#(and we had to diagram a simple decision tree to go with it)

[skyriderwednesday]

theoretically you could say 'no' to the plot into infinity, but i'm not writing all that so even if you outright refuse to ask why holmes is in a box, mrs hudson eventually knocks the door anyway.

[gumnut-logic]

Reactions (Bit 17)

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Now we’re getting somewhere :D

For @soniabigcheese for Fandomversary with Gordon and Bedlam.

-o-o-o-

“It’s a trap.”

Gordon was glaring at John’s hologram hanging in the middle of the comms room. All four brothers were arrayed around the holographic diagram of a giant aircraft launch platform. The massive ship hovered in midair somewhere over the Atlantic offering a take off and landing option along with facilities for an audience.

Unfortunately, at some point the landing option had become a crash option and the whole platform was canted at a horrible angle that needed no engineering skills to know was bad.

Very, very bad.

John’s tone was more on edge than usual, but this was a rescue situation, so he was professional. “Gordon, there are a thousand people on that platform, including the entirety of the World Security Council.”

Gordon froze. “Penelope’s father is on that thing?”

“Unfortunately, yes.”

Scott spoke up as Gordon’s expression froze in shock. “What about escape pods.”

“Not functional.”

Virgil frowned. “What do you mean ‘not functional’?”

“It appears they have been disabled.”

Virgil stared at his space brother. “Why?”

John sighed. “The platform was set up for a series of rescue simulations for the launch of the new ‘World Rescue’ initiative. My guess is that they didn’t want anyone panicking and jumping ship mid-display.”

It was Virgil’s turn to be gobsmacked. How could people be so stupid. “Can we re-enable them?”

John’s fingers darted over unseen controls. “Eos is working on it, but the crash caused electrical havoc and several systems have been irreparably damaged.”

“I agree with Gordon. It has to be a trap.” Alan sat in his hover chair glaring at everything. 

Scott sighed. “John, has our help been requested?”

“If you count individuals, I have received five hundred and forty-two calls for International Rescue from persons aboard the stricken craft in the last five minutes.

“Anything from official channels?”

“Not a blip.”

It was strange. Had this been a rescue prior to the last week, they would already be airborne. As it was, they were all staring at Scott as he stood there frowning and assessing the risk.

It only took a moment before Scott’s head came up. “Request permission.”

“FAB.” John didn’t even blink.

The next few moments were some of the longest ever.

But John’s expression told them all they needed to know before he opened his mouth. “Permission denied.”

“What?!” Gordon glared up at his brother even more. “There are a thousand people in danger!”

“Their answer was ‘World Rescue has the situation under control. Your assistance is not required’.”

Virgil stared at the image of the launch platform. One of its massive hoverjets had been disabled when the aircraft had collided with the landing strip.The whole platform was teetering at an angle that was seriously degrading the effectiveness of the remaining three hoverjets. A few more degrees and the entire ship would fall out of the sky. Physics tolerated only so much abuse.

Virgil’s mind supplied the strategy he would take to stabilise the craft, calculations of mass and thrust, how many airjacks he would need to support all that weight. It would be fairly simple to correct that tilt long enough and strong enough for evac craft to land and get everyone off.

But instead of launching and executing that plan, he was standing here watching a GDF flyer attempting to make a landing beside the crashed plane on the damaged airstrip.

“No!” It was out of his mouth without thinking, his hand held up as if he could grab the hologram and stop the idiots from doing the ultimately stupid.

But he couldn’t. Instead he got to watch as the platform tilted even further, the three remaining hoverjets desperately trying to compensate causing a structural twist in the landing strip’s frame it was not designed to take.

Virgil’s engineering brain supplied the very moment it would snap and it did.

He sucked in a breath as the damaged strip broke and folded almost ninety degrees with the force of gravity, the platform’s whole frame shuddering as it collided with the superstructure.

The GDF flyer flipped and a wing caught in the warped framework. Fortunately. It was the only thing preventing the craft from plummeting to the ground.

The crashed plane shifted, but appeared fused to the platform and didn’t fall either.

A single flailing figure did.

“What the hell were they thinking?” It was a breath exhaled by Scott, his blue eyes staring at the hologram in horror.

“I say we launch.” Virgil made the decision without hesitation.

Those blue eyes latched onto him. “Virgil? It has all the signs of a set up.”

“There’s a thousand people in danger.” He flung a hand at the hologram. “They need our help.”

“We’d be breaking a direct order.”

“It was a stupid ass order.” Gordon glared at Scott. 

The commander looked up at his space brother. “Any change?”

“None official, however I have received another three hundred and thirty-two calls for help, and counting. This appears genuine.”

“Why are we waiting?” Virgil was on edge. “We need to get out there.”

“And I need to make sure I’m making the right decision for all of us.”

“People are in danger. There is no question.”

“Virgil...” But he could see his brother’s dilemma. It was a thousand people versus his family. Because yes, by defying the GDF, this could end everything they had worked for. IR could be shut down. Hell, they could all go to prison.

“If we don’t respond, we will be betraying ourselves.” Virgil eyed the platform as it teetered. “We need to get out there. They need us. We can’t stand by and let those people die.”

Not again.

A fire flickered in his brother’s eyes.

“Scott-“

The commander held up his hand. “Are we all in agreement? Are you aware of the risks?”

Five nods.

Blue eyes lit up with flame.

“Okay then...Thunderbirds are go.”

-o-o-o-

Virgil’s feet hit the deck plates of Two with a reassuring thud. He shoved the overhead hatch closed and revelled in the use of muscles deprived of real work over the last few days.

Slipping into his pilot’s seat gave him such a rush of ‘rightness’ he almost sighed. Behind him, Gordon surfaced through the bottom hatch, no doubt fiddling with his uniform like he always did.

“Alan’s angry.”

Virgil ran through pre-flight with ease, his mental check list ticking of items automatically as his bird began her spin and the great door opened to let the sunlight in.

God, this just felt right! This is where he was supposed to be.

He engaged her warm up sequence as Gordon slipped into his co-pilot’s seat and Virgil found himself obliged to answer. “I don’t blame him. This is an important rescue.” Two’s engines hummed up to readiness and he began her taxi out.

“Do you think it is a trap?”

His bones sung with her thrum. A flick of his wrist and the palm trees on the runway gave way.

The sun was bright this morning.

“I don’t know, Gordon. All I know is that people are in danger. That is where we step in.”

“But what if we step into shit?”

Two slid onto her launch platform and he engaged the hydraulics that lifted her nose towards the sky. He sighed. “Then we go into hell knowing we are doing it for the right reasons.”

Gordon turned away and looked up through the front windows into the blue.

Virgil engaged Two’s rear thrusters and his ‘bird roared into the sky.

-o-o-o-

Next

[ferbmanofactionfletcher]

Interview Process || The Flynn-Fletcher’s

Candace, Phineas, and Ferb sit down to interview Andrea on why she should get the chance to fill the roll she came to town for. 

[TW: bad parenting, past trauma related to bad parenting]

@oh-phineas @i-want-candy

FERB

A time and place had been agreed upon for the interview of Andrea Martin. (Their house, afternoon.) 

Ferb had no idea how to go about it and proceeded to spend the time leading up to it researching the interview process from the interviewer’s perspective. There were so many techniques, ranging from that of an employer looking to learn about a person that best suited a job to that of a screenwriter looking for research on a subject. He didn’t know which one to employ here since— well he didn’t know what exactly they were interviewing her for. What position was she wanting to take up?

A mother? She already had kids that she had a direct relationship to. And was he supposed to meet them? His half siblings? Or her husband? His step-father? What about—? 

And he mostly got overwhelmed when he thought about it as one question would branch off into an infinite tree diagram. Though to anyone looking at him, he still looked like Ferb always did. Neutral and steady. 

There were questions he had prepared but overall, didn’t know what to expect. But, that was the catch when it came to all people— he could never anticipate the outcome. 

He sat at the kitchen table with Phineas and Candace, opposite to Andrea, who looked to be happily sipping tea. His eyes shifted to the Flynn’s, unsure if he was supposed to say something first since— well she was only here because of him. But they were so much better at speaking. 

Andrea cleared her throat, leaning forward against the table top. “So! Where should we start?” 

PHINEAS

Phineas didn’t really do as much research. His idea of an “interview” was mostly based on podcasts about tech startups and his own extremely limited experience. But he wanted to give Andrea hard questions (and yes, this was partially a result of his own humiliation at his Chapter Three interview). Part of it was a power trip, sure, but the other part was his genuine desire to protect Ferb. If this lady really cared about him, she would have to fight to be a part of his life.

“I’ll start us off,” Phineas announced, glancing at Ferb and at Candace. He signed as he spoke and translated for Andrea-- he didn’t want Ferb to miss any of this. It was his decision, at the end of the day. Phineas fixed Andrea with an extremely serious expression. “How many pennies, stacked one on top of the other, would equal the height of the Empire State Building?”

CANDACE:

Candace didn’t see the point of this. In fact, she thought it was incredibly stupid. There was nothing that Andrea could say that would convince Candace that she was truly back. Parents that left always left. They weren’t parents. They were sperm and egg donors. Nothing more. If only she could make Ferb see that. 

Even if he did, she doubted that he would do the right thing and push Andrea away. He was too nice for that, too much of a pushover. 

Well, if Candace was forced to be his big sister, this was how she would do it. By protecting him from a woman he didn’t even remember. So, even though she thought this whole thing was stupid and pointless, she was going to be here. For every step of it. And she’d expose Andrea for being just as flighty as she was before. People like her didn’t change. She’d make sure that Ferb understood that when all this was said and done. 

She sat slightly slumped in her chair, arms crossed, glaring at Andrea. Phineas’ question wasn’t going to get them anywhere but at least it’d tell her if Andrea was willing to play along. Maybe Phineas would just wear her down by being obnoxious. That would be ideal, since at the very least, Candace knew Ferb would stick up for Phineas. 

Candace didn’t say anything. She just watched. 

FERB

Ferb didn’t really know where Phineas was going with that one. It seemed a little out of left field if they were supposed to be getting to know who she was. But he didn’t protest or shoot him a funny look, he trusted Phineas to know what he was doing— Ferb just blinked and turned to see what Ms. Martin would have to say while he worked it out for himself in his head. 

(The height of the Empire State building [1,454ft, which converted to 443,179.2 mm] divided by the thickness of an average American penny [1.52mm] = 291,565.2632 or, rounding up since you couldn’t very well slice the penny, 291,566 pennies.) 

At first Andrea could only stare, brow furrowed, at the question. She had prepared for numerous things to be asked of her. About her life, about why she had left, about why she hadn’t come back, about her other children, about her and Lawrence’s past relationship— but she had never expected she would have to do maths. 

“The Empire State Building.” She smiled as she repeated him. It had still been such a surprise that Lawrence of all people had found someone to marry in America. Then she hummed, lips pressed together trying to think how she was even supposed to begin.

After a moment she simply shrugged, figuring it wasn’t worth answering something so silly. Surely it was some sort of joke Phineas wanted in order to break the ice? Andrea laughed a little before providing her answer.  “I’m afraid I’ve no idea. I don’t even know how tall the Empire State Building is. I’m sorry.” She glanced between the three of them. “How many is it then?”

PHINEAS

Phineas smiled triumphantly, and scribbled down a few notes that didn’t actually mean anything but just to show Andrea he was taking notes. That he had opinions on that answer. He was going to turn it over to Candace for the next question, but Phineas couldn’t help it. He had to interject with his explanation.

“So, that question doesn’t actually have a correct answer-- well, it would, maybe, if I were interviewing you for an engineering job, but even then, there would probably be more efficient ways to test your math skills than a word problem about pennies and the Empire State Building. That was actually a test to see what kind of problem-solver you are. Whether you would even make an attempt, you know? And if you did, would you go at it from a mathematical perspective, or a more practical perspective? Or maybe you would have a question about the problem, like do the laws of physics apply here, and if not, could I stack the pennies length-wise instead of width-wise?” Phineas explained, a superior smile on his face as he signed the words. “So if you want to make another try, you can, but I think I got what I needed from that question.”

He glanced at Candace. ��Did you want to go next?” 

CANDACE: Not that Candace would admit it out loud, but she was actually kind of impressed with Phineas’ logic about the question. She wondered what weirdo interview site he’d read that on. Probably the hiring for Google or something. It sounded like a question they would ask you if you wanted to work at Google. 

And she was unimpressed with Andrea’s answer. 

At least come up with something, yeah? Ask a question? Don’t just give up. It showed a weak sort of character, if you asked Candace. The kind of character that would run out on her son at first opportunity. And would do it again without a second thought. 

When Phineas passed the baton to her, Candace shrugged a little. “Sure, I guess.” 

Candace didn’t know what she wanted to ask. She hadn’t come into this wanting to ask anything. Only looking for the satisfaction of Andrea failing. But, now that the opportunity presented itself: yeah, Candace had a question.

“Why now? Why are you back now? You never said. And I don’t want some bullshit answer. There has got to be a real reason.” 

FERB

In all his research, Ferb hadn’t come across Phineas’ question, which made him wonder if his research had been thorough enough. Then again, that was why Candace and Phineas were here. To fill in the gaps that Ferb couldn’t. 

It also made him uncomfortable once he realized what Ms. Martin’s answer reflected about herself. He couldn’t even muster up the courage to glance her way, knowing the second hand embarrassment would eat him alive if he did. This only grew as he watched Candace’s words popped up along his phone screen. 

Andrea let out a little oh, falling back into her seat at the explanation. She folded her hands, one on top of the other, her confidence level having decreased significantly— and after only the first question.

As Phineas asked his sister if she wished to contribute Andrea picked her head back up, pressing a smile back to her features. Ah, now this she had been prepared for. Even if the way it was said was rather vulgar. That was fine. Even needed. 

“I know it seems a little out of the blue. Believe me, it was for me, too. But— like I had said, I just couldn’t stay away any longer. There was no more reasons I could come up with or excuses that I could push in front of me to blame. I was watching my other children and I— I don’t know but I finally came to my senses. I realized Ferb was going to be a young man soon enough and I knew I didn’t want to miss any more of his life than I already had.” She looked over to Ferb now but when his head remained down, eyes focused on his phone’s screen Andrea returned her attention back to Candace. “I don’t know quite what you mean by the real reason. If it’s finances you think I’m after, I’d obviously be in the wrong place. The house was never in my name, there’s no secret will or treasure said to be buried in the floorboards that’s somehow come to light or whatever else. The only thing here is my son. That’s it, plain and simple.” 

PHINEAS

Phineas liked to pride himself on being scientific and objective with these kinds of things. Logical. Sure, he was an emotional person and emotions often got in the way of good choices, but not with science. And that was what this kind of was, right? A science experiment?

Hypothesis: Andrea couldn’t possibly deserve Ferb.

Conclusion: ...Unclear.

It was getting harder for Phineas to separate his own baggage from this. Because, really, how many times had he imagined this exact scenario for himself? Fred showing up on the Flynns’ doorstep in Danville, begging for forgiveness, saying that he had made a mistake and that he didn’t want to miss another moment of his kids’ lives. Not so much recently, because Phineas had a new life and a new family and he barely thought about Fred anymore. But when he was in middle school? That had been a different time.

“What are you going to do to make it up?” Phineas interjected, his tone different now. Less smarmy, a little more genuine. A hint of a challenge in his tone, but a little bit of fear as well. Hopefully Candace wouldn’t catch on to what was going on here. “If you’re gonna walk out on your kid with no explanation, the least you can do is prove you’re sorry.”

FERB

“I’m not sure that there is any one thing I can do to make it up,” Andrea admitted with a small shrug. (Especially when the one she was even here for wouldn’t spare her a glance!) “Nor do I have any set plan in mind. That’s not really how you gain someone’s trust, is it? You can’t manufacture that. All I can do is make good on my word— which is that I’m here now and I will be for as long as I am welcomed. And even if it takes til the end of my life to repair the damage I have done and to form any sort of relationship with my son, then I’ll do it.” 

This all seemed rather dramatic to Ferb. 

Phineas’ and Candace’s body language read defensive while Ms. Martin was still one giant mystery, but she did seem tense. Immediately he wished he could call the whole thing off. Maybe he could fake an illness or something, say he got a text about some emergency— of course that wouldn’t work considering the only people who would contact him about that were all somewhere in the house.

He wasn’t so selfish to think that all of this was about him. The Flynn’s had lost a parent, one they had actually known personally, and he could guess this was poking at old, but still painful, wounds. But he was so selfish to think that none of this would be happening if it weren’t for him, and it was rather pointless to do so. 

CANDACE:

No, it wasn’t about Ferb. 

Not to Candace. She wasn’t mature enough to separate her own wound from Ferb’s. She projected her own feelings onto him, which was easy to do. He was quiet and reserved. She couldn’t read him, but she didn’t need to. She assumed she knew exactly how he was feeling, because it was how she felt:

Confused. Angry. Hurt. Her whole heart felt like a bruise. A lot of the time, it was easy to ignore Fred’s absence. It had been years and Candace didn’t need him anyway. She did just fine on her own. But, now that Andrea was here with her watery eyes and half-baked promises, Candace’s missing for her father had opened up like a black hole in her chest, sucking everything else into it. 

It made her feel more protective of Ferb than any previous time. He was so soft. Such a pushover. He’d let Andrea back into his life even though she didn’t earn it and then get hurt when she inevitably left again. Candace felt like she had to protect him from this, the way she hadn’t been able to protect Phineas from the heartbreak of their father walking away. 

“And what if he decides he doesn’t want a relationship? And that the damage you caused is irreversible?” 

PHINEAS

Phineas glanced at Candace sharply. That was… an intense thing to say. And even if Phineas had come into this interview determined to drive Andrea away, he was starting to wonder if maybe he had judged her too harshly. 

Because the truth was, Andrea was right. There wasn’t any one thing you could do to make something like this better. Phineas had never wanted Fred to come back with presents or stories or excuses. He just wanted a dad. Period. It didn’t matter, now, though, because he had Lawrence who was way better and would never disappear.

Sometimes he did wonder, though, what he would do. He and Ferb didn’t really talk about this stuff much.

“I mean, irreversible’s a strong word. Ferb isn’t damaged,” Phineas said quickly. “He’s, like, the most mature person I know. But I get what Candace is saying. It’s up to Ferb. I trust him.” He glanced at Ferb encouragingly. “Anything you wanna say, Ferb?”

FERB

Both Candace and Phineas were wrong. 

Ferb was damaged— but it had not been because his mother had left. It was of his own doing. This was why he felt no anger toward the woman sitting on the other side of the table. Of course, it had hurt to have learned why she did not want him. It always hurt. It had hurt every time he had tried to communicate with someone at school or at the park or— anywhere, really, and they would ignore him. When his teachers would talk to Ms. Thompson instead of him despite it being his words she was translating. When his father would have to take over every conversation on his behalf at restaurants, stores, and just about everywhere else. It was why he avoided it now. The world. He had learned to know better than to inconvenience it with himself. 

He watched Phineas’ question addressing him stare back at him from his phone and after a moment he lifted his head. It took him another to finally turn to find Ms. Martin’s eyes. 

“I don’t want to deny you the opportunity you’re asking for but— you have other children. I fail to see what I could give you that they can’t.” 

Andrea’s discomfort grew at the sound of her son’s voice. It was the first time hearing it. Even as a baby he had been rather quiet. She hadn’t expected it. Which was silly, considering, but still. It was off. Different. Made his lack of hearing all the more present to her. She tried not to let that show.

“Oh, darling, it isn’t about what you can give me! I’m supposed to be giving to you. And even if it were the other way around, you’re doing your part by just being you.” 

There was a pause as Ferb had to read this over. She shifted in her seat. (Again, it grew.) “You don’t know me, though.” 

“Right— that’s what I’m here to do!” 

Pause. (Growing, growing, growing.) 

“It won’t be worth it.” 

Andrea’s smile fell. She blinked, brow furrowing as her eyes went to the other two sitting in front of her to make sure she had heard that correctly. “I’m— I’m sorry?”

“Objectively speaking, it won’t be worth it. Getting to know me. You live in another city where you live with your family and go to work. If you wished to see me you would need to travel which would cost you money and time you would otherwise be able to save. People would expect you to learn sign, which also takes up more time from your life. If you only wished to communicate through technology it would be a written relationship since you can’t call me, which would only take up storage space and, again, time. Either way you would have to contact my father, which he does not seem pleased with. People usually do not respond well to not being liked so your interactions will tax the both of you. And— I’m not worth all of that. You gain nothing from knowing me besides extra hardships which will only result in regret or resentment. Both of which are not healthy.”  

CANDACE: Candace rolled her eyes at Phineas. She hadn’t meant that Ferb was like...broken or something, just emotionally damaged. Because having a shitty parent did that to you. Obviously. It broke your heart and your trust and made you feel like shit. It was damaging. End of story.

Listen to Ferb now! Clearly, he felt the same way.

It was hard to listen to because Candace had shit opinions of herself, but she had some redeeming qualities. And she would never admit to feeling them the way that Ferb did now. It was uncomfortable to say the least. It made Candace want to squirm.

So, she did what she usually did when she was uncomfortable: she turned it into something else. Anger. Anger at Andrea and any parent that thought just leaving a child was okay.

“See?” she said furiously. “That’s because of you. He thinks that way, because of you. He thinks he isn’t worth it because you left him. That’s fucked up and it isn’t something that is easily forgiven. You can sit here with smiles all you want, but what you did was horrible.” 

She looked at Ferb then and she’d been signing this whole time...well, doing her best anyway. She still wasn’t totally good at it and she was too pissed. But, what she said now, she said very carefully and very deliberately. 

“No one should make you feel like a transaction,” she told him, even if she had to spell out ‘transaction’ because she didn’t know the sign for it. “And it’s okay if you’re angry or upset. Just because she’s here, doesn’t mean you have to be polite.” 

God, she wished Ferb had more of a backbone and would just tear into this bitch.

PHINEAS

Phineas, in theory, agreed with pretty much everything Candace was saying. Relationships didn’t work like that, the way Ferb was describing it: they were about love and reciprocity, and genuine care for other people. That was the way Phineas saw it, anyway. Sure, it was nice that Ferb could help Phineas when the projects got too technical and complicated for Phineas to do on his own, but Phineas that wasn’t why Phineas cared about him. It was because they were brothers now, and that was what brothers did. That simple.

But Candace’s tone annoyed him. Why did she know better than Ferb? She always acted like she was so much older and wiser, meanwhile, she was barely a year older than Phineas. She was right, but did she have to be so bossy about it? And even if what she did was kind of fucked-up, if Ferb did eventually want to give Andrea a second chance, what made it Candace’s business?

Phineas didn’t realize it, but he was maybe projecting a little too.

He had a lot of things to say, but it wouldn’t be professional to say them out loud, not in front of Andrea. So Phineas did the thing that was probably ruder— he took out his phone and texted the group chat with Candace and Ferb.

@Ferb that’s bullshit and u know it anyone would be lucky to get the opportunity to be in ur family and like obviously ur worth it

@Candace that being said can you chill with the psychoanalysis me and ferb r capable of making our own decisions

Satisfied, Phineas set his phone down and signed to Candace and Ferb, Check your phone, before turning his attention back to Andrea. “I think what we’re actually trying to ask is what you can bring to Ferb’s life, not the other way around. Let’s focus on that. And based on that, Ferb can make his own decision about whether it’s worth it to him.” Phineas shot Candace a look. 

FERB

If Andrea hadn’t already folded under listening to Ferb talk, then she certainly would have upon Candace’s addition. She found she didn’t know what to say to any of that— and she thought she had prepared for the worst. 

Ferb pondered over Candace’s words and concluded that she wasn’t really talking about him. He didn’t think that way because of Ms. Martin, he had always thought that way. His brain had made it easier with its ability to recall everything it had ever come into contact with. He also hadn’t said that he was worthless, just that he wasn’t worth spending time with. That was a fact, proven by many, many, many failed attempts to prove the opposite. 

And he was upset that Ms. Martin was here, but he had taken to not showing his emotions out of self preservation. It wasn’t out of politeness, though, he did have those hardwired into him, too. 

His eyes flickered down to his phone as Phineas’ texts came through. Phineas was obviously biased, but Ferb appreciated the kindness nonetheless. 

This whole thing wasn’t out of a want for a mother or because he sought to gain anything from this— it just seemed like the fair thing to do. Ms. Martin had asked for a chance. Ferb did not want to deny her that, even if she had wronged him. It was the right thing to do. 

Andrea cleared her throat after Phineas addressed her, nodding. “Of course! Yes, you’re right. I completely agree. I don’t mind traveling at all and I’m certain Lawrence and I can be civil to one another, so, please, you’ve nothing to worry about as far as logistics go.” 

Ferb blinked and she was beginning to think that was a good thing rather than him responding. So far, he only replied with bad news. 

“As for what I can offer, it’s only what anyone else could— myself. And while I know my past record doesn’t reflect that being a very good thing, but I want to be here. I want to know him— you. Ferb. To whatever effect that may be! And not because I feel like it’s my obligation to do so.”  She smiled, trying to get away from all the discomfort of the past few minutes. “We can start with interests! What do you like?” 

Again, Ferb blinked, then shrugged, unsure of how to answer that. It was too broad of a question. What did she mean, what did he like? As in food? Colours? Coding method? Time of day? 

“Right.” She glanced to the Flynn’s. “You two know him better than I do. Is he in anything? Sports? Clubs?”

CANDACE:

Candace ignored her phone because she didn’t care what Phineas had to say. She was right. Everyone here knew it. Andrea didn’t deserve to come back into Ferb’s life. Admittedly, she didn’t know what would qualify as enough penitence to come back into Ferb’s life. She hadn’t ever thought about it. When Fred had left, that had been it. Candace had spent months, crying and waiting for him to come home. Calling his cell phone only to receive a dial tone. 

She had held out hope until her birthday, but when he didn’t show up. Or call. Or even send a card, Candace knew that he was gone and she’d cut him out of her heart then. Of course, it was messier than she liked to think when she look back now, but what was done was done. Every missed birthday, graduation, milestone had only hardened her heart against him. Fred was a sperm donor. Not a dad. If he showed back up she’d—

See, she didn’t know, because she never thought about it. 

Whatever Andrea was doing wasn’t it, though. 

“This is stupid,” Candace declared, pushing back from her chair. “You aren’t even talking to him, himself!” Her hands flew erratically as she tried to sign but was too pissed off to do so very well. 

“Whatever. I’m not dealing with this. If you want to “get to know” Ferb, fine, whatever. But count me out.” And with that, she stormed out of the kitchen, Agent P scrambling at her feet playfully. 

PHINEAS

Phineas was annoyed. At everyone. Candace was being unreasonable, Andrea was being awkward, and Ferb was… well, Phineas figured he probably shouldn’t get to decide how Ferb should feel about his estranged mom showing up, but he wished Ferb would say something. Even if Phineas thought Candace needed to calm down, he did agree that it rubbed him the wrong way that Andrea was talking about Ferb instead of to him. 

He watched Candace storm off and raised his eyebrows, shrugging apologetically. 

“Sorry about her,” Phineas said. He glanced at Ferb, trying to see where he was coming from. “But she does have a point. You can’t just talk about people right in front of them. Anyway, we’ll be asking the questions.”

He smiled and folded his hands, satisfied with his own assertive attitude. “Describe what you would do if Ferb got detention.” Ohhh yeah. This was a trick question. Ferb never got detention.

FERB

Goodness, Andrea thought, but forgave the girl as soon as she left. It wasn’t her fault. That came from upbringing, clearly. And Candace hadn’t really been the person Andrea had been here for anyway. 

“Oh, that’s alright. She’s fine, I understand.” She nodded to Phineas, folding her hands back over one another on top of the table. 

Ferb, on the other hand, felt all the more guilty. He shouldn’t have said anything. He should have just sat there. He shouldn’t have invited her back. He shouldn’t have come down stairs at all the day she showed up. He shouldn’t have—. Well. That list could consist of an infinite amount of answers, or just one that would make everything else moot. 

He didn’t look back at Phineas this time, too ashamed now to do anything but keep his eyes on his phone because surely Phineas would be angry with him, too. Yet he kept his anxieties from manifesting and despite the dread sitting in his stomach like a pit, he remained still and seated, even if he wanted to leave the table, too, to go find a hiding place that would last him for all eternity. 

Andrea didn’t really have to think that hard about this question since she did have experience with figuring out punishments for her own children when getting phone calls from their schools! What she hesitated on was the fact that it was a child who was asking the question. Surely he would deduct points if she answered like a parent should. Or maybe he was trying to see if she would sugar coat it for the sake of trying to appeal to them? 

Oh, she was just overthinking it. This was a child! “Well, depending on what he was in detention for, I would vary the consequences. He would have to apologize to whoever, if anyone, he had hurt, and then probably be grounded for some time, again, depending.” 

PHINEAS

Phineas smirked. “Trick question. Ferb doesn’t get detention. The one time he did was because he covered my ass. So… nice try, but incorrect,” he said, a tone of superiority in his voice as he signed. He winked at Ferb. 

Candace was gone and as much as Phineas wanted to milk this opportunity to be in charge, he figured there wasn’t much point in continuing to grill Andrea. Phineas didn’t hate her, after all. He was a little suspicious, but for the most part, she just seemed like a well-intentioned person who didn’t realize she was kind of in over her head. That was Phineas’s assessment anyway.

“Listen, I wouldn’t take Candace personally. She’s just… like that. I do agree that this is kind of out of nowhere, and I think you have a lot of making up for lost time to do, but the end of the day, it’s Ferb’s decision, not ours. Excuse us for a moment.”

He turned to Ferb and signed, Do you want to make a decision now, or sleep on it?

FERB

Andrea sat there a little shocked. He didn’t get detention? She blinked, jaw slack, as Phineas informed her. It wasn’t as if she had been expecting Ferb to be a troublemaker or anything, but never? On his own accord, anyway? Goodness. Even her other children had gotten punishments at school. A call home here or there for something. It was only natural. 

She only gave a weak nod and smile to match as Phineas tried to apologize for his sister. Again, Andrea really paid no mind to Candace. She wasn’t the one she was here for and nor did she seem particularly close to Ferb in the way the boy sitting next to him was. Andrea sat back, left to twiddle her thumbs as the two of them began to speak in a language she couldn’t even begin to make out. (Which was more from a lack of not trying than anything else.) 

Ferb thought over this question and could see no reason to prolong the inevitable. Ms. Martin had given her answers and she had still seemed like she wanted to know Ferb. For whatever reason. In his mind, it was only fair to give her a shot. She had apologized and said she would do more to make amends. There was really nothing else he could think to ask for. 

Also, this was perhaps a chance for him to make up for his own failings. All those years he had spent trying to actively gain people’s friendship only to be ignored. Now, he was met with someone who had ignored him for years who was wanting to do the opposite. That had never happened before.

Now, he signed, both hands at his ribcage, palms to the ceiling, bobbing up and down twice. He then turned to Ms. Martin and spoke aloud. “Okay. If this is what you want.”   

She nodded enthusiastically. “It is! Of course. Erm— oh here.” Andrea reached across the table to take Ferb’s phone, which caused a spike in his nerves since he 1. No longer knew what she was saying and 2. Well. She had his phone. After a few painful seconds of her tapping at it she pushed it back across to him. “I put my number in so you can call or— contact me whenever!” 

Ferb, having not gotten any of that, just nodded. Andrea smiled, eyes moving to Phineas. “And thank you so much! This was delightful, apart from— well. Anyway, I’m sure we’ll be seeing more of each other soon!”

[alifgiant]

Comparative Study on Flutter State Management

Background

I am going to build a new flutter app. The app is aimed to be quite big. I'm going to need a state management tools. So, I think it’s a good idea to spent some time considering the options. First of all, i do have a preference on flutter’s state management. Itu could affect my final verdict. But, I want to make a data based decision on it. So, let’s start..

Current State of the Art

Flutter official website has a listing of all current available state management options. As on 1 Aug 2021, the following list are those that listed on the website.

I marked GetIt since it’s actually not a state management by it’s own. It’s a dependency injection library, but the community behind it develop a set of tools to make it a state management (get_it_mixin (45,130,81%) and get_it_hooks (6,100,33%)). There’s also two additional lib that not mentioned on the official page (Stacked and flutter_hooks). Those two are relatively new compared to others (since pretty much everything about flutter is new) but has high popularity.

What is Pub Point

Pub point is a curation point given by flutter package manager (pub.dev). Basically this point indicate how far a given library adhere to dart/flutter best practices.

Provider Package Meta Scores

Selection Criteria

I concluded several criteria that need to be fulfilled by a good state management library.

Well Known (Popular)

There's should be a lot of people using it.

Mature

Has rich ecosystem, which mean, resources about the library should be easily available. Resources are, but not limited to, documentation, best practices and common issue/problem solutions.

Rigid

Allow for engineers to write consistent code, so an engineer can come and easily maintain other's codebase.

Easy to Use

Easy in inter-component communication. In a complex app, a component tend to need to talk to other component. So it's very useful if the state manager give an easy way to do it.

Easy to test. A component that implement the state management need to have a good separation of concern.

Easy to learn. Has leaner learning curve.

Well Maintained:

High test coverage rate and actively developed.

First Filter: Popularity

This first filter can give us a quick glance over the easiness of usage and the availability of resources. Since both are our criteria in choosing, the first filter is a no brainer to use. Furthermore when it’s not popular, there’s a high chance that new engineers need more time to learn it.

Luckily, we have a definitive data to rank our list. Pub.dev give us popularity score and number of likes. So, let’s drop those that has less than 90% popularity and has less than 100 likes.

As you can see, we drop 6 package from the list. We also drop setState and InheritedWidget from the list, since it’s the default implementation of state management in flutter. It’s very simple but easily increase complexity in building a bigger app. Most of the other packages try to fix the problem and build on top of it.

Now we have 9 left to go.

Second Filter: Maturity

The second filter is a bit hard to implement. After all, parameter to define “maturity” is kinda vague. But let’s make our own threshold of matureness to use as filter.

Pub point should be higher than 100

Version number should be on major version at least “1.0.0”

Github’s Closed Issue should be more than 100

Stackoverflow questions should be more than 100

Total resource (Github + Stackoverflow) should be more than 500

The current list doesn’t have 2 parameter defined above, so we need to find it out.

So let’s see which state management fulfill our threshold.

As you can see, “flutter_redux” is dropped. It’s not satisfied the criteria of “major version”. Not on major version can be inferred as, the creator of the package marked it is as not stable. There could be potentially breaking API changes in near future or an implementation change. When it happens we got no option but to refactor our code base, which lead to unnecessary work load.

But, it’s actually seems unfair. Since flutter_redux is only a set of tool on top redux . The base package is actually satisfy our threshold so far. It’s on v5.0.0, has pub point ≥ 100, has likes ≥ 100 and has popularity ≥ 90%.

So, if we use the base package it should be safe. But, let’s go a little deeper. The base package is a Dart package, so it means this lib can be used outside flutter (which is a plus). Redux package also claims it’s a rich ecosystem, in which it has several child packages:

As i inspect each of those packages, i found none of them are stables. In fact, none of them are even popular. Which i can assume it’s pretty hard to find “best practices” around it. Redux might be super popular on Javascript community. We could easily find help about redux for web development issue, but i don’t think it stays true for flutter’s issue (you can see the total resource count, it barely pass 500, it’s 517).

Redux package promises big things, but as a saying goes “a chain is as strong as its weakest link”. It’s hard for me to let this package claim “maturity”.

Fun fact: On JS community, specifically React community, redux is also losing popularity due to easier or simpler API from React.Context or Mobx.

But, Just in case, let’s keep Redux in mind, let’s say it’s a seed selection. Since we might go away with only using the base package. Also, it’s might be significantly excel on another filter. So, currently we have 4+1 options left.

Third Filter: Rigid

Our code should be very consistent across all code base. Again, this is very vague. What is the parameters to say a code is consistent, and how consistent we want it to be. Honestly, i can’t find a measurable metric for it. The consistency of a code is all based on a person valuation. In my opinion every and each public available solutions should be custom tailored to our needs. So to make a codebase consistent we should define our own conventions and stick on it during code reviews.

So, sadly on this filter none of the options are dropped. It stays 4+1 options.

Fourth Filter: Easy to Use

We had already define, when is a state management can be called as easy to use in the previous section. Those criteria are:

Each components can talk to each other easily.

Each components should be easy to test. It can be achieved when it separates business logic from views. Also separate big business logic to smaller ones.

We spent little time in learning it.

Since the fourth filter is span across multiple complex criteria, I think to objectively measure it, we need to use a ranking system. A winner on a criteria will get 2 point, second place will get 1, and the rest get 0 point. So, Let’s start visiting those criteria one by one.

Inter Component Communication

Let’s say we have component tree like the following diagram,

In basic composition pattern, when component A needs something from component D it needs to follow a chain of command through E→G→F→D

This approach is easily get very complex when we scale up the system, like a tree with 10 layers deep. So, to solve this problem, state management’s tools introduce a separate class that hold an object which exposed to all components.

Basically, all state management listed above allows this to happen. The differences located on how big is the “root state” allowed and how to reduce unnecessary “render”.

Provider and BLoC is very similar, their pattern best practices only allow state to be stored as high as they needed. In example, on previous graph, states that used by A and B is stored in E but states that used by A and D is stored in root (G). This ensure the render only happen on those component that needed it. But, the problem arise when D suddenly need state that stored in E. We will need a refactor to move it to G.

Redux and Mobx is very similar, it allows all state to be stored in a single state Store that located at root. Each state in store is implemented as an observable and only listened by component that needs it. By doing it that way, it can reduce the unnecessary render occurred. But, this approach easily bloated the root state since it stores everything. You can implement a sub store, like a store located in E to be used by A and B, but then they will lose their advantages over Provider and BLoC. So, sub store is basically discouraged, you can see both redux and mobx has no implementation for MultiStore component like MultiProvider in provider and MultiBlocProvider in BLoC.

A bloated root state is bad due to, not only the file become very big very fast but also the store hogs a lot of memory even when the state is not actively used. Also, as far as i read, i can’t find any solution to remove states that being unused in either Redux and Mobx. It’s something that won’t happen in Provider, since when a branch is disposes it will remove all state included. So, basically choosing either Provider or Redux is down to personal preferences. Wether you prefer simplicity in Redux or a bit more complex but has better memory allocation in Provider.

Meanwhile, Getx has different implementation altogether. It tries to combine provider style and redux style. It has a singleton object to store all active states, but that singleton is managed by a dependency injector. That dependency injector will create and store a state when it’s needed and remove it when it’s not needed anymore. Theres a writer comment in flutter_redux readme, it says

Singletons can be problematic for testing, and Flutter doesn’t have a great Dependency Injection library (such as Dagger2) just yet, so I’d prefer to avoid those. … Therefore, redux & redux_flutter was born for more complex stories like this one.

I can infer, if there is a great dependency injection, the creator of flutter redux won’t create it. So, for the first criteria in easiness of usage, i think, won by Getx (+2 point).

There is a state management that also build on top dependency injection GetIt. But, it got removed in the first round due to very low popularity. Personally, i think it got potential.

Business logic separation

Just like in the previous criteria, all state management also has their own level of separation. They differ in their way in defining unidirectional data flow. You can try to map each of them based on similarity to a more common design pattern like MVVM or MVI.

Provider, Mobx and Getx are similar to MVVM. BLoC and Redux are similar to MVI.

In this criteria, i think there’s no winner since it boils down to preference again.

Easy to learn

Finally, the easiest criteria in easiness of usage, easy to learn. I think there’s only one parameter for it. To be easy to learn, it have to introduced least new things. Both, MVVM and MVI is already pretty common but the latter is a bit new. MVI’s style packages like redux and bloc, introduce new concepts like an action and reducer. Even though Mobx also has actions but it already simplified by using code generator so it looks like any other view model.

So, for this criteria, i think the winner are those with MVVM’s style (+2 Point), Provider, Mobx and Getx. Actually, google themself also promote Provider (Google I/O 2019) over BLoC (Google I/O 2018) because of the simplicity, you can watch the show here.

The fourth filter result

We have inspect all criteria in the fourth filter. The result are as the following:

Getx won twice (4 point),

Provider and Mobx won once (2 point) and

BLoC and Redux never won (0 point).

I guess it’s very clear that we will drop BLoC and Redux. But, i think we need to add one more important criteria.

Which has bigger ecosystem

Big ecosystem means that a given package has many default tools baked or integrated in. A big ecosystem can help us to reduce the time needed to mix and match tools. We don’t need to reinvent the wheel and focused on delivering products. So, let’s see which one of them has the biggest ecosystem. The answer is Getx, but also unsurprisingly Redux. Getx shipped with Dependency Injection, Automated Logging, Http Client, Route Management, and more. The same thing with Redux, as mentioned before, Redux has multiple sub packages, even though none of it is popular. The second place goes to provider and BLoC since it gives us more option in implementation compared to one on the last place. Finally, on the last place Mobx, it offers only state management and gives no additional tools.

So, these are the final verdict

Suddenly, Redux has comeback to the race.

Fifth Filter: Well maintained

No matter how good a package currently is, we can’t use something that got no further maintenance. We need a well maintained package. So, as always let’s define the criteria of well maintained.

Code Coverage

Last time a commit merged to master

Open Pull Request count

Just like previous filter, we will implement ranking system. A winner on a criteria will get 2 point, second place will get 1, and the rest get 0 point.

So, with above data, here are the verdicts

Getx 4 point (2+2+0)

BLoC 4 point (1+1+2)

MobX 0 point (0+0+0)

Provider 1 point (0+0+1)

Redux 0 point (0+0+0)

Lets add with the previous filter point,

Getx 10 point (4+6)

BLoC 5 point (4+1)

MobX 2 point (0+2)

Provider 4 point (1+3)

Redux 2 point (0+2)

By now we can see that the winner state management, that allowed to claim the best possible right now, is Getx. But, it’s a bit concerning when I look at the code coverage, it’s the lowest by far from the others. It makes me wonder, what happen to Getx. So i tried to see the result more closely.

After seeing the image above, i can see that the problem is the get_connect module has 0 coverage also several other modules has low coverage. But, let’s pick the coverage of the core modules like, get_core (100%), get_instance(77%), get_state_manager(51%,33%). The coverage get over 50%, not too bad.

Basically, this result means we need to cancel a winner status from Getx. It’s the win on big ecosystem criteria. So, lets subtract 2 point from the end result (10-2). It got 8 points left, it still won the race. We can safely say it has more pros than cons.

Conclusions

The final result, current best state management is Getx 🎉🎉🎉. Sure, it is not perfect and could be beaten by other state management in the future, but currently it’s the best.

So, my decision is "I should use GetX"

[werewolfdays]

Ask Her Out - Snippet

of course I had to write Jayde being a disaster bi trying to ask Nadya out on a date for the first time. Probably my last writing post for this year! it’s been a really really good one for this blog and for my writing so I just wanna thank everyone who follows me and reads my stuff (and sometimes actually enjoys my stuff?? still a weird concept for me) and fingers crossed for a good 2020! - 

Okay, I can do this. I thought to myself for the hundredth time today. I can do this. It’s not like I haven’t asked someone out before. Well, not someone like her. And not necessarily in this kind of context. For the first time I was actually nervous about doing this sort of thing. I just wish I could get it the fuck together and act normal because that’s what Nadya deserves. Normal. 

That made me second guess this. Maybe I shouldn’t ask her. Maybe she doesn’t even like me the way I liked her. Maybe when I kissed her it was only me that wanted it… No. This wasn’t one-sided. I felt it when she kissed me back. But it did little for my confidence because Nadya could do a lot better than me.

The fact that this wasn’t one-sided made me realize that it couldn’t be all my decision. My feelings for Nadya were growing every day. I wanted her in the purest sense and it was becoming impossible for me to ignore unless I wanted to torture myself more than I usually do. The best thing I could do, for both of us, was to be straightforward with her. Ask Nadya what she wanted. If she didn’t feel the same, then I could let it go and that would be that. 

Of course that was what I was terrified of…

I found her sitting at a table outside of the Den, studying her medical textbook like she usually does during her alone time. She was so engrossed in her work that she didn’t even hear me approach. The pen in her hand was busy scribbling notes, her eyes scanning the paragraphs and diagrams in the book and double checking whatever she had open on her laptop. It was a fascinating process to watch. I could only imagine all of the information she was absorbing. 

Not wanting to make her feel cornered, I sat across from her and not beside her. Only when the table shifted slightly from my weight did she notice my arrival. Nadya’s head snapped up and her expression brightened in a smile, made all the more adorable by the glasses resting on the bridge of her nose. 

“Hi, Jay.” Nadya gave her usual greeting with a slight blush. The chilly breeze blew some strands of hair in her face and she brushed it behind her ear with the hand holding the pen, unknowingly leaving a small streak of ink on her jaw. 

I smiled half to myself, “Hey, Nadya.” 

“Do you need something?” her brow furrowed just a fraction in worry, probably picking up on my anxiety. 

“No,” I shook my head, definitely too quickly, “Well, yeah, actually. I wanted to ask you… uh, what kind of food do you like? Or drinks? You know, maybe a special coffee or tea or something… like that.” Jesus, I’m embarrassing myself. The desire to crumble into dust was overwhelming in this moment. 

Nadya, completely oblivious to my deeper meaning, asked, “Are you going to the store?” 

“I wasn’t really planning on it… unless you needed something?” Well, this was falling apart a lot quicker than I thought it would. At least Nadya didn’t seem aware of how badly I wanted to kick myself. 

“I actually might need a few things.” Nadya said apologetically, “We don’t have to go now, but-”

“No, we can go now if you want.” I agreed eagerly. Maybe this would give me some time to pull myself together and regroup my thoughts. 

“Great.” Nadya lit up, gathering all of her work and putting it in her bookbag. “Let me just drop this off in my room.” 

“Of course.” I got up to follow her. 

The entire walk to her room was silent. I spent most of it lost in my thoughts, trying to think of a better way to phrase the question I somehow failed to ask, but became increasingly aware of Nadya’s presence. We were walking side by side, every now and then our gaits would brush our shoulders together and I couldn’t stop thinking about how easy it would be to reach for her hand. Wondering if she would like that. Or if she would even want it. 

Nadya unlocked her door when we got to it and pushed it open, “I’ll only be a sec, but you can still come in if you want.” 

“Thanks.” the last time I was in her room was when I kissed her last night. Obviously nothing has visibly changed, but it somehow felt different. Just like we did. 

I watched Nadya hang her bookbag on the back of her desk chair and rifle through one of the drawers. Watching her do something so simple made me want to kiss her again. Hell, everything nowadays made me want to kiss her. But I wanted our second kiss to be in a different setting where we put everything out in front of us. I didn’t want to kiss her in the limbo of not knowing what we are to each other. And it was a topic I wanted to discuss over something normal, like a dinner date. 

Nadya pocketed a folded piece of paper and put her glasses into a smaller bag that she threw over one shoulder. “Okay, I’m good to go.” 

Our journey out of the Lodge fell back into silence again. That is until I felt Nadya’s hand brush against mine. The sudden touch made me flinch away like I was somehow responsible for an offensive act. 

“Sorry.” I said quickly.

“Oh, it’s okay.” Nadya replied just as fast, “It wasn’t you.” 

That answer dwelled in my head for the rest of the walk. Was she… reaching for my hand? Did she want me to reach for hers? Was it a genuine accident? That wouldn’t be too far-fetched considering how closely we’ve been walking together. I don’t think I have ever spent so much effort overthinking such an insignificant moment before. This girl was driving me crazy. She threw me off so bad that it was a wonder I haven’t forgotten how to walk. 

Once we got outside, I led us towards the truck, but Nadya hesitated when I went to the driver’s side. My brow furrowed in confusion as I looked at her expectantly, thinking there was something wrong. Maybe she realized what I was trying to ask earlier and it made her uncomfortable. The thought made my stomach drop. 

“Can we, um…” her eyes drifted over to my motorcycle parked right next to us, “Can we take the bike?” 

I smiled in relief at the suggestion, turning around to look at the motorcycle with pride. “Sure,” then I raised an eyebrow at her, “You ever ridden one before?” 

“No,” Nadya answered. I fished in the bed of the truck for the only helmet I had and handed it to her after brushing it off, “But it always looked fun.” she put the helmet on with an excited grin that was infectious. 

“It is fun.” I told her, settling on the bike and waiting. 

She didn’t move to get on. “Where’s your helmet?”

“You’re wearing it.” 

“Jay,” Nadya sighed, raising a hand to undo the strap. “I can’t take your helmet.” 

I chuckled a little, reaching up and grabbing her wrist to stop her, “Nadya, it’s fine. I never wear it anyway. It’s yours.” 

“You don’t wear a helmet?” her voice sounded incredulous. 

“Not really.” I shrugged, letting her go. Nadya glared at me in disbelief, “My head’s still intact.” I pointed out, knocking my knuckles against my temple. 

“Yeah, it’s called dumb luck.” she couldn’t have been too mad because she gave me a playful shove.

“You gonna get on or not?” I laughed and tied my hair up in a messy bun. 

Begrudgingly, Nadya climbed on behind me. “You really need to work on that self preservation instinct.” 

Logically, I was prepared for it. But mentally… When Nadya wrapped her arms around my waist, it sent my mind spinning through a hurricane. Being this close to her again threatened to overwhelm me. We were outside where there were thousands of scents and sounds, but my senses zeroed in on her to be immediately consumed. It both excited me and calmed my nerves at the same time. I had to fight not to be completely lost in her proximity. It was a miracle that I even thought of a reply at all. 

“Whatever you say.” 

I felt her heart begin to gallop when I revved the engine and I wondered if it was because of me or the bike. Knowing that I had at least some of the same affect on her that she had on me would be reassuring, but there was no way to tell. Not unless I asked. I wasn’t exactly ready to bring it up right now.

The trail out of the Lodge’s grounds was a few miles of windy road through the dense forest that the property rested in. Some stretches of it became too narrow for more than one car to drive through it at a time, but it was no issue for the bike. Still, I kept a relaxed speed to be safe. After all, I did have precious cargo with me now. I could feel Nadya looking around at the gorgeous scenery we were travelling through, watching the trees and what little sky broke through the branches. If I wasn’t focused on driving, I would’ve done the same. I still smiled at the sighs of wonder that Nadya breathed out against me. 

It was already pretty cold out and the wind from driving made the weather’s bite all the more sharper. Nadya shivered when I picked up speed during a straightforward patch of trail and she held onto me tighter, resting her chin on my shoulder. I knew it was because I was warm, and I certainly didn’t have any problem with that, but it couldn’t stop my heart from singing with hope. Maybe she liked being close to me like this. I would let her know that she could whenever she wanted to. 

We eventually made our way into town and I parked in front of the general market. 

Nadya chuckled when I turned the engine off, “Okay, you were right. That was pretty awesome.” 

“Motorcycles aren’t always death machines.” I replied while she got off. 

Nadya ran a hand through her hair after she took the helmet off and it made me stumble when I dismounted the bike. As if it was instinct, Nadya reached out and grabbed my arm to steady me. That was all it took for the nerves to return. The familiar tingling sensation numbed my arm where her hand made contact and lit the rest of my body on fire. 

“You okay?” 

“Um, y-yeah,” I nodded, nervously tucking loose hair behind my ear while I scrambled for an excuse, “Yeah, my foot got caught, I think.”

She seemed to have accepted that excuse, much to my relief, and we continued on into the store. I followed Nadya past several isles, rehearsing my second attempt in my head. Then I rolled my eyes at myself. Could I be acting any more like a stupid teenager? What, am I on some stupid romance movie? That idea made me cringe and I wanted to forget this whole thing for a frustrating moment.

Nadya was still oblivious to the crisis I was going through, her eyes scanning the shelves in the medical section with that cute concentrated frown that she does. After a minute, she pulled out the piece of paper that she put in her back pocket before we left and put her glasses on to read whatever was written on it. I waited, somewhat transfixed, as she picked out a few bottles and medical supplies, dumping them in the handheld basket I was carrying for her. 

“What do you need all of this for?” I finally asked. 

She casted me a playful side-glance, “For you.”

“Me?” A crooked grin appeared on my face, “What makes me so special?”

“The fact that you can barely go anywhere without getting yourself hurt.” she stated nonchalantly while studying the back of a pill bottle.

“Okay, that’s an exaggeration.” I snatched the bottle out of her hand and placed it back on the shelf. 

Nadya raised her eyebrows at me, as if to say Really? and picked the bottle back up again. “It happens about seventy percent of the time.” 

I scoffed, “Forty.”

“That’s wishful thinking. Sixty.” she fired back.

“Fifty.”

“Fifty-five if you’re behaving.” 

I never really thought about the statistics, but her math was probably correct. So I switched tactics, “What about you miss goodie two-shoes? With a disposition and a face like that, it’s a wonder you haven’t gotten mugged.”

“What does that mean?” Nadya asked through a giggle.

“You know,” I felt the confidence that had built up from our banter start to crumble, “Kind eyes… gorgeous smile…” I couldn’t tear my eyes off of her as I waited for her reaction.

A deep blush colored Nadya’s cheeks when she realized the compliment was genuine. Her eyes dropped down sheepishly to the paper she was still holding. After a long, terrifying pause, she handed it to me. It was a list of words that must’ve been medicine related because I only recognized a few of them and couldn’t pronounce many more than that. 

“They like the idea of me joining the clinic.” she explained and I pushed away the sinking feeling I felt from her changing the subject. “When I went there earlier today, I saw that the place is pretty barren as far as supplies go, so I made a list and Toby’s father gave me his credit card to buy everything I think we might need.”

“Nadya, this is great.” I told her honestly. Just because I didn’t know how to take her silence at my blatant flirting didn’t mean I wasn’t happy for her.

“Well, it’s really all thanks to you.” her blush returned as she beamed at me. That made up for the dread in the pit of my stomach. “You put in a good word for me.” 

I felt my own cheeks grow hot and I handed the list back to her. “Anytime. Anything you need from me is yours.”  

We stared at each other for what felt like forever, but couldn’t have been more than a few seconds. The gentle look in her enchanting brown eyes made the rest of the store completely disappear. Nadya’s silent gaze spoke a thousand words that I couldn’t decipher in this moment where I was lost in her. Then I was distracted by how far I was from her. Maybe it was only a foot or two, but it felt unbearably like miles. There was a voice in the back of my head begging me to move closer. Begging me to kiss her again. 

“I’ve always known I could count on you, Jay.” Nadya told me softly. 

I slowly reached for her. My fingers had just barely made contact with her forearm when a loud crackle from the store’s loudspeaker made me jump. An employee called for someone’s assistance on aisle three and I couldn’t care less, but the echoing monotone voice brought me back to earth. Not here. Not yet. The voice in my mind spoke. So I pulled my hand back. 

“Did you get everything you needed?” I asked in a somewhat hoarse tone.

Nadya blinked like she just came out of a trance too and looked at her list for a few moments. “Um, yeah. Everything that I can get here.” 

I noticed while we were waiting in line to purchase our items that Nadya had grown uncharacteristically quiet. She didn’t speak to me or anyone else except for the cashier when it was our turn. Even her glances were brief. It wasn’t until we got back to the bike that I realized it was definitely something that I did because she didn’t wrap her arms around me this time, she only lightly placed her hands on my sides to hold on. 

That heavy sinking feeling filled my gut again. It made me regret every stupid decision I’ve ever made. My mind ran through every single thing I’ve done to cause her offense and I spent the rest of the ride silently reprimanding myself for it. Rehearsing an apology instead of a way to ask her out now. All of it coalesced as one phrase inside my head, She deserves better than me. 

Once we got home, I was eager for a turn. Wanting to run and banish all of these feelings from my mind. Or at the very least, try to. Pretend like I can. Maybe if I pretend hard enough, it will actually happen. 

I said a quick goodbye while Nadya returned the helmet to the bed of the truck and started to walk away, trying not to look at her because of how painful it was. How painful it was that I already messed this up before it even started. 

“Jayde?” Nadya called. 

My steps halted and I half turned to look at her. “Yes?”

“What were you really asking about?” she hesitated for a second, her feet shuffling in place like she wasn’t sure about where she wanted to go, but then she tentatively came towards me. “Earlier.” 

I knew exactly what she was referring to. And the fact that she suspected was even more surprising considering she had been oblivious up to this point. I suppose I should’ve known that she would put it together. Nadya is too smart to not figure it out. Well, I couldn’t avoid it anymore. More importantly, I didn’t want to. Now was the time to leap into the unknown. 

“I was…” the words were still hard to get out. My hands started to shake just like they did when I went to kiss her last night. “I was trying to ask you out.” 

“On a date?” Nadya asked, completely bewildered, “Romantically…?”

She sounded so baffled that I let out a half-amused, half-nervous breath, “Yeah, exactly like that.”

There was a long moment before she said anything else, “I didn’t think…” 

“Didn’t think what?”

“Well,” Nadya started to fidget with a ring that she was wearing, “I felt something when you kissed me, but… today you were acting weird and I was starting to think that maybe you changed your mind.” 

It was my turn to be baffled. How she could think that I would ever change my mind was an impossible concept for me to grasp. My head shook with disbelief and I took a couple step towards her, “I want to be with you. I want you so bad that I’ve been freaking out about it, trying to figure out the best way to tell you. And even then I wasn’t sure you wanted me.”

Her voice was barely more than a whisper, “Of course I want you.”

The relief I felt at her soft spoken words was so profound that I actually felt my eyes begin to mist. I released something between a sigh and a laugh, trying to rein in the emotions that were threatening to get away from me. I felt utterly ridiculous for all of my worries, but in the best way. “Then what’s stopping us?”

Now that we were finally saying it, now that we finally admitted what we wanted, the energy in me was making my skin tingle as though my very soul was trying to break free. Nadya’s gaze remained locked on mine for a handful of charged beats. Her eyes scanned my features carefully like she was looking for any sign that I might be lying. She would find none, of that I was certain. When her warm eyes settled back on mine, a small, shy smile crept across her lips. 

“Can I kiss you?” Nadya asked.

It took everything in me not to immediately pitch forward, but I wanted this to be all her initiative. “You don’t ever have to ask.” 

Nadya’s lips met mine for the second time and it was just as breathtaking as the first. Just as comforting and fulfilling. Only now there weren’t any doubts between us, only a certainty that was powerful and strong. I felt it in the passionate kiss she gave me, in the way her hands went to my hips at the same time mine reached up to cup her face. It ignited my body and drew me closer to her. My thumbs lightly brushed her cheeks, still careful and not wanting to come on too strong, but I couldn’t not touch her. It didn’t seem to scare her. Nadya actually leaned into me more, wrapping her arms around my lower back to pull me even closer. If I could have this every day for the rest of my life, I would die happy.

We broke away to regain breath and I took the quiet moments to let her fill my erratic senses. The intensity with which I felt her was starting to fully dawn on me. How much I cherished having her in my arms. The fact that I knew there wasn’t anything I wouldn’t do for her if she asked. I knew I was falling for her, but somewhere between that realization and now, I had fully fallen. It took me so long to recognize it because it felt different in a way that was hard to describe. But I knew it was the best feeling I’ve ever felt. It gave me hope. It gave me warmth. Most of all, it made me feel safe, and I haven’t felt this safe since I was a kid with my family. 

I felt Nadya smile against my touch, “You were freaking out, huh?”

When I opened my eyes, I saw her smile was a little cocky. It made me laugh and I dropped my hands to hold hers. “Yeah, uh, I promise I’m not usually so awkward about this sort of thing. It’s just… different with you.” 

She tilted her head slightly, “Why is it different?”

“I don’t know, it just is.” I saw her grin grow wider, so I continued through another laugh, “Don’t make fun of me, I’m trying to be normal!” 

“If I wanted normal, I probably wouldn’t have kissed a werewolf.” Nadya giggled. 

“Excellent point.” I agreed with a slow, amused nod.

“You don’t have to try to be anything other than yourself with me.” 

That meant the world to me. My right hand came up to rest on her cheek again and she leaned into it, looking at me with such a fond gaze. “Let me make you dinner tonight.” I begged softly, caressing her cheek, “And take you somewhere pretty.” 

She cradled my other hand close to her chest and started to play with my fingers, “You want to make me dinner?” 

“Whatever you want, I’ll make it.” 

“Surprise me.” she turned her head to place a kiss on the center of my palm. 

My smile was so broad that it was starting to hurt my cheeks. “You got it.”

Nadya’s eyes brightened even more with my promise. “Then it’s a date.”

[jhonsmith88-blog]

How To Get More Online Funding For Your Crowdfunding Projects

Nowadays raising money is getting progressively harder for philanthropies and not revenue driven associations. There is expanded challenge with such huge numbers of foundations doing combating it out for the consideration of givers and obviously the retreat means individuals are taking up some slack. However an ongoing report by NCVO demonstrated that online gifts had expanded more than 2011/2012 by 7%. That figure alone probably won't sound like much - however considering £9.3 billion worth of gifts were portrayed in that year that fills in as a reasonable old wad of cash. Individuals' giving propensities are changing and accordingly philanthropies and not for benefits need to ensure they are remaining in front of the occasions and making it as simple as feasible for their supporters to give on the web.

In this blog entry we feature a portion of the manners in which you can get increasingly online gifts for your philanthropy.

1. How would I give?

This isn't an inquiry you need your supporters to ever inquire! You need to ensure individuals visiting your site know precisely how they can give to your philanthropy. Your give catch ought to be exceedingly noticeable and show up on each page of your site. Regardless of where your supporters land they ought to have the option to rapidly explore to your give catch and not need to chase around for it. This is on the grounds that the additional time they need to spend searching it out, the more uncertain they are to proceed with making a gift.

Putting the give catch over the overlay implies it is noticeable without looking over.

Partially, this is likewise significant as a result of contributor desires. Guests to your site will expect the give catch to be conspicuous - either in the top route or if nothing else in the top portion of your site.

The Samaritans have an incredible case of a well-put, exceedingly noticeable give catch:

Not exclusively is it situated high up the page, yet it likewise emerges. Regularly give catches can be packed with a bustling page which means they become less unmistakable to guests. By keeping the encompassing zone of the give catch uncluttered it's simpler for potential givers to explore to. What's likewise extraordinary about the Samaritans give catch is the utilization of delineation. It's with regards to the plan of the site but since it's uncommon and diverse it truly emerges. The utilization of the outlined philanthropy tin additionally makes it clear this is the place you can make a gift.

Keeping things straightforward can have a similar impact of making the give catch emerge.

Vitalise do this by utilizing a differentiating shading for their catch yet at the same time utilize a similar rule of keeping the catch clear and uncluttered.

2. Give data on why individuals ought to give

The UK populace would give an additional £665 million every year to philanthropies on the off chance that they were given more data about where their cash was going and what effect it would have.

This was one of the fundamental discoveries in the biggest ever UK think about into the inspirations of benefactors did by NPC not long ago.

Oxfam do this truly well by as a matter of first importance laying out what's going on for example that Syria is in emergency with in excess of a million displaced people escaping brutality and after that featuring what they need for example safe house, nourishment and water.

Above all Oxfam at that point plot what it is they intend to do and how they intend to do it.

It's likewise imperative to layout precisely how the cash raised from gifts will be utilized. This is additionally a decent method to urge potential benefactors to make a normal commitment as opposed to only an irregular gift. Incredible Ormond Street exhibits this well by appearing changed normal gifts can purchase:

The Salvation Army goes above and beyond and utilizes dynamic substance so when the client changes the sum they'd like to give, the substance changes also indicating what that gift could help with.

3. Give individuals the alternative to give to explicit causes or ventures

Once in a while people can be hesitant to give to a philanthropy for dread that their cash isn't generally going to support anybody however essentially subsidize the administrator and running expenses of the philanthropy itself. It is obviously significant that assets are raised to help the running of your philanthropy, however you additionally would prefer not to pass up potential givers who might possibly give in the event that they felt their cash was being spent legitimately on a reason. Featuring individual crusades can be an incredible method for focusing on this specific sort of benefactor. Littler battles regularly have increasingly point by point data and therefore can help urge guests to help a particular reason.

Spare the Children has various battles running without a moment's delay with data on every one of these crusades. This implies their supporters are given a decision and can help bolster a reason they feel emphatically about.

Tree of Hope, despite the fact that an a lot littler philanthropy, likewise do this truly well by setting up explicit intrigue pages for individual kids and sharing their accounts:

4. Give a possibility for coincidental gifts and customary giving

Standard gifts are the favored choice for foundations as it enables you to prepare dependent on income.

Anyway a few people may very well need to give a coincidental installment as it's critical to cook for these supporters as well. For instance at Christmas individuals may be bound to make gifts to destitute foundations yet at the same time probably won't be set up to focus on a normal installment.

Asylum does this truly well by choosing the coincidental installment as the default choice. They additionally recommend set adds up to give yet in addition enable supporters to alter this data to transform it to a gift that suits them:

So as to urge individuals to turn into an ordinary contributor plot what their gift will help accomplish, give data on why this is so significant and demonstrate the effect that normal gifts has had on your philanthropy. Guide Dogs does this phenomenally. On the left hand side of their standard contributors' page they diagram what the gift can do:

In the principle segment of the screen they've implanted a video recounting to the tale of how a little gift of £2 has changed genuine individuals' lives:

They at that point detail precisely what they need the cash for and how a month to month gift could truly help:

5.Recognize the best apparatus for taking on the web gifts

Instead of facilitating on the web gifts yourself which can be expensive as far as improvement work it may be progressively suitable to utilize an outsider apparatus, for example, Just Giving or Virgin Money Giving. These instruments are progressively prominent with philanthropies as they give numerous advantages over keeping your gift structure nearby. Presently over 33% of little philanthropies' absolute gifts are currently gathered online through an outsider device appearing well known they've progressed toward becoming.

Advantages include:

They are anything but difficult to set up and oversee

The entire value-based procedure is overseen for you

There are no powerful forthright expenses

Most apparatuses incorporate with internet based life to help extend your span to the companions of your benefactor

We should take a gander at two instances of outsider apparatuses in more detail:

Simply Giving

Simply Giving is a mainstream decision for philanthropies as it is so outstanding however it additionally offers some extraordinary highlights that make it simple for both your philanthropy and your supporters to utilize.

A Just Giving page can be marked to resemble your site or you can install a "giving" gadget on your site so your supporters don't have to leave your site to donate.Bipolar UK utilize this element on their site:

Simply Giving can be coordinated with a CRM arrangement making it simpler to get to the majority of your supporter information.

The instrument permits social sharing significance givers can impart to their companions on Twitter and Facebook that they have recently made a gift to your philanthropy.

The expenses of utilizing Just Giving is £15 every month in addition to commission of 5% and they take a charge on blessing help gifts

Virgin Money Giving

Virgin Money Giving is a not revenue driven association

They charge £100 in addition to VAT set up expense in addition to 2% on all gifts yet don't take an expense on blessing help or have month to month expenses. They are one of the least expensive outsider instruments accessible.

You can mark your page to resemble your site - including transferring your logo, change text dimension, change shading or content, transfer photographs and can include additional pages. Oxfam is an incredible case of a philanthropy who have done this:

Virgin Money Giving offers announcing including customisable reports

You aren't ready to insert a give structure legitimately on your site

There are numerous outsider instruments accessible and the most ideal approach to pick the one that is directly for your philanthropy is to make a rundown of your needs and what you need the device to do and after that analyze each apparatus against this rundown. You can see a point by point correlation list here.

6. Unmistakably characterize a guest's adventure

Since individuals don't receive anything consequently when making a gift, they are bound to surrender the gift procedure if issues happen than they would in the event that they were just making a buy for themselves. This implies the gift procedure should be consistent, making it as simple and agony free for the benefactor as would be prudent.

Make a point to keep the gift structure negligible and just incorporate fields you truly need as opposed to requesting heaps of data that may be 'pleasant to have'.

It's likewise worth abstaining from having a survey and affirm venture as regularly individuals might suspect this is the affirmation page and consequently surrender the procedure at this stage.

On the off chance that conceivable, attempt and catch all the data you require on one page or if nothing else demonstrate an advancement marker so individuals know what number of pages of the structure they have left. Simply Giving does this well by indicating which stage a giver is on simultaneously:

Regardless of whether you are utilizing an outsider instrument, it is essential to consider the client venture. This can likewise enable you to choose which is the most suitable apparatus to use for your philanthropy.

7. Blessing Aid made simple

One of the incredible rewards of utilizing an outsider instrument is that it makes Gift Aid a lot simpler to process and oversee. On the off chance that you are facilitating your online gift page, at that point you have to get the

[sidewaysdreamer]

I wrote a story a while back...

And I thought I might as well post it. WARNING: This story deals with mature content, such as suicide and depression. If this bothers you, please, either stop reading, or skip to the very last paragraph; I find that one to be the most important. While this story was inspired by real life events, it is not 100% true, and any and all likenesses to people are completely accidental. There comes a time in everyone's life in which they have a story to tell. This story, it reflects the innermost parts of that person, the parts they never would have shown people until that time. Right now, it's my turn to tell a story. My name is Raimond Sorce, and this is the story of how I killed myself. The week had started out like every other week for the past four years: slow and uneventful with a side depression and self-loathing. Cliché, right? I know. Just like every other story. The only difference is, in my story, no one knew. Not my parents, not my best friend - hell, even I didn't really know. I thought I wasn't getting enough sleep or enough vitamins, not that I was suicidally depressed. I remember walking down the hall of my high school, seeing all the teens laughing or holding hands, or those simple few, like me, who were just trying to skate by with a C in Calculus. Later, I remember sitting outside after practice, just watching cars go by, when I realized I was thinking about what would happen if I walked down to Main Street and simply walked into traffic. Then, when I realized I had stood up and started walking that direction, I realized something was wrong in my head.  Now, I was the kind of person who kept to myself. I held open doors, I sat in the back of the class, I set three alarms just to make sure I actually got out of bed in the morning. But learning that I subconsciously wanted to die was something even I, myself, didn't want to believe.  Unfortunately, my being suicidal actually made sense. I wasn't sleepy, I was tired - of life. Tired of the day-to-day looping routine. Tired of having to be. So I made the decision to die. Now, mind you, this was in no way an easy decision to make. I had seen all "you hurt more people than you think" pictures and all the "I believe in you" quotes. I, myself, was guilty of posting these and sharing these to help out others. But I wasn't happy. I hadn't been for a long time. And it was finally time to do something about it.  I decided to finish out the week at school. I don't know why, it just felt important. Like it was almost ironic that I planned to die on a friday. Maybe I was stalling. Maybe I didn't fully understand the magnitude of what I was about to do. That day, I gave my best friend the biggest hug I ever had because I knew on monday, it wouldnt be me there to support him. I turned in all of my late work to every teacher. I returned all of my library books. I cleared out my locker. I gave 110% at practice that night, and when everyone had left for the weekend, I started my journey. In a daze, I walked to the end of the school. As I stood at the chain link fence that surrounded it, I felt numb. Slowly I turned around and started walking. I walked through the hall, peering into each empty classroom, knowing I would never see them again. I stopped and gazed into every display case there was, looking at items and papers from the school's glory days, way back when. I looked into the cafeteria, knowing I would never again mindlessly clamber through, looking for the biggest serving of ravioli I could get my hands on.  I walked past the outdoor tables, thinking of the few friends I had and how we would risk the 30 degree weather just so we didnt have to interact with other people. We practically lived at that table: the one closest to the garbage, but farthest from the people.  I walked around the science wing, looking through the windows at the chemistry class colored test tube experiments and the astronomy class diagrams of the stars. I stopped as I passed the agriculture room. Being a part of the FFA had changed my life, but I still stopped to think back and remember the good times we had in that classroom: making flower diagrams out of play-doh, learning how to act in an interview, gaining skills that would give me a step up in the world. I was grateful for my time within those four walls. After that, I slowly made my way to the common area, the place we all went between classes to chill for a bit or to get something out of our lockers. I walked over to all my old lockers. I had four, in total: first came one by the student services center, then one somewhere in the middle. Next came my locker in the far corner of the room, and finally came the locker I was using this year. Number 33. Code 7-45-37. Being a senior, I was finally eligible for the double digit lockers closest to the doors. As I opened it, I was greeted by a flood of old memories: the notes my friends and I put in each other's lockers, one of my best friends finding out exactly which lockers you could open without a code and putting a paper with pi to a thousand places in each of them. Unreciprocated crushes. I put each memory in my backpack and closed the door. Finally, I walked past the music room, half illuminated by security lights. As I gazed through the "soundproof" glass, I realized I never again would feel the adrenaline pump through my veins as I auditioned for a solo or feel the frustration when that one soprano kept aiming for that high notee that just wasn't in her vocal range. I had never seen the room look so empty, not in my six years of basically living in that room. As I walked past it, I couldn't help feeling a bit forlorn. That room was never going to be the same. As I got into my car, my original plan came back into my mind: drive and don't stop till the car does. Which basically meant drive off a cliff, but I wanted my parents to at least have closure. So I went with Plan B, which took place at home. When I got there, everything was as it usually is: parents car in the driveway, pile of leaves under the tree in the front yard. I walked up the drive with a determination I had never felt before. I was really gonna do it. When I got to the door, I hesitated. How was I going to do this? It was hard enough to do stuff with one parent home, let alone two. If I was going to do this, everything needed to be perfect. With that, I walked in the door. . . and looked my mother in the eye and said "I need help." I'll bet you ten to one that throughout this entire story, you thought my parents were going to find me in my room, lifeless; that I was really going to kill myself. Well, I did. I killed the part I didn't like. I took the biggest leap of faith of my life, telling my mom how I was feeling. We talked it out, and took the necessary steps to work it out. I'm now on three different medications that are working wonders and I couldn't be happier. I even managed to snag a boyfriend out of this whole ordeal. We've been together for almost three years now, and are going to college together.  Please, if you are going through something like this, tell someone. There really are people who care about you. I care about you. There are sites like 7cupsoftea that have real life people waiting to hear anything you wanna talk about, and if its starting to get really serious, please call the National Suicide Prevention Lifeline: 1-800-273-8255. Someone is there 24 hours a day to help you. You are not alone. 

[ts-seychelles]

EP. 3 - “I Want To Work With Someone Who I Don’t Have To Push A Narrative Toward” - ALEX

So to recap what in the fuck just happened. I was able to get Frankie out because no one had the balls to throw a name out, so I was like Fuck it throw the person that hasn't talked to me much under the bus. Turns out it was the right choice because like Alison he was stuck in a palm tree and couldn't vote. Then as my Low key alliance predicted, we would have a swap at 18, way to be original hosts you did this on another season you hosted. However I didn't get swapped fucked as I originally thought, since I thought it was just me and JG however Jared popped up and now our tribe is 3-3. Now Operation Kiss Ass is ago because I have three brand new tribe members to suck up to so we can have a majority vote, or at least I can have a majority if they want to pull cross tribal thing. But on the bright side I wasn't voted out 18th, I wasn't second voted out on my original tribe, and I made a swap so I improved so much already compared to flops yay!

So the first round we voted out a inactive and then I got sent to ghost island. I am really close to Drake and Jared and with this swap we have now solidified and have a split majority where all we might need is just 1 ... I know of Aysa and I know alex pretty well so we shall see what will happen

Getting swap fucked is my kink. It happens in almost every game I play. It’s such a cute look. 

My tribe seems...nice? I know Roxy, Johnny, and Vi from previous games, so I’m hoping we can make something work. I’m terrified being the only one from the old Malabar tribe here, but that just means we’re gonna have to try our hardest to win. I’m just gonna put in my all and hope for the best.

This swap couldn't have gone better. We swapped 5-1 AND I'm in an 4 person alliance with the 5 we had from before the swap. The next couple of rounds should be nice.

https://www.youtube.com/watch?v=5PF0vMamDY4

Alex is a sneaky one all right, he's trying to get information from me about the tribe dynamics but I'm just going to reflect it back onto him to get more info out of him than he is me. I ain't stupid, I know when it comes down to tribe swaps, espicially in a 3-3 situation the goal is to find that one crack to flip it to your favor. Im loyal to Jared and JG at this very moment so I ain't about to reveal shit to him

oh geez. so tribe swap happened and i think me, augusto, sam, and reagan lucked out. (rip dan because he is by himself) i feel bad for voting out frankie. but we didn’t really have a choice... other than that nicole and vilma seem really nice!

they reuploaded my spam video wig https://youtu.be/mpgD-uqZknI

(A BIT LATER)

https://youtu.be/d1wYk2Hkj48

HOLY SHIT WAIT NICOLE AND JARED ARE DATING WHAT KINDA PLOT TWIST REHASH OF RUBEN/LEXI BULLSHIT IS GOING ON HERE

(A BIT LATER AFTER SCREAMING UNDER WATER)

OKAY APPARENTLY EVERYONE ELSE KNEW BUT ME WHAT ELSE IS NEW

https://www.youtube.com/watch?v=_oTbmew7Fa0&feature=youtu.be

Honestly hosts? This challenge that I going to force me out of my comfort zone? Negative. I’m already literally so fucking self conscious and anxiety filled. I’ll probably try to do my best but these are CRUEL. If I die from fucking salmonella poisoning from eating a raw egg imma haunt all your asses for the rest of eternity. Mark my words.

I didn't want to put too much effort into the reward challenge because I don't want to be seen as a threat, and luckily, the immunity challenge is a scavenger hunt that is at the beginning of a work week, otherwise, I'd knock it out of the park immediately. With work, I won't be able to, but I still shared the resources that I have so that they can work on as a group

https://youtu.be/mZ9MVVL_PP4

Okay so I was doing really well until you put me on this shit ass tribe. As people? Lovely. As players? The worst. How am I supposed to do anything with these people? Like literally how am I supposed to do anything. I’m so mad because I actually want to play this game and they are giving literally zero effort, being the absolute worst, all they do is complain and cry....I’m so over it. I haven’t eaten today so maybe just maybe it’s like that I’m hungry but on the other hand, I hate being with losers. Like I was in a tribe of winners who 90% wanted to give their all. Don’t sign up for the damn game if you aren’t willing to eat a raw egg or whatever we are being told to do this round. Why am I going to put in ALL my efforts for these idiots to just vote me out anyway? I’m so annoyed. They’re literally so fucking annoying. And I know the other tribes are actually doing well because they actually want to be here! And when we lose I’m gonna have to kiss more ass with these people because I’m in the minority. It’s just a fucked end of the stick that I got here when everyone else in my old tribe has it so easy. I got stuck with Vilma of all people and then a group of people who won’t ever vote each other out. It’s just like we are never going to win, I ate a fucking egg for these loser asses the best they can do is read some Harry Potter erotica, like get a fucking grip.

Vi doing nothing for this challenge? Expected.

Okay so, I've been on this new tribe for a few days now. I have really been thinking through every strategic situation. There is another layer to my game though, because I have to run through every scenario like I normally would- and then run through them all again, but account for Nicole being present. Not that I don't want to do well if it isn't with her, because that is just not the case. I just have to think about it... everyone else sure will be. Since there is still a long way to go til the merge- this is like a rough outline in different scenarios of getting there. 1- I want to win immunities. Pretty simple. They create a sense of cameraderie, and even though we are in essence 3-3, the longer we are socializing without discourse, the easier it will be for me to maneuver through this in the event that we must attend tribal. --- It's tough to try to account for Nicole's game and my tribe wins affecting how things go for her. I love her but I can't try to speculate what decisions would be the best for both of us mutually, because I could end up destroying us both. She's a smart cookie. She don't need me to get through. 2A- We win 1st place immunity. New Lazare loses. --- Here I don't know if I should make an effort to get Nicole sent to Ghost Island. I'm hoping my conversations with Augusto, her relationship with Regan from prior to this, and her social game outweigh any counter-offence that Vilma could mount. I think a good compromise would be to send Ashen or Sam, someone who is not an enemy of myself. 2B- We win 1st place immunity. Takamaka loses. --- I don't see a situation in where this tribe unanimously agrees to send Dan to Ghost Island. I would not really push the envelope in this spot, but rather see what everyone has to say and give input based off of that. Only person I would be hesitant to send is Johnny. 3A- We lose immunity. JG/Drake is sent to Ghost Island. --- Tough spot. I don't necessarily think that I would be in trouble, but the question is- Do I quietly send off my alliance mate? Do I hard sell Alex under the bus to Ricky and Asya? I think the latter has a higher ceiling outcome, and the lowest floor outcome. (Me getting evicted.) It would all be based on how far I get in conversations with Ricky/Asya without revealing my intentions. There is another option, and that is trying to get Alex to vote off Ricky or Asya. Close to 0% chance of pulling that off, and I don't think I would like the lay of the land coming back from that tribal. 3B- We lose immunity. Alex is sent to Ghost Island. --- Whatever Drake and JG want to do. Not a big discrepancy between Ricky and Asya here for me. I do like Asya a bit more. 3C- We lose immunity. Ricky/Asya is sent to Ghost Island. --- Do I persuade my counterparts to get Alex out in this spot? I do, I do! Why? Because Alex is nowhere on that venn diagram of people that will protect Nicole and myself at the merge. ---------- I had a call with Alex last night and got a lot of information from it. Lots and lots of ammunition that I will hold onto until the time is right. I think he likes me, or at least wants me to think that.

https://youtu.be/xlskto5cgVM

https://www.youtube.com/watch?v=TlpWEnCbpD0

(A BIT LATER)

These fucking animals will be the death of me. First Zumba with Tortoises & now  I'm covered in bat shit and carrying rabies. WTF who knew idol hunting would be so damn hard!!!

Had a call with Ruben last night, where he said he trusts Zach and I the most, and that Roxy has gotten kind of boring to him. I slipped it in there that Roxy is a pretty dangerous player (which she is), and those seeds just give me even more confidence that I can get her out sooner rather than later. Even thinking about making a power move to just have Zach, Ruben and I vote for Roxy, and then Roxy and Vi would vote for Dan, while Dan would likely vote for Vi, so idk...... When I was talking to Ruben, he clarified I'm his #1 and I'm going to start treating him like he's my number one. I don't wanna tell him about my idol yet obviously, but I think I can trust Ruben a lot. I know I've got that same kind of relationship going with Zach AND Crooks, so I'm spreading myself thin, but not trying too hard socially. Still can't find that Takamaka idol. I only got to search once, but I doubt I'm getting as lucky as I did on Lazare. I'm shook we won immunity when Vi didn't do anything.... it's kind of why I would feel bad voting out Roxy or Dan before Vi goes home..... she just doesn't continue to stay here. Ruben and I said we're going to see who goes home from the tribe that's going to tribal this round, and after we strong armed sending JG to Ghost Island, it gives Alex, Ricky and Asya numbers..... IF they stick together. I can see them not sticking together because I know those are the two people that Alex has spoken to roughly the least? So I could see Alex trying to work with Jared and Drake. I won't really be sad either way, but if someone from OG Lazare goes home, then Dan's fate might be sealed, which is kind of what Crooks wants anyways, so I guess good for him. I've never felt in such a secure spot in a tribe before, just because I feel our tribe is so strong, and we're just thinking about winning, and there are two easy outs, with a lowkey third person coming up on the horizons (Roxy). We'll see if I can get what I want this early in the game :P I will try to make this tribe interesting somehow. I promise!

You want the tea huh? We fuckin won binnnnnch. I’m shook honestly haha. Vi was as useful as soup on a hot summer day and we still literally got first place. So I’m liking that my tribe seems motivated to do our best in things, I was so worried I would be boned by the swap, but I seem to be acclimating well. The one thing that scares me is that Jared is vulnerable this week. I was hoping to push for him to go to Ghost Island but my gay ass was asleep of course. JG going is whatever, it’s just gonna give a fucking floater more power. It also puts the old Lizard (or whatever their name is) tribe at a 3-2 advantage over my cute old Malabar’s but whatever my ass is safe and that’s the only ass I care about.

Ugh I hate being in a 5 person tribal, my 3rd tribal in a row, and one where the tribal lines leave me in the minority. I'm trying to vote out someone I just am very cautious when throwing out a name since I am in the minority and Ricky, Alex, and Asya could easily team up and vote me out. I may just go and try throwing out Asya because she's the one I talked least to.  It's ugh im going to have a panic attack rip

Its really hard protecting vi tbh but shes loyal only to me so shes good for me

I did say every one on my tribe was great right? Totally carried me through this as I died physically and mentally. Maybe I can finally get some sleep and then tackle tonight’s thingy ;-;

https://youtu.be/rxYjBRg9u1g

I thought I liked Jared but he’s a little bit of a weasel. Jared’s like Kraft Mac and Cheese. It’s not your favorite food, it’s pretty mediocre. But it’s still good? Like if nothing else we’re there it’d be great. But I’ve got like a sundae with Asya and a Rueben Sandwich with Ricky. He’s smart. But I don’t like that he is. I wanna work with someone who I don’t have to push a narrative towards. However I feel great about Asya and Ricky. But we lost and if I get voted out the day after my three year anniversary I’ll cry.

I'm liking my new tribe so far. Nicole did most of the work last challenge and I am so grateful for that. Vilma is also sweet and funny, love her. Yesterday stressed me the fuck out though. I can handle the workload. Pippa is hosting this game, is in like 5 theatre shows, and school. So, I think i can do this. (PIPPA REALLY APPRECIATED THIS) MY INTERNET is what is fucking me up though. I could have done so many more of the video dares if my internet would just do what it supposed to do. It's so stressful. GOD. I just hope this shit doesn't ruin the game for me.

Smooth sailing as far as last tribal goes, which is surprising since I expected Frankie to come online and for it to be a bloody mess but guess not! Speaking of a bloody mess, I’m avoiding one! With a tribe swap happening, I get to avoid picking any sides as Malabar was about to turn into a Ashen/Drake vs Dan/Regan/Jared with Samantha and I in the middle -type of sticky situation really quick SO now I get to save face. Tbh, I am not really sure what side I would’ve chosen if it was a battle of the Malabar titans since a lot would’ve had to factor into the vote. With this new swap, I want to continue to build connections but do so at a slower pace (unless I end up swap fucked and then I’m kicking my charm into high gear) so I can once again have options. We’ll see what happens!

(A BIT LATER)

The results are in…. And I’m lucky for once? The swap actually worked out almost perfectly and the only instance where that has happened is Flops when I got to choose my new tribe. On New Lazare, Malabar has the numbers with 4 original members (Ashen, myself, Regan, Samantha) and we have Nicole and Vilma from original Lazare. Here, I have the option of sticking to my alliance with Ashen and Samantha, trying to stick it out with Regan as a newfound duo, or try to get super close to Nicole and/or Vilma somehow for options. Fluidity and adaptability is what the game is all about, so I’ll try to make that my mantra per se during this new phase of the game. As far as my other OG tribemates go, they are in shitty positions and I feel awful. My partner in crime Jared is in a 3-3 situation alongside Drake and JG and then Dan is by himself on the newly formed Takamaka. I relate so much to Dan in this moment just because I was all by myself during Socotra and had to face those giants all on my own, so I hope they at least give him an opportunity cause he does not deserve that. This swap is gonna be interesting for sure, though. I’m excited!

(EVEN LATER)

Can I just say I love Nicole? Okay so, her and I had a really good heart to heart both personally and strategically which makes me really excited. Before I swapped onto Lazare, Jared and I had a conversation about him and her and what their relationship entails as far as the game goes. He told me that he would never want to vote her out, which I totally understand and would never expect him to do because that’s a REAL relationship. I’m selfish but not selfish enough to ask him to pick between her or myself and I reiterated that to him. Anyway, I ended up telling Nicole that Jared and I were the best of buddies on OG Malabar to connect better with her as a person and as a gameplayer. I also told her about my conversation with Jared and how he really cares for her, etc. By doing that, Nicole and I really got to bond and had a conversation about this whole Malabar versus Lazare mentality that is likely to plague the swap as it did the first two rounds of the game. She also let me know that Alex was very much assertive in the fact that he wanted to decimate Malabar completely and how people such as Ricky and Asya were not very about it, etc. We also brought up the possibility of us working together in the future, which I am super about because it allows me to be close to someone from Lazare and gives me the bridge I need to those players, plus it helps me get even closer to Jared. Also in a sense, their relationship can be a shield for me in the future as a couple would be more likely to be targeted than a third wheel. Overall, I am feeling really good!

So......we won immunity 😬 I was really thinking my tribe was gonna be lazy because they wasted a full 36 hours. At the last moment they came through and fucked over my boyfriend, a sentiment I’m sad about but...also we won immunity. I’m kind of worried for him but Ricky seemed genuinely on board with me and I don’t think he’d want to make an enemy of me. He knows I can be a villain if he calls for one.

So the challenge is not really over, but I do think we’re doing okay? I am just thinking about a future tribal scenario and I think my game plan for this stage of the swap is to maybe get rid of Vilma and then Ashen so I can stay working with Regan, Nicole, and Samantha. Vilma is a good asset in challenges and overall an amazing person, but I do think it’d be smart to get rid of at least one Lazare person. As far as Ashen goes, I do find her to be a threat only because she seems somewhat social and she is the one that started the alliance between myself, Drake, her, and Samantha. The only thing is that she had no real agency/pull when she tried to get rid of Regan instead of Frankie so who knows. At least I could potentially use the fact that Ashen wanted to axe Regan against her in the future, but we’ll see… maybe I’m moving too fast haha. Let’s just get this bread first and I’ll act crazy later.

wish i didnt have to go to tribal xoxo

I knew this iconic color would produce iconic results… I’m…. WE WON?! This is my first challenge win this season and it’s been long overdue, so I’m super happy about that! Not only that, but this tribe is seriously awesome. I love the girl power, the legendary status of these people, and overall our vibe because we came together pretty well. I am nervous for Drake and Jared just because I KNEW that JG would be sent to Ghost Island since he is not social or very much active, so I will likely lose an alliance member this round and I’m sad about it. If I had to guess, I’d say Drake is the boot since Jared is more social and the Lazare on that tribe probably don’t want to piss off Nicole this early on. If Jared does leave, I’ll be really sad because our duo never got its time to shine, but let’s hope the show can continue whatever the circumstance.

Okay so I like Nicole I like Augusto Vilma kinda hasn't talked to me. I forget who else exists tbh. Sam barely does. Ashen barely does. Me augusto and Nicole pulling the weight on this tribe fr

AGAIN SORRY FOR THE LACK OF CONFESSIONALS, it takes a while to write them and I'm constantly staying up till 5am or even later for this game, so I'm pretty much exhausted during the day. It's tough honestly! Thankfully I haven't had to go to tribal so far in this game so there's not that much tea to spill. Here's my game so far, summarized: I had already forgotten how much my social game sucks. I hate the start of orgs, it's super overwhelming trying to socialize and get to know so many knew people. I get social anxiety every time I see a message pop up on my screen and pretend it didn't ever happen lmao. Thankfully I got put on Lazare which turned out to be a really active tribe and everyone was really nice to me despite the fact that they all seemed to pretty much know each other and I was kind of the odd one out. I think there's a good chance I would have gotten voted out if I ever went to tribal with them, but thankfully we were BEASTS at challenges so we didn't have to vote anyone out. The 99 bottles challenge was a nice bonding moment between Johnny, Zach and I despite me being super awkward as always. And I FINALLY got to compete on the music video challenge for the first time which was an absolute highlight. <<33333 I can pretty much go home happy now, I got what I wanted LMAO. Don't worry I'm not quitting though. Just as the tribe swap was happening Roxy told me that her idol clue said the idol's not in the volcano. I appreciated the info! All in all, I didn't talk much game with anyone on Lazare, which made me think I was on the outs but on the other hand we never went to tribal so there wasn't really any real reason to talk about strategy. Besides, I was horrendous at answering messages so it's partially my own fault. Then I got swapfucked. Haha okay that might be a slight exaggeration, but clearly numbers weren't on my side at the swap as I went from a 10 to 8 advantage to a 4 to 2 disadvantage. The only person from og Lazare that swapped with me was Nicole. We made a promise to have each others backs and she convinced me that she had a good relationship with some of our new tribe mates, so there's chance she could convince them to flip. I hope that's true, but it could very well be the end for me if end up to tribal. It's pretty difficult trying to understand all the game dynamics since so many players already know each other! I'm just trying to observe as many conversations as possible to figure out who's close with who, and who's against who. Even though my new tribe lost the reward challenge by a pretty big margin, we were able to come together in the last minute at immunity and placed 2nd, so we didn't have to go to tribal. WOOOOOOO I live to see yet another day in this game!!!! Awesome. I really don't think I have any chance to win this, so my plan at this point is just to hang in there as long as possible. So far I've done pretty good at that if I say so myself. I'm gonna try and continue to be a challenge beast for now, just so that my tribe would think they need me if we end up having to go to tribal. Summary: Loving the challenges, failing hard at the social game, but most importantly HAVING GREAT FUN THANKS

Omg I feel so bad for not contributing much to the comp but like also I’m super busy so I guess it’s fine?? We won by 15 points and I only sent in 12 points hahaha at least I did something! I really like Nicole and Vilma, and I’m happy that my old tribe has majority here, so I don’t want to lose because I don’t know how that vote would go! Oh well :(

[mshai002]

Wekinator

I was recommended by a number of people to look into wekinator machine learning. I learned a lot which actually helped me grasp my Data Mining module better. Machine learning pipeline was explained in a simple way as follows: the structure of machine learning is made up of three main linear aspects; the first being (1) Input, whereby a sensor of some sort (game controller, webcam...) receives information, which gets transferred to a (2) processor decision-making process, where the process basically figures out what to do with the info that just came in. Once it decides what to do, the decision is transferred to (3) the output, where it basically ends up doing something in the end (an animation or a sound...). 

The second step’s processor is basically the core and, in my case, the most relevant topic in the subject. This step entails what is called ‘supervised learning,’ which, unlike un-supervised learning, is supervised by the person who assigns certain samples to train it on. These samples are called training sets. This training set is what the model learns from. And each sample is basically a simple combination of the first two steps: the first-step and the last-step: input and output. The process of learning and creating an algorithm is a three-step process: training set, algorithm learning, and model creation. The process of using a learning algorithm to build models is called ‘induction,’ or ‘learning a model,’ or ‘building a model.’ However, a classifier is not the same as a model. Models define the classifiers, and not every classifier is defined by a model. 

How Wekinator and my Data Mining module worked together in my understanding of the basics of machine learning: While learning an algorithm there needs to be a pattern recognition based on the training set. The pattern is then used to ultimately classify the end decision. What action or output should be selected for this particular output. Classification is the most salient feature in my project. 

For classifications, the computer recognises a certain number of categories, or classes. These categories are independent and are the last column in the table. There are multiple ways of classifying data. Common ones that I learned are Decision Trees and Nearest Neighbours algorithms. A decision tree is diagramed below on paper. The tree starts with a root node from which follow certain attribute test, which are the columns headings apart from the first and last one as the former is just ID and the last one is the classification itself. After the root node, when an test condition is checked, there are multiple answers, however, we prefer just two to simplify the process. And after each test we check the answer for which we apply a further test or, if it is suitable to get the classification, one is applied. The classification is not necessarily binary, there can be more than two classifications, which is termed ‘multi class classification problem.’ For classifications, not every attribute test is conducive to classifiability. Optimal combination of attributes that best discriminates instances from different classes is key. 

As already mentioned the process of learning an algorithm from the training set is called induction, and the same way the output is called deduction. And induction and deduction steps need to be independent of each other. The deduction has to be evaluated for its accuracy by no. correct predictions/total no. of predictions. The error rate has to be measured--what it got wrong: no. of predictions/total no. of predictions. A learning algorithm has to attain the highest accuracy and the lowest error rate. The selection of the attribute test is crucial and has to be done based on which attribute leads to what was just mentioned optimally. A known measure here is the purity check. How ‘pure’ is it? The higher the purity the lower the error rate. Purity is measured by the following algorithms: Entropy, Gini and Classification Error. However, the purest does not mean the best because the attribute error would be 0, that would mean simply checking the ID and seeing the result, there is nothing for it to go against but the classification itself. So is the first ID this classification or not, basically. We thus need one that can be elaborated, descriptive. 

Decision Trees as can be seen at the bottom in this image, is fast but is exponentially large, getting bigger and bigger with the conditions as each decision boundary involves only a single attribute. 

There is one major problem here: model overfitting. Even if a model fits perfectly well on the given training set, it will not be generalisable as it learns it all too well and adapts to that set permanently and rigidly. A reason for this is that the training set may be too small and thus not generalisable. Another problem is that the training set may be too complex due to which it become too unique and the tree becomes too specific, niche. Therefore, the resulting tree will not provide a good estimate of how well the tree will perform with unseen attributes. 

Wekinator had a lot of limitations, worked on limited ports, and once at a time.I understand from discussing it my friends that it was just there to introduce the concept of machine learning. There is no coding involved, if you want to measure a distance, just press the button basically. It helped understand how general algorithms learn from data; how to choose one over another. Working with small datasets created by the users themselves, we get a perspective of the human process behind creating real-world machine learning systems.

[bentonpena]

Machine Learning Basics

Machine Learning Basics http://bit.ly/2zNxI4i

By Rekhit Pachanekar and Shagufta Tahsildar

Before we start this article on machine learning basics, let us take an example to understand the impact of machine learning in the world.

According to a recent article in Forbes,

Machine learning patents grew at a 34% Compound Annual Growth Rate (CAGR) between 2013 and 2017, the third-fastest growing category of all patents granted.

International Data Corporation (IDC) forecasts that spending on AI and ML will grow from $12B in 2017 to $57.6B by 2021.

We can safely assume that machine learning has been a dominant force in today’s world and has accelerated our progress in all fields. No matter which industry you look at, machine learning has dramatically altered it. Let’s take an example from the world of trading.

Man Group's AHL Dimension programme is a $5.1 billion dollar hedge fund which is partially managed by AI. After it started off, by the year 2015, its machine learning algorithms were contributing more than half of the profits of the fund even though the assets under its management were far less. 

Machine learning has become a hot topic today, with professionals all over the world signing up for ML or AI courses for fear of being left behind. 

But exactly what is machine learning? It will be clear to you when you have reached the end of this article. We will cover the following topics in machine learning basics:

What is Machine Learning

Difference between Machine Learning, Deep Learning, AI

Components of machine learning

Python Libraries for Machine Learning basics

Popular machine learning algorithms categorised

Common terms to know in machine learning basics

Difference between Machine Learning and Deep Learning

Working of Deep Neural Network

Application of Machine Learning

Growth and Future of Machine Learning

What is Machine Learning?

Machine Learning, as the name suggests, provides machines with the ability to learn autonomously based on experiences, observations and analysing patterns within a given data set without explicitly programming. When we write a program or a code for some specific purpose, we are actually writing a definite set of instructions which the machine will follow. 

Whereas in machine learning, we input a data set through which the machine will learn by identifying and analysing the patterns in the data set and learn to take decisions autonomously based on its observations and learnings from the dataset. While Machine learning is a vast topic which will take more than a few articles and courses, today we will focus on machine learning basics, so that you will know what to expect when you dive into machine learning algorithms.

Think of Facebook’s facial recognition algorithm which prompts you to tag photos whenever you upload a photo. Even voice assistants use machine learning to identify and service the user’s request. Tesla’s autopilot feature is another example.

How do Machines learn?

Well, the simple answer is, just like humans do! First, we receive the knowledge about a certain thing and then keeping this knowledge in mind, we are able to identify the thing in the future. Also, past experiences help us in taking decisions accordingly in the future. Our brain trains itself by identifying the features and patterns in knowledge/data received, thus enabling itself to successfully identify or distinguish between various things.

Machine Learning timeline

Machine learning is not a recent phenomenon. In fact, neural networks were first introduced as a concept in a research paper in the year 1943!

Although in the early days progress in machine learning was somewhat slow due to the high cost of computing which made this domain only accessible to large academic institutions or multinational corporations. There was also the fact data in itself was difficult to acquire for a company’s needs. 

But with the advent of the internet, we are now generating quintillions of data everyday!

Couple that with the reduction in the price of computations and we find that machine learning is a more than a viable proposition.

Let’s try to plot a timeline with some of the notable events in the history of machine learning below:

Difference between Machine Learning, Deep Learning, Artificial Intelligence?

While learning about machine learning basics, one often confuses Machine Learning, Artificial Intelligence and Deep Learning. The below diagram clears the concept of machine learning.

We hope that the diagram has helped dispel any doubts you had regarding the three disciplines. Now we move to the heart of the matter. 

Components of Machine Learning

Let’s break down the machine learning process so that we can understand it in detail. We will take a small example as we go through it.

Collecting and preparing data

The first step in machine learning basics is that we feed knowledge/data to the machine, this data is divided into two parts namely, training data and testing data.

Consider that we want to build software which can identify a person as soon as their photo is shown. We start by collecting data, ie photos of people. Now in this phase, we have to make sure that our data is representative of the entire population ie, if we include only adults from 20 -40 years of age, the software will fail if it is shown a picture of a baby. 

The data is usually split into 80/20 or 70/30 to make sure that the model once sufficiently trained can be tested later.

Choosing and training a model

This is the second step in machine learning basics. We have a variety of machine learning algorithms and models which have been created and modified further so that it can solve a particular type of problem. Thus, it is imperative we choose and train a model depending on its suitability for the problem at hand.

Evaluating a model

The machine learns the patterns and features from the training data and trains itself to take decisions like identifying, classifying or predicting new data. To check how accurately the machine is able to take these decisions, the predictions are tested on the testing data.

In this case, we will first work on the training data and once the model is sufficiently trained, we use it on the testing data to understand how successful it is in recognising the faces in the photo.

Hyperparameter tuning and Prediction

In Machine learning terminology, the hyperparameters are parameters that cannot be estimated by the model itself, but we still need to account for them as they play a crucial role in increasing the performance of the model.

Traditionally speaking, hyperparameters in a machine learning model are the parameters which need to be specified by the user, in order to run the algorithm. Classical parameters are learned from the data, hyperparameters may or may not be learned from data.

For example, in a decision tree shown below. The hyperparameters are

Number of leaf nodes

Depth of tree

Minimum sample required to split the node

  A model can have many hyperparameters and the process of finding the best possible combination of hyperparameters is referred to as hyperparameter tuning. Some of the machine learning basics methods for hyperparameter tuning include grid search. Randomised Search, Gradient-based optimisation. To go into detail about these methods would perhaps be overkill as we are concentrating on Machine learning basics, but a general understanding of these processes is enough for now.

Once the hyperparameter optimisation process is completed, we can say that the machine learning model is built and depending on its success rate or rather, the prediction ability, we can deploy it in the real world.

Thus, in this manner, we can build a machine learning algorithm. 

Just like every building needs a foundation, we need to import python libraries which will help us build the machine learning algorithm. Let’s go through a few python machine learning libraries now.

Python Libraries for Machine Learning basics

Scikit-learn

It is a Python Machine Learning library built upon the SciPy library and consists of various algorithms including classification, clustering and regression, and can be used along with other Python libraries like NumPy and SciPy for scientific and numerical computations. Some of its classes and functions are sklearn.cluster, sklearn.datasets, sklearn.ensemble, sklearn.mixture etc.

TensorFlow 

TensorFlow is an open-source software library for high-performance numerical computations and machine learning applications such as neural networks. It allows easy deployment of computation across various platforms like CPUs, GPUs, TPUs etc. due to its flexible architecture. Learn how to install TensorFlow GPU here.

Keras 

Keras is deep learning library used to develop neural networks and other deep learning models. It can be built on top of TensorFlow, Microsoft Cognitive Toolkit or Theano and focuses on being modular and extensible.

You can read more about Python trading libraries and its functions here.

We have now laid the groundwork and covered most of the machine learning basics till now. Let’s move further and understand a few machine learning algorithms.

Types of Machine Learning Algorithms

Machine Learning algorithms can be classified into:

Supervised Algorithms - Linear Regression, Logistic Regression, KNN classification, Support Vector Machine (SVM), Decision Trees, Random Forest, Naive Bayes’ theorem

Unsupervised Algorithms - K Means Clustering.

Reinforcement Algorithm

Let us dig a bit deeper in these machine learning basics algorithms

Supervised Machine Learning Algorithms

In this type of algorithm, the data set on which the machine is trained consists of labelled data or simply said, consists both the input parameters as well as the required output. 

Let’s take the previous example of facial recognition and once we have identified the people in the photos, we will try to classify them as baby, teenager or adult. Here baby, teenager and adult will be our labels and our training dataset will already be classified into the given labels based on certain parameters through which the machine will learn these features and patterns and classify some new input data based on the learning from this training data.

Supervised Machine Learning Algorithms can be broadly divided into two types of algorithms; Classification and Regression.

Classification Algorithms

Just as the name suggests, these algorithms are used to classify data into predefined classes or labels. We will discuss one of the most used classification algorithm known as the K-Nearest Neighbor (KNN) Classification Algorithm.

KNN Classification Machine Learning Algorithm

This algorithm is used to classify a set of data points into specific groups or classes based on the similarities between the data points. Let’s consider an example where we need to check whether a person is fit or not based on the height and weight of a person. Suppose we give the following table as the training data set:

Now consider a new person needs to be classified as fit/not fit. Let us consider the value of K=3, which means will consider 3 nearest neighbours. The nearest neighbours can be found out by determining the Euclidean difference between the height and weight of one person and the height and weight of the persons given in the table. The persons with the 3 least differences will be considered as the nearest neighbours. Now we will check how many out of these 3 are fit. If 2 or more out of the 3 are fit, then we will classify the new person as fit and vice versa. In case, we get an equal number of neighbours with different outcomes, then we can increase the value of K and check again.

KNN learns as it goes, in this sense, it does not need an explicit training phase and starts classifying the data points decided by a majority vote of its neighbours.

The object is assigned to the class which is most common among its k nearest neighbours.

Another way to explain the KNN Machine learning classification algorithm is in the following manner:

Let’s consider the task of classifying a green circle into class 1 and class 2. Consider the case of KNN based on the 1-nearest neighbour. In this case, KNN will classify the green circle into class 1. Now let’s increase the number of nearest neighbours to 3 i.e., 3-nearest neighbour. As you can see in the figure there are ‘two’ class 2 objects and ‘one’ class 1 object inside the circle. KNN will classify a green circle into class 2 object as it forms the majority.

Regression Machine Learning Algorithms

These algorithms are used to determine the mathematical relationship between two or more variables and the level of dependency between variables. These can be used for predicting an output based on the interdependency of two or more variables. 

For example, an increase in the price of a product will decrease its consumption, which means, in this case, the amount of consumption will depend on the price of the product. Here, the amount of consumption will be called as the dependent variable and price of the product will be called the independent variable. The level of dependency on the amount of consumption on the price of a product will help us predict the future value of the amount of consumption based on the change in prices of the product.

We have two types of regression algorithms: Linear Regression and Logistic Regression

Linear Regression Machine Learning

Initially developed in statistics to study the relationship between input and output numerical variables, it was adopted by the machine learning community to make predictions based on the linear regression equation.

The mathematical representation of linear regression is a linear equation that combines a specific set of input data (x) to predict the output value (y) for that set of input values. The linear equation assigns a factor to each set of input values, which are called the coefficients represented by the Greek letter Beta (β).

The equation mentioned below represents a linear regression model with two sets of input values, x1 and x2. y represents the output of the model, β0, β1 and β2 are the coefficients of the linear equation.

y = β0 + β1x1 + β2x2

When there is only one input variable, the linear equation represents a straight line. For simplicity, consider β2 to be equal to zero, which would imply that the variable x2 will not influence the output of the linear regression model. In this case, the linear regression will represent a straight line and its equation is shown below.

y = β0 + β1x1

A graph of the linear regression equation model is as shown below:

Linear regression can be used to find the general price trend of a stock over a period of time. This helps us understand if the price movement is positive or negative.

You can learn about Linear Regression and how it can be used to predict the stock prices in detail in this blog.

Logistic Regression Machine Learning Algorithm

In logistic regression, our aim is to produce a discrete value, either 1 or 0. This helps us in finding a definite answer to our scenario.

Logistic regression can be mathematically represented as,

The logistic regression model computes a weighted sum of the input variables similar to the linear regression, but it runs the result through a special non-linear function, the logistic function or sigmoid function to produce the output y.

The sigmoid/logistic function is given by the following equation.

y = 1 / (1+ e-x)

In simple terms, logistic regression can be used to predict the direction of the market.

So far, the machine learning algorithms explained above were exclusively classification or regression-based algorithms. Now we will look at certain Supervised machine learning algorithms which can be both.

Support Vector Machine (SVM) Learning Algorithm

Support Vector Machine was initially used for data analysis. Initially, a set of training examples is fed into the SVM algorithm, belonging to one or the other category. The algorithm then builds a model that starts assigning new data to one of the categories that it has learned in the training phase.

In the SVM algorithm, a hyperplane is created which serves as a demarcation between the categories. When the SVM algorithm processes a new data point and depending on the side on which it appears it will be classified into one of the classes.

When related to trading, an SVM algorithm can be built which categorises the equity data as a favourable buy, sell or neutral classes and then classifies the test data according to the rules.

Decision Trees

Decision trees are basically a tree-like support tool which can be used to represent a cause and its effect. Since one cause can have multiple effects, we list them down (quite like a tree with its branches).

We can build the decision tree by organising the input data and predictor variables, and according to some criteria that we will specify.

The main steps to build a decision tree are:

Retrieve market data for a financial instrument.

Introduce the Predictor variables (i.e. Technical indicators, Sentiment indicators, Breadth indicators, etc.)

Setup the Target variable or the desired output.

Split data between training and test data.

Generate the decision tree training the model.

Testing and analyzing the model.

The disadvantage of decision trees is that they are prone to overfitting due to their inherent design structure.

Random Forest

A random forest algorithm was designed to address some of the limitations of decision trees.

Random Forest comprises of decision trees which are graphs of decisions representing their course of action or statistical probability. These multiple trees are mapped to a single tree which is called Classification and Regression (CART) Model.

To classify an object based on its attributes, each tree gives a classification which is said to “vote” for that class. The forest then chooses the classification with the greatest number of votes. For regression, it considers the average of the outputs of different trees.

Random Forest works in the following way:

Assume the number of cases as N. A sample of these N cases is taken as the training set.

Consider M to be the number of input variables, a number m is selected such that m < M. The best split between m and M is used to split the node. The value of m is held constant as the trees are grown.

Each tree is grown as large as possible.

By aggregating the predictions of n trees (i.e., majority votes for classification, the average for regression), predict the new data.

Naive Bayes theorem

Now, if you remember basic probability, you would know that Bayes theorem was formulated in a way where we assume we have prior knowledge of any event that related to the former event.

For example, to check the probability that you will be late to the office, one would like to know if you face any traffic on the way.

However, the Naive Bayes classifier algorithm assumes that two events are independent of each other and thus, this simplifies the calculations to a large extent. Initially thought of nothing more than an academic exercise, Naive Bayes has shown that it works remarkably well in the real world as well.

Naive Bayes algorithm can be used to find simple relationships between different parameters without having complete data.

We will now look at the next type of Machine learning algorithms, ie Unsupervised machine learning algorithms.

Unsupervised Machine Learning Algorithms

Unlike supervised learning algorithms, where we deal with labelled data for training, the training data will be unlabelled for Unsupervised Machine Learning Algorithms. The clustering of data into a specific group will be done on the basis of the similarities between the variables. Some of the unsupervised machine learning algorithms are K-means clustering, neural networks. Let us look at the K-means clustering machine learning algorithm.

K-means clustering Machine Learning Algorithm

Before we understand the working of the K-means clustering algorithm, let us first break down the word K-means clustering to understand what it means.

Clustering: In this algorithm, we form clusters which are a collection of data points grouped together due to their similarities.

K refers to the number of centroids which will be considered for a specific problem whereas ‘means’ refers to a centroid which is considered as the central point of any cluster.

Working of K-means Clustering Algorithm

Define the value of K. For eg: if K= 2, then we will have two centroids.

Randomly select K data points as centroids.

Check the distance of each data point with the centroids.

Assign the data point to the centroid with which it has a minimum distance, thus forming a cluster of similar data points.

Recalculate the centroid of each newly formed cluster and reassign the data points to the cluster whose centroid is at a minimum distance from the data point.

You can decide the number of iterations for repeating step 5 to optimize the algorithm. When the centroid stops changing or remains the same after some amount of iterations then that will be our stopping point and the algorithm will be fully optimized.

A simple example would be that given the data of football players, we will use K-means clustering and label them according to their similarity. Thus, these clusters could be based on the strikers preference to score on free kicks or successful tackles, even when the algorithm is not given pre-defined labels to start with.

K-means clustering would be beneficial to traders who feel that there might be similarities between different assets which cannot be seen on the surface.

While we did mention neural networks in unsupervised machine learning algorithms, it can be debated that they can be used for both supervised as well as unsupervised learning algorithms. Let’s understand Artificial and Recurrent Neural networks now.

Artificial Neural Network 

In our quest to play God, an artificial neural network is one of our crowning achievements. We have created multiple nodes which are interconnected to each other, as shown in the image, which mimics the neurons in our brain. In simple terms, each neuron takes in information through another neuron, performs work on it, and transfers it to another neuron as output.

Each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another.

Neural networks can be more useful if we use it to find interdependencies between various asset classes, rather than trying to predict a buy or sell choice.

Recurrent Neural Networks (RNN)

Did you know Siri and Google Assistant use RNN in their programming? RNNs are essentially a type of neural network which has a memory attached to each node which makes it easy to process sequential data i.e. one data unit is dependent on the previous one.

A way to explain the advantage of RNN over a normal neural network is that we are supposed to process a word character by character. If the word is “trading”, a normal neural network node would forget the character “t” by the time it moves to “d” whereas a recurrent neural network will remember the character as it has its own memory.

Reinforcement Machine Learning Algorithms

Reinforcement Learning is a type of Machine Learning in which the machine is required to determine the ideal behaviour within a specific context, in order to maximize its rewards. It works on the rewards and punishment principle which means that for any decision which a machine takes, it will be either be rewarded or punished. Thus, it will understand whether or not the decision was correct. This is how the machine will learn to take the correct decisions to maximize the reward in the long run.

For reinforcement algorithm, a machine can be adjusted and programmed to focus more on either the long-term rewards or the short-term rewards. When the machine is in a particular state and has to be the action for the next state in order to achieve the reward, this process is called the Markov Decision Process.

A more technical explanation of the Reinforcement Learning problem can be explored as follows: 

The environment is modelled as a stochastic finite state machine with inputs (actions sent from the agent) and outputs (observations and rewards sent to the agent):

State transition function P(X(t)|X(t-1),A(t))

Observation (output) function P(Y(t) | X(t), A(t))

Reward function E(R(t) | X(t), A(t))

State transition function: S(t) = f (S(t-1), Y(t), R(t), A(t))

Policy/output function: A(t) = pi(S(t)))

The agent's goal is to find a policy and state-update function so as to maximize the expected sum of discounted rewards

E [ R_0 + g R_1 + g^2 R_2 + ...] = E sum_{t=0}^infty gamma^t R_t 

Where, 0 <= gamma <= 1 is a discount factor, which models the fact that future reward is worth less than the immediate reward. 

The Reinforcement Learning problem requires clever exploration mechanisms. Selection of actions with careful reference to the probability of an event happening is required so that the desired results can be obtained. Further, other drawbacks also make Reinforcement Learning a challenge for the practitioners. Firstly, it turns out to be memory expensive to store the values of each state, as the problems can be very complex. Moreover, problems are also generally very modular; similar behaviours reappear often. Also, limited perception can contribute to the limitations of Reinforcement Learning. 

We have now covered most of the popular machine learning algorithms which are used today. As you have understood them, it is imperative that we go through a few terms to make sure we are well versed with machine learning basics.

Common terms in machine learning basics

Here are a few machine learning basics terms which would be of help as you start your journey in machine learning algorithms.

Bias

A machine learning model is said to have low bias if its predictability level is high. In other words it makes fewer mistakes when it is working on a dataset.

Bias plays an important role when we have to compare two machine learning algorithms for the same problem statement.

Cross-validation bias

Cross-validation in machine learning is a technique that provides an accurate measure of the performance of a machine learning model. This performance will be closer to what you can expect when the model is used in a future unseen dataset.

The application of the machine learning models is to learn from the existing data and use that knowledge to predict future unseen events. The cross-validation in machine learning model needs to be thoroughly done to profitably trade in live trading.

You can learn how to perform cross-validation on a machine learning model by going through this article.

Underfitting

If a machine learning model is not able to predict with a decent level of accuracy, then we say that the model underfits. This could be due to a variety of reasons, including, not selecting the correct features for the prediction, or simply the problem statement is too complex for the selected machine learning algorithm.

Overfitting

In both machine learning and statistics, overfitting occurs when the model fits the data too well or simply put when the model is too complex. Overfitting model learns the detail and noise in the training data to such an extent that it negatively impacts the performance of the model on new data/test data.

Overfitting problem can be solved by decreasing the number of features/inputs or by increasing the number of training examples to make the machine learning algorithms more generalized. The more common way of solving the overfitting problem is by regularization.

These were a few terms we discussed in Machine learning basics. Most of the popular machine learning algorithms are mentioned above. While we could have ended the article here, we thought of going into more detail on one of the hot topics of today ie deep learning. Usually, a neural network consists of three layers, input, hidden layer, and output layer. While the conventional neural network is good enough for solving a lot of problem statements, researchers realised that adding more hidden layers can help us build complex models in an effort to solve different types of complex problems. This is deep learning in a nutshell.

Difference between Machine Learning and Deep Learning

Machine Learning models lack the mechanism to identify errors, in such cases the programmer needs to step in to tune the model for more accurate decisions, whereas deep learning models can identify the inaccurate decision and correct the model on its own without human intervention.

But for doing so, deep learning models require a huge amount of data and information, unlike Machine Learning models.

Working of Deep Neural Network

The deep neural network gets its name due to a high number of layers in the networks. Let us now understand what these layers are and how are they used in the deep neural network to give a final output by referring to the diagram given below:

Layers in Deep Neural Network

By looking at this diagram, we see that there are 4 layers present in this deep neural network namely Layer 1, Layer 2, Layer 3 and Layer 4. Every deep neural network consists of three types of layers, which are:

Input Layer (Layer 1): This layer is the first layer in a deep neural network and it provides the input parameters required to process the information. It simply passes these parameters to the further layers without any computation at this layer.

Hidden Layers (Layers 2 and 3): These layers in the deep neural network perform the necessary computations on the inputs received from the previous layers and pass on the result to the next layer. It is crucial to decide the number of layers and the number of neurons in each layer so as to increase the efficiency of the deep neural network. More the number of hidden layers, deeper is the network.

Output Layer (Layer 4): This layer in the deep neural network gives us the final output after receiving the results from the previous layers.

Now that we have understood the types of layers present in a network, let's learn how these layers actually function and give the output data.

Each neuron is connected to all the neurons in the next layer and all these connections have some weights associated with them. But what are these weights and why are they used?

Weights in Deep Neural Network

Weights, as the name suggests, are used to attach some weightage to a certain feature. Some features might be more important than other features to get the desired output.

For example, close prices and SMAs of the previous days will be considered as more important features than high or low prices while predicting the stock prices for the next day, this will affect the weights attached to these parameters.

These weights are used to calculate the weighted sum for each neuron. x1, x2, x3, x4 represent the weights associated with the corresponding connections in the deep neural network.

Along with the weights, each hidden layer has an activation function associated with it.

Activation Function in Deep Neural Network

Activation functions decide whether a neuron should be activated or not based on their weighted sum. These are also used to introduce non-linearity by using functions like sigmoid and tanh thus allowing computations for more complex tasks. Without the activation function, the deep neural network would act as a simple linear regression model.

Here are examples of a few activation functions which are used:

Tanh: Avoids bias in gradients

Rectified Linear Unit (ReLU): Used for Image Processing

Softmax: To retain the relevance of outliers

In addition to this, we also add a ‘bias’ neuron to each layer to enable moving the activation function along the x-axis to the left or to the right thus allowing us to fit the activation function better. The bias term which is a constant term also acts as an output whenever the input is absolute zero.

Processing of Deep Neural Network

The processing starts by calculating the weighted sums for each neuron in the first hidden layer using the inputs received from the input layer. The weighted sums are the sum of the products of the input with the corresponding weights for each connection.

The activation function corresponding to each layer then acts upon these weighted sums to give a final output. This process can also be known as forward propagation.

Once the processing is completed, the predicted output is compared with the actual output to determine the error or loss. For a deep neural network to work accurately, this loss function must be minimized so that the predicted output is as close to the actual output as possible. As we initially choose random weights for the connections in the deep neural network, they might not be the best choice.

Hence, to minimize the loss function, we need to adjust the weights and biases to get accurate results. Backpropagation is the process used to tune the weights and biases such that we get the optimal values of weights and biases thus giving us higher accuracy in our results.

Deep Learning Applications

We have to remember that Deep learning is actually a subset of Machine learning and thus there will be an overlap between the two when it comes to their applications.

Applications of Machine Learning

We have covered most about machine learning basics that would clear fundamentals of machine learning, the machine learning process, machine learning concepts and examples of machine learning that would be essential to a machine learning beginner.

Machine Learning for Trading

As we can observe from the above image, machine learning has a myriad number of applications and is being used in almost all the major fields. Similarly, machine learning has gained huge traction in the field of trading as well with domains such as Algorithmic Trading are witnessing exponential growth. Machine learning in trading is eventually automating the process of trading, wherein the machines themselves are becoming capable to learn from the previous data and take decisions to maximize profit or minimize loss.

Trading strategies, too, can be implemented through machine learning algorithms to optimize the trading process. Some of the open-source machine learning technologies used include TensorFlow, Keras, Scikit-learn, Microsoft Cognitive Toolkit etc.

If you are looking to learn how machine learning can be used for trading, then here is a comprehensive course on Machine Learning for Trading that covers machine learning basics for trading, and it not only consists of video lectures but also provides an interactive platform to practice coding and starts right from the machine learning basics to advanced concepts of machine learning.

Growth and Future of Machine Learning

Machine Learning is growing at a tremendous rate and we will soon be able to see its applications across all of the major domains. Various reports regarding machine learning have all pointed to an upward growth curve for this domain. According to IFI Claims Patent Services (Patent Analytics), Machine Learning patents witnessed a growth of 116% CAGR between 2017 and 2018, of which the major patent producers included companies like IBM, Microsoft, Intel, Samsung, Google etc.

A survey by MIT and Google Cloud demonstrates that 60% of the organisations have already been using Machine Learning strategies and one-third of them are at an early stage of development. This report by Forrester predicts huge growth for Machine Learning, which forecasts that the Predictive Analytics and Machine Learning (PAML) market will grow at 21% CAGR through 2021.

According to a study by Preqin, 1,360 quantitative funds are known to use computer models in their trading process, representing 9% of all funds. Firms like Quantopian organise cash prizes for an individual's machine learning strategy if it makes money in the test phase and in fact, invest their own money and take it in the live trading phase. Thus, in the race to be one step ahead of the competition, everyone, be it billion-dollar hedge funds or the individual trade, all are trying to understand and implement machine learning in their trading strategies. Companies are encouraging their employees to start learning machine learning basics.

Businesses and other major domains are not just adopting new technologies but are adopting new machine learning technologies to automate many of the processes which are helping them increase their productivity. We are now entering into the age of Artificial Intelligence and Machine Learning, thus, making it a domain impossible to ignore and a lot to explore!

Conclusion

In this article, we have understood machine learning basics as well as the different types of machine learning algorithms used by professional traders in Python. We also know that machine learning is becoming indispensable to the trading world and will become an integral part of the trader’s work life in the years to come. 

CTA: 

Learning Track: Machine Learning & Deep Learning in Financial Markets

Trading via QuantInsti http://bit.ly/2Zi7kP2 September 3, 2019 at 08:14AM

[irarelypostanything]

Slice of Life[8]

[Kevin]

Kevin could have spent those last critical hours learning something appropriate to this interview, but instead he spent all of them trying to figure out a tie.  Ties were far too complicated.  Ties were old-fashioned, out-dated, older than even legacy software...and they were pointless.  Besides needing them every now and then for an occasion like this, they were about as functionally useful as button-up shirts or apartment decorations.  And like apartment decorations, Kevin struggled with this to the point of giving up.

He left the tie at home and drove.  This healthcare company was actually a little bit closer than his actual workplace.  To find it he’d gone through a head hunter in India, two HR representatives in London, and a logistical organizer from St. Louis.  After all that, he was happy to find that the company site was actually a 12-minute drive from his apartment.

He arrived an hour early, as was his custom.  He met with a software engineer manager, a scrum master, and two senior engineers at once.  Kevin thought of himself as the kind of person who got intimidated by mildly impatient drivers and arguably rude grocery store cashiers; this was, in a word, terrifying.

“So,” said one of the two senior engineers (the other one said nothing the entire time; Kevin later learned that he was just there to make the experience more intimidating), “we’re going to start right off the bat with some technical questions.”

“Shit, it didn’t say you’d be asking technical questions on your Glassdoor,” mumbled Kevin.

“What?”

“I said go ahead.”   Kevin smiled like it was a joke, when in reality his thought process frequently found its way out of his mouth.

“What are the advantages of object-oriented programming?”

“Come again?”  Kevin was stunned.  He had prepared for the interview by studying binary search tree insertion, how to convert a binary search tree into a doubly linked list, and the full history of binary search trees.

“What are the advantages of object-oriented programming?” he repeated.

“They...they make things organized.”

“Anything else?”

“No...yeah…” Kevin was stumbling on his words, “they make things more organized.”

“How comfortable are you with Java?”

“Fairly comfortable.”

“What are some differences between using an abstract class and using an interface, in Java?”

“Actually, on second thought, I have more experience in C++...”  The interviewer did not respond to that.  Kevin did not expect him to, since the role was very clearly a Java developer role.

“Does C++ have interfaces?”

“I don’t know, it might?”

“Sticking to Java, do you know when you’d choose something like an interface over an abstract class?”

“I do not.”

The senior software engineer exchanged glances with the others present.  They were frantically taking notes on respective laptops.

The senior software engineer handed Kevin a whiteboard marker.  “We’d like to do an exercise.  We’d like you to write a simple string reversal algorithm.”

“Oh, okay.  I’ll just write a for loop to traverse it backwards.”

“Let me finish.  You have to reverse the string in place.  You’re not allowed to make a new string.”

“Fuck, you’d think I’d have looked this up the last time I failed on it.”

“What?”

“What?”

In the end, everyone seemed polite enough.  The building was nice.  The culture seemed like a good fit.  But in spite of the fact that no one insulted him bluntly, Kevin felt a sinking feeling that he didn’t have a snowball’s chance in hell of getting this job.

The manager walked Kevin to his car.  

“About a year ago, someone at my company applied here.  Remember him?”

“Yes, actually,” said the manager.

“Do you know what happened?”

“I heard.  It was really sad.”

“Did he get the job, though?”

“No.”

It was mid-afternoon.  It was 90 degrees even though it had rained the week before.

“I want to ask you something,” said the manager.  They locked eyes.  “Did you apply here because you wanted a job, or because your dead friend wanted it?”

“A bit of both?  I thought it would be poetic if I took the job he never lived to do.”

“Poetic how?”

“I don’t know.  But be honest, I didn’t get it...did I?”

The manager looked around uncomfortably.  “We’ll still do a thorough review and try to make the best decision, but no.  Probably not.”

“Because I’m not good enough.”

“Because you’re not ready.  Review your fundamentals.  Do some practice, maybe some independent projects.  Then see us again in a year or so.”

“Good luck.”

“You too.”

[Nora]

It was well past midnight.  Nora and Kevin were on the phone as they worked on their computers.

“Andy’s always been kind of a douche,” said Nora, “but we love him anyway.  I don’t see why that exchange bugged you so much.”

“He said I don’t understand the big picture of how our system works.”

“Well, do you?”

“Yeah.  When I said I did, he quizzed me.”

“Did you pass his quiz?”

“I guess.”

“Then you proved him wrong.  What’s the problem?”

“Maybe he’s right.  Maybe I don’t understand.  Or maybe I at least really give off the impression that I don’t understand.”

“Whenever I find myself in situations like this,” said Nora, “I look at myself in the mirror, try to give my situation a hard metaphorical look, think things through, and then ask my reflection…’DO I ACTUALLY GIVE A FUCK?’”

“I do, though.”

“Do you?”

“Okay, maybe not that much.”

The two worked a little longer in silence.  Nora broke the ice.  “Speaking of people and things I don’t give a fuck about, how’s Dan?”

“Slowly cracking, whatever sanity he once possessed turning to nothing but a shadow.”

“Oh sorry, I wasn’t listening.  A creeper almost snuck up on me.”  She moved her Minecraft avatar to collect gunpowder, which she knew she could use to make a splash potion.  “You said Dan is doing well?”

“No, terribly.  His code quality is suffering too.”

“What do you think the problem is?”

“I don’t know.  He seems uncharacteristically depressed, like things are going really badly for him at home or in his personal life.”

“Sweet, sounds like an awesome opportunity to finally do what you’ve always been dreaming of.”

“And what’s that?”

“Get the little bitch fired.”

[Ryan]

Two days.  Two days was how long they’d given Ryan to prepare a work presentation.  Shared in front of six sites, he was to provide a “playbook presentation” on what his team had learned about effective software engineering.

By the laws of company policy, which was attempting to keep up with the pace of start-ups, all employees were permitted to drink on site.  Ryan did so, a lot, which was kind of dumb because he did strange things when he was drunk.

“Assets and liabilities,” began Ryan, “these are the two things I want to present to you today.  Assets are people who help us, who make us the best we can be.  Liabilities are exactly what they sound like.  These are people we need to eradicate.”  Ryan had intended to say terminate, but the alcohol was not working well with his system.  “Eradicate from the company, I mean.

“I believe, passionately, that incompetence is as much of a crime as intentional sabotage.  I believe that those responsible for writing the shitty software that downed a commercial airplane are every bit as responsible as if they were terrorists who took it down with a rocket.  The greatest threat to our company and our country isn’t an outside threat, it’s the idiots right here in this room who do a shitty job day after day, not because they’re trying to but because they’re too stupid to even comprehend the depth of their own stupidity.

“This brings me to my next point, the necessity of eugenics-”

“Okay,” interrupted the chief engineer as he ushered Ryan off stage, “that was a wonderful presentation from our newest engineer.  Unfortunately we are out of time, and must move onto the next section: The importance of properly color-coding your sequence diagrams in Microsoft Visio.”

[Andy]

Andy was in a manager meeting as the only non-manager.

“Ryan has apologized profusely for his inflammatory presentation today,” Andy began, “ordinarily we would have taken disciplinary action, but unfortunately he’s the only one at our site who knows what the fuck error code 256 means.”

“Then what was the point of calling this meaning?” asked a manager.

“As outrageous as his presentation may be,” explained Andy, “he may have a point.  Has anyone noticed Dan’s erratic behavior?”

“Yes,” said another manager, “there have been numerous reports of him sobbing uncontrollably, hanging up pictures of a coworker who passed away a year ago, and then taking unscheduled visits to his late friend’s tombstone.”

“Wow, this guy sounds like a serious liability.”

“Exactly!  I believe this may seriously detriment our ability to perform well in our next demo, which we know is critical, unless we take action immediately.”

“What do you recommend?”

“Finding a replacement.”

“Get to it.”

[israelkloss]

Donor Churn Risk for Non-profits

"You cannot manage what you cannot measure... and what gets measured gets done" - Bill Hewlett, Hewlett Packard

Non-profits and Donor Churn 

Individual and corporate contributors are the lifeblood of non-profits, tied to the fabric by supporting the organization’s viability, sustainability, and advancement. Yet, non-profit groups know very little about their donor segments. It’s easy to create a profile for a reliable, faithful supporter who writes a check every year. But, why do short-term donors leave without a trace and where can organizations find future contributors who are ready, willing and able to donate? Retailers and digital marketers have perfected the art of targeted segmentation, but non-profits have yet to fully utilize technology to create profiles and intimately understand the interests, preferences and underlying behavior of their donors. Now, nonprofits can now capture critical donor base information and learn more about those who leave (churn), reduce the risk of future churn and attract new donors in real time. 

Non-profits are Losing $4.2 Billion, Yearly 

The magnitude of donor churn is pervasive across the industry. The “Fundraising Effectiveness Project (FEP)” by the Association of Fundraising Professionals reports that nonprofits are losing approximately $4.2 billion[1], yearly due to donation attrition in various forms including donation abandonment. And that is only the nonprofits studied. The actual number is much higher.

               The findings of the study say it best:

“It usually costs less to retain and motivate an existing donor than to attract a new one… taking positive steps to reduce gift and donor losses is the least expensive strategy for increasing net fundraising gains.” -- Fundraising Effectiveness Project

Under-utilized data and analytics can account for as much as 30-40% of losses in donations and smaller nonprofits are well advised to prioritize their donor retention strategy before (or at least concurrently with) their donor activation strategies. 

The Missing Link is Data 

To segment and improve your prospective donor base, you must begin by gathering critical information. The following potential data can be readily collected from a donation page without compromising any of the donor’s personal information.

1.       Individual’s identity

2.       Net worth

3.       Household Income

4.       Home value

5.       Luxury purchase history (luxury purchases are highly correlated with non-profit donations)

6.       Age, gender, zip code, latitude/longitude and a host of other demographics available free from the US Census bureau.

7.       Personal interests and affinities

8.       All the topics and phrases of interest in a visitor’s history of reading your web page  

9.       The names of your donation page visitor’s employer is available to you (for the vast majority of visitors)

 If you work in a non-profit, there are also some questions to consider in determining if a prospective donor will donate, churn or abandon their donation:

1.       Do I get notifications when visitors to our donation page abandon the page without donating?

2.       Do I know the names of the companies and the names of the individuals who most frequently visit our non-profit website?

3.       What is the zip code of our “almost donated” crowd who quit the donation process or tried to donate and failed?

Three Years of Donor Abandonment Analysis

In a 3-year project, we were able to identify and map 96 potential donors with a minimum one-million dollar (and greater) ability to donate but who had abandoned various donation pages, online. This following pilot study resulted in a 30% recovery rate from all abandoners: https://www.linkedin.com/pulse/donation-retention-universities-israel-kloss

The total capacities of the 96 abandoners (who had greater than $1 Million giving capacities) in our study was $96,000,000

By recovering just 10%, there was a potential for $9,600,000 in recovery. By pursuing the 96 abandoners alone with the 30% recovery rate proved that the above pilot study (and a similar pilot study by Dickenson University) demonstrated was possible, Conservatively, $28.8 Million could have been recovered for this non-profit.[1]

What is Donor Churn Risk? 

Donor churn risk is simply a calculation of the probability of a donor to stop donating (whether online once or recurring). This is also called donor attrition risk or donor turnover probability.

Historical Donor Data 

Historical behavioral data about your donors is critical for generating an actionable donor churn risk score.  If you don’t already have someone responsible for collecting (and protecting) historical data associated with the donors, make it a priority.

By exporting and crunching some of the key Google Analytics data through a program like R Studio, you can get more donor behavioral insights than most people imagine. In fact, you might be surprised about how much historical data you’re already recording. Got Google Analytics? Got log files? Almost everybody with a web site has log files. It may only be Apache log files, but that’s still data. You are measuring, whether you know it or not. You’ve got data.

While Google Analytics data is a fine place to start you’ll likely quickly need to go beyond just Google Analytics data to effectively address donor churn. There are more powerful analytics products available that will let you do things like segment traffic-to-donations ratios and do campaign personalization based on personal data about your logged-in (and other) visitors.  

Why Behavioral Analytics Matter 

Tracking donor-level behaviors allow non-profits many advantages:

1) Preemptive Intervention: By intercepting potential problems before your donors quit the donation process, you

can help stop loyalty problems from cropping up for your non-profit.

2) Donor Acquisition and Retention: Behavior-triggered donor churn alerts can allow your non-profit lead time on new retention strategies because, of course, donor retention is the job of everyone in any non-profit.

Let’s Dive In! 

So here are some steps to help you use your internal non-profit data to slow down your donor churn rates. 

Step 1. Collect and Analyze Donor Concerns  

If you haven’t already, do the following:

1.       Make a list of observations that the frontline donor care team believes has directly lead to churn for each donor (institution or individual) with whom they are familiar.

2.       Make a list of observations from the frontline donor care team has heard from each donor (institution or individual) regardless of whether they believe it has directly led to donor turnover.

3.       Request that a team with the closest knowledge of donors ranks concerns and objections they’ve heard over their careers from donors in this Donor Concern Matrix Model,  It can be downloaded here: http://archetypeconsulting.com/donor-matrix-model. The Donor Concern Matrix Model helps our non-profit customers to diagram donor concerns with 3 institutional priorities:

o    Ease of addressing the donor concern

o    Likelihood of addressing the donor concern

o    Impact of address the donor’s concern

4.       Record these concerns in the permanent record of each donor (institution or individual). If you don’t have a database, that can be solved but this guide assumes that you have one or will acquire one. 

How to Use the Donor Concern Matrix 

Let’s say that your donor-facing team members have heard from multiple donors that they are concerned about a lack of alignment between their philanthropic interests and your organizational priorities.

These concerns will show up in a statistical analysis of your institution’s donor churn rate (step 2). 

Thanks to University of Illinois Office of Business and Financial Systems for the risk map template[2].  

Step 2: Generate Your Donor’s Churn Risk Scores 

If you’ve never done any work with a statistical package, you might want to start with a basic data mining with decision trees (see below). Statistical packages available for this step include R Studio, SPSS, SAS or even Excel’s solver features. In fact, there are multiple Excel plugins available for basic statistical modeling. 

Let’s start building an actionable donor churn risk score. Make sure your donor dataset includes the donor concerns expressed for each donor no matter whether they are current or past donors. You must include their last donation, the amount and the full history of their donations). With all this data you can calculate a single score representing the risk that they will stop donating (the donor churn risk score). 

R Studio (free) will help you generate churn risk scores from your data. Below is an example of how just a few lines of code can provide a statistically valid churn risk score for every donor in your dataset. 

Try it Yourself!

Check out this short tutorial to start your journey with R: https://www.youtube.com/watch?v=Gzfo4piKwdw. To be statistically valid, your dataset needs to meet a statistically valid minimum for the size of your dataset. You can find the minimum requirements for the levels of confidence that you seek at this site: https://www.surveysystem.com/sscalc.htm (and many others).  

There are multiple approaches to churn risk scores. One is called RFM. Try out some RFM sample code (in R) here:  https://github.com/apurvadeshmukh/Churn/blob/master/churnmodel.R. You can also do a simple search on GitHub for other RFM code and learn many different approaches to calculating donor churn risk.

Too Advanced? 

It’s ok! Not everyone has the time or the interest to learn to code in R. In this case, why not try basic data mining? Decision trees are is often considered a more accessible (and event fun) starting point for learning more about donor data. Decision trees are part of a group of data modeling methods called “supervised learning”.

Here’s an easy video introduction to get your feet wet in the power of decision trees. And you can start your journey with decision trees using a free Excel plugin. 

Step 3: Map the Risk  

After a churn risk score has been calculated for every donor, it’s time to take a closer look. You can export the data from R as a CSV file and import it into Excel, or you can get fancy and visualize the data in Tableau. Below is a very basic view of how churn risk scores look after an export from R into Excel.

This is great, but sometimes you just need a map to make it more real. I’ve included some donor latitude/longitude columns and donor zip codes for the next step, donor churn-risk mapping. 

Predictive analytics is not always as understandable in the non-profit world as a good old-fashioned map. You can bundle up the highest-risk visitors by town (over population) using Tableau (a visualization tool), so the cities with the highest donation churn risk are visible by population center.

Step 4: Map Donation Abandonment 

Using Tableau, you may want to overlay the donors with the non-donors (donation page abandoners). We found patterns in the results that lead to significant increases in total donations and per-donation increases for one customer by mapping donor abandonment. You can read more about our findings in these articles:

Mapping Alumni Data: https://www.linkedin.com/pulse/mapping-alumni-data-israel-kloss

Donor Retention for Universities: https://www.linkedin.com/pulse/donation-retention-universities-israel-kloss

Wrapping Up 

There is so much under-used data in the non-profits world. The insights available for better operations are nearly inexhaustible when the data is accurate and the analysis methods are statistically sound and valid. Non-profit leaders have so much to gain from better data collection, data wrangling, data analysis and donor predictive analytics. $4.2 Billion is no chump change. There are so many great opportunities to recoup that loss. Data is the new oil. Now you have the know how to go mine it! 

[1]  From “Winning Donor Mindshare in the Attention Economy” (https://www.eab.com/research-and-insights/advancement-forum/events/webconferences/2017/winning-donor-mindshare-in-the-attention-economy)

[2] From  https://www.obfs.uillinois.edu/enterprise-risk-management/resources-tools

[aamilkameena]

Rich Dad, Poor Dad by Robert Kiyosaki

Chapter 1: The rich don’t work for money

At a very young age, Robert Kiyosaki had his first business partner, his schoolmate Mike. They worked for Mike’s dad, who taught them lessons on how to make money. The first rule they learn was that the rich don’t work hard for money, their money works hard for them. The first thing Mike’s dad did was to pay Robert and Mike 10 cents/hr so that they could see what is like to get a salary they find short – and imagine how would that be if multiplied over the time-span of 50 years. Then, rich dad had them working for free, which taught them two lessons: 1) most people are guided by fear (of not being able to pay for their bills) or desire (e.g. greed) and 2) we need to think of alternatives to make money, which Robert and Mike did – at a very a young age they set up a small library room, where they provided leftover magazines to other kids for a small fee.

Chapter 2: Why teach financial literacy?

Kiyosaki makes an analogy between retirement and a tree: if you water it for a few years, at some point it doesn’t need to be watered anymore, because its roots are deep in enough. Kiyosaki teaches lesson 2, which is why to be financially educated. He argues that, first, its important because of the ups and downs of the market (page #59). In page #61, he explains how he and Mike learned it from the rich dad. Then, he goes on to explain rule #1, which he considers the foundation of a good financial literacy: the definition of an asset and a liability. Although simple, this is a very profound concept. He also introduces cash-flow diagrams which are useful to understand the concepts, but basically, it explains how to filter between assets and liabilities. In page #70, he provides a list of assets. Kiyosaki also argues that not only cash flow tells the story of how a person handles money, but how more money can actually be a problem to many people. He provides the example of his poor dad, who considered his house to be his biggest investment, and how it is to be trapped in the rat race: people buy expensive houses which they pay for 30 years and use a bill consolidation loan to pay off their credit cards (page #74).

The story of this couple keeps repeating: as a result of their income increasing, they buy their dream home, and a new car. Soon they find themselves trapped in the rat race. All to often, the middle class lets the power of money to control them. Kiyosaki argues that people simply lack financial education. For instance, the decision of owning a house in lieu of an investment portfolio results in loss of time, additional capital and education (page #83). The chapter then explains that the rich get richer because they acquire assets and the middle class struggles because they increase their liabilities. Some analogies are made: income means working for a company and the government (since it gets its share before you see it) and owning a house means working for the bank (pages #89 and #90).

Chapter 3: Mind your business

Kiyosaki starts off by explaining the difference between each one’s profession and business. At school, we learn how to work for somebody else for the rest of our lives. Rich people focus on assets, the masses focus on their income, and that is why they are always asking for a promotion or raise. This leads to financial struggle. Kiyosaki recommends you to keep your job and build your asset column; keep your expenses low, reduce liabilities and buy assets. Think of your dollars as employees – they work 24/7 for you without complaining. Build this list first, afford some luxuries after.

Chapter 4: The history of taxes and the power of corporations

Kiyosaki explains that in the beginning there was no taxes and government created taxes to punish the rich and give to the poor. The rich are smart, the middle class ends up paying way more than the rich. In particular, rich use corporations to pay fewer taxes, as they are very effective at reducing the tax burden. Knowledge is power. Money should work for you, not the other way around: don’t give the power to the government or your employer. There is a link to the previous chapter of minding your business. Kiyosaki says that working for Xerox Corp. was essential for him to build his RE holding, which payed for his Porsche. Stresses the importance of financial IQ again (pages 116-120).

Chapter 5: The rich invent money

Kiyosaki starts by emphasizing that fear often suppresses genius in people. As a teacher for financial education, he often fosters his students to take risks. At this point, you may have the question of why to improve your financial IQ. Kiyosaki argues that only you can answer that question. However, we live in astonishingly fast-changing times, and people are often caught off guard, or “pushed around” as Kiyosaki says. They often blame the economy or their boss, and only seldom they consider that the problem is actually them.

Kiyosaki uses CASHFLOW, a game he created, to teach investment to make a series of other points. First, some people playing it can see the game reflecting them. This is good because they can quickly learn what is causing them to struggle. Some people playing the game gain lots of money but don’t know what to do with it. Others claim that “the right cards” are not coming to them, and they just sit there, waiting. Some get the right opportunity but they don’t have the money. Some get the opportunity, have the money but don’t know what to do. And all this comes down to the meaning of having financial IQ, which really means having more options.

After this, Kiyosaki shows a series of deals he did himself, buying-and-selling Real Estate and increasing his asset columns, showing a practical example of what financial intelligence brought him. Then, on page 138, he summarizes financial intelligence as being a set of 4 skills: accounting, investing, understanding markets and law. In page 148, two kinds of investors are defined, those that buy packaged investments, and those that put investments together, and Robert Kiyosaki says that the latter are the more professional investor and his rich dad encouraged him to be. To be this kind of investor, you need to find opportunities that other investors missed, know how to raise capital and organize smart people. There is always risk, learn to manage it instead of avoiding it.

Chapter 6: Work to learn, don’t work for money

This chapter is primarily about the benefit of learning new skills, showing how trapped one become when we are too specialized. The chapter starts off with an example that Robert Kiyosaki when himself through. He interviewed by a journalist who was an excellent writer but didn’t do well in selling her books, so Kiyosaki advised her to take sales-training courses. He generalizes this problem, stating that many talented people struggle financially because they only master one skill.

Kiyosaki provides a few examples of his own journey. First, he says that he entitled his first book “If You Want to Be Rich & Happy Don’t Go to School” on purpose; not because he is against education, but because he knew that title would take him to more TV and radio shows, and sell more. Then, he goes on to explain that he kept changing jobs to learn more skills, and skills that became important for him to succeed. He joined Xerox Corp because he was a shy person, and Xerox has one of the best sales-training programs in America. While his rich dad was proud of him, his poor dad was more disappointed at every job change. To other people, Robert Kiyosaki recommends to join a marketing company as a second job, or join a union if they are too specialized and refuse to widen their skills.

One of the examples that is provided is that, although most his students claim to cook a better hamburger than fast food chains, the fast food chains make way more money than his students. He uses examples of other people who are not doing so well financially, mostly due to their lack of knowledge of business. He also says that people who run major companies are usually transferred between departments within the company till they get to the top, to acquire knowledge in all areas of business. In page 168, Kiyosaki summarizes the main management skills needed to succeed: cash flow, systems, and people, while the most important specialized skills are sales and marketing. One of the analogies made with specialized people being vulnerable is an athlete who becomes injured or too old to play. Finally, Kiyosaki remembers the importance of giving, in order to get.

Chapter 7: Overcoming obstacles

This chapter starts with a bold claim: “the primary difference between a rich person and a poor person is how they manage fear”. Once people become financially educated, they still face some obstacles to become rich. In particular, Robert Kiyosaki enumerates 5 obstacles: 1) fear, 2) cynicism, 3) laziness, 4) bad habits and 5) arrogance. Robert Kiyosaki explores all these obstacles, one by one. Regarding fear, Kiyosaki says that everyone fears to lose money. Rich dad recommended Kiyosaki to think like a Texan when it comes to fearing losing money: they think big, and they’re proud when they win and brag when they lose. Rich dad also said that the greatest reason for lack of financial success was playing too safe. “People are so afraid of losing that they lose”, rich dad said. Kiyosaki leaves another bold claim: “for most people, the reason they don’t win financially is because the pain of losing money is far greater than the joy of being rich”. In essence, they play not to lose, not to win. Balanced portfolios are fine if you have to lose, but make sure you start early.

The second obstacle is cynicism. Kiyosaki uses the story of the Chicken Little to illustrate that we all have doubts, or noise, as Kiyosaki calls it. It can come from within or for the outside. A savvy investor has to know how to make money even in bad times. Kiyosaki tells the story of a friend who backed out on a deal Kiyosaki and his wife had arranged. Another example is tax-lien certificates, which Kiyosaki keeps a portion of his money in, and people often evaluate as “risky” even without investing in them. As rich dad said, “cynics never win”. They criticize, and winners analyze. At the end of this section, a motivational story is provided: Colonel Sanders lost his business at age 66 and started to live off a Social Security check. He went around the country and over more than a thousand rejections, he finally became a multi-millionaire, after selling his fried chicken recipe.

A “little greed” is the solution to laziness, the third obstacle. Although we are raised thinking of greed as bad, as Kiyosaki reports, that greed is what really makes us tend laziness off. Again, he relates this greed with the reactions of both dads to Kiyosaki’s asks, when he was younger. While the poor dad use to say “I can’t afford it”, rich dad use to say “how can I afford it?”, opening up possibilities and excitement. When Kiyosaki wanted to quick the rat race, he questioned himself and thought about how would his life be if he didn’t have to work again. Our lives are a reflection of our habits more than our education. An example of a good habit preached by the rich dad was to pay himself first, and bills last. This motivated him to deliver: if he could not pay his creditors, he was forced to find additional sources of income.

Arrogance is the last obstacle. Rich dad lost money whenever he was arrogant. People are arrogant to hide ignorance. If you’re arrogant in a subject, start educating yourself by finding an expert or reading a book.

Chapter 8: Getting started

This chapter lists 10 things to start improving your financial life. First, find a reason to succeed. Kiyosaki mentions the example of a young woman who had dreams of swimming for the US Olympic team. She used to wake up very early to practice, among many other sacrifices she used to go through. She said it was due to love because she did that for herself and the people she loved. Two, make good daily choices. Kiyosaki explains that we have the power of choosing what we want. He takes courses, listens to CDs and what not. Essentially, he invests a lot in his financial education, which he considers crucial to succeed. Arrogance, on the other hand, impedes you from listening and learning and will get you nowhere. Three, choose friends carefully. Be friends with people you can learn from, don’t listen to the Chicken Littles. If possible, get access to legal insider trading. Four, master a formula and learn quickly. Kiyosaki explains that in a fast-changing world, it’s often not what you know, but how quickly you can learn that makes the difference. Five, pay yourself first and master self-discipline. Kiyosaki argues that self-discipline is the biggest difference between the rich and the poor. Six, pay your brokers well. In general, Kiyosaki argues that you’ll have a better service (aka make more money) if you do pay them well. If you cut on their commissions – why would they want to help you?

Seven, be an Indian giver: ask yourself how quickly you get your money back. Look at the ROI, but not only that. They also look at what you get for free in deals too. Eight, use assets to buy luxuries. When you desire something, use your assets to pay for it. If you get $10.000 as a gift, you can either use that money to make more money and pay for what you want, or you can get yourself into further debt if you use that as a down-payment for something else. Nine, have your own heroes, people who inspire you. It will make investing a lot easier. The latest advice is about the power of giving: teach and you should receive.

[trendingnewsb]

The Power of Mind Map: Get More Things Done & Make Creativity Easy

You have a big project coming up, or a bunch of tasks, and are overwhelmed by the thought of it. This overload happens to me way too often,[1] but once I began using a mind map to clarify the direction, the project and tasks don’t seem so frightening.

Whatever your position or place of work is, it is natural that you will have more than one task to handle at any given moment. This could be due to hectic deadlines, a large project made up of many different tasks, or simply you’re loading more and more on yourself in hopes to get more done; how’s the latter been working out for you?

Prior to mind mapping, I jotted all tasks I knew of onto my notepad or in workflowy. On one hand, this is great for as you complete a task, you cross it over and have the amazing feeling of getting things done.[2] On the other hand, I continuously felt that I wasn’t getting ‘the right things’ done. I was crossing off tasks left and right, but are they in line with my overall goals and targets? Or am I just listing tasks in order to cross them off?

With a mind map, I had a more organized process to follow, which helped me avoid missing tasks, while also keeping in line with overall needs.

The Purpose of a Mind Map

Mind mapping is a simple organization process using diagrams to list the information, ideas, and details and assess the big picture.

You begin with a blank page, write the main subject, project, or idea in the middle of the page, and then like a web or branches, expand from it to additional ideas connected to it, and smaller branches to those connected to it, and so on. Think of a family or decision tree, but to help you organize a large project.

How to Start Mind Mapping from Scratch

The best way to begin mind mapping is with a blank page. Write down the main subject in the middle and begin brainstorming and breaking it down into more-focused categories that aren’t as abstract. This can be done in your notebook, printer paper, as well as online solutions for those who cannot disconnect. (I will recommend some online tools later in this article.)

As you progress, you expand out more branches from the categories and subcategories, in order to make sure to ‘cross your t’s and dot your i’s’ and that nothing is forgotten.

Note: this is to organize project needs, not a manifesto which you begin attending every task within the map. In other words, if there is a branch under content for ‘product pages’, you don’t branch out and start adding the actual text/context of any specific page. We want to keep it clean and clear; completing individual tasks is external from the map.

Let’s take for example a project of a new website. Depending on your needs, this may be a simple or very complex project, but whatever your needs are, with a mind map, you can easily break it down.

You begin in the middle with the project “New Website” and can expand the various branches to it such as: “design”, “development”, “content”, and so on. But it doesn’t stop there, as each subject can be broken down further; with design, it can go into ‘specifications’ (wishes of the new site), ‘characterization’ (behaviors and experience), ‘branding’ (color schemes, themes, style) and much more.

Breaking down a main project into separate categories (and then sub-categories) allows us to take a huge project and make it digestible and tangible. What began as an abstract wish is now clear with real direction and is broken down into bite-sized tasks.

From there, we can create a separate mind-map for each of the main categories to further clarify (if needed), as well as actually prioritize and set timetables to fit across complimentary and awaited tasks.

What began as the preliminary plan of the new website design expanded to all various corners which go beyond design but the actual context as well, i.e new site content, and its own extension into type of content, such as product pages, company pages, support pages, blog and more.

The mind mapping process allowed me to think bigger and organize the project a lot clearer, making sure I don’t proceed until the entire scope of the project is organized in front of me. From there, I prioritize the tasks, so I am well aware if any task is dependent on another, or is attended to in parallel to another, and where I stand in the entire process.

There are plenty of applications, sites, and more to get you on the mind mapping wagon. The latter may include various features such as color-coding, sharing/collaborating, linking (to notes, sites, imagery), design/style extras, and more bells and whistles to help you create the map needed to organize your thoughts, brainstorming, task breakdown and more.

Whichever tool or mind mapping method floats your boat is great, but it is best to begin simple, realize the benefits, and only after such a process proves beneficial for you, then upgrade to all the extras.

At the end of the day, mind mapping is aimed to help you organize thoughts, tasks, ideas, and such to get you closer to the main goal, which is getting things done. If the extra features are making your map prettier but don’r bring you closer to the goal, you haven’t yet benefited from such an amazing process.

I found myself going back to Coggle, as it is very easy to use, the free offering would be sufficient for most, and collaboration is available to allow your team to expand. Still, a regular piece of paper offline always does the trick, granting your pen the freedom it deserves.

The Power of Mind Mapping

There are so many different tools, methods, processes, and more which individuals can use when attempting to organize their thoughts or brainstorm toward a new project. I find that mind-mapping is a very simple process to follow, and is rather natural to use and expand upon.

Its practice helps the individual get the big picture visually and clearly without too much effort. Its flexibility makes it easy to grow, so we’re allowing ideas to flow while maintaining a singular focus which stares at you from the beginning to the end.

As always, adapting to a new tool or process isn’t an easy thing; you may realize the benefit but feel it is time-consuming and you could have already been submerged in the project, or you’re unable to clarify which is a category or a task, and more.

I had some difficulties when I began to adapt to a freehand style and draw a diagram, rather than listing an outline on my computer. But as it clarified the project, organized my thoughts and tasks, as well as helped me avoid missing any crucial steps along the way, I was hooked.

Since I began, I also realized that I am ending up getting more done, as every project is clear, all tasks listed, and I can better maneuvre through items and multi-tasks effectively (not just for the sake of multi-tasking).[3]

I can take more upon myself, and while at it, complete things towards my goals and targets in a more effective and efficient way.

Reference

[1]^Knowmail: A simple approach to task or project overload[2]^Craig Jarrow: Why Checking Things Off Your List Feels So Good[3]^Knowmail: Can multitasking produce results?

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How and Why (or Why Not) to Build a Chatbot

Let’s say your organization has the best content in your industry. Prospective customers go to your site, enter their questions in your search box, navigate through a few clicks, and – voilà – they get instant answers to their questions.

Excellent. For today. But are you ready for tomorrow, when your competitors lure those customers away with a superior Q-and-A experience? They won’t do it by hiring thousands of people to take phone calls. In 2011, Gartner predicted “by 2020, customers will manage 85% of their relationship with the enterprise without interacting with a human.”

Customers will manage 85% of business relationships w/o humans by 2020, says @Gartner_Inc. Click To Tweet

How will your competitors lure those curious customers away from your superior content if not with human beings?

With chatbots. Friendly, helpful chatbots.

Unless you beat them to it.

So says Cruce Saunders, founder and principal content engineer at [A], in his Intelligent Content Conference talk Engineering Content for Chatbots, AI, and Marketing Automation. In this article, I sum up some of Cruce’s advice. Unless noted, all images and quotations in this post come from his talk.

Chat-whats?

Chances are, you’ve interacted with a chatbot, even if you didn’t know it. A chatbot (also called a bot, a virtual assistant, or an intelligent personal assistant) is “software that automates the task of talking with people, especially over the internet,” says Kristina Podnar in this article from which I borrowed the animated example below. This example shows Taco Bell’s chatbot – “tacobot” – sounding downright personable (“Sounds good,” and so on.)

  Some chatbots use artificial intelligence (AI) and some don’t. A simple, scripted chatbot, like tacobot, uses programmed-response technology based on rules or decision trees. “Its paths are limited, and users select from defined options,” according to a recent UX Booth article. On the other hand, the article explains, an AI-powered chatbot – like Google, Siri, or Alexa – responds based on machine-learning or natural-language-processing systems. It deciphers people’s input, responds based on what it knows so far, and then “turns the user’s input into more data,” continuously updating its algorithms.

AI-powered or not, chatbots aim to respond to basic requests in real time, “freeing up humans to do more creative problem solving,” says Cruce. He describes chatbots as an “increasingly interactive and vital way to get at content.”

#Chatbots are an increasingly interactive & vital way to get at content, says @MrCruce. #intelcontent Click To Tweet

Why you might want to build a chatbot

Not every company needs a chatbot. You may want to build one if certain queries could be handled in an automated way. A successful implementation, according to the UX Booth article, can have the following benefits:

Increased brand affinity and loyalty

Reinforced brand voice and personality

Differentiation from the competition

Increased engagement and interaction times

Higher conversion rates

Rich data to better understand users

Chatbot examples

Cruce cites three progressively human-like chatbots:

Mastercard chatbot (via the Facebook Messenger app)

Alexa (via Amazon Echo)

Nadia (created by Soul Machines and powered by IBM’s Watson software)

The Mastercard chatbot, which communicates via text messages in Facebook Messenger, answers questions that don’t need to be handled by a person: How much did I spend in restaurants in September? What are my offers? What are the benefits of my card? How do I reset my password?

Amazon Echo’s intelligent personal assistant, Alexa, moves the conversation from text to voice. You talk to Alexa and Alexa talks back. This assistant goes beyond answering your questions; it can play music, create to-do lists, set alarms, stream podcasts, play audiobooks, and provide updates on weather, traffic, and news. This type of assistant can even be programmed to have a little fun. (Siri is a similar example. Try telling Siri, “I see a little silhouetto of a man”; the response especially tickles me when delivered in one of the Aussie voices.)

The Soul Machine’s intelligent personal assistant, Nadia, is an experimental avatar designed by a team in New Zealand and Australia. Cate Blanchett created the voice recordings. If you talk with Nadia, “she” sees and hears you, adapting her answers according to your tone and facial expression to fit your presumed emotional state. Here’s what Nadia looks and sounds like:

Chatbot content behind the scenes

Most chatbot platforms depend on authors to develop an independent repository of questions and answers. Often, the authors duplicate this content from other systems. This redundant effort is expensive. As Cruce says,

Subject-matter experts need to be able to maintain content in a single source. The more overlapping content repositories we introduce, the more effort, cost, and risk we introduce. Chatbot content should exist in the core CMS.

#Chatbot content should exist in the core CMS, says @MrCruce. #intelcontent Click To Tweet

Within your CMS, chatbot content could look something like this:

Within the CMS, the chatbot content can live alongside related articles and documentation instead of living in a separate chatbot platform. “The chatbot ideally calls that content in real time,” Cruce says.

Chatbots and humans

When visitors interact with a chatbot, they don’t necessarily know it’s not a person. It’s up to the chatbot owner to clarify – by the image and the chatbot’s name, for example – that visitors are interacting with a machine. Here, for example, you can tell (or can you?) that Cruce is talking with a chatbot.

Eric Savitz, a Forbes writer, describes chatbots as giving people a self-service experience that combines “the conversational attributes of live chat or a phone call” with “the ultimate in automation – zero human contact.”

Granted, robot-powered customer experiences can be annoying or weird in these early generations of the technology. Today’s chatbots often miss the mark, sometimes embarrassingly so (for the company anyhow – entertainingly so for the rest of the world). Many high-performing customer service and sales chatbots have an option to hand off questions they can’t answer to human representatives. In this way, robots and humans work together to serve customers.

Even though chatbot technology is far from perfect, it holds undeniable potential – in a way that scales – for answering the most common questions asked by your most promising audiences. There’s no fighting the use of chatbots. Someday, they will be as common as automated phone systems. How about we make them better?

Chatbots hold undeniable potential to scale answers to common questions of audience. @MrCruce #intelcontent Click To Tweet

HANDPICKED RELATED CONTENT: Automating Your Customer Interactions: Get Ready for Chatbots

Why chatbots need input from content strategists and content engineers

When creating chatbot content, companies often make the same mistake as when creating any new type of content: They copy and paste from existing sources instead of a single source. Ideally, you set up your system so that all chatbot content – primarily compact answers to common questions – flows directly from the same CMS that supplies your other customer-facing content.

Cruce is talking about classic content reuse, aka COPE content: create once, publish everywhere.

On the theme of reuse, Cruce announced, midway through his talk, that, at that moment, history was being made. SpaceX, Elon Musk’s private space-flight company, had just launched Falcon 9, “the world’s first re-flight of an orbital-class rocket” according to this video.

A part of the rocket had been refurbished after an earlier flight, something that had never been done before. The savings were estimated in many millions of dollars.

Cruce drew the parallel: Our content assets are like those rocket assets. We make significant investments in our content assets. Why in the world wouldn’t we reuse them if we can?

Setting up your content for reuse is easier to say than to do. You need to work with a content strategist (or at least think like one) to develop appropriate content models and metadata, among other things. You may need to work with a front-end and a back-end content strategist.

And you’ll need to work with a content engineer to connect the chatbot with your CMS, among other things.

In case you’re thinking of going DIY and tackling the strategy and engineering of chatbots yourself, I can only wish you luck. A blog post like this can’t give you the guidance you need. I include this diagram below (with chatbot functionality represented by robot heads) not so much for you to study as for you to appreciate the need to collaborate with people with backgrounds in content strategy and content engineering. Few of us could create this sort of diagram on our own:

Going DIY for your #chatbot? Good luck, says @MarciaRJohnston. #intelcontent Click To Tweet

The new multichannel content stack:

Click to enlarge

Image source

For a deeper dive into the techier aspects of this topic, see Cruce’s Resource Guide: Engineering Content for Bots, AI, and Marketing Automation.

HANDPICKED RELATED CONTENT: New Tech Friends on the Marketing Block

5 steps to develop a chatbot

When you’re ready to develop a chatbot, follow these steps: journey, research, model, engineer, and deploy, as shared by Cruce.

Step 1: Map the customer journey.

Journey maps “show us the customer experience in context.” Work on your journey maps with a content strategist and business stakeholders “to understand what your content needs to be doing in its context.”

HANDPICKED RELATED CONTENT: What to Do When Your Buyers’ Journey Isn’t Linear (Hint: It Never Is)

Step 2: Research what your audience wants to know.

Figure out your audience’s burning questions and the terms they use to phrase those questions. You can do this research in various ways: gather SEO data, review session data – even get radical and talk with people. Whatever it takes to get inside prospective customers’ heads.

HANDPICKED RELATED CONTENT: What Are Your Customers Thinking? Search Secrets Hiding in Plain Sight

Step 3: Build the content model.

Create a content model that specifies the structure of each of your team’s commonly created content types. Also, specify the ways those content types relate to each other. Build your content model with a content engineer and content strategist. Without an accurate model of your organization’s content, you can’t know what kind of technology you need or how it needs to be set up.

HANDPICKED RELATED CONTENT: Structured Content: Get Started With Content Models

Step 4: Engineer the technology to support your content model.

The content engineer maps your content model to technology. Cruce had a lot to say about content engineering that I couldn’t squish into this post even if I had understood it all. (Code snippets, anyone? Microdata, schemas, taxonomy, and clean-content APIs?) Here’s what you need to know: Find yourself a good content engineer.

Step 5: Deploy your chatbot – when it’s ready.

Before you unleash your bot on the world, test it in development, staging, and production environments. Work out as many kinks as you can before your prospective customers make it say silly things. Work on the voice, tone, and message targeting and interactions with a content strategist who understands interactive content.

HANDPICKED RELATED CONTENT: Why Automation Is the Future of Content Creation

Conclusion

Increasingly, when we humans get curious about something, we expect answers to materialize instantly. Chatbots – done well – provide a scalable way for companies to fulfill that expectation.

Done poorly, of course, chatbots frustrate people and damage brands. Don’t bother making a chatbot unless your company is committed to dedicating the resources needed for creating a positive user experience.

As Cruce says, “Consumers are increasingly talking with our content, asking it questions. We need to make sure our content can talk back.”

Is your content team creating chatbots? Thinking about them? Let us know in a comment.

Here’s an excerpt from Cruce’s talk:

Sign up for our weekly Content Strategy for Marketers e-newsletter, which features exclusive stories and insights from CMI Chief Content Adviser Robert Rose. If you’re like many other marketers we meet, you’ll come to look forward to reading his thoughts every Saturday.

Cover image by Joseph Kalinowski/Content Marketing Institute

The post How and Why (or Why Not) to Build a Chatbot appeared first on Content Marketing Institute.

from http://contentmarketinginstitute.com/2017/08/how-why-build-chatbot/

[hotspreadpage]

How and Why (or Why Not) to Build a Chatbot

Let’s say your organization has the best content in your industry. Prospective customers go to your site, enter their questions in your search box, navigate through a few clicks, and – voilà – they get instant answers to their questions.

Excellent. For today. But are you ready for tomorrow, when your competitors lure those customers away with a superior Q-and-A experience? They won’t do it by hiring thousands of people to take phone calls. In 2011, Gartner predicted “by 2020, customers will manage 85% of their relationship with the enterprise without interacting with a human.”

Customers will manage 85% of business relationships w/o humans by 2020, says @Gartner_Inc. Click To Tweet

How will your competitors lure those curious customers away from your superior content if not with human beings?

With chatbots. Friendly, helpful chatbots.

Unless you beat them to it.

So says Cruce Saunders, founder and principal content engineer at [A], in his Intelligent Content Conference talk Engineering Content for Chatbots, AI, and Marketing Automation. In this article, I sum up some of Cruce’s advice. Unless noted, all images and quotations in this post come from his talk.

Chat-whats?

Chances are, you’ve interacted with a chatbot, even if you didn’t know it. A chatbot (also called a bot, a virtual assistant, or an intelligent personal assistant) is “software that automates the task of talking with people, especially over the internet,” says Kristina Podnar in this article from which I borrowed the animated example below. This example shows Taco Bell’s chatbot – “tacobot” – sounding downright personable (“Sounds good,” and so on.)

  Some chatbots use artificial intelligence (AI) and some don’t. A simple, scripted chatbot, like tacobot, uses programmed-response technology based on rules or decision trees. “Its paths are limited, and users select from defined options,” according to a recent UX Booth article. On the other hand, the article explains, an AI-powered chatbot – like Google, Siri, or Alexa – responds based on machine-learning or natural-language-processing systems. It deciphers people’s input, responds based on what it knows so far, and then “turns the user’s input into more data,” continuously updating its algorithms.

AI-powered or not, chatbots aim to respond to basic requests in real time, “freeing up humans to do more creative problem solving,” says Cruce. He describes chatbots as an “increasingly interactive and vital way to get at content.”

#Chatbots are an increasingly interactive & vital way to get at content, says @MrCruce. #intelcontent Click To Tweet

Why you might want to build a chatbot

Not every company needs a chatbot. You may want to build one if certain queries could be handled in an automated way. A successful implementation, according to the UX Booth article, can have the following benefits:

Increased brand affinity and loyalty

Reinforced brand voice and personality

Differentiation from the competition

Increased engagement and interaction times

Higher conversion rates

Rich data to better understand users

Chatbot examples

Cruce cites three progressively human-like chatbots:

Mastercard chatbot (via the Facebook Messenger app)

Alexa (via Amazon Echo)

Nadia (created by Soul Machines and powered by IBM’s Watson software)

The Mastercard chatbot, which communicates via text messages in Facebook Messenger, answers questions that don’t need to be handled by a person: How much did I spend in restaurants in September? What are my offers? What are the benefits of my card? How do I reset my password?

Amazon Echo’s intelligent personal assistant, Alexa, moves the conversation from text to voice. You talk to Alexa and Alexa talks back. This assistant goes beyond answering your questions; it can play music, create to-do lists, set alarms, stream podcasts, play audiobooks, and provide updates on weather, traffic, and news. This type of assistant can even be programmed to have a little fun. (Siri is a similar example. Try telling Siri, “I see a little silhouetto of a man”; the response especially tickles me when delivered in one of the Aussie voices.)

The Soul Machine’s intelligent personal assistant, Nadia, is an experimental avatar designed by a team in New Zealand and Australia. Cate Blanchett created the voice recordings. If you talk with Nadia, “she” sees and hears you, adapting her answers according to your tone and facial expression to fit your presumed emotional state. Here’s what Nadia looks and sounds like:

//player.ooyala.com/static/v4/stable/4.10.6/skin-plugin/iframe.html?ec=w2a2FnYTE6VCuTLs_DI6Kl66gh-0wJLB&pbid=5ad1946db28d45cdb4325c91c7751266&pcode=FvbGkyOtJVFD33j_Rd0xPLSo0Jiv

Chatbot content behind the scenes

Most chatbot platforms depend on authors to develop an independent repository of questions and answers. Often, the authors duplicate this content from other systems. This redundant effort is expensive. As Cruce says,

Subject-matter experts need to be able to maintain content in a single source. The more overlapping content repositories we introduce, the more effort, cost, and risk we introduce. Chatbot content should exist in the core CMS.

#Chatbot content should exist in the core CMS, says @MrCruce. #intelcontent Click To Tweet

Within your CMS, chatbot content could look something like this:

Within the CMS, the chatbot content can live alongside related articles and documentation instead of living in a separate chatbot platform. “The chatbot ideally calls that content in real time,” Cruce says.

Chatbots and humans

When visitors interact with a chatbot, they don’t necessarily know it’s not a person. It’s up to the chatbot owner to clarify – by the image and the chatbot’s name, for example – that visitors are interacting with a machine. Here, for example, you can tell (or can you?) that Cruce is talking with a chatbot.

Eric Savitz, a Forbes writer, describes chatbots as giving people a self-service experience that combines “the conversational attributes of live chat or a phone call” with “the ultimate in automation – zero human contact.”

Granted, robot-powered customer experiences can be annoying or weird in these early generations of the technology. Today’s chatbots often miss the mark, sometimes embarrassingly so (for the company anyhow – entertainingly so for the rest of the world). Many high-performing customer service and sales chatbots have an option to hand off questions they can’t answer to human representatives. In this way, robots and humans work together to serve customers.

Even though chatbot technology is far from perfect, it holds undeniable potential – in a way that scales – for answering the most common questions asked by your most promising audiences. There’s no fighting the use of chatbots. Someday, they will be as common as automated phone systems. How about we make them better?

Chatbots hold undeniable potential to scale answers to common questions of audience. @MrCruce #intelcontent Click To Tweet

HANDPICKED RELATED CONTENT: Automating Your Customer Interactions: Get Ready for Chatbots

Why chatbots need input from content strategists and content engineers

When creating chatbot content, companies often make the same mistake as when creating any new type of content: They copy and paste from existing sources instead of a single source. Ideally, you set up your system so that all chatbot content – primarily compact answers to common questions – flows directly from the same CMS that supplies your other customer-facing content.

Cruce is talking about classic content reuse, aka COPE content: create once, publish everywhere.

On the theme of reuse, Cruce announced, midway through his talk, that, at that moment, history was being made. SpaceX, Elon Musk’s private space-flight company, had just launched Falcon 9, “the world’s first re-flight of an orbital-class rocket” according to this video.

A part of the rocket had been refurbished after an earlier flight, something that had never been done before. The savings were estimated in many millions of dollars.

Cruce drew the parallel: Our content assets are like those rocket assets. We make significant investments in our content assets. Why in the world wouldn’t we reuse them if we can?

Setting up your content for reuse is easier to say than to do. You need to work with a content strategist (or at least think like one) to develop appropriate content models and metadata, among other things. You may need to work with a front-end and a back-end content strategist.

And you’ll need to work with a content engineer to connect the chatbot with your CMS, among other things.

In case you’re thinking of going DIY and tackling the strategy and engineering of chatbots yourself, I can only wish you luck. A blog post like this can’t give you the guidance you need. I include this diagram below (with chatbot functionality represented by robot heads) not so much for you to study as for you to appreciate the need to collaborate with people with backgrounds in content strategy and content engineering. Few of us could create this sort of diagram on our own:

Going DIY for your #chatbot? Good luck, says @MarciaRJohnston. #intelcontent Click To Tweet

The new multichannel content stack:

Click to enlarge

Image source

For a deeper dive into the techier aspects of this topic, see Cruce’s Resource Guide: Engineering Content for Bots, AI, and Marketing Automation.

HANDPICKED RELATED CONTENT: New Tech Friends on the Marketing Block

5 steps to develop a chatbot

When you’re ready to develop a chatbot, follow these steps: journey, research, model, engineer, and deploy, as shared by Cruce.

Step 1: Map the customer journey.

Journey maps “show us the customer experience in context.” Work on your journey maps with a content strategist and business stakeholders “to understand what your content needs to be doing in its context.”

HANDPICKED RELATED CONTENT: What to Do When Your Buyers’ Journey Isn’t Linear (Hint: It Never Is)

Step 2: Research what your audience wants to know.

Figure out your audience’s burning questions and the terms they use to phrase those questions. You can do this research in various ways: gather SEO data, review session data – even get radical and talk with people. Whatever it takes to get inside prospective customers’ heads.

HANDPICKED RELATED CONTENT: What Are Your Customers Thinking? Search Secrets Hiding in Plain Sight

Step 3: Build the content model.

Create a content model that specifies the structure of each of your team’s commonly created content types. Also, specify the ways those content types relate to each other. Build your content model with a content engineer and content strategist. Without an accurate model of your organization’s content, you can’t know what kind of technology you need or how it needs to be set up.

HANDPICKED RELATED CONTENT: Structured Content: Get Started With Content Models

Step 4: Engineer the technology to support your content model.

The content engineer maps your content model to technology. Cruce had a lot to say about content engineering that I couldn’t squish into this post even if I had understood it all. (Code snippets, anyone? Microdata, schemas, taxonomy, and clean-content APIs?) Here’s what you need to know: Find yourself a good content engineer.

Step 5: Deploy your chatbot – when it’s ready.

Before you unleash your bot on the world, test it in development, staging, and production environments. Work out as many kinks as you can before your prospective customers make it say silly things. Work on the voice, tone, and message targeting and interactions with a content strategist who understands interactive content.

HANDPICKED RELATED CONTENT: Why Automation Is the Future of Content Creation

Conclusion

Increasingly, when we humans get curious about something, we expect answers to materialize instantly. Chatbots – done well – provide a scalable way for companies to fulfill that expectation.

Done poorly, of course, chatbots frustrate people and damage brands. Don’t bother making a chatbot unless your company is committed to dedicating the resources needed for creating a positive user experience.

As Cruce says, “Consumers are increasingly talking with our content, asking it questions. We need to make sure our content can talk back.”

Is your content team creating chatbots? Thinking about them? Let us know in a comment.

Here’s an excerpt from Cruce’s talk:

Sign up for our weekly Content Strategy for Marketers e-newsletter, which features exclusive stories and insights from CMI Chief Content Adviser Robert Rose. If you’re like many other marketers we meet, you’ll come to look forward to reading his thoughts every Saturday.

Cover image by Joseph Kalinowski/Content Marketing Institute

The post How and Why (or Why Not) to Build a Chatbot appeared first on Content Marketing Institute.

How and Why (or Why Not) to Build a Chatbot syndicated from http://ift.tt/2maPRjm