#unfortunately it is impossible to get good BBQ here

[risu5waffles]

If Roppongi Is Six Trees, What's the Word for TEN Fires?

Here we are again, something, something pleasure.

We played these on stream and i just fell in love wiv them. The concept is just so good. Little dioramas clearly conveying the feel of the different story mode stages? Like, that's inspired. And it largely works. There're a few bits here and there where they could have tightened and polished, but it totally works, and i'm surprised it took this long for me to stumble across someone who did it and did it well.

The concept here is cute and funny, and it's another one where i found myself thinking "why haven't i seen someone try something like this sooner"? The execution leaves a little to be desired, tho'. i feel like the course takes you away from the scoreboard to soon and for too long, so you kinda lose that little extra spice that sells you on the play. Still, the race is a solid one, not top-tier, but definitely good and fun. Kuro_96_33 did another really good level that i may take a look at in the future, but that @soupum has covered on his channel, where they go through the basics of level design and how connectors work, wiv a little bit of simple logic (LBP1-era) set-up for good measure (that area's unfortunately broken, but you can leave the level after the basics have been covered). It's really good advice for new creators, and presented clearly (bilingually, no less!), and not in any kind of jerky way. i really enjoyed that one.

Someone on the subreddit asked "hey, how do i get the Simon Says pin", and someone else rec'ed this level, and i thought "hey, i don't have the Simon Says pin either, and now i do, and you could too. i should play wiv state sensors more. i feel like you could get up to all kinds of shenanigans. Maybe next level.

It's a level about K-trucks. i like K-trucks. i have fond memories of riding home from bbq's when we'd visit honeybunny's folks down in Amami. Everyone drunk as hell and piled in the truck bed. i've also got a pretty bad story that didn't directly involve me, but hurt some folx i care a lot about, so maybe i should shut my mouth before i go and make myself sad. Still love k-trucks tho', and this level's a fun little race, so i can't complain about that.

We talked about this on Friday. It's still very much what it is. This particular footage was from before i figured out you could get that score bubble sign, unfortunately, but i was too busy putting out fires to go and swap the videos. Such is life, sometimes.

i really loved this level. Some of the bits were actually kind of tricky, and i was kind of proud of myself that i got them figured out. i feel like it's pretty rare anymore to see these kinds of puzzles; working both the brainmeats, but also actually physically manipulating things. It was super cool to run into this one.

This one was alright enough, i guess? Pretty standard post-MGS DLC kill everything wiv the paintinator platformer. It's fine. Not particularly inspired, but fine. It is a shame it's broken the way it is. The level might not have been top of the pops, but i was having a good enough time wiv it. Just LBP1 things, i guess.

This one's actually surprisingly good. Solid, robust platformer, nicely presented. The enemies are a bit of a mixed bag, but i think that's fairly par for the course for an LBP1 level. It could be a bit tough to make things act wiv dynamism. Not impossible, but i feel like it was a limited palette for the types of enemies you could work in. Still, just looking at them from an objects standpoint, they're nice enough, and that dragon-y thing at the end was pretty cool.

---------------------------------------------------

So that's the ten for this go about. If you missed the last post, i have to write a term paper about why i'm trans, and why i totes need this fucking on-paper diagnosis, and it can't be 5000 words of "because immigration fucked me and won't update my paperwork the way they're supposed to." Actually, i don't know how long it's supposed to be; the nurse didn't specify. She just said "from childhood," and i wanna be, like, "bitch, i am 45 fucking years old. 'From childhood' covers a goddamned lot of goddamned ground by this point." i may be experiencing a little stress.

[adultswim2021]

Aqua Teen Hunger Force #60: “Hand Banana” | October 29, 2006 – 10:30PM | S05E05

Meatwad wants a dog and of course he is a piece of shit who refuses to adopt a pit bull from a kill shelter, so he resorts to using Frylock's dog-making kit/computer program and Carl's pool, which is converted into some kind of science-fiction vat. The only thing you need to supply for the kit to work is som DNA. Shake winds up being the one to supply the DNA after he absent-mindedly dips his hand into the concoction to have a nice splash. The result: a friendly little critter that looks like Shake's yellow hand. Wait, I assumed he wore gloves? Well, in this episode that's his skin, I guess. Don't have a cow, Homer.

The dog is a dream pet for the Aqua Teens; in fact he is a world-class pastry chef. Unfortunately for Carl he mindmelds with him, turning him into a Son-of-Sam. The dog, named Hand Banana by Meatwad, begins threatening Carl in somewhat vague ways. Then he rapes Carl. Multiple times.

Okay, so obviously there is a type of retard who would like to condemn this episode for promoting rape culture (remember when everyone said “rape culture”? Now I'm the one that says it! :D) and there's also a type of retard who would like to specifically tout this episode as being the best one ever because they can't handle a world that no longer directly reflects their values of being able to laugh at horrific sexual crimes so they make a point to like stuff with rape jokes on purpose. I, an extremely reasonable person who never uses slurs like “retard” unless I'm being toxically ironic on tumblr, am here to weigh in with the exact correct opinion: this episode is extremely funny.

Look, I'll get sincere for a minute: I'm not a fan of political laughter. If you don't know what I mean (because it's a term I am pretty sure only I have ever used), think about an obnoxious male-feminist type laughing extra hard at an objectively tepid female stand-up at an open mic. I'm not calling all women who do stand-up bad! I'm just saying, imagine one that isn't actually that good but is getting over-supportive laughter because she's the only woman whose gone up in the last hour (or she's actually great but this particular set is bad, happy now?).

Now imagine your redneck uncle, telling his friend a racist joke that you've personally overheard him tell this same person about a dozen times. And yet, he exclaims with laughter as if it's the first time he's heard it, even though jokes are supposed to have diminishing returns. You get the sense he's laughing because the joke flatters him, propping him up as a straight white man who deserves to be celebrated for being exactly that. The racist joke makes him feel superior or at the very least normal. So he laughs not because it’s funny, but because it’s “true”. At least, he hopes it’s true.

Is the supportive male feminist who is over-laughing at the heavily-accented Indian woman telling nothing but shitty puns less evil than the two yokels telling a joke that was designed to make non-whites feel less-than-human? Well, that depends. There aren't any black people at your redneck uncle's BBQ and there was never going to be weather he tells that joke or not. The male feminist at the open mic has a few beers in him, Gonorrhea, and is planning to try and have unprotected sex with the female comic after the show. Harmless?

So please don't mistake me for some FREAKING RIGHT-WING MAGA CHUD for liking this episode. I've acknowledged that it's impossible to enjoy things in a total vacuum, and I've observed something sorta disturbing about myself: I am a bit of a reactionary contrarian. If I were to find a posting about this episode that I wrote right after this aired, I would bet that somewhere in there I'd complain about how there's too many rape jokes in comedy. Does this make me a hypocrite? Eh. Maybe. But it is absolutely worth noting something about the comedy landscape at the time: to mainstream audiences in 2006 rape jokes were seen as merely “acceptably edgy”. Not saying everyone loved them; I'm just saying it was like making fun of religion. You could get away with it only at the expense of a few zealots. With rape jokes now, that it just isn’t the case. (to be clear: I think this is a good thing. But I see no need to pretend I don’t laugh at problematic stuff a lot of the time).

You know who RedLetterMedia are? Youtube Channel by a bunch of Midwestern slobs who manage to put a phenomenal amount of production value in their content which is mostly them extemporaneously critiquing movies? I like them. They're funny and pleasant to listen to. I put them on a lot when I just want noise to fill my head, you know? There's a phrase for this, and I've seen it applied to other shows (mostly podcasts) of this ilk: “friendship simulator”. Well, they tend to not take too much flack for their content even though they can, ON OCCASION, exhibit the insensitive sense of humor that Gen-X white dudes did well for decades and now get yelled at for. It's not, by any means, the main drum they beat, but if you watch them drunkenly mock bad movies on Best of the Worst sometimes some slightly ugly humor comes out and I can imagine them not being for everyone. It's fine. Society is progressing. It's good. I don't care if people hate the stuff I love. What else is new?

Anyway, they recently got a tiny amount flack (most of it was just ribbing from fans) because they were among the most-subscribed sub-reddits among those subscribed to the incel sub-reddit! Fans of RLM just laughed about it and made jokes. I, an appreciator of them, understood that they were just MASSIVELY POPULAR and the overlap between them and incels was probably not a coincidence per se, but a function of the same reason I enjoy them: it's just nice to hear other people who are roughly your demo make each other laugh and have healthy camaraderie in a world where that seems to be harder and harder to come by. In fact, I can attest that their following seems to be all over the map politically generally speaking. I’ve been surprised to find out certain people I knew were fans that I’d expect to take a moral stand against them for being sporadically problematic.

People who had no context started approaching their content from a “I've never heard of these guys they must be bad” position and started digging into their channel, slightly puzzled. I made a joke (or maybe not even) about it on twitter, and some rando replied to me saying something like “gee those rape jokes in the Mr. Plinkett Reviews sure feel different now that we know who their fans are, huh?” I didn't want to fight with this probably-well-meaning person on twitter, so I simply didn't respond, which is a thing you can totally do.

The Mr. Plinkett Review in question was the thing that made them famous. It was a long-form video essay that explains in detail why the Star Wars film The Phantom Menace could be considered objectively terrible. While the points being made were genuine, the humor of the video came from the Mr. Plinkett character; a deranged elderly man who has an unhealthy obsession with the Star Wars prequels, and whose disgusting home life we see little glimpses of. When he wants to show us some kind of Phantom Menace promotional item to drive home some point about consumerism, we see that he has a kidnapped prostitute tied up in his basement, begging to be let go. Mr. Plinkett reviewed the other Star Wars prequels and her kidnapping becomes part of a story arc. That was 2009, a few years after this. I guess I'm just illustrating how much of a thing it was to casually include male-on-female sexual abuse as a dark punchline.

I should probably come up with more examples instead of over-explaining that last one. But I promise if you examined a lot of alternative comedy from 2005-2010 you’d find a lot of rape jokes. I guess my point is: everyone was doing it. Now barely anyone is doing it. I guess that's a good thing. But will I laugh? Sometimes. Will god look upon me and smile? Yes. God is a man. And he's old as fuck. He probably thinks rape is hilarious.

MAIL BAG:

Honestly I don't care whose harp strings he plucks in his offtime, as long as Andy Samberg keeps bringing the funny he's alright with me.

MESSAGE TO ANDY AND ANDY ALONE: Bring on the funny! We can’t wait to laugh!

[mostlysignssomeportents]

A stroll through Magnolia Park

One of the weird ironies of living in the US and having family, friends and colleagues abroad is the vast, iniquitous gap in vaccine availability based on where you live, and, more particularly, whether you live in a poor country or a rich one.

Vaccine Apartheid is a global terror and horror, but that’s not the “ironic” part. That would be the American vaccine deniers who have effectively killed the dream of herd immunity, and taken anti-vax from a threat to public health to a threat to civilization itself.

The way this manifests is often quirky and personal — like the news that some of my beloved cousins in Canada and the US have become anti-vax, anti-mask conspiracists, losing themselves in the Qanon cult.

They’re never far from my thoughts, but doubly so yesterday. You see, here in LA, we have high levels of vaccination and a general lifting of restrictions that — in contrast to the premature “re-openings” elsewhere that led to lethal outbreaks — feel prudent and safe.

That’s given my neighborhood — Burbank’s Magnolia Park — a new vitality. The centerpiece of the neighborhood is a couple miles’ worth of pedestrian friendly, retail, dominated by independent and idiosyncratic retailers that draw people from all over the city.

Many of these did not survive the pandemic, but a heartening number of them held on, and it’s great to see crowds out there on a Saturday. Yesterday, I rode my bike up to one end of the strip, outside Porto’s, the regionally famous Cuban sandwich shop, locked up and strolled.

Magnolia Park’s retail is dominated by vintage clothes and memorabilia stores, a legacy of our proximity to the studios (Disney, Warner and Universal are all a few minutes’ drive), which created demand for wardrobe and set pieces, and a supply of post-shoot surplus items.

It’s also got some great restaurants, like The New Deal. Unfortunately, thanks to Burbank’s antiquated blue laws, almost no one has a real liquor license (wine and beer licenses are easy to get, but spirits licenses are all but impossible).

The sole exception on the strip is…unfortunate. Tinhorn Flats (AKA “Tinfoil Hats”) is a fake saloon with a nice back garden that had one of those rare liquor license, and paired it with mediocre bar food. The best thing about it is its fantastic neon sign.

The worst thing about is that it’s owned by mask-denying, covid-denying far right Trumpian conspiracists who defied public health orders, flooded their social media with culture war bullshit, and became a rallying point for every Bircher, Klansman and Qanon in the Valley.

I do mean “rallying point.” As Tinhorn Flats waged war — installing generators after its power was cut, removing the boards over the door, etc — it hosted weekly Sat gatherings of unmasked, unhinged conspiracists waving American flags and signs decrying “Hollywood pedos.”

They’re still out there, every Sat. If you’re one of the many people who comes to our great family owned grocery Handy Market (whose neon is better than Tinhorn’s!) for their weekly Saturday parking-lot BBQ, you’ve seen ’em, screaming about frazzledrip and “small business.”

They were there yesterday, between my stops at The Mystic Museum and Halloween Town, two of our three goth superstores (the third being Dark Delicacies) — Burbank will costume you, sell you an articulated bat skeleton and fill your bookshelves.

Then you can tour the museum-grade replica of the horror section at a 1980s video store:

https://www.themysticmuseum.com/slashback

and buy merchandise from a wholly hypothetical slasher summer-camp:

https://beardedladysmysticmuseum.square.site/#MJosnZ

It’s such an odd juxtaposition, to be walking around a neighborhood that is making a brave recovery from the lockdown, stopping in at these improbable, scrappy shops, and then walk past these superspreaders screaming in front of the chainlink-surrounded derelict bar.

But my first fully vaccinated Saturday stroll down Magnolia was rescued by a discovery at Halloween Town: the discovery of Round2’s “Haunted Manor” model kits, cheeky remakes of the classic “Zap/Action” MPC Haunted Mansion kits of the 1970s.

https://www.round2corp.com/?s=HAUNTED+MANOR&post_type=product&type_aws=true

The original models were from the high-water mark of Haunted Mansion merch, the era of the UV-paint-doped “changing portrait” cards, the magnificent board-game, and Randotti skulls, models and plaques.

http://www.hauntedmansion.com/spgm/index.php?spgmGal=Vintage_Collectibles&spgmPic=2#spgmPicture

They ingeniously incorporated rubber bands into their interiors to create pop-up effects, like a corpse that popped out a grave, causing the poor grave-digger to spin about. Between the kinetics, glow-in-the-dark plastic, and a good paint job, these were just fantastic.

Even if you never owned one of these kits, if you read comics in the 70s and early 80s, you can’t have missed their distinctive, brilliantly conceived full-page comics ads. Small wonder that these kits sell for stupid money in the secondary market.

The Take2 models (sold under the Polar Lights mark) are not quite replicas of the MPC models (presumably they couldn’t get a license), but they’re fabulous reinterpretations of the vintage designs and I love the renaming (i.e. ”Play It Again, Sam” becomes “Play It Again, Tom”).

Alas, I couldn’t find any sign of a Polar Lights remake of the MPC Zap/Action Pirates of the Caribbean models (whose ads were even better!).

https://pirates.fandom.com/wiki/Pirates_of_the_Caribbean_model_kits

After all that, I confess I didn’t buy the kits (though I may go back today and rectify that). My daily work-load is so high that I’m lucky if I can manage to carve out half an hour every couple days to read a book, let alone put together and paint a model.

But of all the aspirational hobbies I’m wishing I was engaging in, assembling these models tops the list. Building a “Grave Robber’s Demise” kit wouldn’t quite be a “nature is healing” moment, but I know it would give me joy.

In the meantime, I hope you get vaccinated, too — and if you’re ever in Burbank, be sure to patronize our wonderful indie stores (and don’t miss Iliad Bookshop, one of the great used bookstores of the region!).

[cloudthehusky]

Top 10 favorite Bluey episodes ( with your thoughts and facts )

Ooh okay! This will just be season 1 cause I don’t have access really to season two yet so I’ve only seen like bits of those episodes, so maybe I’ll do another once season 2 comes out. (spoilers)

10. Verandah Santa: I enjoyed this episode cause I really like seeing all the heelers together. I think it also has a really good lesson to teach younger kids how to play properly and how to get along with them. Bluey was kinda mean to her little cousin, Socks, after biting her cause she didn’t know any better, and we see later after Bluey makes Socks sad and Bluey tries to apologize Socks licks where she had bitten her and they hug it out. Very cute!!

9. Copycat: I really enjoyed Bluey and Bandit’s interaction in the beginning of this episode and this was an interesting episode as far as little kid shows go. Bluey and her dad find an injured bird and take it to the vet. Unfortunately it dies and Bluey then decides to go play a game with her little sister, Bingo, when she gets home. She then pretends that Bingo is an injured bird an acts like her dad did when dealing with the situation. Her mom acts as the vet and says that the bird (Bingo) is going to be okay but Bluey insists that she say “no it died”. The name of this episode “Copycat” had two meanings. The obvious one was at the very start of this episode she’s playing a copycat game with her father, but then later when she reenacts her day, she takes on the role of her dad. Bluey also, at six years old, excepts what happened to the small bird. I think this was a deep episode that kinda explained a situation like this well to kids.

8. Markets: Bluey gets five bucks from the tooth fairy and decides to spend it on something at the markets. So I loved the beginning of this episode when Bluey runs in saying she got five bucks from the tooth fairy and her father then yells to her mother “Five Bucks!?” and her mom responds saying “That’s what she gave all Bluey’s friends.” As an adult this joke here is hilarious without letting a younger audience know the truth. Bluey at the markets finds her friend, Indy, and wants to spend her money on something for both of them. I really liked this whole sequence cause Indy is allergic to sugar, wheat, gluten, and dairy so it was hard for them to find something to eat together. I think this really helps others who don’t have allergies understand the small struggles like this. I can relate to Indy personally cause I am severely allergic to wheat and gluten since I was two years old, so I’ve basically lived my entire life living how Indy did in this one episode. In the end Bluey ends up impulse buying a toffee apple which Indy says she can’t have so the two end up being sad. But in the end Indy’s mom gives her five bucks to put into a guitar case for the musician to play another song, which Bluey and Indy find out its the same one that Bluey had originally spent. Overall nice episode, and very relatable to me.

7. Wagon Ride: This episode has my favorite Bluey line ever when Bluey starts to think of a plan to get to the park without her father stopping to talk to his friends on the way. The amount of sass both Bluey and her father have is so hilarious. Great episode for teaching patients and I’m sorry I just love Bandit so much! He is one of the best dads in fiction I have ever seen and he’s so patient with his two hyper girls. Daddy/daughter episode I love it!!

6. Yoga Ball: I like this episode cause its another father centered episode, this time with Bingo. Poor Bingo feels like Bandit is playing too rough with her, and honestly you can’t not love Bingo, its impossible. This poor puppy just wants to speak up to her dad, and she ultimately does in the end and her father then knows he’s being too rough. Again, Bandit being best dad.

5. BBQ: Another episode focused around the Heeler family together. Also Bingo is so done. Poor Bingo. Worked so hard to get recognition. JUST LOVE HER!

4. The Weekend: I like this episode for a similar reason to why I liked Yoga Ball. Bingo wants Bandit to see a “walking leaf” but he doesn’t hear which ultimately leads to Bingo feeling sad that he didn’t come see. But he apologizes and turns the conversation around by bringing up the game they were playing earlier that day. Bandit gets all the points for best dad. He is the best dad ever in a fictional thing I’ve ever seen!

3. The Dump: So the car ride to the dump is very enjoyable to watch, but seriously the authenticity of this episode is absolutely amazing! Bandit getting angry at other drivers being on their phone. Bandit doesn’t pay attention and doesn’t go on a green light so he gets beeped at and Bluey starts calling him out on it over and over again. Once they get to the dump Bandit tries to discretely throw away some of Bluey’s drawings to get recycled, and Bluey gets all upset. Kids make a ton of drawings, and you just can’t keep them all. Being an artist since I was three, I remember going through having to get rid of drawings and it made me upset, but it’s something that parents got to do, and I think that this was a good episode that makes it a bit easier for little kids to throw things out. Bandit tries to say that they get recycled into new paper for other kids to draw on and Bluey later accepts this and willingly throws her drawings away for other kids to draw on. What a great apisode.

2. Bumpy and the Wise Old Wolfhound: Okay this episode legit made me laugh. So essentially Bluey and her dad along with the help of her Uncle Stripe, Aunt Trixie, Cousin Muffin, and Cousin Socks put together a movie about a young girl who gets a puppy and eventually gets sick. The video is made for poor Bingo who had to go to the hospital and stay over another night with her mom. Bingo didn’t find it fair that she was sick and the video cheered Bingo up. The video has a good message in saying that everyone gets sick sometimes. This lets Bingo accept that she’s sick and understands it better. Has a good message, and I honestly never thought a show for preschoolers could get me genuinely laughing unironically.

1. Camping: Camping is my overall favorite episode of Bluey. Even if you haven’t seen a single episode of Bluey watch this one, this writing is so amazing I don’t know how to express how artistic and heartfelt this episode was. In this episode Bluey goes on a camping trip with her family and eventually meets a young black lab named Jean-Luc. Jean-Luc approaches Bluey desiring to play with her, however he can only speak French so Bluey doesn’t quite understand him. Nevertheless, Bluey and Jean-Luc become fast friends and eventually learn to communicate through visuals. Jean-Luc and Bluey plant a tree to make food, and each day they try to see if the sprout came up..... but it hadn’t. Each day they tried to solve the same problem of capturing a “wild pig” (Bluey’s dad) however they fail everyday cause they can’t understand one another. On the third day they decide to draw on a rock to figure out a new strategy and eventually come up with a plan that succeeds. When they each most return to their tents at the end of that specific day, Bluey hugs Jean-Luc saying “By Jean-Luc, see you tomorrow.” However Jean-Luc responds in French with a sad face and then watched Bluey run off happily saying “Au Revoir, Bluey.” The next day Bluey returns to where they had planted the seed and a tiny tree had finally sprouted up. Super happy, Bluey goes in search of Jean-Luc but can’t find him anywhere. Her mother says that Jean-Luc’s family was packing up that morning and left leading to Bluey being very sad and crying by the tiny tree they had planted. (omg this was so sad) Chili, Bluey’s mom, had to explain that “sometimes special people come into our lives, stay for a bit, and then they have to go.” Bluey comes to accept this and the episode does a time skip to a teenage Bluey going under the tree that they had planted that was now fully grown to sit under it and read a book. Someone in the distance says “Hello Bluey” and Bluey looks around quite confused only to find her old friend, Jean-Luc grown up waving and smiling at Bluey, in which Bluey smiles and wags her tail gently as the screen fades to black. This episode was so cute and gave the feels at the same time! I love it. I also could semi understand Jean-Luc cause I took French for five years, so it was interesting to see these two interact where I knew what each other was saying but they didn’t. This episode had multiple good lessons in it and I love that basically no matter who you are and what the language barrier you could still make a friend!

[onthesandsofdreams]

Where Everybody Knows Your Name

Fandom: ASoIaF Pairing: Sansa/Sandor (Pre-relationship) Rating: G Words: 1100 Summary: It wasn’t very often that Sandor ventured into new bars. Much less gastropubs. He was a man of tradition, he was a fan of the smaller bars. Those bars where people left you alone and the bartender knew your name.

But today, an impulse took him to a gastropub. Sandor would later blame it on his hunger, he had stood in front of “The She-Wolves” gastropub deciding if he should go inside. In the end, he did. Notes: Chapter 1 out of 2

Read @ AO3

It wasn’t very often that Sandor ventured into new bars. Much less gastropubs. He was a man of tradition, he was a fan of the smaller bars. Those bars where people left you alone and the bartender knew your name.

But today, an impulse took him to a gastropub. Sandor would later blame it on his hunger, he had stood in front of “The She-Wolves” gastropub deciding if he should go inside. In the end, he did.

The place was clean and very well tended. On one side, a television played a hockey game, on the other, there were music videos on. Sandor looked at the bar and froze, the bartender was the loveliest woman he’d ever seen. Once he regained his wits, he squared his shoulders and walked to the bar, sitting himself on a stool.

“Welcome!” The woman was even prettier up close. Pale flawless skin, bright blue eyes and dark red hair, a smile that was friendly and welcoming made her all the more attractive. She also had curves, he noticed, it was impossible not to. “Welcome to ‘The She-wolves��, first time in?”

“Yeah,” his voice was gruff and watched as the woman stiffened a bit, her smile dropping a bit. He nearly kicked himself, way to make a nice first impression on the woman. He had been told (too many times to count) that he could intimidate people, but for once in his life, he didn’t want to do that. He wanted to know that woman behind the bar. So he gave her half a smile, trying to set the woman at ease. “First time. Got a menu? I’m starved.”

“Of course!” The woman said and walked away for a bit, returning with a small menu. “We’re just starting, so there’s not a large variety of food.”

Sandor gave the menu a cursory glance, then with a hand scratched his chin. “Which burger do you recomend?”

“The Bbq one,” The woman’s voice was full of earnest enthusiasm. “Honestly, I love it. It’s very tasty and I think you’ll enjoy it very much.”

Sandor looked at the specific burger, read over the ingredients and nodded. “Yeah,” he said handing back the menu. “I’ll try that. And a black ale.”

“Bbq burger and a black ale coming right up!” The woman wrote it down and passed his ticket along. “I’m Sansa,” she extended her hand to Sandor. “One of the owners. My sister’s a brewmaster, she’s the other owner.”

Sandor’s eyebrows shot upwards in surprise. “Sandor,” he took her hand and shook it. “So your sister’s a brewmaster? And you tend the bar? Interesting.”

Sansa gave him a dazzling smile. “Yeah, all the recipes are mine. But I just like talking to people, so I hired two cooks. Friends of my sister too. And yes, I’m not a beer person at all, but Arya - my sister - makes amazing beers.” She left for a bit, Sandor watched her as she grabbed a tall glass and poured dark (almost black) ale and walked back to him. “Here you go, hope you like it!”

Sandor was all the more intrigued. He would not have expected a gastropub to be owned by a family, all the gastropub owners he’d ever been unfortunate to meet, they’d been pretentious assholes. And yet, here he was. In a gastropub owned by two sisters, one who apparently was a cook and the other a brewmaster. Sandor stared at the ale that Sansa placed in front of him. Taking the glass, he sniffed it. It smelled nice and took a tentative sip. He nearly groaned in bliss. “This is amazing.”

Sansa’s smile was almost dazzling. “See? I told you so. My sister makes amazing beers. I only hope you like my burger.”

“I’m sure I will,” Sandor said taking a deeper gulp of his ale. “If it’s half as good as the ale, you’ll have a new regular.”

Sansa laughed, “Oh. Excuse me a second.” She left to tend to another client, Sandor watched as she bounced, smiled at the customer and grabbed the glass he offered and refilled it. After she was done, Sandor watched her disappear near to what he could assume was the kitchen, only to reappear with a burger on her hands. She placed the burger in front of him. “One bbq burger, as requested.”

Sandor looked at it. The burger looked good, so he grabbed it and bit into it. Chewed carefully, savoring the symphony of flavors that made for an amazing burger. “This is great,” he complimented her, immediately taking another bite and washing it down with the ale. “Best burger I’ve had in a while.”

“I’m glad! Honestly, I am. I like it when people enjoy my food!”

“Well, I guess you have a new regular.”

“You’d be one the first,” Sansa nodded solemnly. “We are new, so all we have is people who walk in, only two of them are regulars already.”

Sandor looked at Sansa. He could tell, even if he had only just met her, that she was one of those people who liked to look at the positive side of life. Her eyes were shining with pride, her voice was clear and filled with excitement. Yeah, he’d be a regular alright, but not for either the burger and the ale, but to see the woman who served him both. “Good, I might bring some friends here.”

“That’d be fun,” Sansa agreed. 

“Yeah,” he said. Brienne would like this place. So would that bastard Jaime. And as much as he’d like to keep this place as his spot, Brienne would make a good eating companion. Jaime… he’d tune him out. Sandor kept eating, talking with Sansa on the moments where he could. He would watch her as she tended to the customers alongside another young woman. Then, Sansa came back, with a gentle smile that softened her whole face and Sandor felt himself be enthralled. Yes, he was definitely coming back. He might just have found his perfect spot.

“Anything else?” Sansa asked when she saw that Sandor had finished his burger.

Sandor racked his brain trying to think of something, he wasn’t ready to go and say goodnight to Sansa. “Pie?”

Sansa nodded. “May I recommend the lemon pie? It’s not too sweet.”

“I’ll get that then… and another ale, please.”

Sandor was never one for sweets… but he did what he had to do to delay his leaving. It was just a slice of pie, it wasn’t going to kill him. So long as he could continue to watch Sansa, all would be fine. Really.

[tvehyungs-gf]

Could you do 1, 12, and 94 with jimin :)

Birthday Kisses and Cacti - Fuckboy!Jimin DrabbleI DIDN’T MEAN TO IT MAKE SO LONG OMG THIS IS AN IMAGINE NOW

✧ What if I kissed you right now?✧ Quit looking at me, you’re making me nervous.✧ This is why we can’t have nice things.

Jimin is a fuckboy, it was evidently obvious with the number of girls and boys who left his apartment every other day. Being his across the hall neighbor was hell and you weren’t sure why you haven’t moved to a new place yet. Maybe it was because you silently hoped that he would come knocking on your door and fuck you till noon or for him to confess his true feelings to you.

You were indisputably pathetic for being head over heels in love with a fuckboy, but you had no fucking shame. It was wack, yet, you didn’t care. Of course, however, you forbid anyone from knowing about your undeniable massive crush on the man. Despite being a nerdy love-struck girl, you did have a title to withhold; the fuckgirl of college.

It was cheesy really. You being the fuckgirl that no one could call theirs and Jimin being the fuckboy that no girl had the chance to ever be in a relationship with. For both of you, you only believed in one night stands and nothing more. However, oddly enough, the only fucking person you wished to be in a relationship was with Jimin. It was weird. And unfortunately, you had no answer that can explain the question of why you liked him. You just do.

And it was already weird enough that the two biggest players on campus haven’t already fucked another yet. Now, that is another question that’s left unanswered.

Speaking of unanswered questions, this leaves us with this scenario. Your best friend, Jungkook, has been nagging you for the past hour about some party that was going down in the ‘Three Kim’s’ dormitory.

“Dude, you have to take me with you!” Jungkook whined, his phone held tightly in the grasp of his hands. Twitter was pulled up on his phone, the tweet mocking you in the face as you read what Jungkook was showing you on his feed. “And I can’t go if you’re not going! Plus, we would be absolute losers if we don’t go!”

You rolled your eyes after reading the tweet from one of the Kim’s themselves, specifically by Kim Taehyung. “Party tonight at the house! You’d be fucking losers if you’re not showing up for the biggest Halloween party! Also, bring presents lol!”

“Why should we go?” You asked with a brow raised. “You don’t even go to college here.” You folded your arms across your chest and watched the younger boy roll his eyes at you.

“And?” He scoffed before looking back at his cellular device. “I was about to go to college here, so technically, it counts.”

Boy, he was reaching. “That really doesn’t count, bro.”

“Aaaand!” He dragged. “I have lots of friends who go here too!” He shrugged. His eyes were glued to his phone before a malicious smirk played on his face. “I think I know what will get you to come to the party.” His eyes peeked up at you, making his face look like a devil in disguise.

“And it is?” You questioned kind of curious.

Jungkook chuckled evilly, the phone he held now shoved in front of your face. It was another tweet by Taehyung. “Lots of people are asking why it’s important to bring presents & its because we’re celebrating our favorite fucker, Jimin’s birthday!”

You were going to fucking go. And Jungkook knew by the look on your face that you guys were definitely going to the party. “Fine.”

Panic. You were in panic mode for many reasons, but let’s start off with the fact that you had no idea what the hell to dress up as. Should you look cute or sexy? Should you cosplay or just go simple? These were tough questions to answer and it made you feel stressed out. And stressed out for what? A party? You really were pathetic.

“Dude, just pick something.” Jungkook groaned. “It’s literally not that serious.”

You scoffed, flicking him off. “Fuck off. I can’t just go there looking like I didn’t put some type of effort. I have a reputation to withhold.”

“Yeah, right.” Jungkook sighed and stood up on his feet. He was seriously one to talk because he had dressed all out. From the doc martins to the black military pants and the matching military top, Jungkook looked like he just got done with his military service but with a twist. He had a deep fake gash on his cheek and vein marks on his skin. To put it lightly, he was zombified. “You should dress up as Lara Croft since you have all those accessories from when you did that Attack on Titan cosplay.”

“Wow.” You gasped surprised. “You actually have a good idea for once.” Quickly, you reached for your grey tank top and a pair of military-style khaki pants.

Jungkook shook his head in disagreement. “You’re so rude today. After everything I’ve done for you!” He sighed dramatically and fell back onto your bed with a bounce. “I get treated like a bag of dirt by my own best friend. Wow!” He gasped. “Life is great.” He continued, eyes closing.

“Oh shut up.” You rolled your eyes and began to get undressed out of Jungkook’s oversized t-shirt and shorts. Quickly, you threw on your cargo pants. “Anyway,” You pulled the tank top over your head. “Who did your zombie makeup?”

Jungkook sat up, his head resting on his palm as he readjusted himself to lay sideways. “Ya know how Hoseok works at the Korean BBQ place?” You nodded with a hum. “Well, his coworker goes to school here and she’s super into makeup and whatever so I stopped by her place before coming back here.”

“Oh, nice.” You gave him a thumbs up and turned back to your closet. Now all you need was to find all the gear you needed.

After a while of digging, you eventually found everything you needed. Once that was done, you did your makeup and styled your hair. “Alright!” You smiled happily. You looked fucking great! “Now we just have to find Jimin a present and then we can head to the party.”

“Finally!” Jungkook cried out excitedly. “I thought I was going to die of old age by the time you finished.”

“I’m actually this close,” You showed him an okay sign to represent how annoyed you were. “To smacking you in the face.”

The boy rolled his eyes. “You haven’t done it already so…” He shrugged. “Let’s go.”

By the time you the two of you finished getting Jimin a present, you found yourselves standing in front of a big house. “Damn, they must be loaded if they can afford to live in this type of house.” Jungkook gasped in awe. His eyes were wide open and you swore you could see a fly dancing in his mouth with how big his mouth was open.

You chuckled. “Yeah, well,” You shrugged your shoulders. “They are rich. Anyway, c’mon lets get inside.”

Jungkook closed his mouth and followed you inside the house.

There were people everywhere with red solo cups in their hands and costumes adorned on their body. You were surprised with how many who actually dressed to par and went all out. But not even a minute later, you both were in the kitchen getting drinks.

Eventually, Jungkook bid farewell without even telling you. You only noticed he was gone when you saw him chatting up with some chick across the room. Typical.

Taking the present you got Jimin, you decided it was probably a good idea to give it to him than to hold onto it all night. So with the quest set in mind, you left the kitchen to find the man of the night. Surprisingly, it didn’t take long to find him because you actually bumped into him in one of the hallways on the second floor.

“Ah, Y/N! I didn’t know you were going to come.” Jimin side hugged you. He was wearing a typical cop costume with the all-black attire, handcuffs, a fake gun, and a hat. “I would’ve dressed up with a better costume.”

You giggled. “Ah, you already have a nice costume, Mr. Officer.” You winked. Gosh, you were in too deep with this crushing thing.

“You do too, Ms. Croft.” Jimin bit down on his very plump bottom lip. “Ah, is that a cactus?” He pointed to the small plant you held in your left hand.

“Oh!” You blushed and handed it to him. “Yes, it’s your birthday present. Happy birthday!”

Jimin laughed and gladly accepted the plant. “Thank you!” He grinned, eyes turning into crescents. “I should probably put this in a safe place.” He turned around and walked towards a door, but before he opened the door, he turned to you with a raised brow. “Aren’t you going to come?”

“Huh?” You were confused. “I mean, yeah.” You followed him inside the office room with a questionable look. Why did he want you to follow him? You stood by the door and watched him set the plant down carefully on the desk that was placed in front of a window.

Suddenly, Jimin turned to you. “Quit looking at me, you’re making me nervous.”

“Nervous?” You asked with your arms folded across your chest. “The Park Jimin is nervous because I’m looking at him?” You chuckled. “Weird.”

He rolled his eyes. “I mean, you are the Y/L/N Y/N. Why wouldn’t I be nervous about you?”

“Because you’re used to girls ogling you.” You shrugged. “I don’t see how I’m any different.”

The cop scoffed as you walked towards him. He leaned back against the desk with arms on either side of him. “You’re the hottest girl here and it’s impossible to get into something more than a one night stand with you.”

If your heart hasn’t already fallen to the floor, it definitely did now. To say you were in shock was an understatement; you were astounded. “Wait, what?” You had to be hearing things!

And now were you seeing things too? Because the blush on Jimin’s cheek wasn’t there before. “I-” He sighed standing up. “Fuck, well, I’m already in a bit too deep and maybe it’s the 5 and a half shots I took that’s making me too fucking confident but I think I like you. I mean, I find you extremely fucking attractive.”

That’s it. You were hallucinating! “There’s no way you feel the same way I do.”

“You feel the same way about me?” Jimin was surprised too. Wow, the odds were in your favor tonight. “Fuck, that’s amazing.”

You literally had no words because everything seemed as if you were just imaging things. “To be fair, we should’ve expected it. Literally everyone on campus expected it.”

“Then,” Jimin took your hand in his and pulled you closer to him. He leaned back against the desk again and looked at you with eyes that screamed hunger and lust. “What if I kissed you right now?”

Biting down on your lip, you took the initiative to take the last step closer to the cop before you. At this point, you could climb on top of Jimin if you wanted to. “Do it and find out.” You winked.

Jimin smirked, his hands released yours as he placed them on your hip and leaned in closer to your lips. His lips were soft, fuck, they were super soft and moisturized that you took a mental note to ask him what type of chapstick he was using when you have the chance.

The kiss itself was fantastic, however, it was quick to escalate into a heated makeout session that resulted in Jimin spinning you around and placing you to sit on the desk. His hands found their way from your shoulders to your hands, holding them.

Your legs voluntarily wrapped themselves around his hips as your hands knocked off the forage hat he was wearing so that you can tangle your hands in his hair.

If someone were to walk in and see what the two of you were doing, they would definitely say that it was some pretty hot shit going on. However, the moment was quickly ruined when Jimin accidentally pushed your arm back making you come into contact with the cactus plant. “Oh fuck!” You whined, your arm stinging from the thorns.

“Fuck, I’m so sorry!” He took hold of your arm and examined it. You were lucky enough to be only pricked with two thorns but it still hurt. “Can I pull them out?”

You nodded. It was quick when he pulled the thorns out. With a sigh of relief, you looked at your arm with a frown. “I should probably put some ointment and a bandaid on it.”

“Yeah…” Jimin agreed. “I’m so sorry, I really didn’t mean to push your arm into it.”

You shook your head, your hands gripping his shoulders. “You’re fine! It’s fine, don’t worry.” You laughed.

“This is why we can’t have nice things.” Jimin frowned as you hopped off the desk. “I’m really sorry.”

“Jimin!” You rolled your eyes. “If I said that we can continue doing whatever we were doing back at your place tonight, would you stop saying sorry?”

“What was I saying sorry for again?”

You smirked. “Thought so.” You held his hand and pulled him out of the room. “Let’s go.”

As you both walked out, you failed to notice Jungkook standing there with his arms across his chest as a knowing smirk played on his lips. He knew that you guys were definitely going to fuck and that Jungkook was definitely going to use this against you.

What an amazing night.

AN: anD I OOP- I GOT WAY TO CARRIED ON WRITING THIS JSNCIK

➝ ask box ➝ bts masterlist

[lindoig8]

Hughenden - 29 July-1 August

Thursday

(It's weeks since I have been able to post anything. We have rarely had a signal and never good enough to set up a hotspot and connect my PC in order to post anything. The only time when it might have been possible was when we were in Alice Springs for several hours – but I forgot to take my PC. It was in the van and we only took the car to Alice. And I don’t have a lot of photos to post so there will be a lot of text coming up!)

As I said, we didn’t really enjoy Winton this trip so we were glad to be leaving it behind but first, we had to have our Anderson plug replaced (at more than twice the price of the previous two) and to put a bit more diesel in the tanks – and then we were off.

It was an uneventful 200-odd kilometres to Hughenden where we were visiting to experience it’s Country Music Festival – another unlikely activity for us. Perhaps the thing that stood out from the drive here was that we saw a few sheep – the first for at least 10,000 kilometres. The literature indicates that the area we have been in for the past few days is sheep country, but we have still been seeing almost exclusively cattle.

The van park here is fairly full (although only part of it seems to be open) and it is exactly as I wrote a few days ago: very narrow short sites, no pull throughs, with cars necessarily being parked on both sides of the narrow driveways. It is almost impossible to reverse into most sites and all have high curbs on both sides of the wheel tracks so there is no room for error. We simply couldn’t get on to any of the designated sites so eventually parked on what appears to be a roadway, but the manager said it was OK. Once we put our awning out, the ‘roadway’ was effectively closed. Then a camper reversed in behind us and put its awning out too.

We set up and had a cuppa and went out to explore. We drove around town and ended up at the Information Centre where we got our questions answered and collected some brochures. One question was about a massive array of solar panels we saw near the stockyards. It must be at least a kilometre square and is crammed with solar panels. The Information Centre woman said that there was another one on the other side of town too (we saw it a few days later – even more massive) and neither of them have ever worked – not even for a minute she said. They were supposed to feed power to the grid with no benefit whatsoever to the locals who seem fairly opposed to them – probably hundreds of millions of dollars’ worth of gear that will simply sit there abandoned forever.

I asked about birding and the woman at the counter and her partner are apparently birders too and she told me two places to go but I am not sure that I will take it up. She said the best one was a walk along the river that is best immediately after dawn when I want to be comfortable in bed. The other one is at their artificial lake, preferably at dusk, when we hope to be enjoying some country music. We drove around the lake today and it didn’t inspire me. I saw nine species but all birds you expect to see in a public recreation area. The town’s official bird list is pretty impressive for all that.

Friday

We did a load of washing in the morning and it was all dry by soon after lunch. Apart from that, we sat in the van out of the heat and worked on our blogs and photos pretty much all day. There is always a few emails to answer and bills to pay, so access to the Hotspot provided by my phone is always essential. Many van parks offer free Wi-Fi, but it is nearly always open and even if it works (less than 50% of the time), it is always inexorably slow.

We had booked to attend the opening concert of the Music Festival at night and we could buy food and drink at the venue so we did that. Not quite what we would have eaten if we had prepared it ourselves, but tasty enough. We had BBQ and salad, plenty of it and not expensive, but it was all unfortunately cold. Catering for about a hundred people must be difficult.

Robbie Katter is the State member for that electorate and he opened the Concert – and then performed – sang and played the guitar. It was quite a fun evening, definitely a small country event, but better than Hicksville. There was a really good band (steel guitar was excellent) and three main performers. One was really quite good and the other two acceptable but perhaps not quite destined for immediate stardom. Having said that, it was an enjoyable night and we went away with a few familiar tunes repeating in our heads.

Saturday

It was a fairly full day, despite us only spending 2 or 3 hours at the Music Festival.

We drove out to Porcupine Gorge in the morning. It is a very impressive gorge – 27 kilometres long according to the sign near the parking area, but over 100 kilometres according to other material we have read. We only went to the lookout at the top but there is a camping area at the bottom, but accessibility is a problem – a rough track in and a long difficult walk from the camping area to the gorge itself. It is a massive rift in the ground, I think it is 230 metres deep from memory, with a river flowing through it, obviously continuing to cut the gorge ever deeper.

We drove back into town, but just before we got there, we detoured 98(?) kilometres on the Basalt Byway, a big loop west and north of town. Again, very impressive, with unusual rocky tors and interesting structures: a really lovely peaceful drive, all on good gravel and virtually no other traffic – most enjoyable.

We arrived back in town just before 4pm and went straight to the Music Festival. Competition was well on its way, but we enjoyed nearly 3 hours of music, from about 8-year-olds up to adults – with relative quality reflecting skill levels and experience. It was quite entertaining and we enjoyed just relaxing and soaking it all in – very much Country and a lot of fun. Senator Bob Katter (Robbie’s father) was there for the duration sitting close to us. There were a few stalls with wares for sale and Heather struck up a conversation with a woman selling jams, sauces and similar goodies and we made a couple of purchases and they swapped recipes for a couple of items. (We were refuelling the next day – automated 24-hour self-service – when I noticed the same woman in the car next to us driving away. She lives just around the corner but hadn’t used the automated system before and was a bit hesitant about it all. She parked in the middle of the road and Heather went over for a chat (quite a long chat) and the woman really appreciated it. She is apparently quite lonely and was pleased that we recognised her and that Heather took the time for a chat.)

Sunday

We started by getting fuel and bread and then went fossicking about 17 kilometres up the Porcupine Gorge road. This whole area, including most of Queensland and other areas, was originally under a shallow sea and there were myriads of creatures fossilised when the land rose and the sea abated. We had read about the gully where people had found thousands of belemnite fossils – little creatures believed to be the antecedents of squid and cuttlefish. It was very hot, but we dug through a lot of compacted soil and Heather found two of them. Doesn’t sound a lot, but given that they are between 140 and 110 million years old, we were hugely satisfied with our find. We could have kept digging and maybe found some more but it was very hot and dusty in full sun so we left some for other prospectors to find.

We then drove back to town and turned east for 40-odd kilometres to Prairie – a town that is really nothing much more that a quaint old pub. Of course, we had the obligatory coldie and chatted with the owners, probably brothers although very unalike, both a bit brusque and outspoken, but fun to talk to anyway.

We ate our lunch outside the pub and then headed south to the Eromanga Sea Byway, a good gravel road about 50 clicks south of Hughenden. We didn’t see the sea but it was fascinating to imagine that all those years ago, we would have needed a ship or a submarine to traverse the track, dodging belemnites, pliosaurs and zillions of other marine creatures along the way. This Byway took us back to the Winton-Hughenden road and thence back to Hughenden. Our travels on the day took us a bit over 200 kilometres with the van in tow so we decided to top up with fuel again before leaving town – the fuel there was cheaper than anywhere else in the area and we didn’t want to buy more until we reached Boulia.

We then headed due west toward Richmond but the afternoon was drawing to a close and we decided not to go on to Richmond in the dark. Finding places to camp was not easy so we ended up in a rest area 48 clicks short of Richmond – along with at least 14 other rigs. We really try to avoid these crowded places but we didn’t have a lot of choice and we can’t stop people pulling up beside us once we are set up anyway. It was a really hot night and didn’t cool off much at all – well into the 20s I reckon all night and not easy to sleep.

[hightechdad]

After about 20 years, I finally got a new BBQ grill. I had been threatening the family that I would do it, and I finally went ahead and got myself an “early Father’s Day gift.” The funny thing is, my daughters are not huge fans of meat. They are ok with chicken, somewhat, and fish, and even pork occasionally. So, they were incredibly excited to try out some Impossible Food burgers (and I was quite curious to see how the grilling and taste experience of the Impossible Burgers would be as well). (*Disclosure below.) Both of my younger daughters have taken an Environmental Science class in high school. And both of them have come out of these classes saying, “we have to eat less meat!” and they would rattle off a bunch of scientific and environmental reasons that were a bit over my head. Impossible has actually created an entertaining and educational website for kids to learn how to educate their backward and old-fashioned parents (like me) about the importance of keeping our planet healthy. Just take a look at The Birds and the Trees and there are several simple chapters that discuss various items designed to help talk to parents about climate change. Below is the teaser video from that site: I trust my kids to keep me on track when it comes to the environment and saving the planet. So, they have, as I said, been pushing us to eat less red meat. I’m a huge hamburger fan…so I was a bit reluctant. But I’m willing to try new things. My response was, “ok, well, give me something that smells, tastes, and acts like a burger, and we will grill that.” What is Impossible Foods anyway? How can you have something that looks, cooks, and tastes like a burger but has no red meat in it at all? First, let me say that I normally write about tech and gadgets on my site. So, writing about food…how does that work? Well, there is A LOT of technology involved in the creation of Impossible Foods non-meat, meat. It’s all about Food Technology. According to Impossible, there is an essential molecule called Heme which is in every living plant and animal. This molecule supposedly “makes meat taste like meat” but is also present in plants, just not a prevalently. It is an iron-containing molecule that exists in your blood and grabs oxygen from your lungs, and distributes it throughout your body. According to Pat Brown, founder and CEO at Impossible Foods, the craving for meat is actually the body’s craving for Heme and the iron and protein that it represents in the diet. So, all of this is science-based. Impossible’s mission is to be scalable, sustainable, and safe yet produce yummy plant-based meats in the process. I highly recommend you read more about the science and technology behind the making of Impossible Foods and Impossible Burgers (there are other Impossible products as well, like sausage and pork – all made from plants). What are Impossible Burgers REALLY Like? So, what are Impossible Burgers REALLY like? Could you trick someone into thinking they were eating or even cooking a traditional meat-based burger instead of a plant-based burger? I’m going to go out on a limb here as say YES. I received a sample pack of Impossible Burgers and ground “meat” to test it all out. And, since I’m a burger fan, I went straight to telling the family we would be having burgers for dinner, AND, that it would be the maiden voyage of my new grill. I will walk you through the unpacking and cooking process. Unfortunately, you won’t be able to smell or taste the end result, so I hope that the pictures will help fill in the blanks. You can purchase the Impossible Burgers in pre-measured burgers that are ready to cook. (I haven’t tested out the ground meat version yet…perhaps on a taco night or if I make spaghetti.) As I went through the process of cooking the Impossible Burgers, I kept coming across some great differentiators and even a few advantages. Here’s one: you don’t have to thaw out the Impossible Burger fully. Yes, you can just put it on the grill frozen if you need to. It does help to thaw it out – I did that by just leaving them overnight in the refrigerator. With meat-based burgers, it is usually recommended that you don’t cook frozen meat patties because you have to ensure the meat is cooked well enough to kill out bacteria. Guess what? Impossible Burgers aren’t meat, so you don’t have to be as religious about cooking to that pre-defined temperature to kill that bacteria. I wouldn’t recommend under-cooking anything though. To cook the Impossible Burgers, it is 2 minutes per side at Medium-High to High heat for thawed patties. For frozen ones, it’s 4 minutes per side. If you have the ground-up version of the Impossible meat, you can add your own spices, or onions, or hot peppers, or whatever to it. For my initial test, we just did the pre-measured burgers as I said – we would add the other assortments later. The raw Impossible Burgers actually look and feel like burgers. They are not a single consistency nor color, and they even seem to have “fat” globules within the “meat.” To my unprofessional eye, they looked like meat burgers…they were even a bit “bloody!” After heating the grill, I just slapped the Impossible Burgers on. They popped and sizzled and smoked just the way you would expect a meat burger to. There were even little flare-ups from the grill. After a few minutes (I actually waited more than 2 minutes for the first side as this was my first time grilling Impossible Burgers), I flipped them over. My new grill was able to make some great sear marks! I continued to cook them – things were smokey and smelling great – just like what you would expect. Next came the cheese! And I toasted some artisanal buns as well. The one thing that I did notice about grilling the Impossible Burgers was that they didn’t shrink as much as traditional meat burgers. I honestly hated when I grilled regular burgers, and they would shrink up and be essentially hidden in the bun. The Impossible Burgers pretty much maintained their size – so when you had your burger, it was truly a burger and not a bun with some meat in it. You can clearly see this in the photo above (and I did actually overcook the Impossible Burgers slightly – but they didn’t shrink). Next up came all of the garnishes! We kept things pretty traditional with condiments, pickles, onions, tomatoes, and lettuce. Next time, I will probably experiment more with different types of toppings like hot peppers or grilled onions or something like that – my mouth is watering just writing this! So what about the Impossible Burger taste? I have to say, I really liked the Impossible Burger. It had the consistency of a meat burger (even even though I over-cooked it slightly). You can, actually, have the Impossible Burger be a bit pink in the middle – remember, it’s not meat. It smelled, tasted, and chewed like a meat burger. So much so that I actually had two of them. And how did my family like them? They loved them! And, my daughters were happy to be eating something that was plant-based and sustainable. To be completely candid, I had tried another plant-based meat burger. It was pretty good as well. But, one of my daughters and I both got quite gassy a few hours later. Hmmm. Did that happen with the Impossible Burger? Nope! And I wolfed down two of them. I will leave you with the image above to make your mouth water and get you hungry! I do highly recommend you go to your local supermarket (or you can order directly from Impossible) and pick up a grilling pack of patties. Disclosure: I have a material connection because I received a sample of a product for consideration in preparing to review the product and write this content. I was/am not expected to return this item after my review period. All opinions within this article are my own and are typically not subject to the editorial review from any 3rd party. Also, some of the links in the post above may be “affiliate” or “advertising” links. These may be automatically created or placed by me manually. This means if you click on the link and purchase the item (sometimes but not necessarily the product or service being reviewed), I will receive a small affiliate or advertising commission. More information can be found on my About page. HTD says: With quite a bit of tech, Impossible Foods has made their Impossible Burgers a truly mouth-watering experience, complete with all of the taste, smell, looks, and feel of a meat burger, but without the meat – saving our planet in the process!

https://www.hightechdad.com/2021/05/16/maiden-voyage-on-new-grill-was-impossible-burgers/

[gosunsolarenergy]

Everyone Loves the Brew: GoSun's New Portable Coffee Maker

GoSun, which has invented everything from incredible solar stoves to off-grid fridges to entire solar kitchens -- believes that solar energy and battery technology can make almost any appliance better. To prove it, GoSun has created the Brew, the best portable coffee maker on the market.

The GoSun Brew is an all-in-one coffee pot that combines a 130-Watt heater, integrated French Press, drink-thru opening and leak-proof lid, all in one cup. This doubled insulated mug will keep your coffee hot for hours,

Others agree. Check out these reviews, which echo our belief that the Brew is the best portable coffee pot you can find.  

Here's what CNET says:

The Brew is insulated like any travel mug, but includes a 12-volt heater and built-in French press, making it good for coffee- or tea-brewing while off the grid. The idea is that you can boil water, and thus drink coffee, while camping, without needing to build a fire. Whether you're unable to make a fire because of unfavorable weather or a current campfire ban, the GoSun allows you to stay caffeinated.

Here's Sprudge's review:

Created by GoSun—the makers of pretty much solar-powered everything from ovens to coolers, lights to chargers, even a solar-powered water purifier—the brand new GoSun Brew might just be one of the most compact, all-in-one portable brew devices to come out in a while. Yes, I am aware you don’t need power to make a French press, but where’s that hot water gonna come from, huh hotshot?

The Portable Coffee Maker That Gives You The Power to Make Coffee in the Palm of Your Hand

The GoSun Brew is the ultimate off-grid portable coffee maker for people on the go.

Gas station coffee doesn't cut it and setting up a cook stove requires a bunch of parts, time, and planning. But with the GoSun Brew, all you need is coffee grinds, water and a 12 volt outlet - found in every car, truck, boat or RV or GoSun Powerbank.

There’s so much to love about GoSun Brew. Here are some other reasons why this portable coffee maker will soon become your best friend.

Holds 12oz, plenty to last all morning

Fits in car cup holder within easy reach of your 12V outlet

Keeps drinks hot for hours

Easy to clean stainless steel Mug is safe to hand was

BPA free Lid is safe in dishwasher.

A Portable Coffee Maker That Can't Start Fires  

Brewing coffee or tea while traveling or camping, or during a power outage, is nearly impossible without burning something. Unfortunately, fire bans (including stoves and BBQ) are increasing because fire threats are elevated. Fire is dangerous and highly destructive. GoSun Brew, in contrast, enables you to get a good cup of hot coffee, no matter what.

There are a million ways to make a cup of coffee, but GoSun Brew is by far the most simple and mobile. Just add coffee grounds or loose tea and water, Brew will do the rest. Here are all the things you don’t need with the Brew that you do need with a normal coffee maker for camping:  stove (gas, lighter, fire, etc.), kettle or pot, grid power, separate french press or filters, and cups.

[terkaznebes]

Unpopular opinions

Boy wasn’t my life so much simpler when I believed that feminists are hairy and unhappy women and the ones who always have a date on Valentine’s have nothing to do with it. Life was so much simpler when I closed my eyes to whatever is happening outside of my bubble. I was definitely more popular when I believed that sapiens have only made it so far because of eating meat. Why did I have to find out that 90% of the big animals on the planet are domesticated? And these domesticated animals are living the shortest most miserable lives. 

Damn it, I want to write about this for so long, but I just can’t stand to be so annoying. For some reason whenever I post something online, I always imagine the handful of people who might be reading it and think: would they roll their eyes reading it? Especially the people that I used to crack jokes with about anything and anyone. Would they still think I am funny? I know we haven’t spent a lot of time together in the last four years because I am living in a goddamn paradise at the end of the world, but I promise you, I am still the same idiot that I was before when it comes to all the inappropriate jokes. I still love getting drunk, I still occasionally roll a joint when my daughter goes to sleep. I still dance when making breakfast, I still browse 9gag and find memes to be relevant form of communication. 

But yes I am a mother and I feel like I have this responsibility now to not take shortcuts at the expense of the planet or our wellbeing. This has nothing to do with motherhood, it’s just what really brought it onto the surface in me personally. It’s an uncontrollable urge thought. So here we go, here’s a blog about our plant based diet. We have been eating like this for 10 months, which I realise is nothing, but it also means the passion is still there.

It started in a totally innocent way. I wanted Lukas to do the whole30 diet with me and he kept telling me that eating so much meat just doesn’t agree with him and that he feels too heavy. Nonsense I told him, we need meat, it’s super important. And I started doing a bit of a research to prove it. Instead I proved myself wrong. This is what happens when you dive too deep into an actual research. I felt so cheated by all the propaganda. I thought when I stopped with the dairy and sugar few years ago that I knew it all. I silently laughed at everyone who told me they have strong bones because they drink so much milk.

So we gradually stopped eating meat every day and soon we were only eating it once a week. Then the Game Changers came out, or in other name the Vegan propaganda. Lukas suggested we try it for a month in November. A month has gone by without us noticing and it has become the new norm. It’s really interesting how once you start doing something slightly better, it’s almost impossible to go back into your old habits without feeling totally guilty. It doesn’t matter if it’s something small like not using plastic shampoo bottles or something big like trying to avoid the industry that’s responsible for an incredible water use, pollution, deforestation and a noticeable percentage of human produced greenhouse gases. There’s definitely a lot of vegan propaganda that greatly exaggerates these statistics.

Nevertheless the one thing that nobody can deny is the struggle of the animals farmed for meat and dairy. They have the most miserable lives, which have kept me emotionless for many years. Partly because I hadn’t a clue and partly because since becoming a mum I am definitely a lot more emotional. Meaning when somebody tells me: It’s been scientifically proven that cows form a bond with their calf right at birth and when they get separated, they struggle, it moves me a lot more than it used to. Yes I can see the fact that I have just compared myself to a cow. I have no regrets.

 The information continues and I find out that lot of the calves are kept unfed for 24-48 hours and then slaughtered. I will stop right here. There’s actually a lot more information and a lot of horrible videos that can stick with a person for life. I did this research when I was breastfeeding and my friend said: “I don’t think there’s anything wrong with drinking cow’s milk unless you have an intolerance.” This simple comment has sent me into a spiral of research and ironically it has caused me to lose my own milk and Lukas had to forbid me to use the internet for a few days so I cold keep my sanity and our daughter fed.

I just want to clarify that by no means I think that extreme veganism is the only way. I think that any lifestyle has to be sustainable. Therefore if you are having trouble sustaining it or struggling with it, you are likely going to snap one day or another. And I know it will happen one day or another, on occasion or once a year that I will do something contrary to my belief. Although the more time goes on, the less inclined I am to wanting to eat meat.

Also the information is never black and white. There will be further studies using the arguments that land needs to be grazed by livestock and they will also have a point. However spending some time reading both sides of the story, my conclusion and moral values still incline towards less meat and dairy consumption.

Since we have stopped eating meat as a regular item on our menu, we have still had it a few times. There was one occasion when me and Lukas were on our own in our favourite brunch place and I couldn’t resist the bacon and egg roll. When we were feeling under the weather, I made chicken soup because I didn’t know what else to make. I have had real ice cream (not sorbet) twice. I definitely had some cheese as a part of some dining out meal. So what. At home we eat about 95% plant based and I think it’s great.

The other day my friend was telling me that he loves nothing more than a good BBQ, but he only has it once a week and that’s still great too.

I think what bothers me the most is the whole propaganda around meat industry. I honestly wish, I knew a little bit sooner. It’s incredible how much judgement we have received from friends and family for feeding our daughter plant based meals, wholefood plant based to be clear. We had to go to a nutritionist, not because we were unsure about what we are doing – god knows we have spent hours reading scientific papers, but because we wanted a proof for our families from a professional. It’s crazy how this meat industry propaganda has driven people (including me) to really believe that you need meat for iron, to be healthy, for energy and strength. It’s absolutely distorted that you get more judged more for cooking every single meal from scratch plant based than for taking a child to McDonald’s.

Not to mention that Josie is the healthiest kid, thousand times knock on wood! I am really a big believer that what you eat is what you are. With so much extensive scientific research available that proves how what you eat can deter so many illnesses, I think it’s really worth doing the effort. At least it definitely opened doors for me to see the variety in cooking, using ingredients that I didn’t know existed and I have to say – I am a pretty good chef.

Anyway all these things that we do, that are an “inconvenience” in our lives, always come down to one thing. When Josefina asks me one day if I knew what was happening with the world, I will be able to say yes and I really did try to do my best to create a better one for you. Unfortunately I am not a sexy activist so here, eat your chilly sin carne.

[marymosley]

RestauRant: Why Dining Out Is No Longer Worth It To Me.

By Darren Smith, Weekend Contributor

It’s taken over fifty years, but I’ve come to the conclusion that the punishment/reward scales have tipped and I am losing my appetite for dining in restaurants. Mostly this is due to what the industry has needed to stay competitive, so I am not here to bash business owners for trying to make a living, it is simply that it is no longer in the interest of my health, budget, and growing intolerance of settling for less.

I wish the industry could do better, but I do not see this ending well.

Having had recently to travel out of town on extended business and family related maters over the past few months put me into restaurants daily. Surely much of it was convenience but then again it isn’t exactly easy to prepare something of any quality without the means to do so. The problem being is that I do not like processed foods that one can buy at a grocery store and simply heat up in a microwave oven. So if I make something, it is always from scratch. At home I only eat organic food, most of which is grown at a CSA I buy shares in. The food is better tasting, it is of healthier quality, and it COSTS LESS! The problem though is I have, for lack of better words, detoxified myself over the past five years from the sterile soil grown commercial farm GMO food that is bathed in chemicals several times a year. Maybe I am a snob when it comes to the food I prefer to eat but it’s my body so I can choose what I put into it.

Yet when being out and dining out, it is a setup for at least one disappointment. I will say that it is not always the case. There are a few family owned restaurants that actually try to be better but the industry suffers some serious flaws:

Price

One spillover cost of politicians giving the highest minimum wage benefit in the United States in the vicinity of where I reside is that the cost of eating out is ridiculously high at nearly all markets, save maybe the dollar menu at chain fastfood joints (A cardiologist’s dream). If two of us dine at any run-of-the-mill restaurant it costs forty bucks for one dinner.

A few restaurants around here are becoming insidious in crafting novel methods to extract money out of their customers. The most underhanded I saw was one place where in the smallest font presented on the menu, in a corner of the menu that most customers did not bother to read, they indicated that on top of the price of the food, there was a mandatory 20% surcharge proffered to cover employee health and 401k benefits. Of course there was no offer to reduce the socially obligatory tip of 18% to offset this surprise. And, thesurcharge is subject to a 9% sales tax. A relative of mine actually confronted one of the employees at this establishment and asked them about the 401k. The waitress stated they do have a 401k, but the company pays no matching contributions of those made by the employee. In other words, the restaurant extracts money from customers to pay for this 401k but is so cheap it grants nothing on behalf of the employees.

My average home meal expense calculated and averaged over the past twelve months is $3.86 per person. Yes, that is a low cost, but when you do not eat Hot Pockets and microwavable frozen lasagna you can eat frugally. And I’ll let you in on a little secret. Humanity made its own food for tens of thousands of years before the creation of a radio frequency, molecule exciting means of heating food. And it was all organic.

Salt

It is nearly impossible to find a menu item having more than three ingredients that is not laden with unnecessary amounts of salt. It is a cheap, bang for the buck ingredient that not only covers adequately for otherwise flavor lacking food but it makes the customer order drinks just to address the ensuing dry mouth. But then again most consumers like salted foods. I unfortunately am not one of them. For me it ruins my sleep and I rapidly gain weight just from one over-salted meal and after a week of this during travels I come home physically exhausted. Excessive salt is more consequently unhealthy.

It does require some creativity and care to craft menu items that have no added salt. I think with most people who formerly consumed high amounts of sodium in their diets, if they abstained completely from it in time their ability to taste food would return and they would discover a preference for salt free dining and have a better ability to taste what is there.

Mediocrity

While there are certainly outliers abound, overall the quality of restaurant food has declined over the years. There is a significant trend toward precooked and processed food that gives restaurants the advantage of lower labor costs and quicker order turnaround. And for the most part it works well for a customer that wants to be served in two minutes as cheaply as possible. The trade off for that is quality and healthiness.

A few years ago a major “Italian” chain restaurant finally admitted that most of its menu items were made outside of the local restaurant. In other words it was made in a factory, delivered to the restaurant, and then boiled or otherwise heated. The pasta was boiled and mixed in with the sauce that was basically out of a bag. They attempted to minimize this embarrassing yet true fact by showing an employee in the kitchen dumping a large bag of pasta into a boiling cauldron of water, claiming it was still inspired by great chefs of Tuscany, or was it Tucson–who knows these days.

A good way to measure the quality of a particular restaurant is to try the underlying meat or fish without the smathering of sauces and cheeses. If it is bland you are being cheated. In all probability you are getting either low quality or you are being fed a T.V. dinner on a plate with a garnish, and charged accordingly. I also saw a Canadian news expose where investigators took DNA samples of what was proffered to be chicken in the chicken breast sandwich of a major chain sandwich shop. A significant portion of this was not chicken but instead soy. Fowl deceivers!

Back in 1997 or thereabouts a friend of mine and I traveled to Turkey. We ate in restaurants where everything they made was from scratch–it was during the spice harvest–and it was astoundingly cheap. Nearly every dinner we had was delicious, bountiful in flavors of which you could often discern nearly each spice individually. Upon our return, he called me about a week later and asked me “Have you noticed the food (in the US) seemed bland after we came home?” I noticed the same disappointment. The food here DID seem bland after three weeks in Turkey and Greece. And then I realized why this happened.

Over the course of our lifetimes the quality of food here declined. While we had different spices and nuances than they Turks did with their cuisine it was apparent that slowly the taste of food diminished. He and I went from our parents having our own gardens to moving toward more industrial farms where the soil became depleted through monocrops and over-reliance of fertilizer and pesticides. It wasn’t until I started eating better quality food that the flavor of my youth began to resurrect itself in my memory of taste. I was lucky to extricate myself from the bland, but it seems, unfortunately, that restaurants did not.

Cheesiness

No, I’m not talking about the cheddar. It’s the over-the-top cross promotion and advertising manifest in chains.

Does it come with green eggs and ham?

I do not plan to dine with buffoonery such as The Grinch who IHOP managed to finagle into a menu item, or the Addams Family with purple milkshakes or a Registered Trade Mark after a third of the item names at others. I just want a say steamed salmon and mixed vegetables, not a Deadliest Catch(R) King-Kong(R) Krab Salad with Jack Daniels(TM) inspired BBQ Shrimp Munchies (TM).

I might recommend going to an antiques store or if not readily available finding on e-Bay a copy of a restaurant menu from the 1930s. Look at what people ate: Eggs, Pancakes, Tuna Sandwich, Coffee, etc. It was simply normal, ordinary food without the pretentiousness, hype or advertising. Plus, it probably was a menu from small home-town restaurant owned by a guy named Tom and his wife Gwen who “looked the part”. Not some gaudy throwback to a 50’s style diner that was more 1950s than it was in the 1950s and is owned by a Japanese hedge fund.

Aggregation Aggravation

The sum of all these irritants, I finally had enough. As I said in a previous article, perhaps it is more honest of a living to make one’s own meals like a regular guy. It’s better in all respects to pay less for better living and in this case eating at home (which is tax free) as opposed to paying 9.1% tax on 20% service fees to benefit government bureaucracies. I still enjoy though a good cup of coffee, so I might indulge myself with that and an occasional croissant that was accidentally labeled as a quiche lorraine.

By Darren Smith

The views expressed in this posting are the author’s alone and not those of the blog, the host, or other weekend bloggers. As an open forum, weekend bloggers post independently without pre-approval or review. Content and any displays or art are solely their decision and responsibility.

RestauRant: Why Dining Out Is No Longer Worth It To Me. published first on https://immigrationlawyerto.tumblr.com/

[clamjumper5-blog]

5 Spanish Wines to Sip & Serve this Summer

If you're anything like me (and like most people!), you tend to buy and order the same wines over and over again. There's nothing wrong with that, but I think summer should be a time of trying new and different things, and in this case? That means new and different wines!

That's why I pulled together this easy summer wine cheat sheet for you. Whether you're looking to mix things up with something new, or just looking for the best wine to mix in your next batch of party sangria, I've got you covered!

I recently had the chance to join a group of winemakers and importers from Spain who introduced me to an incredible variety of wines from Ribera del Duero and Rueda. These two sister regions are located about two hours north of Madrid in the North-Central part of Spain, and are kind of like the Spanish equivalent to California's Napa and Sonoma Valleys.

But unlike the gorgeous and temperate rolling California hills, the climate in this part of Spain ranges wildly from blazing hot summers to cruel freezing winters. Not great living conditions for humans (or even many animals!), but it does magnificent things to the grapes...and the wines.

Also unlike California wine country, the winemaking tradition here goes back much much further; ancient mosaics depicting wine-loving Bacchus discovered in Ribera del Duero suggest they've been making and enjoying wine in the region for at least a couple thousand years! I guess that means they know what they're doing, right? Here are the wines you'll want to look out for when scrolling down the wine menu over the next few months:

1. Love Sauvignon Blanc (and eating)? Try Verdejo: More full-bodied and aromatic than Sauvignon Blanc, Verdejo is actually Spain’s most popular white grape--and with good reason! It comes from the region of Rueda in North-Central Spain, and is a lovely smooth, citrusy, and refreshing white to enjoy on hot summer days.

This food-friendly Spanish white pairs beautifully with so many of your summer faves.  It's seriously hard to go wrong with this wine.

From seafood like shrimp or cold briny oysters to grilled chicken or marinated veggies hot off the grill to that bowl of spicy salsa you can’t seem to stay away from (Don't even try. It's impossible.), the bright acidity and sharp fruity notes means it can stand up to and complement a wide variety of cuisines and dishes.

This means you can bring a bottle to your friend's BBQ or dinner party safe in the knowledge that it will probably pair perfectly with whatever they serve.

2. Do you always order Cabernet Sauvignon? Try a Tempranillo from Ribera del Duero: I know some people prefer whites in the summer, but come night time I usually still want a glass of something red. If you're like me, give a Tempranillo from Ribera del Duero a try!

At the dinner, one of the winemakers shared a fun fact about Tempranillo. Apparently 95% of people who love Cabernet, also enjoy Tempranillo. These wines have that same boldness as a cab, but with a bit more balance making them another summer food-friendly pick.

Ribera del Duero wines are categorized by their age from Crianza (1+ year) to Reserva (2+ year) to Gran Reserva (3+ year). Older does not mean better and there's great quality wines at each age, so give them each a try and see what you prefer!

Perfect for Summer Dinners. Tempranillo is great with grilled meats like steak and burgers, roasted pork and lamb, and they also play nicely with a simple cheese and charcuterie board (preferably featuring manchego and jamon, of course!).

It's vegetarian friendly. Grilled portabellos, stewed eggplant, and rich pasta dishes are all fine matches for this wine!

3. Love Champagne? Try Rueda Espumosa: Nothing says celebration better than bubbles, but those good-quality bubbles unfortunately often come with a hefty price tag, right? Nope! Rueda Espumosa is a high-quality, yet affordable, Spanish sparkling wine from Rueda.

Rueda Espumosa get their bubbles via the same “method traditionnelle” (or “metodo classico” in Spanish) as Champagne. It involves having the verdejo wine go through a secondary fermentation process inside the bottle, producing an elegant bubbly that is just as lovely on its own as it is paired with your summer meals.

4. Always Order Dessert...and more wine! Your dessert may be sweet, but that doesn't mean the wine you serve or order with it also has to be! Spanish wines are a great match for so many desserts.

Love sweet & salty combos? Pair salty-sweet desserts like salted caramel, sea salt-topped milk chocolate, or caramel popcorn with a crispy white verdejo.

Tempranillo is a great match for desserts made with dark chocolate, cherries, and almonds. If you've never paired a large slice of rich chocolate cake with a glass of bold red, you've been missing out!

We already covered that these wines are amazing with grilled foods, and this goes for dessert, too! Try verdejo with and easy summer dessert like grilled peaches, or pair a thick slice of grilled poundcake with tempranillo! 

Want to turn wine into your dessert? Try poaching ripe cherries in tempranillo wine, then serving over ice cream or a simple plain cake. Or add a cup of the dry red to your favorite chocolate cake recipe.

And you can't go wrong with sticking to the region! A traditional caramel custard (like flan!) or even piping hot churros con chocolate will work nicely with the tempranillo as well!

5. Looking to make a great sangria? These are your new go-to sangria wines: Sangria often gets a bad wrap due to the restaurants (or party hosts!) that use it as an excuse to disguise and mark-up their cheapest unremarkable wines with some fruit and sugar, often leaving you with a big bill and a nasty headache. But just like with anything else, when you start with great quality ingredients (aka a fabulous #sangriawine!), the final product will be just as good!

For Red Sangria, start with a bottle of Ribera del Duero Joven or Crianza. These young and juicy tempranillo wines are light, tart, and a great start to a summer-ready sangria. Combine with fruits like red cherries, ripe plums, and strawberries, to bring out the natural fruity notes in the wine. Tuck in a bay leaf or two and let sit overnight in the fridge. Just before serving, add a can or two of naturally-flavored orange or lemon seltzer and serve over ice. 

For White Sangria, get a bottle of crisp and fruity white like Rueda Verdejo. Choose fruits that complement the wine’s citrusy and peachy notes like sliced lemons, ripe peaches, and maybe even just a few cucumber slices. Add herbs like fresh basil or tarragon to bring out the natural fennel notes of the wine. I recommend combining all the ingredients the night before you serve, then letting the natural sugars in the fruit infuse the wine overnight.

And there you have it! 5 fabulous Spanish wines to help you mix things up this summer. Cheers!

This post was sponsored by Ribera y Rueda--the organization representing these two wine regions and their winemakers. They provided me with several wine bottles to try at home and compensated me for sharing these tips with you. As always, all ideas and opinions are entirely my own. Please remember to enjoy these delicious Spanish wines responsibly!

Source: http://www.alwaysorderdessert.com/2018/05/5-spanish-wines-to-sip-serve-this-summer.html

[katedangworld-blog]

ATKINS DIET - LADY’s CHOICE for a SLENDER SHAPE

As a woman, I always want to keep my body in good shape. Unfortunately, these days I was so busy with studying and working so my dining times were unstable and dinners often had lately at night (9:00 -10:00pm). As a result, I gained weight slightly, especially my belly had accumulated rather thick fat. Therefore, I decided to try Atkins Diet aim to control my weight within 7 days. The Atkins Diet is a well-known low-carbohydrate eating plan was designed by the cardiologist Robert C. Atkins in the years 1960s. This diet limits carbohydrate and encourage protein and fat.

A standard Atkins includes four phases of diet:

·        Phase 1 (induction): daily carbohydrate consumption limited under 20g within 2 weeks. High-fat, high protein and low-carb vegetable should be eaten in meals.

·        Phase 2 (balancing): gradually supplement nuts, low-carb vegetable and some fruits in meals.

·        Phase 3 (fine-tuning): once dieters are close to their desired weight, more carb can be add into meals to slow down weight loss.

·        Phase 4 (maintenance): at this period, eaters can enjoy more beneficial carbohydrates with toleration of body without weight regaining.

Nevertheless, it is impossible for me to follow these complex procedures, therefore I have obeyed basic requirements of this diet: Foods to avoid vs. Foods to eat as well as some of acceptable beverages as below guideline:

 *Food to avoid:

1.      Sugar: Soft drinks, fruit juices, cakes, candy, ice cream, etc.

2.      Grains: Wheat, spelt, rye, barley, rice.

3.      Vegetable oils: Soybean oil, corn oil, cottonseed oil, canola oil and a few others.

4.      Trans fats: Usually found in processed foods with the word "hydrogenated" on the ingredients list.

5.      "Diet" and "low-fat" foods: These are usually very high in sugar.

6.      High-carb vegetables: Carrots, turnips, etc (induction only).

7.      High-carb fruits: Bananas, apples, oranges, pears, grapes (induction only).

8.      Starches: Potatoes, sweet potatoes (induction only).

9.      Legumes: Lentils, beans, chickpeas, etc. (induction only).

*Food to eat:

1.      Meats: Beef, pork, lamb, chicken, bacon and others.

Fatty     fish and seafood: Salmon, trout, sardines, etc.

Eggs: The     healthiest eggs are omega-3 enriched or��pastured.

Low-carb     vegetables: Kale,     spinach, broccoli, asparagus and others.

Full-fat     dairy: Butter, cheese, cream, full-fat yogurt.

Nuts     and seeds: Almonds, macadamia nuts, walnuts,     sunflower seeds, etc.

Healthy     fats: Extra virgin olive oil, coconut oil, avocados and     avocado oil.

*Beverages

1.      Water: As always, water should be your go-to beverage.

Coffee: Many     studies show that coffee is high     in antioxidants and quite healthy.

Green     tea: A very healthy beverage.

(Healthline, Kris Gunnars, 2018)

 As the guideline, primarily grains (like wheat, rice) and high-sugar content vegetables/ fruits are prohibited in Atkins meals. This is definitely a huge challenge to me because rice is consumed every day in Asian cuisine, I must say it is vital ingredient account 60% in our meals portion as it provides half of energy source for one shot of dining. If we do not have rice, bread can be replaced sometimes but here bread is not allowed, too. To tackle this problem, I increase the amount of protein (fish, meat), fiber (vegetable, low-sugar content fruit) and drink more than 2L of water every day to fill up my stomach and reduce the feeling of hunger.

My typical daily menu in general:

-        Breakfast: A glass of milk, nuts.

-        Lunch: I eat much more than breakfast. One main course of meat and some vegetable added

-        Dinner: Same dish as lunch but smaller portion, some fruits are added. I have less demand for dinner because at night from 7:00pm, the body will slow down its metabolic process and surplus energy will be accumulated as fat layers.

 My menu during a week as following:

 *Friday 21 Jun:

Main courses: Lyonnaise Salad + Pan Broiled Garlic - Sweet Fish Sauce Chicken Wing.

Dessert: Peach, almond, walnut

Supplement: 2% skimmed milk

In first day, I still use some slices of bread to substitute rice aim to let my body get along slowly well with this diet and milk is drunk to supplement nutrients and make me full.

*Saturday 22 Jun:

Main courses: Grilled Toro Beef + House Dressing Salad + Miso soup

Dessert: grapefruit, apricot, sunflower seeds

Supplement: 2% skimmed milk

*Sunday 23 Jun:

Main courses: Sauce marinated pork jowl + Avocado Salad + Miso soup

Dessert: strawberry, apricot, pitascho

Supplement: 2% skimmed milk

At weekend Sat-Sun, I treated myself by Japanese BBQ styles dishes. Grilled Toro beef and pork jowl are often combined to rice but I remove it out of meal and add a bowl of salad. Along with them are miso soup including tofu (plant-based protein) and seaweed (minerals and vitamins, fiber – supporting loosing weight by postponing hunger)

*Monday 24 Jun:

Main courses: Chick Breast Rice Soup

Dessert: strawberry, mandarin, sunflower seeds

Supplement: 2% skimmed milk

Dilute rice soup can calm down my body’s desire of rice. The chicken breast is less fat than other parts so used to add rice soup.

*Tuesday 25 Jun:

Main courses: Chicken Salad

Dessert: strawberry, almond, walnut

Supplement: 2% skimmed milk

I stayed home so energy is not required too much, I enjoyed my favorite chicken salad. You can see some brown saute’ shallot on top of salad, which aim to replace roasted peanut. Oil from shallot can balance the sour of salad as well as make body feel full longer.

*Wednesday 26 Jun:

Main courses: BBQ Pork Rib

Dessert: strawberry, peach, mandarin

Supplement: 2% skimmed milk

At this time, my body survived better without rice, a piece BBQ sauce pork rib could satisfy my hunger. Then I refreshed with some cool fruits.

*Thursday 27 Jun:

Main courses: Poached eggs, Grilled garlic butter sauce salmon & blanched bok choy

Dessert: apricot, peach, mandarin, pistachio

Supplement: 2% skimmed milk

The last day of diet, my body has less demand of energy which most generated from carbohydrate and it does not desire for rice like first days. So brunch was simply poached egg & blanched bok choy. Then I grilled salmon with garlic butter sauce cover on top, really tasty and yummy! Salmon is rich of nutrients and omega 3 which are useful for female’s skin.  

I admits that I was crazily hungry as the amount of bread and rice got in the stomach too little, it seemed to shout every second that “Rice, please!” and I was almost exhausted when I worked at my restaurant, I ought to drink water constantly to overflow my stomach. If I could drink coke, my hunger would be satisfied (when the restaurant so busy and I have no time for break) but in this diet, soft drinks are not permitted. 

One week probably is not enough to conclude this diet is suitable to me or not but generally I think this diet solution is good and healthy. It is not too strict about volume of foods and nutrients input therefore dieters might reduce weight gradually with less health troublesome. Lastly, I just lose weight slightly of 600g and my belly seems to be unchanged (I wish I could know why). Anyway, not bad for one week!

Among my dishes in my weekly menu, I would like to recommend you try to make Chicken Salad. It is easy to prepare and can be stored in fridge 3 days. The recipe as below:

 - Chicken breasts: 2 pcs

- Cabbage: ½ pc

- Pickled carrot and daikon: 1 cup

- Garlic: 3 cloves

- Lime juice: 1 tbs

- Vietnamese coriander: ½ bunch

- Carrot: 1 pc

- Thai chilies: 2 pcs

- Shallot: 1 pc (saute’ until brown)

- Green mango: ½ pc

- Sugar: 2 tbs

- Fish sauce: 2 tbs

- Warm water: ½ cup

Step 1: boiled or steamed chicken breasts until tender.

Step 2: Slice cabbage, carrot and mango in stripes (1cm of width), collect coriander leaves and wash all vegetable. Strain them and put 2 pinches of salt to dehydrate water/moisture from vegetables and make them be crunchy

Step 3: chop and mince chilies and garlic -> put in a small bowl to make sweet fish sauce. Add sugar and pound hardly to mix well with chilies and garlic -> lime juice -> warm water -> fish sauce -> stir well.

Step 4: tear chicken meat in lardon -> mix with strained vegetable + pickle + sweet fish sauce + chopped coriander + saute’ shallot -> mix well and enjoy!

In my hometown, my sister often put roasted peanut into the salad instead of shallot. Peanut’s nutty taste helps to balance the sour of salad and encourage aroma and palate. Regretfully, when I arrived Canada, I had slight allergic symptom when I ate peanut while this had not happened in Vietnam. Therefore, I got rid peanut of my dish and change to shallot without changing the characteristic of the salad.

The Atkins diet is popular thanks to its weight-lost effectiveness and health benefits such as prevent or limit dangerous health conditions like metabolic syndrome, diabetes, high blood pressure and cardiovascular disease, improve heart health. Nevertheless, every coin has two sides, some experts concerns Atkins has some negative impacts to eater’s body like headache, dizziness, weakness, fatigue, constipation. Low card input result in nutritional shortage or inadequate fiber – cause constipation, diarrhea and nausea.

Eating and tasting foods are the happiness of a cook or chef, therefore I would not follow any Diet solution. Just try to balance daily foods intake to body not exceed my body needs also do daily simple exercise like walking and stepping stairs (TTC subway is a good choice as we use it every day).

In point of view of a service provider – a potential cook, of course, I will study more about various Diet solutions and give advice to my customers. Also, adjust the formula to match well with their digestive demands is necessary.

 References of data & sources:

Mayo Clinic. (nil). Atkins Diet: What’s behind the claims?. Retrieved June 27, 2019, from https://www.mayoclinic.org/healthy-lifestyle/weight-loss/in-depth/atkins-diet/art-20048485

Healthline. (2018). The Atkins Diet: Everything You Need to Know. Retrieved June 27, 2019, from https://www.healthline.com/nutrition/atkins-diet-101

Healthline. (2018). 7 Surprising Health Benefits of Eating Seaweed. Retrieved June 27, 2019, from https://www.healthline.com/nutrition/benefits-of-seaweed

Right Shape. (nil). Atkins Diet. Retrieved June 27, 2019, from https://www.rightshape.com/atkins-diet/

Smile Delivery Online. (nil). 4 Foods that you should not eat while losing weight. Retrieved June 27, 2019, from http://smiledeliveryonline.com/food/4-foods-that-you-should-not-eat-while-losing-weight/

Health Fitness Revolution. (nil). 10 Good Reasons to Drink Green Tea.  Retrieved June 27, 2019, from https://www.healthfitnessrevolution.com/10-good-reasons-to-drink-green-tea/

Manor Surgery. (nil). Healthy Lifestyle.  Retrieved June 27, 2019, from https://www.manorsurgery.co.uk/healthy-lifestyle

[turbogrill]

How to control the fire temperature for grilling and smoking

Chuck Blount March 11, 2019

1of10Chuck Blount pours charcoal briquettes from a chimney starter into a Weber grill as he prepares to grill chicken and sausage with an off-set fire, with all the briquettes placed on one side of the grill.Photo: William Luther /Staff photographer

2of10Sausage and chicken cook over indirect heat in a Weber grill.Photo: William Luther /Staff photographer

Two split logs rest on a small pile of charcoal briquettes to get an offset smoker to the correct 250 degree temperature.Photo: William Luther /Staff photographer

There is such a thing as a dedication to the craft of outdoor cooking, and then there is Fred Robles.

Robles, a world champion barbecue cook based out of Weslaco, is the type of guy who constantly tinkers with his recipes, cooking devices and meat preparations. He’s so precise with his demanding command of temperature, he counts the number of charcoal briquettes that are used to grill up his chicken.

“The magic number is 47,” Robles said. “That will usually get my grill to about 350 degrees, which is the temperature that will cook and finish the chicken the way I like it in about an hour.”

If you don’t want to spend hours experimenting briquette by briquette, here is a simplified formula: Take the diameter of your grill, and multiply that number by two. That’s how many briquettes are needed to ballpark 350 degrees with the cover applied and your meat placed away from the hot coals.Taming the flames | Chuck Food ShackVolume 0%Follow these tips and techniques to keep your outdoor cooking devices working with oven-like precision.Video: San Antonio Express-News

There are other ways to take command of your outdoor fire, making the cooking process as simple and consistent as anything that could be done in a conventional kitchen oven. Here are some ways to do it:

Setup

The charcoal: You can go either the hardwood lump or the conventional briquette route. Both have key strengths and weaknesses.

The lump charcoal will burn about 5 to 10 degrees hotter than the briquettes, provides a cleaner wood flavor and won’t cook down into pure flaky ash. That makes it perfect for the caveman style of cooking directly on the coals. However, since the charcoal pieces are randomly sized (some chunks as big as a human fist), it can be a bit unpredictable.

On ExpressNews.com: Youth pitbuilders showcase their handmade pits that rival the pros at San Antonio Stock Show & Rodeo

Briquettes are of uniform size and will hold the heat a little longer, with a signature flavor that reminds everybody of the backyard cookouts they grew up with. Kingsford charcoal, the industry leader in briquette charcoal by a wide margin, is a staple on the competition barbecue circuit because of its ability to win over judges that score with a nostalgic palate.

Wood: Manny Olivo, owner of the Schertz-based Cow Tippin BBQ food truck, keeps his fire pure with pecan wood by taking the scraps, starting the fire small, and building it up into a blaze. “It take a little more time, but it’s worth it for the flavor,” he said.

Remove the bark from the logs and accumulate the shavings and scraps that can be pulled off the wood. As it burns, add larger pieces until you are burning chunks that are about the size of a rolling pin. One or two logs on a bed of coals will get a traditional off-set steel pit into that magic temperature window between 225 and 250 degrees. Avoid large logs, which have a tendency to smolder and can add a funky taste to the meat.

Ignition

Lighter fluid: It can make life easier in a pinch, but I avoid it at all costs, including the charcoal that comes coated with it. The fuel never completely burns off, and the flavor will transfer into the meat like a seasoning.

On ExpressNews.com: 1 smoker, 10 store-bought sauces. Which got smoked?

Chimney starter: The metal contraption that’s shaped like a German beer stein is the perfect vehicle for getting a good fire going. Stuff a few sheets of newspaper or a couple paper towels coated in cooking oil underneath your briquettes, light it up, and you should have a perfect blend of hot charcoal that glows like lava in about 20 minutes. A full starter will hold about 70 briquettes.

Flamethrower: Don’t laugh. This is a thing, and it’s legal. They sell open-flame devices, often marketed as a weed-killer in the garden section of your local hardware store, that hook up to a propane tank and will light the charcoal or wood in seconds.

Temperature control

The full spread: Unfortunately, too many outdoor cooks think that the proper way to set up a grill is to blanket the bottom with coals. That’s a disaster recipe for burgers that end up looking like charred hockey pucks because of out-of-control flames that erupt when the meat grease hits the coals. The heat above the coals is usually about 550 to 600 degrees, making it impossible to cook with precision outside the realm of a quick steak cook.

Two-zone setup: Stack all of the charcoal to one side of the grill for a hot and a cold zone that provides tremendous flexibility with anything put on the grates. This is the Robles method, and it should be yours, too. Put the meat on the hot zone to finish or establish blackened grill marks, but most of the cooking time should be spent on the cool side. If your cook lasts more than hour, add eight to 10 new coals to the hot side after an hour.

Other two-zone setups promote putting the coals on the outside with a metal pan filled with water in the middle. Eh. The water does little to moisten the meat, and the end result is mostly a wasted pan.

Vent control: All nongas grills and smokers come with vents that are located below and on top of the device. They can help control the temperature, but I’ve always found it best to keep them open all the way from start to finish. Airflow gives every fire life, and it delivers better flavor. If the fire is burning too hot or too cold, it’s probably because an error was made in the original setup.

It shouldn’t take very long for these tips to become second-nature in your outdoor grilling process. When fanning the flames, it’s always best to maintain control.

Chuck Blount is a food writer and columnist covering all things grilled and smoked in the San Antonio area. Find his Chuck’s Food Shack columns on our subscriber site, ExpressNews.com, or read his other coverage on our free site, mySA.com. | cblount@express-news.net | Twitter: @chuck_blount   | Instagram: @bbqdiver

If you would like to get involved in supporting our wounded troop – go to tatubbq.shutterfly.com/ and sign up. If you have problems, simply call me and I’d be glad to answer any questions.

How to control the fire temperature for grilling and smoking published first on https://turbogrill.us/

[theresawelchy]

Machine Learning for Everyone - In simple words

This article in other languages: Russian (original) Special thanks for help: @sudodoki and my wife <3

Machine Learning is like sex in high school. Everyone is talking about it, a few know what to do, and only your teacher is doing it. If you ever tried to read articles about machine learning on the Internet, most likely you stumbled upon two types of them: thick academic trilogies filled with theorems (I couldn’t even get through half of one) or fishy fairytales about artificial intelligence, data-science magic, and jobs of the future.

I decided to write a post I’ve been missing all that time. There's a simple introduction for those who always wanted to understand machine learning. Only real-world problems, practical solutions, simple language, and no high-level theorems. One and for everyone.

Let's roll.

Why do we want machines to learn?

This is Billy. Billy wants to buy a car. He tries to calculate how much he needs to save monthly for that. He went over dozens of ads on the internet and learned that new cars are around $20,000, used year-old ones are $19,000, 2-year old are $18,000 and so on.

Billy, our brilliant analytic, starts seeing a pattern: so, the car price depends on its age and drops $1,000 every year, but won't get lower than $10,000.

In machine learning terms, Billy invented regression – he predicted a value (price) based on known historical data. People do it all the time, when trying to estimate a reasonable cost for a used iPhone on eBay or figure out how many ribs to buy for a BBQ party. 200 grams per person? 500?

Yeah, it would be nice to have a simple formula for every problem in the world. Especially, for a BBQ party. Unfortunately, it's impossible.

Let's back to cars. The problem is, they all have different manufacturing date, dozens of options, technical condition, seasonal demand spikes, and god only knows how many more hidden factors. An average Billy can't keep all that data in his head while calculating the price. Me too.

People are dumb and lazy – we need robots to do the maths for them. So, let's go it computational way here. Let's provide the machine a data and ask it to find all hidden patterns related to price.

Aaaand it worked. The most exciting thing is that the machine copes with this task much better than a real person does when carefully analyzing all the dependencies in mind.

That was the birth of machine learning.

Three components of machine learning

The only goal of machine learning is to predict results based on incoming data. That's it. All ML tasks can be represented this way, or it's not an ML from the beginning.

The greater variety in the samples you have, the easier to find relevant patterns and predict the result. Therefore, we need three components to teach the machine:

Data Want to detect spam? Get samples of spam messages. Want to forecast stocks? Find the price history. Want to find out user preferences? Parse their activities on Facebook (no, Mark, stop it, enough!). The more and diverse the data, the better the result. Tens of thousands of rows is the bare minimum for the desperate ones.

There are two main ways of collecting data — manual and automatic. Manually collected data contains far fewer errors but takes more time to collect — that makes it more expensive in general.

Automatic approach is cheaper — you only need to gather everything you can find on the Internet and hope for the best.

Some smart asses like Google use their own customers to label data for them for free. Remember ReCaptcha which forces you to "Select all street signs"? That's exactly what they're doing. Free labor! Nice. In their place, I'd start to show captcha more and more. Oh, wait...

It's extremely tough to collect a good collection of data (aka dataset). They are so important that companies may even reveal their algorithms, but rarely datasets.

Features Also known as parameters or variables. Those could be car mileage, user's gender, stock price, word frequency in the text. In other words, these are the factors for a machine to look at.

When data stored in tables it's simple — features are column names. But what are they if you have 100 Gb of cat pics? We cannot consider each pixel as a feature. That's why selecting the right features usually takes way longer than all the other ML parts. That's also the main source of errors. Meatbags are always subjective. They choose only features they like or find "more important". Please, avoid being human.

Algorithms Most obvious part. Any problem can be solved differently. The method you choose affects the precision, performance, and size of the final model. There is one important nuance though: if the data is crappy, even the best algorithm won't help. Sometimes it's referred as "garbage in – garbage out". So don't pay too much attention to the percentage of accuracy, try to acquire more data first.

Learning vs Intelligence

Once I saw an article titled "Will neural networks replace machine learning?" on some hipster media website. These media guys always call any shitty linear regression at least artificial intelligence, almost SkyNet. Here is a simple picture to deal with it once and for all.

Artificial intelligence is the name of a whole knowledge field, such are biology or chemistry.

Machine Learning is a part of artificial intelligence. Important, but not the only one.

Neural Networks is one of machine learning types. A popular one, but there are other good guys in the class.

Deep Learning is a modern method of building, training, and using neural networks. Basically, it's a new architecture. Nowadays in practice, no one separates deep learning from the "ordinary networks". We even use the same libraries for them. To not look like a dumbass, it's better just name the type of network and avoid buzzwords.

The general rule is to compare things on the same level. That's why the phrase "will neural nets replace machine learning" sounds like "will the wheels replace cars". Dear media, it's compromising your reputation a lot.

Machine can Machine cannot Forecast Create smth new Memorize Get smart really fast Reproduce Go beyond their task Choose best item Kill all humans

The map of machine learning world

If you are too lazy for long reads, take a look at the picture below to get some understanding.

It's important to understand — there is never a sole way to solve a problem in the machine learning world. There are always several algorithms that fit, and you have to choose which one fits better. Everything can be solved with a neural network, of course, but who will pay for all these GeForces?

Let's start with a basic overview. Nowadays there are four main directions in machine learning.

Part 1. Classical Machine Learning

The first methods came from pure statistics in the '50s. They solved formal math tasks, looking for patterns in numbers, evaluating the proximity of data points, and calculating vectors' directions.

Nowadays, half of the Internet is working using these algorithms. When you see a list of articles to "read next" or your bank blocks your card at random gas station in the middle of nowhere, most likely it's the work of one of those little guys.

Big tech companies are huge fans of neural networks. Obviously. For them, 2% accuracy is an additional 2 billion in revenue. But when you are small, it doesn't make sense. I heard stories of the teams spending a year on a new recommendation algorithm for their e-commerce website, before discovering that 99% of traffic came from search engines. Their algorithms were useless. Most users didn't even open the main page.

Despite the popularity, classical approaches are so natural, that you can easily explain them to a toddler. They are like a basic arithmetics — we use it every day, without even thinking.

1.1 Supervised Learning

Classical machine learning is often divided into two categories – Supervised and Unsupervised Learning.

In the first case, the machine has a "supervisor" or a "teacher" who gives machine all the answers, telling is it a cat at the picture or a dog. The teacher is already divided (labeled) the data into cats and dogs, and the machine is using these examples to learn. One by one. Dog by cat.

Unsupervised learning means the machine is left on its own with a pile of animal photos and a task to find out who's who. Data is not labeled, there's no teacher, the machine is trying to find any patterns on its own. We'll talk about these methods below.

Clearly, the machine will learn faster with a teacher, so it's more commonly used in real-life tasks. There are two types of such tasks: classification – an object's category prediction and regression – prediction of a specific point on numeric axis.

Classification

"Splits objects based at one of the attributes known beforehand. Separate socks by based on color, documents based on language, music by genre"

Today used for: – Spam filtering – Language detection – A search of similar documents – Sentiment analysis – Recognition of handwritten characters and numbers – Fraud detection

Popular algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbours, Support Vector Machine

Here and onward you can comment with additional information to these sections. Feel free to write your examples of tasks. Everything is written here based on my own subjective experience.

Machine learning is about classifying things, mostly. The machine here is like a baby learning to sort toys: here's a robot, here's a car, here's a robo-car... Oh, wait. Error! Error!

In classification, you always need a teacher. The data should be labeled with features so the machine could assign the classes based on them. Everything could be classified — users based on interests (as algorithmic feeds do), articles based on language and topic (that's important for search engines), music based on genre (Spotify playlists), and even your emails.

In spam filtering was widely used Naive Bayes algorithm. Machine counted the number of "viagra" mentions in spam and normal mail. Then it multiplied both probabilities using Bayes equation, summed the results and yay, we got Machine Learning.

Later, spammers learned how to deal with Bayesian filter by adding lots of "good" words at the end of the email. Ironically, the method was called Bayesian poisoning. It stayed at history as most elegant and first practically useful one, though, other algorithms now used for spam filtering.

Here's another practical example of classification. Let's say, you need some credit money. How bank will know will you pay it back or not? There's no way to know it for sure. Though, the bank has lots of profiles of people who took the money before. Bank has data about age, education, occupation and salary and – most importantly – the fact of paying the money back. Or not.

With that data, we can teach the machine, find the patterns and get the answer. There's not an issue. The issue is that bank can't blindly trust the machine answer. What if there's a system failure, hacker attack or a quick fix from a drunk senior.

To deal with it, we have Decision Trees. All the data automatically divided to yes/no questions. They could sound a bit weird from a human perspective, e.g., whether the creditor earns more than $128.12? Though, the machine comes up with such question to split the data best at each step.

That's how a tree made. The higher the branch — the broader the question. Any analyst can take it and explain afterward. He may not understand it, but explain easily! (typical analyst)

The trees widely used in high responsibility spheres: diagnostics, medicine, and finances.

The two most popular algorithms for forming the trees are CART and C4.5.

Pure decision trees are rarely used now. However, they often set the basis for large systems, and their ensembles even work better than neural networks. We'll talk about that later.

When you google something, there are precisely the bunch of dumb trees which are looking for range the answers for you. Search engines love them because they're fast.

Support Vector Machines (SVM) is rightfully the most popular method of classical classification. It was used to classify everything in existence: plants by types faces at the photos, documents by categories, etc.

The idea behind SVM is simple – it's trying to draw two lines between categories with the largest margin between them. It's more evident in the picture:

There's one very useful side of the classification — anomaly detection. When a feature does not fit any of the classes, we highlight it. Now it used at the medicine — on MRI, computer highlights all the suspicious areas or deviations of the test. Stock markets use it to detect abnormal behavior of traders, to find the insiders. When teaching the computer the right things, we automatically teach it what things are wrong.

Today, for classification more frequently used neural networks. Well, that's what they were created for.

The rule of thumb is the more complex the data, the more complex the algorithm. For text, numbers, and tables, I'd choose the classical approach. The models are smaller there, they learn faster and work more clear. For pictures, video and all other complicated big data things, I'd definitely look at neural networks.

You may find face classifier built on SVM only 5 years ago you. Now, you can choose from hundreds of pre-trained networks. Nothing changed for spam filters, though. They are still written with SVM. And there's no good reason to switch from it anywhere.

Regression

"Draw a line through these dots. Yep, that's the machine learning"

Today this is used for:

Stock price forecast

Demand and sales volume analysis

Medical diagnosis

Any number-time correlations

Popular algorithms are Linear and Polynomial regressions.

Regression is basically classification where we forecast a number instead of category. Such are car price by its mileage, traffic by time of the day, demand volume by growth of the company etc. Regression is perfect when something depends on time.

Everyone who works with finance and analysis loves regression. It's even built-in to Excel. And it's super smooth inside — machine simply tries to draw a line that indicates average correlation. Though, unlike a person with a pen and a whiteboard, machine does at mathematically accurate, calculating the average interval to every dot.

When the line is straight — it's a linear regression, when it's curved – polynomial. These are two major types of regression. The other ones are more exotic. Logistic regression is a black sheep in the flock. Don't let it trick you, as it's a classification method, not regression.

It's okay to mess with regression and classification, though. Many classifiers turn into regression after some tuning. We can not only define the class of the object but memorize, how close it is. Here comes a regression.

1.2 Unsupervised learning

Unsupervised was invented a bit later, in the '90s. It is used less often, but sometimes we simply have no choice.

Labeled data is luxury. But what if I want to create, let's say, a bus classifier? Should I manually take photos of million fucking buses on the streets and label each of them? No way, that will take a lifetime, and I still have so many games not played on my Steam account.

There's a little hope for capitalism in this case. Thanks to the social stratification, we have millions of cheap workers and services like Mechanical Turk who are ready to complete your task for 0.05$. And that's how things usually get done here.

Or you can try to use unsupervised learning. But I can't remember any good practical appliance of it, though. It's usually useful for exploratory data analysis but not as the main algorithm. Specially trained meatbag with Oxford degree feeds the machine with a ton of garbage and watch it. Are there any clusters? No. Any visible relations? No. Well, continue then. You wanted to work in data science, right?

Clustering

"Divides objects based on unknown feature. Machine chooses the best way"

Nowadays used:

For market segmentation (types of customers, loyalty)

To merge close points on the map

For image compression

To analyze and label new data

To detect abnormal behavior

Popular algorithms: K-means_clustering, Mean-Shift, DBSCAN

Clustering is a classification with no predefined classes. It’s like dividing socks by color when you don't remember all the colors you have. Clustering algorithm trying to find similar (by some features) objects and merge them in a cluster. Those who have lots of similar features are joined in one class. With some algorithms, you even can specify the exact number of clusters you want.

An excellent example of clustering — markers on web maps. When you're looking for all vegan restaurants around, the clustering engine groups them to blobs with a number. Otherwise, your browser would freeze, trying to draw all three million vegan restaurants in that hipster downtown.

Apple Photos and Google Photos use more complex clustering. They're looking for faces at photos to create albums of your friends. The app doesn't know how many friends you have and how they look, but it's trying to find the common facial features. Typical clustering.

Another popular issue is image compression. When saving the image to PNG you can set the palette, let's say, to 32 colors. It means clustering will find all the "reddish" pixels, calculate the "average red" and set it for all the red pixels. Fewer colors — less the file size — profit!

However, you may have problems with colors like Cyan◼︎-like colors. Is it green or blue? Here comes the K-Means algorithm.

It randomly set 32 color dots in the palette. Now, those are centroids. The remaining points are marked as assigned to the nearest centroid. Thus, we get kind of galaxies around these 32 colors. Then we're moving the centroid to the center of its galaxy and repeat that until centroids won't stop moving.

All done. Clusters defined, stable, and there are exactly 32 of them. Here is a more real-world explanation:

Searching for the centroids is convenient. Though, in real life clusters not always circles. Let's imagine, you're a geologist. And you need to find some similar minerals at the map. In that case, the clusters can be weirdly shaped and even nested. Also, you don't even know how many of them to expect. 10? 100?

K-means does not fit here, but DBSCAN can be helpful. Let's say, our dots are people at the town square. Find any three people standing close to each other and ask them to hold hands. Then, tell them to start grabbing hands of those neighbors they can reach out. And so on, and so on until no one else can take anyone hand. That's our first cluster. Repeat the process until everyone clustered. Done.

A nice bonus: a person who have no one to hold hands — is an anomaly.

It all looks cool in motion:

Just like classification, clustering could be used to detect anomalies. User behaves abnormally after signing up? Let machine ban him temporarily and create a ticket for the support to check it. Maybe it's a bot. We don't even need to know what is "normal behavior", we just upload all user actions to our model and let the machine decide is it a "typical" user or not.

This approach works not that well compared to the classification one, but it never hurts to try.

Dimensionality Reduction (Generalization)

"Assembles specific features into more high-level ones"

Nowadays is used for:

Recommender systems (★)

Beautiful visualizations

Topic modeling and similar document search

Fake image analysis

Risk management

Popular algorithms: Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Latent Dirichlet allocation (LDA), Latent Semantic Analysis (LSA, pLSA, GLSA), t-SNE (for visualization)

Previously these methods were used by hardcore data scientists, who had to find "something interesting" at the huge piles of numbers. When Excel charts didn't help, they forced machines to do find the patterns. That's how they got Dimension Reduction or Feature Learning methods.

Projecting 2D-data to a line (PCA)

It is always convenient for people to use abstraction, not a bunch of fragmented features. For example, we can merge all dogs with triangle ears, long noses, and big tails to a nice abstraction — "shepherd". Yes, we're losing some information about the specific shepherds, but the new abstraction is much more useful for naming and explaining purposes. As a bonus, such "abstracted" model learn faster, overfit less and use fewer number of features.

These algorithms became an amazing tool for Topic Modeling. We can abstract from specific words to their meanings. This is that Latent semantic analysis (LSA) do. It is based on how frequent you see the word on the exact topic. Like, there are more tech terms in tech articles, for sure. The names of politicians are mostly found in political news, etc.

Yes, we can just make clusters from all the words at the articles, but we will lose all the important connections (for example the same meaning of battery and accumulator in different documents). LSA will handle it properly, that's why its called "latent semantic".

So we need to connect the words and documents into one feature to keep these latent connections. Referring to the name of the method. Turned out that Singular decomposition (SVD) nails this task, revealing the useful topic clusters from seen-together words.

Recommender Systems and Collaborative Filtering is another super-popular use of dimensionality reduction method. Seems like if you use it to abstract user ratings, you get a great system to recommend movies, music, games and whatever you want.

It's barely possible to fully understand this machine abstraction, but it's possible to see some correlations on closer look. Some of them correlate with user's age — kids play Minecraft and watch cartoons more; others correlate with movie genre or user hobbies.

Machine get these high-level concepts even without understanding them, based only on knowledge of user ratings. Nicely done, Mr.Computer. Now we can write a thesis why bearded lumberjacks love My Little Pony.

Association rule learning

"Look for patterns in the orders' stream"

Nowadays is used:

To forecast sales and discounts

To analyze goods bought together

To place the products on the shelves

TO analyze web surfing patterns

Popular algorithms: Apriori, Euclat, FP-growth

This includes all the methods to analyze shopping carts, automate marketing strategy, and other event-related tasks. When you have a sequence of something and want to find patterns in it — try these thingys.

Say, a customer takes a six-pack of beers and goes to the checkout. Should we place peanuts on the way? How often people buy it together? Yes, it probably works for beer and peanuts, but what other sequences can we predict? Can a small change in the arrangement of goods lead to a significant increase in profits?

Same goes for e-commerce. The task is even more interesting there — what customer is going to buy next time?

No idea, why the rule learning seems to be the least elaborated category of machine learning. Classical methods are based on a head-on looking through all the bought goods using trees or sets. Algorithms can only search for patterns, but cannot generalize or reproduce those on the new examples.

In the real world, every big retailer builds their own proprietary solution, so nooo revolutions here for you. The highest level of tech here — recommender systems. Though, I may be not aware of a breakthrough in the area. Let me know in comments if you have something to share.

Part 2. Reinforcement Learning

"Throw a robot into a maze and let it find an exit"

Nowadays used for:

Self-driving cars

Robot vacuums

Games

Automating trading

Enterprise resource management

Popular algorithms: Q-Learning, SARSA, DQN, A3C, Genetic algorithm

Finally, we got to something looks like real artificial intelligence. In lots of articles reinforcement learning is placed somewhere in between of supervised and unsupervised learning. They have nothing in common! Is this because of the name?

Reinforcement learning is used in cases when your problem is not related to data at all, but you have an environment to live. Like a video game world or a city for self-driving car.

Neural network plays Mario

Knowledge of all the road rules in the world will not teach the autopilot how to drive on the roads. Regardless of how much data we collect, we still can't foresee all the possible situations. This is why its goal is to minimize error, not to predict all the moves.

Surviving in an environment is a core idea of reinforcement learning. Throw poor little robot into real live, punish it for errors and reward for right deeds. Same way we teach our kids, right?

More effective way here — to build a virtual city and let self-driving car to learn all its tricks there first. That's exactly how we train auto-pilots right now. Create a virtual city based on a real map, populate with pedestrians and let the car learn to kill as few people as possible. When the robot is reasonably confident in this artificial GTA, it's freed to test in the real streets. Fun!

There may be two different approaches — Model-Based and Model-Free.

Model-Based means that car needs to memorize a map or its parts. That's a pretty outdated approach since it's impossible for the poor self-driving car to memorize the whole planet.

In Model-Free learning, the car doesn't memorize every movement but tries to generalize situations and act rationally while obtaining a maximum reward.

Remember the news about AI beats a top player at the game of Go? Although, shortly before this, it was proved that the number of combinations in this game is greater than the number of atoms in the universe.

This means, the machine could not remember all the combinations and thereby win Go (as it did chess). At each turn, it simply chose the best move for each situation, and it did well enough to outplay a human meatbag.

This approach is a core concept behind Q-learning and its derivatives (SARSA & DQN). 'Q' in the name stands for "Quality" as a robot learns to perform the most "qualitative" action in each situation and all the situations are memorized as a simple markovian process.

Such a machine can test billions of situations in a virtual environment, remembering which solutions led to greater reward. But how it can distinguish previously seen situation from a completely new one? If a self-driving car is at a road crossing and traffic light turns green — does it mean it can go now? What if there's an ambulance rushing through a street nearby?

The answer is today is "no one knows". There's no easy answer. Researches are constantly searching for it but meanwhile only finding workarounds. Some would hardcode all the situations manually that lets them solve exceptional cases like trolley problem. Others would go deep and let neural networks do the job of figuring it out. This led us to the evolution of Q-learning called Deep Q-Network (DQN). But they are not a silver bullet either.

Reinforcement Learning for an average person would look like a real artificial intelligence. Because it makes you think wow, this machine is making decisions in real life situations! This topic is hyped right now, it's advancing with incredible pace and intersecting with a neural network to clean your floor more accurate. Amazing world of technologies!

Off-topic. When I was a student, genetic algorithms (links has cool visualization) were really popular. This is about throwing a bunch of robots into a single environment and make them try reaching the goal until they die. Then we pick the best ones, cross them, mutate some genes and rerun the simulation. After a few milliard years, we will get an intelligent creature. Probably. Evolution at its finest.

Genetic algorithms are considered as part of reinforcement learning and they have the most important feature proved by the decade-long practice: no one gives a shit about them.

Humanity still couldn't come up with a task where those would be more effective than other methods. But they are great for students experiments and let people get their university supervisors excited about "artificial intelligence" without too much labor. And youtube would love it as well.

Part 3. Ensemble Methods

"Bunch of stupid trees learning to correct errors of each other"

Nowadays is used for:

Everything that fits classical algorithms approaches (but works better)

Search systems (★)

Computer vision

Object detection

Popular algorithms: Random Forest, Gradient Boosting

It's time for modern, grown-up methods. Ensembles and neural networks are two main fighters paving our path to a singularity. Today they are producing the most accurate results and are widely used in production.

However, the neural networks got all the hype today, while the words like "boosting" or "bagging" are scare hipsters on TechCrunch.

Despite all the effectiveness idea behind those is overly simple. If you take a bunch of inefficient algorithms and force them to correct each other's mistakes, the overall quality of a system will be higher than even the best individual algorithms.

You'll get even better results if you take the most unstable algorithms that are predicting completely different results on small noise in input data. Like Regression and Decision Trees. These algorithms are sensitive to even a single outlier in input data to have model go mad.

In fact, this is what we need.

We can use any algorithm we know to create an ensemble. Just throw a bunch of classifiers, spice up with regression and don't forget to measure accuracy. From my experience: don't even try a Bayes or kNN here. Although being "dumb" they are really stable. That's boring and predictable. Like your ex.

Although, there are three battle-tested methods to create ensembles.

Stacking Output of several parallel models is passed as input to last one which makes final decision. Like that girl, who asks her girlfriends whether to meet with you in order to make the final decision herself.

Emphasize here the word "different". Mixing the same algorithm on the same data would make no sense. Choice of algorithms is completely up to you. However, for final decision-making model, regression is usually a good choice.

Based on my experience stacking is less popular in practice, because two other methods are giving better accuracy.

Bagging aka Bootstrap AGGregatING. Use the same algorithm but train it on different subsets of original data. In the end — just average answers.

Data in random subsets may repeat. For example, from a set like "1-2-3" we can get subsets like "2-2-3", "1-2-2", "3-1-2" and so on. We use these new datasets to teach the same algorithm several times and then predict the final answer via simple majority voting.

The most famous example of bagging is the Random Forest algorithm, which is simply bagging on the decision trees (that was illustrated above). When you open your phone's camera app and see it drawing boxes around people faces — it probably results of Random Forest work. Neural network would be too slow to run real-time yet bagging is ideal given it can calculate trees on all the shaders of a video card or on these new fancy ML processors.

In some tasks, the ability of the Random Forest to run in parallel, even more, important than a small loss in accuracy to the boosting, for example. Especially in real-time processing. There is always a trade-off.

Boosting Algorithms are trained one by one sequentially. Every next one paying most attention to data points that were mispredicted by the previous one. Repeat until you are happy.

Same as in bagging, we use subsets of our data but this time they are not randomly generated. Now, in each subsample we take a part from the data previous algorithm failed to process. Thus, we make a new algorithm learn to fix errors of the previous one.

The main advantage here — very high, even illegal in some countries precision of classification that all cool kids can envy. Cons were already called out — it doesn't parallelize. But it's still faster than neural networks. It's like a race between dumper truck and racing car. Truck can do more, but if you want to go fast — take a car.

If you want a real example of boosting — open Facebook or Google and start typing in a search query. Can you hear an army of trees roaring and smashing together to sort results by relevancy? This is it, they are using boosting.

Part 4. Neural Networks and Deep Leaning

"We have a thousand-layer network, dozens of video cards, but still no idea where to use it. Let's generate cat pics!"

Used today for:

Replacement of all algorithms above

Object identification on photos and videos

Speech recognition and synthesis

Image processing, style transfer

Machine translation

Popular architectures: Perceptron, Convolutional Network (CNN), Recurrent Networks (RNN), Autoencoders

If no one ever tried to explain you neural networks using the "human brain" analogies, you're a happy guy. Tell me your secret. But first, I'll explain it as I like.

Any neural network is basically a collection of neurons and connections between them. Neuron is a function with a bunch of inputs and one output. His task is to take all numbers from its input, perform a function on them and send the result to the output.

Here is an example of simple but useful in real life neuron: sum up all numbers on inputs and if that sum is bigger than N — give 1 as a result. Otherwise — zero.

Connections are like channels between neurons. They connect outputs of one neuron with the inputs of another so they can send digits to each other. Each connection has its only parameter — weight. It's like a connection strength for a signal. When the number 10 passes through a connection with a weight 0.5 it turns into 5.

These weights tell the neuron to respond more to one input and less to another. Weights are adjusted when training — that's how the network learns. Basically, that's all.

To prevent the network from falling into anarchy, the neurons are linked by layers, not randomly. Inside one layer neurons are not connected, but connected to neurons of the next and previous layer. Data in the network goes strictly in one direction — from the inputs of the first layer to the outputs of the last.

If you throw in a sufficient number of layers and put the weights correctly, you will get the following - by applying to the input, say, the image of handwritten digit 4, black pixels activate the associated neurons, they activate the next layers, and so on and on, until it lights up the very exit in charge of the four. The result is achieved.

When doing real-life programming nobody is writing neurons and connections. Instead, everything is represented as matrices and calculated based on matrix multiplication for better performance. In two favorite videos of mine, all the process is described in an easily digestible way on the example of recognizing hand-written digits. Watch those if you want to figure this out.

A network that has multiple layers that have connections between every neuron is called perceptron (MLP) and considers the simplest architecture for a novice. I didn't see it used for solving tasks in production.

After we constructed a network, our task is to assign proper ways so neurons would react to proper incoming signals. Now is the time to remember that we have data that is samples of 'inputs' and proper 'outputs'. We will be showing our network a drawing of same digit 4 and tell it 'adapt your weights so whenever you see this input your output would emit 4'.

To start with all weights are assigned randomly afterward we show it a digit, it emits a random answer (the weights are not proper yet) and we compare how much this result differs from the right one. Afterward, we start traversing network backward from outputs to inputs and tell every neuron 'hey, you did activate here but you did a terrible job and everything went south from here downwards, let's keep less attention to this connection and more of that one, mkay?'.

After a hundred thousands of such cycles 'infer-check-punish', there is a hope that weights are corrected and act as intended. Science name for this approach is called Backpropagation or 'method of backpropagating an error'. Funny thing it took twenty years to come up with this method. Before this neural networks, we taught, however.

My second favorite vid is describing this process in depth but still very accessible.

A well trained neural network can fake work of any of the algorithms described in this chapter (and frequently work more precisely). This universality is what made them widely popular. Finally we have an architecture of human brain said they we just need to assemble lots of layers and teach them on any possible data they hoped. Then first AI winter) started, then thaw and then another wave of disappointment.

It turned out networks with a large number of layers required computation power unimaginable at that time. Nowadays any gamer PC with geforces outperforms datacenter of that time. So people didn't have any hope at that time to acquire computation power like that and neural networks were a huge bummer.

And then ten years ago deep learning rose.

In 2012 convolutional neural network acquired overwhelming victory in ImageNet competition that world suddenly remembered about methods of deep learning described in ancient 90s. Now we have video cards!

Differences of deep learning from classical neural networks was in new methods of training that could handle bigger networks. Nowadays only theoretics would try to divide which learning to consider deep and not so deep. And we, as practitioners are using popular 'deep' libraries like Keras, TensorFlow & PyTorch even when we build a mini-network with five layers. Just because it's better suited than all the tools coming before. And we just call them neural networks.

I'll tell about two main kinds nowadays.

Convolutional Neural Networks (CNN)

Convolutional neural networks are all the rage right now. They are used to search for the object on photos and in the videos, face recognition, style transfer, generating and enhancing images, creating effects like slow-mo and improving image quality. Nowadays CNN's are used in all the cases that involve pictures and videos. Even in you iPhone several of these networks are going through your nudes to detect objects in those. If there is something to detect, heh.

Image above is a result produced by Detectron that was recently open-sourced by Facebook

A problem with images was always the difficulty of extracting features out of them. You can split text by sentences, lookup words' attributes in specialized vocabularies. But images had to be labeled manually to teach machine where cat ears or tail were in this specific image. This approach got the name 'handcrafting features' and used to be used almost by everyone.

There are lots of issues with the handcrafting.

First of all, if a cat had its ears down or turned away from the camera you are in trouble, the neural network won't see a thing.

Secondly, try naming at the spot 10 different features that distinguish cats from other animals. I for once couldn't do it. Although when I see black blob rushing past me at night, even I see it in the corner of my eye I would definitely tell a cat from a rat. Because people don't look only at ear form or leg count and account lots of different features they don't even think about. And thus cannot explain it to the machine.

So it means machine need to learn such features on its own building on top of basic lines. We'll do the following: first, we divide the whole image into 8x8 pixels block and assign to each type of dominant line – either horizontal [-], vertical [|] or one of the diagonals [/]. It can be that several would be highly visible this happens too and we are not always absolutely confident.

Output would be several tables of sticks that are in fact are simplest features representing objects' edges on the image. They are images on their own but build out of sticks. So we can once again take a block of 8x8 and see how they match together. And again and again…

This operation is called convolution which gave the name for the method. Convolution can be represented as a layer of a neural network as neuron can act as any function.

When we feed our neural network with lots of photos of cats it automatically assigns bigger weights to those combinations of sticks it saw the most frequently. It doesn't care whether it was a straight line of a cat's back or a geometrically complicated object like a cat's face, something will be highly activating.

As the output, we would put a simple perceptron which will look at the most activated combinations and based on that differentiate cats from dogs.

The beauty of this idea is that we have a neural net that searches for most distinctive features of the objects on its own. We don't need to pick them manually. We can feed it any amount of images of any object just by googling billion of images with it and our net will create feature maps from sticks and learn to differentiate any object on its own.

For this I even have a handy unfunny joke:

Give your neural net a fish and it will be able to detect fish for the rest of its life. Give your neural net a fishing rod and it will be able to detect fishing rods for the rest of its life…

Recurrent Neural Networks (RNN)

The second most popular architecture today. Recurrent networks gave us useful things like neural machine translation (here is my post about it), speech recognition and voice synthesis in smart assistants. RNNs are the best for sequential data like voice, text or music.

Remember Microsoft Sam, the old-school speech synthesizer from Windows XP? That funny guy builds words letter by letter, trying to glue them up together. Now, look at Amazon Alexa or Assistant from Google. They don't only say the words clearly, they even place the right accents!

Neural Net is trying to speak

All because modern voice assistants are learned to speak not letter by letter, but whole phrases at once. We can take a bunch of voiced texts and train a neural network to generate an audio-sequence closest to the original speech.

In other words, we use text as input and its audio as the desired output. We ask a neural network to generate some audio for the given text, then compare it with the original, correct errors and try to get as close as possible to ideal.

Sounds like a classical leaning process. Even a perceptron is suitable for this. But how should we define it outputs? Fire one particular output for each possible phrase is not an option — obviously.

Here we'll be helped by the fact that text, speech or music are sequences. They consist of consecutive units like syllables. They all sound unique but depend on previous ones. Lose this connection and you get dubstep.

We can train the perceptron to generate these unique sounds, but how will he remember previous answers? So the idea was to add memory to each neuron and use it as an additional input on the next run. A neuron could make a note for itself - hey, man, we had a vowel here, the next sound should sound higher (it's a very simplified example).

That's how recurrent networks appeared.

This approach had one huge problem - when all neurons remembered their past results, the number of connections in the network became so huge that it was technically impossible to adjust all weights.

When a neural network can't forget it can't learn new things (people have the same flaw).

The first decision was simple - let's limit the neuron memory. Let's say, to memorize no more than 5 recent results. But it broke the whole idea.

The much better approach came up later — to use special cells, similar to computer memory. Each cell can record a number, read it or reset it. They were called a long and short-term memory (LSTM) cell.

Now, when neuron needs to set a reminder, it puts the flag in that cell. Like "it was a consonant in a word, next time use different pronunciation rules". When the flag is no longer needed - the cells are reset, leaving only the “long-term” connections of the classical perceptron. In other words, the network is trained not only to learn weights but also to set these reminders.

Simple, but it works!

CNN + RNN = Fake Obama

You can take speech samples from anywhere. BuzzFeed, for example, took the Obama's speeches and trained the neural network to imitate his voice. As you see, audio synthesis is already a simple task. Video still has issues, but it's a question of time.

There are many more network architectures in the wild. I recommend you a good article called Neural Network Zoo, where almost all types of neural networks are collected and briefly explained.

The End: when the war with the machines?

The main problem here is that the question "when will the machines become smarter than us and enslave everyone?" is initially wrong. There are too many hidden conditions in it.

We say "become smarter than us" like we mean that there is a certain unified scale of intelligence. The top of which is a man, dogs are a bit lower, and stupid pigeons are hanging around at the very bottom.

That's wrong.

In this case, every man must beat animals in everything but it's not true. The average squirrel can remember a thousand hidden places with nuts — I can't even remember where are my keys.

So the intelligence is a set of different skills, not a single measurable value? Or remembering nuts stashes' location is not included in the intelligence?

However, an even more interesting question for me - why do we believe that the human brain possibilities are limited? There are many popular graphs on the Internet, where the technological progress is drawn as an exponent and the human possibilities are constant. But is it?

Ok, multiply 1680 by 950 right now in your mind. I know you won't even try, lazy bastards. But give you a calculator — you'll do it in two seconds. Does this mean that the calculator just expanded the capabilities of your brain?

If yes, can I continue to expand them with other machines? Like, use notes in my phone to not to remember a shitload of data? Oh, seems like I'm doing it right now. I'm expanding capabilities of my brain with the machines.

Think about it. Thanks for reading.

DataTau published first on DataTau

[unretainable]

Gov Ball NYC 2017

Hey everyone! This past weekend, I attended 2 out of the 3 days of the Governors Ball Music Festival on Randall’s Island. I’m writing this to talk about my experience and hopefully help people decide whether or not they want to go to the festival next year. 

My boyfriend, Ian, and I decided to take the train to Penn Station (from Long Island) and then take a ferry to Randall’s Island. I did a lot of research on different ways to get there before coming to the conclusion that the ferry was the safest and most time-efficient way to go. Thankfully, I was right and it ended up being super convenient and a rather fun experience. However, the overall travel time each way ended up being about 2.5 hours after adding up all the wait-times, Uber times, etc.

We first arrived at the festival on Friday around 7:30. We chose to go later in the evening because we only really wanted to see Chance the Rapper on that day, and I also had school. Shortly after arriving, we went to get something to eat. I was really excited to try the food stand called “The Nugget Spot,” which exclusively served chicken nuggets. Once we found the tent for it, we had to wait for almost 45 minutes in a densely packed, disorganized crowd. Although the nuggets ended up being amazing, I definitely did not think they were worth the unpleasant wait and my $16. 

Since Ian was still hungry, we went to the tent called “Pizza Nova”, where we both got a slice. The pizza tasted like high quality cafeteria pizza, which was overall very good. However, it was not exactly a bargain at $7 a slice. 

By the time we had finished eating, the DJ Flume was about to begin his performance. We headed over to the stage that he was at just as he began. I was quite excited to see his performance, but shortly after he started, Ian and I decided that we were uninterested. Because he had a few songs that we knew and liked, we both expected the performance to be great, but it turned out to be pretty annoying and overall disappointing. 

I had heard that the ice cream shop called “Wowfulls” was going to be at the festival, so we headed to their tent to get their famous waffle with ice cream. We waited about 20 minutes on line for it, but it went quickly as I was talking to some girls on the line about how bad Flume was. After ordering my $10 ice cream, I had to wait another 5 minutes to actually receive it. Of course, once I got it, I had to take a picture with it or else the whole experience would be pointless, right? The ice cream was actually amazing and I totally recommend it to anyone interested. 

Ian and I headed back to the main stage to see Chance the Rapper, who we were most excited to see on that day. We set up our towel (side note: if you’re going to a festival, I highly recommend bringing some type of blanket or towel to sit on) on a hill overlooking the stage from the left. Chance’s performance was as great as we had hoped, but we unfortunately had to leave at 10:15 (the show ended at 11:00) in order to get on the 11:42 train back home. 

Getting on the ferry back to Manhattan didn’t take as long as we’d expect (it took about 20 minutes), but the car ride to the train station was time consuming. Thankfully, we had an experienced NYC Uber driver who was not afraid to drive on the sidewalk to get around bad drivers. We made it back to Penn Station with enough time to go to the bathroom and find good seats on the train. I finally got back to my house around 1:00 AM, which for me is extremely late. Although I was exhausted, I was taking an SAT Subject Test in the morning, so I studied for another half an hour. Finally, I was able to go to sleep around 2 AM. 

The next morning, I had to wake up at 7 AM to get to my Subject Test. After that, I had to go to driver’s ed. Being a junior in HS is so fun! I was hoping get to the festival early enough to see Rae Sremmurd, but the poorly timed trains and my unnecessarily long driver’s ed class made this impossible. We ended up getting to the island around 4:45, where we immediately went to get food. We decided to go to Takumi, which specializes in asian-style tacos and nachos. Ian and I shared short rib nachos, which were incredible and fairly priced ($12 for a big portion). 

I was really looking forward to getting my hair braided at the “got2b Braid Bar”, but by the time we got to their tent, they told me that they were closed for the day. Even though I was disappointed, I was excited to enjoy the festival. We walked over to the area where A$AP Ferg was performing, who did a great job getting the crowd excited. Ian was looking forward to getting “Mighty Quinn’s BBQ” which ended up being delicious and high quality (and there was no wait!). 

We then laid down our towel to watch Marshmello’s performance, which I found to be enjoyable but Ian didn’t like it. We decided to go back to the main stage where Wu-Tang Clan was performing, whose music I am unfamiliar with. Overall, the ended up being really great entertainers and performers. 

Throughout the day, we had seen many people drinking juice out of full fruits, which Ian and I thought was awesome. We found the tent that was selling them (”John’s Juice”), but the line was massive. While Ian waited on the line, I went to the donut stand (”Dough”) and got us a slated caramel chocolate donut and a cinnamon sugar donut. They were one of the less expensive things I bought while there ($5 each), and they were completely worth it as they were delicious. John’s Juice ended up being as cool as it seemed! Ian got a pineapple with pineapple juice and I got a watermelon with watermelon juice. Both drinks were $12 each, which I thought was a bit pricey but ultimately worth it. Plus, they made for a great picture!

After that, we went to a restaurant called “Hippie Dips” at Ian’s suggestion. He wanted to get their turkey bacon panini, while I ordered the toasted PB&J with bananas and bacon. My sandwich wasn’t as good as I had hoped it to be, but I was glad to have tried it. Shortly after we finished eating, Childish Gambino was set to perform. Ian and I were probably most excited to see his performance, and he did not disappoint. If you even get the chance to see him live, I definitely recommend that you do because it was such a great performance. 

Unfortunately, we had to leave early again (10:30 this time) to catch our train home. The ferry line was a bit longer than the night before, but it moved quickly and we made it back to Manhattan in good time. We got to Penn earlier than expected, so we were bale to take an earlier train home, which was great because I was so exhausted. 

I was thankful to get home and go to sleep since I was so drained and my feet were killing me. Although we had 3-Day passes, Ian and I decided to skip going the third day because we didn’t have the energy and we weren’t particularly excited about any of the performers. 

For anyone considering going next year, here’s my advice to you: 

1. Make sure that you don’t have anything else going on that weekend

2. Wear comfortable shoes and make sure to bring a jacket (it gets cold at night)

3. Set a budget for how much money you want to spend; it will add up quickly 

4. Plan your travel in advance and in detail (include times, waits, etc.)

Overall, Ian and I had a great time at the festival, but I would wait to see next year’s lineup before going again. It was a draining and expensive experience, but the food was great and most of the performances were amazing. If you made it all the way to the end, thanks for reading! I’ll be posting a few pictures from my weekend separately.