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snickerdoodlles · 1 year ago

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pulling out a section from this post (a very basic breakdown of generative AI) for easier reading;

AO3 and Generative AI

There are unfortunately some massive misunderstandings in regards to AO3 being included in LLM training datasets. This post was semi-prompted by the ‘Knot in my name’ AO3 tag (for those of you who haven’t heard of it, it’s supposed to be a fandom anti-AI event where AO3 writers help “further pollute” AI with Omegaverse), so let’s take a moment to address AO3 in conjunction with AI. We’ll start with the biggest misconception:

1. AO3 wasn’t used to train generative AI.

Or at least not anymore than any other internet website. AO3 was not deliberately scraped to be used as LLM training data.

The AO3 moderators found traces of the Common Crawl web worm in their servers. The Common Crawl is an open data repository of raw web page data, metadata extracts and text extracts collected from 10+ years of web crawling. Its collective data is measured in petabytes. (As a note, it also only features samples of the available pages on a given domain in its datasets, because its data is freely released under fair use and this is part of how they navigate copyright.) LLM developers use it and similar web crawls like Google’s C4 to bulk up the overall amount of pre-training data.

AO3 is big to an individual user, but it’s actually a small website when it comes to the amount of data used to pre-train LLMs. It’s also just a bad candidate for training data. As a comparison example, Wikipedia is often used as high quality training data because it’s a knowledge corpus and its moderators put a lot of work into maintaining a consistent quality across its web pages. AO3 is just a repository for all fanfic -- it doesn’t have any of that quality maintenance nor any knowledge density. Just in terms of practicality, even if people could get around the copyright issues, the sheer amount of work that would go into curating and labeling AO3’s data (or even a part of it) to make it useful for the fine-tuning stages most likely outstrips any potential usage.

Speaking of copyright, AO3 is a terrible candidate for training data just based on that. Even if people (incorrectly) think fanfic doesn’t hold copyright, there are plenty of books and texts that are public domain that can be found in online libraries that make for much better training data (or rather, there is a higher consistency in quality for them that would make them more appealing than fic for people specifically targeting written story data). And for any scrapers who don’t care about legalities or copyright, they’re going to target published works instead. Meta is in fact currently getting sued for including published books from a shadow library in its training data (note, this case is not in regards to any copyrighted material that might’ve been caught in the Common Crawl data, its regarding a book repository of published books that was scraped specifically to bring in some higher quality data for the first training stage). In a similar case, there’s an anonymous group suing Microsoft, GitHub, and OpenAI for training their LLMs on open source code.

Getting back to my point, AO3 is just not desirable training data. It’s not big enough to be worth scraping for pre-training data, it’s not curated enough to be considered for high quality data, and its data comes with copyright issues to boot. If LLM creators are saying there was no active pursuit in using AO3 to train generative AI, then there was (99% likelihood) no active pursuit in using AO3 to train generative AI.

AO3 has some preventative measures against being included in future Common Crawl datasets, which may or may not work, but there’s no way to remove any previously scraped data from that data corpus. And as a note for anyone locking their AO3 fics: that might potentially help against future AO3 scrapes, but it is rather moot if you post the same fic in full to other platforms like ffn, twitter, tumblr, etc. that have zero preventative measures against data scraping.

2. A/B/O is not polluting generative AI

…I’m going to be real, I have no idea what people expected to prove by asking AI to write Omegaverse fic. At the very least, people know A/B/O fics are not exclusive to AO3, right? The genre isn’t even exclusive to fandom -- it started in fandom, sure, but it expanded to general erotica years ago. It’s all over social media. It has multiple Wikipedia pages.

More to the point though, omegaverse would only be “polluting” AI if LLMs were spewing omegaverse concepts unprompted or like…associated knots with dicks more than rope or something. But people asking AI to write omegaverse and AI then writing omegaverse for them is just AI giving people exactly what they asked for. And…I hate to point this out, but LLMs writing for a niche the LLM trainers didn’t deliberately train the LLMs on is generally considered to be a good thing to the people who develop LLMs. The capability to fill niches developers didn’t even know existed increases LLMs’ marketability. If I were a betting man, what fandom probably saw as a GOTCHA moment, AI people probably saw as a good sign of LLMs’ future potential.

3. Individuals cannot affect LLM training datasets.

So back to the fandom event, with the stated goal of sabotaging AI scrapers via omegaverse fic.

…It’s not going to do anything.

Let’s add some numbers to this to help put things into perspective:

LLaMA’s 65 billion parameter model was trained on 1.4 trillion tokens. Of that 1.4 trillion tokens, about 67% of the training data was from the Common Crawl (roughly ~3 terabytes of data).

3 terabytes is 3,000,000,000 kilobytes.

That’s 3 billion kilobytes.

According to a news article I saw, there has been ~450k words total published for this campaign (*this was while it was going on, that number has probably changed, but you’re about to see why that still doesn’t matter). So, roughly speaking, ~450k of text is ~1012 KB (I’m going off the document size of a plain text doc for a fic whose word count is ~440k).

So 1,012 out of 3,000,000,000.

Aka 0.000034%.

And that 0.000034% of 3 billion kilobytes is only 2/3s of the data for the first stage of training.

And not to beat a dead horse, but 0.000034% is still grossly overestimating the potential impact of posting A/B/O fic. Remember, only parts of AO3 would get scraped for Common Crawl datasets. Which are also huge! The October 2022 Common Crawl dataset is 380 tebibytes. The April 2021 dataset is 320 tebibytes. The 3 terabytes of Common Crawl data used to train LLaMA was randomly selected data that totaled to less than 1% of one full dataset. Not to mention, LLaMA’s training dataset is currently on the (much) larger size as compared to most LLM training datasets.

I also feel the need to point out again that AO3 is trying to prevent any Common Crawl scraping in the future, which would include protection for these new stories (several of which are also locked!).

Omegaverse just isn’t going to do anything to AI. Individual fics are going to do even less. Even if all of AO3 suddenly became omegaverse, it’s just not prominent enough to influence anything in regards to LLMs. You cannot affect training datasets in any meaningful way doing this. And while this might seem really disappointing, this is actually a good thing.

Remember that anything an individual can do to LLMs, the person you hate most can do the same. If it were possible for fandom to corrupt AI with omegaverse, fascists, bigots, and just straight up internet trolls could pollute it with hate speech and worse. AI already carries a lot of biases even while developers are actively trying to flatten that out, it’s good that organized groups can’t corrupt that deliberately.

#generative ai #pulling this out wasnt really prompted by anything specific #so much as heard some repeated misconceptions and just #sighs #nope #incorrect #u got it wrong #sorry #unfortunately for me: no consistent tag to block #sigh #ao3

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learnhowtocreatemusic · 1 month ago

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How to Effectively Market Yourself as a Musician by Telling Your Authentic Story

In today’s crowded music industry, where countless artists are competing for attention, your music alone may not be enough to stand out. One of the most powerful ways to differentiate yourself is by telling your authentic story. Sharing your personal journey, struggles, and triumphs helps you connect with your audience on a deeper level. In this blog post, we'll explore how to effectively market yourself as a musician by tapping into the power of storytelling.

1. **Embrace Vulnerability in Your Story**

One of the key elements of an authentic story is vulnerability. Sharing your personal experiences, struggles, and the obstacles you've overcome humanizes you and makes it easier for your audience to relate to you. When people see your authentic self, they’re more likely to form a deeper connection with your music.

**How to Do It:**

- **Share your journey honestly:** Whether you’ve struggled with stage fright, financial hardship, or creative blocks, opening up about these challenges makes your story more relatable.

- **Highlight defining moments:** Was there a pivotal moment when you decided to pursue music full-time, or a life event that shaped your sound? Share these moments to add depth to your narrative.

- **Be true to your personality:** Don’t try to craft a persona you think people will like. Authenticity shines through when you embrace who you truly are.

2. **Identify the Themes in Your Story**

Your life as a musician may have different layers, such as your background, the experiences that shaped you, and the message you want to convey through your music. Identifying the central themes in your story helps you create a cohesive narrative that resonates with your audience.

**How to Do It:**

- **Focus on key themes:** Are you driven by perseverance, self-discovery, or a passion for social change? Make these themes central to your story, reflecting them in your branding, interviews, and social media content.

- **Connect your story to your music:** How do your life experiences influence your songwriting? Share stories that reflect the emotions or ideas in your music. For example, if your music speaks about overcoming hardship, talk about the personal struggles that inspired those songs.

3. **Use Visual Storytelling**

In today’s digital world, visuals are an important part of telling your story. Whether it's through album covers, music videos, social media content, or even live performances, visuals add another layer of depth to your narrative.

**How to Do It:**

- **Create visual content that reflects your story:** Think of ways to visually represent your personal journey. Album art, behind-the-scenes videos, or even stylized photoshoots that capture different moments of your career can tell a powerful story.

- **Leverage social media:** Use platforms like Instagram, TikTok, and YouTube to share snippets of your life—whether it’s how you write songs, personal reflections, or snapshots from your daily routine. These moments help fans feel like they’re part of your journey.

- **Be consistent in your branding:** Use a consistent visual style across your platforms to reinforce your story. Whether you’re going for a minimalist aesthetic or something bold and expressive, make sure it aligns with your authentic narrative.

4. **Engage with Your Audience Through Your Story**

Storytelling is not a one-way street; it's about building a relationship with your audience. Engaging with your fans by sharing your personal story creates loyalty and fosters deeper connections. People want to support artists they feel they know and understand.

**How to Do It:**

- **Share updates and milestones:** Keep your audience in the loop as you reach new goals, whether it's recording new music, booking a big gig, or hitting a personal milestone. Fans love being part of your journey.

- **Ask for fan input:** Engaging your audience with questions or inviting them to share their own stories helps create a deeper bond. Whether it’s asking for feedback on new music or sharing fan stories that relate to your music, this interaction strengthens connections.

- **Show gratitude:** Make it clear that your fans are an essential part of your story. Acknowledging their support, whether through social media shoutouts or personal messages, can go a long way in building a loyal fanbase.

5. **Show the Growth in Your Journey**

One of the most powerful elements of any story is growth. People are naturally drawn to stories of personal development, and your audience will be interested in seeing how you evolve as an artist and a person over time.

**How to Do It:**

- **Document your growth:** Whether it’s experimenting with new sounds, collaborating with other artists, or taking on bigger projects, share the growth that you’re experiencing. This gives your fans insight into your creative process and shows that you’re constantly evolving.

- **Reflect on your journey:** Share how you’ve grown since starting your music career. Talk about the lessons you’ve learned, the struggles you’ve overcome, and how those experiences have shaped you as an artist.

- **Be transparent about your future goals:** Sharing your ambitions for the future keeps your audience invested in your journey and gives them something to root for.

Final Thoughts

Effectively marketing yourself as a musician isn’t just about promoting your music; it’s about sharing your authentic story with the world. When you open up about your journey, struggles, and growth, you invite listeners to connect with you on a deeper level. By embracing vulnerability, identifying key themes, using visual storytelling, engaging with your audience, and showing growth, you can craft a compelling narrative that will resonate with fans and set you apart in the music industry.

Your story is unique, and it’s one of the most powerful tools you have for building a lasting career in music.

#ArtistDevelopment #MusicMarketing #MusicPromotion #PersonalBranding #IndependentArtists

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lawtayari · 2 months ago

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Preparation Tips For CLAT 2026

The Common Law Admission Test (CLAT) is one of the most important exams for aspiring law students in India, and with CLAT 2026 fast approaching, it's crucial to start preparing effectively. If you’re ready to dive into the world of law (and maybe a little bit of legal drama), here are some preparation tips to help you get through it all:

1. Understand the Exam Pattern & Syllabus

First things first: know what you’re getting into! CLAT 2026 consists of 150 multiple-choice questions (MCQs) covering five main areas: English Language, Current Affairs and General Knowledge, Legal Reasoning, Logical Reasoning, and Quantitative Techniques. Understanding the exam structure and syllabus will help you plan your study sessions without wasting time on unnecessary topics.

2. Create a Realistic Study Plan

Having a study plan is key to staying on track, but make sure it’s a realistic one. Break down the syllabus into manageable sections and set achievable goals each week. Don’t forget to mix up your study routine to keep things fresh-focus on English and reading comprehension one day, and logical reasoning or legal reasoning the next. Regular, small sessions will keep you from feeling overwhelmed and help with retention.

3. Focus on English and Reading Comprehension

The English section isn’t just about knowing big words-it’s about understanding context, spotting the main idea, and quickly analyzing passages. The best way to improve is by reading regularly: pick up a newspaper, read novels, or explore online articles. Practice reading comprehension questions and work on vocabulary. The quicker you get at understanding passages and answering questions, the better you’ll do on exam day!

Also Read: How to prepare for clat

4. Stay Updated on Current Affairs

CLAT places a strong emphasis on Current Affairs and General Knowledge, so keeping up-to-date with recent events is crucial. Get into the habit of reading newspapers like The Hindu or The Indian Express to stay informed about national and international events, legal developments, and social issues. This will not only help you in CLAT but also broaden your understanding of the world-who knows, you might even win a trivia night with all that knowledge!

5. Practice Logical Reasoning

Logical Reasoning can sometimes feel like solving a puzzle-what seems tricky at first becomes easier the more you practice. This section typically involves questions on seating arrangements, puzzles, blood relations, and coding-decoding. Practice these types of questions regularly to improve your speed and accuracy. The more you solve, the more confident you'll become. Think of it as playing a game of strategy rather than just "study time."

6. Join an CLAT Coaching Program

If you find studying on your own challenging, consider joining an online CLAT coaching program. Many coaching platforms offer structured courses, expert guidance, and study materials to help you prepare more effectively. Plus, you’ll have access to live sessions, doubt-solving forums, and mock tests that will help you stay on track and boost your confidence.

7. Strengthen Legal Reasoning Skills

While you don’t need to be a legal expert for CLAT, the Legal Reasoning section tests your ability to apply legal principles to everyday scenarios. You don’t need a law degree for this—just practice. Start with sample questions and work through them. Understanding legal jargon and reasoning through various cases will sharpen your skills. It’s like preparing for a mini courtroom drama—without the wigs and robes.

8. Solve Previous Year Papers & Take Mock Tests

Once you’ve covered enough material, start solving previous year’s CLAT papers. This will give you a feel for the actual exam and help you get used to the question types and format. Taking regular mock tests will help you assess your strengths and weaknesses. Plus, you’ll improve your time management skills and learn how to handle the pressure of the real exam. Pro tip: Don’t get discouraged by a low score in your first few mocks-it’s all part of the learning process!

9. Work on Speed and Accuracy

CLAT is a time-bound exam, so speed and accuracy are essential. Practice solving questions within the time limits to improve both. Speed doesn’t mean rushing through questions-it’s about developing the ability to make quick decisions without sacrificing accuracy. Find your rhythm, so you’re not left scrambling for time at the end.

Checkout: NLSIU Bangalore

10. Stay Consistent and Take Breaks

Consistency is key! Stick to your study schedule, but also know when it’s time to take a break. Overloading your brain can be counterproductive. Take short breaks during study sessions-stretch, go for a walk, or just breathe for a few minutes. Your mind needs to recharge, and regular breaks will keep you energized and focused. Remember, it's about steady progress, not sprinting to the finish line.

11. Stay Positive and Confident

Finally, don’t stress out! Keep a positive attitude and trust the preparation you’ve put in. CLAT can seem daunting, but with the right strategy, you'll be ready. Don’t let one tough mock test or tricky question throw you off course. And if you're ever feeling overwhelmed, just remember: even the best lawyers started as students just like you.

#college #exams #study motivation #clat 2025 #clat preparation #clat coaching

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alxawesome · 2 months ago

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How I Committed (and Failed) a 30-Day Instagram Reels Challenge

Introduction: Setting the Stage

With Instagram pushing Reels as its main content feature, I decided to go all in on a 30-day Reels challenge. The plan was simple: post one Reel every day, complete with a behind-the-scenes follow-up for each video. I aimed for a mix of creative tricks, transitions, and humorous moments, all designed to tap into trending content and (hopefully) reel in some major engagement.

I have a pretty old Instagram account with 1185 followers. Most of them are “dead”. 8 years ago I used this account for mass following in the entrepreneurial niche. At that time I was thinking about starting a website development company. I had been killing my Instagram account for years, posting almost nothing.

I’ve always been passionate about making videos, both recording and editing.

I have made several videos at different times of my life. It seemed very time-consuming. I have a job (I’m a programmer), and I thought I wouldn’t have enough time for videography. I found this short video format reels-tiktok the perfect way to reignite my passion. You still need a strong idea and beautiful visuals for these videos, but you don’t have to create 20-minute films like a YouTuber.

Starting out, I had 1,185 followers, and my videos averaged around 400 views each — not exactly viral, but it was a steady base to build on. My realistic goal was to hit at least 2,000 followers by the end of the month, with a big, optimistic dream of breaking the 10,000-follower mark. Given the success stories I’d read from others, it seemed possible… or at least worth the try.

The second goal was to understand whether I liked videography enough to do it full-time as a part-time job.

Section 1: Why the Challenge Seemed Like a Good Idea

Instagram’s push for Reels was impossible to ignore. With the platform prioritizing short-form video to compete with TikTok, Reels had become the most visible and engaging format on Instagram. People were saying Reels could be the golden ticket to increased reach, with even smaller accounts suddenly landing thousands of new followers. I’d read success stories of creators gaining traction by posting consistently and creating content that played to Instagram’s strengths. Watching others succeed by riding the algorithm’s wave made me think: why not give it a try?

I found tips from Meta on how to grow your Instagram here https://creators.instagram.com/grow . The cornerstone was consistent posting. What could be more consistent than posting every day? Posting twice a day. I planned to post a Reel every day, plus a behind-the-scenes clip, in hopes of capturing both engagement and reach.

On a personal level, the challenge was exciting. It felt like a chance to not only grow my account but to experiment and level up my editing skills. I’ve always enjoyed getting creative with video, and I knew the challenge would push me out of my comfort zone. Plus, the thrill of potential growth was a huge motivator. If all went well, I could finally expand my audience, boost my engagement, and maybe even start doing partnership videos and making money.

Armed with optimism, I launched myself into the 30-day Reels challenge, eager to see where it would take me.

Section 2: The Plan and Execution

The plan was clear: one main Reel each day, followed by a quick behind-the-scenes (BTS) post to keep engagement rolling and give followers a glimpse into the process. I have selected over 30 ideas from Instagram that are somehow related to video editing.

To achieve this, I created a workflow that balanced filming, editing, and daily publishing.

A friend of mine has a YouTube blog and he sets aside Sundays for it. It’s a good approach.

So, Filming sessions would take up part of my Sundays. I would pick 7 videos from my list and set out to shoot them. Some of them were outdoors and I would shoot them first.

Some of the videos I edited on Sundays and some during next week in the evening.

I tried using trending audio for the video, but sometimes added sound effects such as ‘whoosh’ or keyboard ‘tapping’.

Producing daily content wasn’t as smooth as planned, though. Some days, creative blocks slowed me down; other days, technical issues — like lighting or editing glitches — added unexpected hurdles. There were also days when time was short, and producing a high-quality, trend-worthy Reel felt like an impossible task. But I kept at it, juggling my process and refining each step as I adapted to the reality of daily posting. It wasn’t always seamless, but I committed to hitting “post” every day.

Section 3: The Metrics and Reality Check

With 30 days of daily Reels and BTS posts under my belt, I was excited to see the results: follower growth, higher engagement, and a bump in video views. However, reality didn’t quite live up to my expectations.

After 30 days of non-stop posting, the results were underwhelming, to say the least. I stayed consistent, hitting “post” daily without fail, but instead of growth, I saw barely any traction. In the end, I gained just 20–30 new followers, and only a few videos saw any uptick in views beyond my usual numbers.

This challenge might technically be complete, but with such minimal impact, I can’t help but consider it a “fail” — though, as with all failures, there was plenty to learn along the way. Here’s what happened and why this challenge didn’t turn out quite as planned

My follower count is a far cry from my realistic goal of 2,000 or the ambitious dream of 10,000. Engagement didn’t soar as hoped either — while a few Reels did see slightly higher views than my usual numbers, the average engagement stayed about the same. Instead of exponential growth, I was looking at incremental increases that felt more like trickles than waves.

However, neither saw the reach I’d hoped to achieve through daily posting.

It’s tough to say why the challenge fell flat, but I suspect a few things might have contributed:

Weak first seconds of the video. It is too important to have a strong hook. Some of my videos lack it.

Algorithm unpredictability: Instagram’s algorithm is notoriously inconsistent, and despite daily posting and following trends, the platform didn’t seem to reward my account with any extra visibility.

Content saturation: With so many people creating Reels, especially around trending topics, it’s easy to get lost in the noise. Even unique ideas struggled to stand out.

Audience interest: It’s possible my existing followers weren’t as drawn to the daily Reels format or found the BTS updates less engaging than I anticipated.

In the end, the metrics were a reality check that consistent posting doesn’t always lead to big numbers, even when following algorithmic advice and platform trends.

While the hard data showed a lack of growth, the challenge still provided a new perspective on the unpredictable nature of social media, reinforcing that while consistency is important, it’s often only one piece of a much larger puzzle.

Section 4: Lessons Learned and Unexpected Outcomes

Although this 30-day challenge didn’t result in the follower growth I’d hoped for, it was far from a total loss. In fact, diving into daily content creation taught me more about social media — and myself — than I could have anticipated.

Probably the main thing I’ve learned is that I want to keep making videos. I should choose a niche and make my videos more meaningful and valuable.

Consistency Isn’t a Magic Bullet One of the biggest takeaways was that consistency alone doesn’t guarantee results. While consistent posting helps build a routine, it’s not necessarily rewarded with instant growth, especially when the algorithm remains unpredictable. In the future, I’d balance consistency with more in-depth planning for content that aligns closely with what my audience wants to see.

The Power of Experimentation Posting daily meant I had the freedom to experiment with different content formats, styles, and editing tricks. Some of these experiments, like https://www.instagram.com/reel/DBqZ3juI8X-/ , had better-than-average engagement and may serve as a base for future content ideas.

Managing Creative Burnout The grind of producing daily content brought me face-to-face with creative burnout. I quickly realized that creativity can’t always be forced on a strict schedule. This experience taught me to pace myself and respect my creative process, balancing fresh ideas with realistic production timelines to avoid burnout.

Taking Pressure Off the Algorithm Finally, I realized the importance of creating content for my own enjoyment and improvement, rather than constantly trying to hack the algorithm. Instagram’s algorithm changes frequently, and trying to keep up can easily overshadow creativity. Now, I’m more focused on creating content that feels authentic rather than stressing about instant growth.

Section 5: Final Thoughts and Advice

After 30 days of pouring time and effort into daily Reels, my takeaway is this: growth on social media is anything but straightforward. Despite doing everything “by the book” — posting consistently, following trends, and keeping up with the latest algorithm tips — the results didn’t match my initial expectations. But in the end, this experience highlighted the fact that the journey itself holds its own value, even if the outcome isn’t what we envision.

If you’re thinking of diving into a similar challenge, here are a few things I’d suggest:

#1 Focus on Quality Over Quantity

While it’s tempting to think daily posting will boost growth, your audience will remember the quality of your content more than your posting frequency. Aim to create Reels that you’re genuinely excited to share, even if that means posting a bit less often.

The Meta article says you should post 10 reels a month to have a better chance of being recommended. That’s about 3 videos a week. That’s not a lot. But there are a lot of very popular accounts that post less frequently.

#2 Experiment and Take Risks

A challenge like this is the perfect opportunity to experiment with styles, topics, or editing techniques. Don’t be afraid to take creative risks; you might stumble upon something that resonates strongly with your audience — or that you personally enjoy making.

#3 Expect the Unexpected with the Algorithm

Remember that the Instagram algorithm is unpredictable, and growth isn’t guaranteed even with consistent posting. Focusing on what you can control — like connecting with your audience or producing unique content — will help you get more satisfaction from your efforts, algorithm or not.

#4 Don’t Let the Numbers Define Success

Followers, views, and likes are just one measure of success. A low view count doesn’t negate the creative growth, new skills, or connections you might gain along the way. Consider what success means to you beyond metrics.

#5 Be Kind to Your Creative Process

Conclusion: Why This “Failed” Challenge Wasn’t a Total Loss

Although I didn’t hit the ambitious follower goals I set for myself, this 30-day Instagram Reels challenge turned out to be anything but a failure. In fact, the experience taught me valuable lessons that numbers alone could never capture.

Through daily content creation, I gained a clearer sense of what the audience enjoys, honed my editing and creative skills. While I may not have achieved viral fame, I did gain a sense of satisfaction from pushing myself, trying new things, and seeing just how far my ideas could go.

Perhaps most importantly, this challenge reshaped my approach to social media. I’m now less fixated on quick follower counts and more focused on sustainable, quality content that aligns with what I genuinely enjoy creating. Now I have a plan and next steps. I will choose a niche, I will publish videos less often, but I will make more meaningful and qualified videos.

By embracing the process over the outcome, I learned that “failure” in terms of follower count doesn’t mean the journey was a waste. On the contrary, it revealed strengths, weaknesses, and opportunities for growth that I wouldn’t have found any other way.

Ultimately, this “failed” challenge reminded me that numbers are only part of the story. The real reward lies in the skills I developed, the resilience I built, and the perspective I gained.

Follow me for the further journey https://www.instagram.com/shogentle

#instagram #instagram reels #30 day challenge #video editing #videography #video

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shraddhamatre · 3 months ago

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How to Pass the GATE XE Exam: Succeed with Tried-and-True Methods.

The GATE XE (Engineering Sciences) paper is a rare chance for students hoping to get admission to esteemed universities such as IITs and IISc. GATE XE gives students the opportunity to focus on just two essential technical courses, providing a quicker route to success than other GATE examinations that call for attention to several disciplines. To pass the test, though, you must have the appropriate approach. This is a thorough tutorial that will help you ace the GATE XE exam.

1. Understand the GATE XE Paper Structure The GATE XE exam consists of three sections: Section A (Engineering Mathematics): This is a compulsory section and accounts for 15% of the total marks. Section B (Optional Subjects): You can choose two subjects from seven options: Fluid Mechanics (XE-B), Materials Science (XE-C), Solid Mechanics (XE-D), Thermodynamics (XE-E), Polymer Science and Engineering (XE-F), Food Technology (XE-G), and Atmospheric and Oceanic Sciences (XE-H). Each of these sections makes up 70% of the total marks. General Aptitude (GA): Common to all GATE papers, the GA section contributes 15% to the total score. Before you begin preparation, thoroughly understand the syllabus and exam pattern. This will help you tailor your study plan according to the subjects that best align with your strengths.

2. Pick the Appropriate Optional Subjects The elective courses you select might have a big impact on your grade. Choose topics in which you are deeply interested and where your foundation is solid. Although it may be tempting to select courses that seem simple, it is better to select those where, considering your knowledge and experience, you may achieve good marks. For example, select Fluid Mechanics or Thermodynamics if you have a solid foundation in these areas for targeted preparation.

3. Create a Targeted Study Schedule A well-organized study schedule is necessary to effectively cover the extensive material. Divide the time you spend studying into three parts:

Phase of Foundation: Establish a solid foundation in Engineering Mathematics and the electives you have selected. To dispel uncertainties, start by comprehending the principles and then go through instances. Practice Phase: After you understand the fundamentals, start answering questions from past exams and practice exams. This will assist you in acclimating to the exam's format and comprehending the degree of difficulty of the questions. Final Phase: Edit your notes and pay attention to any areas that need improvement. Make sure you spend enough time studying engineering mathematics because it's essential to getting good grades.

4. Complete the Question Papers from Last Year Solving last year's question papers is one of the finest strategies to be ready for GATE XE. You'll get a sense of the key subjects and question trends by practicing. Additionally, it will help you become more adept at managing your time throughout the test. You can become more used to the diversity and degree of difficulty of the questions with regular practice. You may focus your preparation by identifying recurring themes that come up year after year.

5. Stress accuracy and time management GATE XE assesses not just your knowledge but also your time management and accuracy skills. Multiple-choice (MCQ), multiple-select (MSQ), and numerical answer type (NAT) questions are all included in this study. Each of these requires a different approach:

For MCQs, ensure you avoid negative marking by answering only those you are sure about. For NAT questions, practice mental calculations to avoid wasting time on lengthy calculations. MSQs have no negative marking, but don’t rely on guesswork as all options need to be correct for full marks.

6. Make Use of Test Series and Online Resources For those preparing for the GATE XE, online platforms offer good information. Through forums, practice exams, and video lectures, they can help you get your questions answered and stay up to speed on the most recent exam trends. Participating in a test series is very helpful as it allows you to experience an exam in real time and helps you discover your areas of weakness.

7. Remain Motivated and Consistent When studying for a competitive test such as the GATE, consistency is essential. Maintain a consistent study schedule, evaluate your progress frequently, and maintain your motivation as you become ready. Join study groups or online discussion boards to talk with other candidates about your questions and share your expertise. This can help you stay motivated in addition to helping to clarify topics.

8. Take Care of Your Health and Well-Being Lastly, remember to look after your physical and emotional well-being. You may maintain your attention while preparing by taking regular pauses, getting enough sleep, and eating a healthy diet. Exam preparation sometimes causes stress and worry, but keeping a calm head helps improve performance and memory.

In summary Strong principles, regular practice, and a strategic approach are all need to pass the GATE XE exam. You may pass the GATE XE test and get into a prominent university by concentrating on the proper subjects, finishing last year's exams, spending your time wisely, and keeping an optimistic outlook.

Start Your Preparation With: https://gameacademy.in/ / https://clppenny.page.link/cTBm

Recommended: https://www.youtube.com/@gblions / https://www.youtube.com/@gblionsaeje

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madhukumarc · 1 year ago

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What is the quick step to get more followers for my social media platform?

Getting more followers on your social media platform is a common goal for many individuals and businesses.

While there is no magic formula that guarantees instant success, here are the most important steps you can take to boost your follower count.

In fact, these are the secrets of successful social media players.

So, Let's dive into the World of Social Media Growth:

1. Define Your Target Audience:

Before you start chasing followers, it's essential to understand who you want to attract.

Identifying your target audience will help you tailor your content and messaging to resonate with them.

Additionally, it saves you time and energy so that you can get the most out of your work.

Booster insight - “Your audience will grow naturally if you put effort and time into creating engaging, informative, or inspirational content without worrying about "quick fixes" for boosts in followers” – HubSpot

2. Optimize Your Social Profile:

Your profile is the first impression people get of your social media platform. Make sure it's complete, compelling, and showcases what your platform is all about.

Use a professional profile picture, write a catchy bio [show what value you can offer or how you can benefit your visitors or followers], and include keywords and relevant links.

3. Consistency is Key:

To attract and retain followers, you need to consistently deliver high-quality content that resonates with your target audience.

Develop a content strategy that aligns with your brand and post regularly.

Experiment with different types of posts, like images, videos, quotes, and articles, to keep your feed interesting.

4. Engage with Your Audience:

Social media is all about building relationships. Respond to comments, messages, and mentions promptly.

Engage in conversations with your followers and show them that you value their input.

The more you interact with your audience, the more likely they are to become loyal followers.

“Today, it’s not enough to have social media followers and blast out content to them. To have ongoing and meaningful engagement with their target audience, businesses need to build a community” – Business

5. Utilize Hashtags Strategically:

Hashtags are a powerful tool for increasing your reach and visibility on social media platforms.

Research relevant hashtags in your industry and include them in your posts strategically.

Avoid using too many hashtags or irrelevant ones, as it can make your posts appear spammy.

6. Collaborate with Influencers:

Partnering with influencers in your niche can help expose your social media platform to their established audience.

Look for influencers whose values align with yours and reach out to them for collaborations or shout-outs. This can significantly boost your follower count.

“Big audiences don’t always mean high engagement. Be sure your influencers of choice are able to really connect with their followers. This audience must also have an interest in your niche” – Semrush

7. Cross-Promote on Other Platforms:

Leverage the power of cross-promotion by sharing links to your social media platforms on other channels such as your website, blog, email newsletter, and other social media profiles.

Encourage your existing followers to follow you on other platforms as well.

8. Use Paid Advertising:

If you're looking for a quick boost in followers, consider investing in paid advertising on social media platforms.

Platforms like Facebook and Instagram offer targeted advertising options that allow you to reach a specific audience based on demographics, interests, and behaviors.

Remember - “A social media post's comment section is a lens into what your followers care about and what content of yours interests them” – PLANOLY

Social Media Content based on Audiences Needs and Interests:

Image Content Source: Social Trends 2024 Report by Hootsuite

Pro-Tip: To increase discoverability, reach, and engagement, apply SEO strategies tailored to the specific social media platform.

In summary, gaining followers takes time and effort. It's important to stay committed and consistently implement these strategies to see results.

Keep analyzing your metrics, experimenting with different tactics, and adjusting your approach accordingly. With perseverance and a solid strategy, your follower count will start growing organically!

Here's related information that you may also find helpful – Social Media Marketing Statistics [Social is the new powerful].

#social media followers #social media growth #social media marketing #social media platforms

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rakneodesigns-blog · 1 year ago

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How to make money on YouTube Without Recording Videos

Recently I wrote a blog post about affiliate marketing, In this blog post I'm going to show you how you can make money on YouTube without recording videos All I want to do is, share my journey of building an affiliate marketing business online and an online business around. I am planning to do thid step by step transparently with you all in real-time. .So keep in touch with me to see how it goes

1. Choosing a Niche

The first thing you need to do before starting a YouTube channel is pick your niche. Now if you choose a topic that you're passionate about, it would be great, because you are going to create content consistently on a daily basis for a long time to make money on YouTube without recording videos. There are a few best niches that you can pick from, They are: - Health - Wealth - Relationships - Happiness - Finance but you need to narrow down these niches because all these niches are huge. It would be too big to pick a niche like this and it would be hard for you to rank for these keywords. so you need to find something more narrowed down and more specific. for example, if you don't have any idea, this is how you are going to research for a niche

2. Researching a Niche

Steps to Follow: First, go to Google Trends and then select explore, and then type whatever search term you want. make sure you change the country to either worldwide or the United States.

Filter Options - for this, I will use the United States and make sure it is set for at least 12 months or you can go even further. As categories, you can select any of these factors as you wish and search for any idea that you have or any niche that you want to pursue. - for now, I would just search for personal finance - let's see what the trend for personal finance search term. You will get the data of people who searched this term in the United States

Analyzing the trend results

See this trend is quite stable overall, it stays around 75 which is a good number. At the top you get the trend then you can check other details like from which states and how many searches are made. You will also get related topics to the keyword you searched. All the related topics they show are rising, so there are opportunities for you to get traffic with these terms.

Additionally, they display queries. You can analyze what kinds of questions people ask related to your search term. For instance, you can see here a query “best-selling personal finance guru”. Therefore, if you create content around these queries, there's a good chance for you to get significant traffic and views for your videos.

Using Prpmoterkit for Niche Research

Another tool that you can use is Promoterkit, Go to promoterkit.com it's totally free you can sign up for a free account and then just log in since I've already logged in and signed up for it, I'll just log in. This website has 30+ free tools that you can use for marketing and keyword research. I'm gonna choose Keyword Research Tool for now Here you type your keyword and see how it goes. Let's check personal finance for now. and choose the language English and country to the United States. make sure you tick I'm not a robot first, before clicking the find button.

The number of people who searched personal finance is around 2,900 searches and paid competition is about 10 and CPC is around 2.44. It also shows other keywords that you can get content ideas from. For personal finance, you can see the CPC, the amount that you can earn, and the trend this search term has. You can see whether the trend has been stable or has it gone up, or down. You can check everything here for free. Choose whatever topic you wanna create content around and on this platform, you get to perform keyword research about 20 times per day, so that's more than enough for you to do keyword research. That's how you come up with a niche that you wanna pursue

Factors to Consider When Selecting the Niche

Just picking a niche because you're passionate about it wouldn't be a sustainable strategy. You need to consider three things before picking a niche, - That niche should have videos related to the niche topic that get at least a hundred thousand views consistently. - The niche should have a high CPM. CPM means the amount of money you earn per thousand views. - The niche should also have affiliate products or digital products that you can promote and earn revenue in addition to the revenue you earn from your views. if you can tick all three factors in the niche you have chosen, you are ready to go

Example of YouTube Channels that Make Money on YouTube Without Recording Videos

Check out this channel called Body Hub, It's creating content under the health and fitness niche and you can see that they have just made these videos using stock videos

This channel called Natural Cures creates content on the health niche. They are also doing the same thing as the YouTube channel above, they've got 3.4 million subscribers already.

The next channel called Chris Invests, has 134,000 subscribers already. They create videos from a software called Videoscribe.

3. Creating the YouTube Video to make money on YouTube Without Recording Videos

After you decide which niche you wanna create content around, and your specific topic for the video, the next thing you gotta do is create your own unique YouTube script. If you find it difficult to create a YouTube script by yourself, you can use the chat GPT to get some ideas and get some help to create one. You could also outsource these services from platforms like Fiverr, and Upwork. After creating a script, you just have to go to websites like Pixabay.com or Pexels.com and then download the relevant stock videos for your video. After that, all you have to do is compile these videos using free video editing software like Capcut, Hitfilm Express, or Openshot. Then get a voice-over of reading the script you have created. If you can do it by yourself that will be ideal, if you find it difficult, you can always outsource that part of creating a voice-over by outsourcing the task to freelancers. Finally, you gotta put them all together and create your video. These are some niches that have high CPMs and that you could try: - Health - Travel - Technology - Finance - Wealth - Happiness - Relationships If you want to learn more about how to make money on YouTube without recording videos, I got a free training for you. Read the full article

#Earnmoneyonline #makemoneyonline #wealth #youtube

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my-music-viral · 1 year ago

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Nine Ways To Get More Soundcloud Followers https://ift.tt/WC8O3kY Alright, let’s get down to bottom dollars here – you’re here because you want to make it big on SoundCloud, right? A gang of followers, your music ringing in everyone’s ears and pockets fat. Well, it can be done, just check out the Soundcloud musicians who added at least 100K followers in only 30 days: Rank Musician Followers gained in 30 days First signed up on SoundCloud 1 Sub Urban 1,000,000 2014 2 The Kid Laroi 750,000 2017 3 Lil Nas X 500,000 2015 4 Lil Tjay 450,000 2018 5 Juice WRLD 400,000 2015 6 XXXTentacion 350,000 2014 7 Roddy Ricch 300,000 2016 8 Lil Baby 250,000 2017 9 NBA YoungBoy 200,000 2015 10 DaBaby 150,000 2015 But how do you get there? How do you increase your Soundcloud followers? 1. Music, Music, Music Listen, the first rule of thumb is pretty simple – your music is your meal ticket. No two ways about it. If you’ve got great music, people are gonna listen. Period. The likes of Billie Eilish and Post Malone all started from SoundCloud – they had great music and it showed. Ellish was an absolute beast, releasing more than a track per month in 2021. Now look at her: Yeah, look at her now! Cash like confetti! 2. Make Your Profile a Powerhouse Now, hold on, great music ain’t enough. People have to not just know you, they need to know you. And how do they get to know you? Your profile. Make your profile interesting. A great pic, a revealing bio, and links to your other platforms – all these add up to engagement. Read up on our profile tutorial in our How To Get More Plays On SoundCloud article. 3. Regular Music Means Regular Fans Consistency. Yeah, I know it sounds like a chore. But, hey, it’s crucial. Release your tracks regularly. Keeps your fans on their toes, you know. Plus, it shows you’re serious about your craft. That’s what we’re saying, Miss Cortez! 4. Get Chummy with Others SoundCloud ain’t a lonely place. It’s buzzing with artists, listeners, and music lovers. So, why not make some friends? Comment on others’ tracks. Share your favorites. Collaborate with other artists. Who knows, you might just gain some of their followers too. 5. Hashtags Are Your Friends Now, I know what you’re thinking. Hashtags? Really? But trust me, they’re more than just a trend. They’re like breadcrumbs leading people to your music. So, pick your hashtags wisely. Make them relevant. 6. Expand Your Territory SoundCloud is great and all, but don’t forget about the rest of the internet. Promote your music on other platforms. Take Instagram, for instance. Did you know that you could share your SoundCloud links on your Instagram stories? Now you know. We taking territory, dog! 7. Consider SoundCloud’s Paid Promotion Tools Now, here’s a tricky bit. SoundCloud has some paid promotion tools, like Next Plus. It might feel like a bit of a stretch, but sometimes, you gotta spend money to make money, you know? 8. Talk to Your Fans And lastly, don’t forget to engage with your fans. Thank them for their comments, share their posts, and appreciate their likes and shares. Remember, these folks are your audience. Treat ’em right, and they’ll keep coming back. 9. Buy Soundcloud Plays & Look Legit Now Buying SoundCloud plays can create a sense of credibility and popularity by quickly boosting your play count. When other users see a high number of plays on your tracks, they’re more likely to give your music a chance. Alright, there you have it. Nine steps to get you more followers on SoundCloud. But remember, it ain’t gonna happen overnight. It’s a marathon, not a sprint. Stay patient, keep pushing, and let your music do the talking, you do a lot of talking yourself and you will get more followers. The post Nine Ways To Get More Soundcloud Followers appeared first on My Music Viral. via DJ Rede Beats – My Music Viral https://ift.tt/4MewjbS August 12, 2023 at 07:02PM

— Nine Ways To Get More Soundcloud Followers https://ift.tt/WC8O3kY Alright, let’s get down to bottom dollars here – you’re here because you want to make it big on SoundCloud, right? A gang of followers, your music ringing in everyone’s ears and pockets fat. Well, it can be done, just check out the Soundcloud musicians who added at least 100K followers in only 30 days: Rank Musician Followers gained in 30 days First signed up on SoundCloud 1 Sub Urban 1,000,000 2014 2 The Kid Laroi 750,000 2017 3 Lil Nas X 500,000 2015 4 Lil Tjay 450,000 2018 5 Juice WRLD 400,000 2015 6 XXXTentacion 350,000 2014 7 Roddy Ricch 300,000 2016 8 Lil Baby 250,000 2017 9 NBA YoungBoy 200,000 2015 10 DaBaby 150,000 2015 But how do you get there? How do you increase your Soundcloud followers? 1. Music, Music, Music Listen, the first rule of thumb is pretty simple – your music is your meal ticket. No two ways about it. If you’ve got great music, people are gonna listen. Period. The likes of Billie Eilish and Post Malone all started from SoundCloud – they had great music and it showed. Ellish was an absolute beast, releasing more than a track per month in 2021. Now look at her: Yeah, look at her now! Cash like confetti! 2. Make Your Profile a Powerhouse Now, hold on, great music ain’t enough. People have to not just know you, they need to know you. And how do they get to know you? Your profile. Make your profile interesting. A great pic, a revealing bio, and links to your other platforms – all these add up to engagement. Read up on our profile tutorial in our How To Get More Plays On SoundCloud article. 3. Regular Music Means Regular Fans Consistency. Yeah, I know it sounds like a chore. But, hey, it’s crucial. Release your tracks regularly. Keeps your fans on their toes, you know. Plus, it shows you’re serious about your craft. That’s what we’re saying, Miss Cortez! 4. Get Chummy with Others SoundCloud ain’t a lonely place. It’s buzzing with artists, listeners, and music lovers. So, why not make some friends? Comment on others’ tracks. Share your favorites. Collaborate with other artists. Who knows, you might just gain some of their followers too. 5. Hashtags Are Your Friends Now, I know what you’re thinking. Hashtags? Really? But trust me, they’re more than just a trend. They’re like breadcrumbs leading people to your music. So, pick your hashtags wisely. Make them relevant. 6. Expand Your Territory SoundCloud is great and all, but don’t forget about the rest of the internet. Promote your music on other platforms. Take Instagram, for instance. Did you know that you could share your SoundCloud links on your Instagram stories? Now you know. We taking territory, dog! 7. Consider SoundCloud’s Paid Promotion Tools Now, here’s a tricky bit. SoundCloud has some paid promotion tools, like Next Plus. It might feel like a bit of a stretch, but sometimes, you gotta spend money to make money, you know? 8. Talk to Your Fans And lastly, don’t forget to engage with your fans. Thank them for their comments, share their posts, and appreciate their likes and shares. Remember, these folks are your audience. Treat ’em right, and they’ll keep coming back. 9. Buy Soundcloud Plays & Look Legit Now Buying SoundCloud plays can create a sense of credibility and popularity by quickly boosting your play count. When other users see a high number of plays on your tracks, they’re more likely to give your music a chance. Alright, there you have it. Nine steps to get you more followers on SoundCloud. But remember, it ain’t gonna happen overnight. It’s a marathon, not a sprint. Stay patient, keep pushing, and let your music do the talking, you do a lot of talking yourself and you will get more followers. The post Nine Ways To Get More Soundcloud Followers appeared first on My Music Viral. via DJ Rede Beats – My Music Viral https://ift.tt/4MewjbS August 12, 2023 at 07:02PM

#IFTTT #DJ Rede Beats – My Music Viral

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hoodssery · 1 year ago

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Recommending Video Essays #1

I am currently sick and dealing with the constant bodily betrayal of having a chronic illness. This means I don't really have much to do (or much I can do comfortably), and am doing what little things I can and distracting myself with games like Battlefield and Arma 3 King of the Hill and video essays playing in the background of 24/7 Operation Locker servers.

Video essays are a pretty popular format for YouTube considering the design of all large scale social media platforms centers around a short attention span, click more things kind of philosophy that seems to be doing very well in the modern digital age. More than that video essays on all kinds of topics seems to be becoming the new form documentary is taking, with a notable amount of video essays even taking up the title of “documentary”.

But, that doesn't really matter because I'm sure you all can agree that what really matters is someone with 12 subscribers and no followers telling you what's good content to shove into your sensory holes that come standard with every human being.

The style and format of certain Youtubers have dramatically shaped not just my content, but the way I consume all media now. That's a pretty big deal because at the end of the day most of these people are schmucks like me who sit in their house, play video games, and say what's right and wrong. Even weirder is that I don't even know these people!

So in honor of these fellow weirdos, and because I don't have anything much better to do, I'm gonna recommend a video essay everyday of me being sick. And who knows, maybe I'll even manage find the little will it takes to do this less frequently after I can move more than 100 feet at a time.

Recommendation: God of War - Almost a Masterpiece by Joseph Anderson (https://youtu.be/pJPOvLvdugw)

POTENTIAL SPOILERS AHEAD FOR BOTH God of War (2018) AND JOSPEH ANDERSON'S VIDEO

youtube

For most of these people I will be recommending you may have already heard of them, or seen videos from them, especially if you're traveling in obscure enough circles to find these posts. Joseph Anderson may or may not be one of these people. I can say from first hand experience that for a long time, I only recognized his profile picture of an illustration of a Dragon from the cover of his book The Wizard and the Dragon. While I can't speak for his talent and merit as an author of fantasy novels, Joseph Anderson is someone whom I greatly respect for their work as a critic of video games. He has had a very consistent voice for gaming since his first his first set of five videos entitled Dark Souls Critique – Part One – Five. Since then Joseph Anderson has consistently gotten better at producing and making these essays, with his best work being multi-hour, in-depth critiques of games ranging from Super Mario Odyssey, to Bloodborne.

However, the one that I find is his best, and the one that I would recommend to people who haven't watched his videos before is God of War – Almost a Masterpiece. This is the video where Joseph Anderson's methodical, long form breakdown of video games shines through the most. Despite covering almost every facet of the game over just about three hours flat, the video remains very focused on his view of the narrative that is set up for later games in this new God of War series. Even when he does stray from the path of the narrative, it's never for nothing and provides a more interesting insight into the gameplay, open world, structure of side quests, and comparisons to the other game's stories and gameplay than I've seen from anyone else covering this game.

Joseph Anderson is a huge inspiration for me. There is very little waste in his videos, and what waste there is always is providing some information, even if it's redundant or excessive. He is someone I honestly cannot recommend enough even if he's never gonna finish that Witcher 3 video.

Other recommendations from his channel:

-The Villian of Edith Finch, a video I would recommend if you don't have time to watch a movie length video. (https://youtu.be/6bMn4CoyUkM)

-A Critique of SOMA, another short one. (https://youtu.be/J4tbbcWqDyY)

-Super Mario Odyssey – It's no Masterpiece, is a video I don't agree with, but is also a video where his arguments about the game make me analyze my own position on the game. In my opinion, the sign of a good critique. (https://youtu.be/kYJx5xt2cB0)

#joseph anderson #video games #video essay #god of war #reccomendation #please tell me if there are any errors in my writing thanks <3

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stepphase · 2 years ago

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How to make your social networks profitable

Your internet profiles can earn you a lot of money. You just have to know how to amortize each 'post' posted. Here we tell you.

If you are dying of envy that bloggers rake in exorbitant amounts of money for their posts, it's time you took action. You don't need to be like Dulceida or Chiara Ferragni to monetize your social networks. It will be enough for you to know how to get likes or follows. Take note and get ready to fatten up your piggy bank.

TEAM UP WITH BRANDS

Do you want the big badges of fashion and beauty to share their money with you? Well, you are going to need a lot of fans. "You will have to have at least 50,000," says Paloma Miranda, director of Go Talents, an influencer marketing agency specializing in Instagram. To reach that number, the main thing, according to the expert, is "to maintain an identity, make it clear what you want to convey, be consistent and create quality content." That's what the influencer and youtuber Grace Villarreal did, which brings together half a million followers: "I started uploading songs that I liked, very careful, and that I thought could be interesting for people," he says.

Micro-influencers accounts generate more trust.

But also, you must know your demographic 'target'; that is, knowing the city and country where your audience lives (firms value this a lot). And that the list grows day by day or at least is maintained. A good option to increase your followers is to locate a page that already has many and publishes topics similar to yours, and connect with them. The next step is to ask the brands you want to attract to include you on their email list and, of course, attend their events. You will have them at your feet if you create content that suits their style and tag them in your posts. "The money comes after investing a lot of personal sacrifice and hours of work," clarifies Grace Villarreal. And what you win will depend on your negotiating skills.

PRACTICE AFFILIATE MARKETING

If you have between 5,000 and 10,000 followers, affiliate marketing is your thing, because micro-influencers' accounts (yes, you are one of them) generate greater trust and their 'engagement' (the degree of interaction and involvement of their followers) is high.

It basically consists of linking your client's article in your posts and, if someone enters and buys it, you get a percentage of the sale (around 2%). Platforms like Octoly, Brantube or Coobis work in a similar way. “You sign up and then firms like Yves Saint Laurent or Clarins send you their products for you to review; They do not offer remuneration, but if they like your profile, they can sign you as an ambassador. Then, it's easy for them to offer you paid collaborations », explains Macarena Conde, manager of Octoly .

LEARN THE VALUE OF ENGAGEMENT

"The 'engagement' rate is calculated based on the likes and comments of your fans - Macarena Conde says. If you have only 1,000, but your publications get 200 comments, for brands it is a sign that you are nailing it. Those who pay attention to this a lot are online marketing agencies, such as Takumi or Brandnew, where you sign up for a firm's campaign and they pay you in exchange for uploading a photo with their hashtag. To increase your 'engagement', answer all the opinions. The more you interact, the easier it will be for you to appear in the search section of Instagram, the perfect place to progress.

SELL YOUR PHOTOS AND VIDEOS TO COMPANIES

Thanks to pages like foap.com , you can upload your photos and sell them. You just have to put the label they tell you (if it is food, for example #foodporn) and they will use it to support their clients' campaigns. The price is 8 euros and the web is half.

If yours are videos, YouTube gives you the possibility to monetize them (for this they must be sponsored): every time someone watches or clicks them, they will make you add coins (1 euro for every thousand views). Experts believe that a successful channel is one that is constant, has quality and knows how to differentiate itself (lately those of books have been successful). And most importantly, the one that gives the visitor a reason to return. Grace Villarreal knows this well: «In my case, the videos that I like the most are those in which I show my daily life, my routine. Followers love to identify with you, see that you are real and that you do not have a perfect life. Perfection is boring.

KEYS TO MAKING YOUR ACCOUNT PROFITABLE

KEEP YOUR IDENTITY

"Do not abandon your essence, be constant and keep in mind that excessive advertising - especially if it is not truthful - makes followers lose their attention," says Daniela C. Rodríguez, from Soy Olivia, an agency for new digital trends.

GOODBYE TO PRIVACY

“Brands and users must be able to access all the content you upload. Therefore, your profile must be public. Nor is it advisable to continually privatize and reopen your account ”, says Paloma Miranda, director of Go Talents.

BE CONSISTENT

"It is necessary to maintain a balance in your publications: you cannot disappear for two weeks and then post 20 photos in a row," insists Miranda. "One or two images a day works well on Instagram, and posting at 9pm is the best option."

DON'T CHANGE YOUR PHOTO

“People usually recognize accounts more from photographs than from names. If you change your profile picture often, you may confuse your followers and they will abandon you, "says the expert on Instagram.

Wrap Up

In Conclusion, You will get money online sitting at home, but my friends are not going to get such money just by sitting. You have to work hard for this. And yes, after a lot of hard work, it will definitely be rewarded. Always remember this thing.

So these are some of the best ways to earn money. In addition, The ways are in front of you and you also have time. If so let’s decide one way or method and do the work that you like. And yes…If you liked this post of our How to make your social networks profitable, then definitely comment and share this post. Furthuremore, if you also have some good ideas of earning money online, then do let us know through a comment. And tell us that which way you liked and easy. Thank You…!!!

Also Read more amazing

Earn Thousands per Post - Easiest way to Make Money Online with Instagram

How to get Followers on Instagram? Basic rules for a Successful Profile

Easy Online Jobs From Home - Completely Free With Zero Investment

#Stepphase #technologies #technology #tech #technews #techworld #techtrends #smartphone #apple #techupdates #futuretechnology #newtech #techgeek #technologynews #technologythesedays #smarttechnology #technologylover #technologytrends #technologyblog #gadgets #smartphone #gadget #marketing #digital #india #technologyisawesome #amazing #repost

#earn_money #make_money #SOCIAL_NETWORKS

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snickerdoodlles · 1 year ago

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AO3 and Generative AI

1. AO3 wasn’t used to train generative AI.

Or at least not anymore than any other internet website. AO3 was not deliberately scraped to be used as LLM training data.

2. A/B/O is not polluting generative AI

3. Individuals cannot affect LLM training datasets.

So back to the fandom event, with the stated goal of sabotaging AI scrapers via omegaverse fic.

…It’s not going to do anything.

Let’s add some numbers to this to help put things into perspective:

LLaMA’s 65 billion parameter model was trained on 1.4 trillion tokens. Of that 1.4 trillion tokens, about 67% of the training data was from the Common Crawl (roughly ~3 terabytes of data).

3 terabytes is 3,000,000,000 kilobytes.

That’s 3 billion kilobytes.

So 1,012 out of 3,000,000,000.

Aka 0.000034%.

And that 0.000034% of 3 billion kilobytes is only 2/3s of the data for the first stage of training.

Generative AI for Dummies

(kinda. sorta? we're talking about one type and hand-waving some specifics because this is a tumblr post but shh it's fine.)

So there’s a lot of misinformation going around on what generative AI is doing and how it works. I’d seen some of this in some fandom stuff, semi-jokingly snarked that I was going to make a post on how this stuff actually works, and then some people went “o shit, for real?”

So we’re doing this!

This post is meant to just be informative and a very basic breakdown for anyone who has no background in AI or machine learning. I did my best to simplify things and give good analogies for the stuff that’s a little more complicated, but feel free to let me know if there’s anything that needs further clarification. Also a quick disclaimer: as this was specifically inspired by some misconceptions I’d seen in regards to fandom and fanfic, this post focuses on text-based generative AI.

This post is a little long. Since it sucks to read long stuff on tumblr, I’ve broken this post up into four sections to put in new reblogs under readmores to try to make it a little more manageable. Sections 1-3 are the ‘how it works’ breakdowns (and ~4.5k words total). The final 3 sections are mostly to address some specific misconceptions that I’ve seen going around and are roughly ~1k each.

Section Breakdown: 1. Explaining tokens 2. Large Language Models 3. LLM Interfaces 4. AO3 and Generative AI [here] 5. Fic and ChatGPT [here] 6. Some Closing Notes [here] [post tag]

First, to explain some terms in this:

“Generative AI” is a category of AI that refers to the type of machine learning that can produce strings of text, images, etc. Text-based generative AI is powered by large language models called LLM for short.

(*Generative AI for other media sometimes use a LLM modified for a specific media, some use different model types like diffusion models -- anyways, this is why I emphasized I’m talking about text-based generative AI in this post. Some of this post still applies to those, but I’m not covering what nor their specifics here.)

“Neural networks” (NN) are the artificial ‘brains’ of AI. For a simplified overview of NNs, they hold layers of neurons and each neuron has a numerical value associated with it called a bias. The connection channels between each neuron are called weights. Each neuron takes the sum of the input weights, adds its bias value, and passes this sum through an activation function to produce an output value, which is then passed on to the next layer of neurons as a new input for them, and that process repeats until it reaches the final layer and produces an output response.

“Parameters” is a…broad and slightly vague term. Parameters refer to both the biases and weights of a neural network. But they also encapsulate the relationships between them, not just the literal structure of a NN. I don’t know how to explain this further without explaining more about how NN’s are trained, but that’s not really important for our purposes? All you need to know here is that parameters determine the behavior of a model, and the size of a LLM is described by how many parameters it has.

There’s 3 different types of learning neural networks do: “unsupervised” which is when the NN learns from unlabeled data, “supervised” is when all the data has been labeled and categorized as input-output pairs (ie the data input has a specific output associated with it, and the goal is for the NN to pick up those specific patterns), and “semi-supervised” (or “weak supervision”) combines a small set of labeled data with a large set of unlabeled data.

For this post, an “interaction” with a LLM refers to when a LLM is given an input query/prompt and the LLM returns an output response. A new interaction begins when a LLM is given a new input query.

Tokens

Tokens are the ‘language’ of LLMs. How exactly tokens are created/broken down and classified during the tokenization process doesn’t really matter here. Very broadly, tokens represent words, but note that it’s not a 1-to-1 thing -- tokens can represent anything from a fraction of a word to an entire phrase, it depends on the context of how the token was created. Tokens also represent specific characters, punctuation, etc.

“Token limitation” refers to the maximum number of tokens a LLM can process in one interaction. I’ll explain more on this later, but note that this limitation includes the number of tokens in the input prompt and output response. How many tokens a LLM can process in one interaction depends on the model, but there’s two big things that determine this limit: computation processing requirements (1) and error propagation (2). Both of which sound kinda scary, but it’s pretty simple actually:

(1) This is the amount of tokens a LLM can produce/process versus the amount of computer power it takes to generate/process them. The relationship is a quadratic function and for those of you who don’t like math, think of it this way:

Let’s say it costs a penny to generate the first 500 tokens. But it then costs 2 pennies to generate the next 500 tokens. And 4 pennies to generate the next 500 tokens after that. I’m making up values for this, but you can see how it’s costing more money to create the same amount of successive tokens (or alternatively, that each succeeding penny buys you fewer and fewer tokens). Eventually the amount of money it costs to produce the next token is too costly -- so any interactions that go over the token limitation will result in a non-responsive LLM. The processing power available and its related cost also vary between models and what sort of hardware they have available.

(2) Each generated token also comes with an error value. This is a very small value per individual token, but it accumulates over the course of the response.

What that means is: the first token produced has an associated error value. This error value is factored into the generation of the second token (note that it’s still very small at this time and doesn’t affect the second token much). However, this error value for the first token then also carries over and combines with the second token’s error value, which affects the generation of the third token and again carries over to and merges with the third token’s error value, and so forth. This combined error value eventually grows too high and the LLM can’t accurately produce the next token.

I’m kinda breezing through this explanation because how the math for non-linear error propagation exactly works doesn’t really matter for our purposes. The main takeaway from this is that there is a point at which a LLM’s response gets too long and it begins to break down. (This breakdown can look like the LLM producing something that sounds really weird/odd/stale, or just straight up producing gibberish.)

Large Language Models (LLMs)

LLMs are computerized language models. They generate responses by assessing the given input prompt and then spitting out the first token. Then based on the prompt and that first token, it determines the next token. Based on the prompt and first token, second token, and their combination, it makes the third token. And so forth. They just write an output response one token at a time. Some examples of LLMs include the GPT series from OpenAI, LLaMA from Meta, and PaLM 2 from Google.

So, a few things about LLMs:

These things are really, really, really big. The bigger they are, the more they can do. The GPT series are some of the big boys amongst these (GPT-3 is 175 billion parameters; GPT-4 actually isn’t listed, but it’s at least 500 billion parameters, possibly 1 trillion). LLaMA is 65 billion parameters. There are several smaller ones in the range of like, 15-20 billion parameters and a small handful of even smaller ones (these are usually either older/early stage LLMs or LLMs trained for more personalized/individual project things, LLMs just start getting limited in application at that size). There are more LLMs of varying sizes (you can find the list on Wikipedia), but those give an example of the size distribution when it comes to these things.

However, the number of parameters is not the only thing that distinguishes the quality of a LLM. The size of its training data also matters. GPT-3 was trained on 300 billion tokens. LLaMA was trained on 1.4 trillion tokens. So even though LLaMA has less than half the number of parameters GPT-3 has, it’s still considered to be a superior model compared to GPT-3 due to the size of its training data.

So this brings me to LLM training, which has 4 stages to it. The first stage is pre-training and this is where almost all of the computational work happens (it’s like, 99% percent of the training process). It is the most expensive stage of training, usually a few million dollars, and requires the most power. This is the stage where the LLM is trained on a lot of raw internet data (low quality, large quantity data). This data isn’t sorted or labeled in any way, it’s just tokenized and divided up into batches (called epochs) to run through the LLM (note: this is unsupervised learning).

How exactly the pre-training works doesn’t really matter for this post? The key points to take away here are: it takes a lot of hardware, a lot of time, a lot of money, and a lot of data. So it’s pretty common for companies like OpenAI to train these LLMs and then license out their services to people to fine-tune them for their own AI applications (more on this in the next section). Also, LLMs don’t actually “know” anything in general, but at this stage in particular, they are really just trying to mimic human language (or rather what they were trained to recognize as human language).

To help illustrate what this base LLM ‘intelligence’ looks like, there’s a thought exercise called the octopus test. In this scenario, two people (A & B) live alone on deserted islands, but can communicate with each other via text messages using a trans-oceanic cable. A hyper-intelligent octopus listens in on their conversations and after it learns A & B’s conversation patterns, it decides observation isn’t enough and cuts the line so that it can talk to A itself by impersonating B. So the thought exercise is this: At what level of conversation does A realize they’re not actually talking to B?

In theory, if A and the octopus stay in casual conversation (ie “Hi, how are you?” “Doing good! Ate some coconuts and stared at some waves, how about you?” “Nothing so exciting, but I’m about to go find some nuts.” “Sounds nice, have a good day!” “You too, talk to you tomorrow!”), there’s no reason for A to ever suspect or realize that they’re not actually talking to B because the octopus can mimic conversation perfectly and there’s no further evidence to cause suspicion.

However, what if A asks B what the weather is like on B’s island because A’s trying to determine if they should forage food today or save it for tomorrow? The octopus has zero understanding of what weather is because its never experienced it before. The octopus can only make guesses on how B might respond because it has no understanding of the context. It’s not clear yet if A would notice that they’re no longer talking to B -- maybe the octopus guesses correctly and A has no reason to believe they aren’t talking to B. Or maybe the octopus guessed wrong, but its guess wasn’t so wrong that A doesn’t reason that maybe B just doesn’t understand meteorology. Or maybe the octopus’s guess was so wrong that there was no way for A not to realize they’re no longer talking to B.

Another proposed scenario is that A’s found some delicious coconuts on their island and decide they want to share some with B, so A decides to build a catapult to send some coconuts to B. But when A tries to share their plans with B and ask for B’s opinions, the octopus can’t respond. This is a knowledge-intensive task -- even if the octopus understood what a catapult was, it’s also missing knowledge of B’s island and suggestions on things like where to aim. The octopus can avoid A’s questions or respond with total nonsense, but in either scenario, A realizes that they are no longer talking to B because the octopus doesn’t understand enough to simulate B’s response.

There are other scenarios in this thought exercise, but those cover three bases for LLM ‘intelligence’ pretty well: they can mimic general writing patterns pretty well, they can kind of handle very basic knowledge tasks, and they are very bad at knowledge-intensive tasks.

Now, as a note, the octopus test is not intended to be a measure of how the octopus fools A or any measure of ‘intelligence’ in the octopus, but rather show what the “octopus” (the LLM) might be missing in its inputs to provide good responses. Which brings us to the final 1% of training, the fine-tuning stages;

LLM Interfaces

As mentioned previously, LLMs only mimic language and have some key issues that need to be addressed:

LLM base models don’t like to answer questions nor do it well.

LLMs have token limitations. There’s a limit to how much input they can take in vs how long of a response they can return.

LLMs have no memory. They cannot retain the context or history of a conversation on their own.

LLMs are very bad at knowledge-intensive tasks. They need extra context and input to manage these.

However, there’s a limit to how much you can train a LLM. The specifics behind this don’t really matter so uh… *handwaves* very generally, it’s a matter of diminishing returns. You can get close to the end goal but you can never actually reach it, and you hit a point where you’re putting in a lot of work for little to no change. There’s also some other issues that pop up with too much training, but we don’t need to get into those.

You can still further refine models from the pre-training stage to overcome these inherent issues in LLM base models -- Vicuna-13b is an example of this (I think? Pretty sure? Someone fact check me on this lol).

(Vicuna-13b, side-note, is an open source chatbot model that was fine-tuned from the LLaMA model using conversation data from ShareGPT. It was developed by LMSYS, a research group founded by students and professors from UC Berkeley, UCSD, and CMU. Because so much information about how models are trained and developed is closed-source, hidden, or otherwise obscured, they research LLMs and develop their models specifically to release that research for the benefit of public knowledge, learning, and understanding.)

Back to my point, you can still refine and fine-tune LLM base models directly. However, by about the time GPT-2 was released, people had realized that the base models really like to complete documents and that they’re already really good at this even without further fine-tuning. So long as they gave the model a prompt that was formatted as a ‘document’ with enough background information alongside the desired input question, the model would answer the question by ‘finishing’ the document. This opened up an entire new branch in LLM development where instead of trying to coach the LLMs into performing tasks that weren’t native to their capabilities, they focused on ways to deliver information to the models in a way that took advantage of what they were already good at.

This is where LLM interfaces come in.

LLM interfaces (which I sometimes just refer to as “AI” or “AI interface” below; I’ve also seen people refer to these as “assistants”) are developed and fine-tuned for specific applications to act as a bridge between a user and a LLM and transform any query from the user into a viable input prompt for the LLM. Examples of these would be OpenAI’s ChatGPT and Google’s Bard. One of the key benefits to developing an AI interface is their adaptability, as rather than needing to restart the fine-tuning process for a LLM with every base update, an AI interface fine-tuned for one LLM engine can be refitted to an updated version or even a new LLM engine with minimal to no additional work. Take ChatGPT as an example -- when GPT-4 was released, OpenAI didn’t have to train or develop a new chat bot model fine-tuned specifically from GPT-4. They just ‘plugged in’ the already fine-tuned ChatGPT interface to the new GPT model. Even now, ChatGPT can submit prompts to either the GPT-3.5 or GPT-4 LLM engines depending on the user’s payment plan, rather than being two separate chat bots.

As I mentioned previously, LLMs have some inherent problems such as token limitations, no memory, and the inability to handle knowledge-intensive tasks. However, an input prompt that includes conversation history, extra context relevant to the user’s query, and instructions on how to deliver the response will result in a good quality response from the base LLM model. This is what I mean when I say an interface transforms a user’s query into a viable prompt -- rather than the user having to come up with all this extra info and formatting it into a proper document for the LLM to complete, the AI interface handles those responsibilities.

How exactly these interfaces do that varies from application to application. It really depends on what type of task the developers are trying to fine-tune the application for. There’s also a host of APIs that can be incorporated into these interfaces to customize user experience (such as APIs that identify inappropriate content and kill a user’s query, to APIs that allow users to speak a command or upload image prompts, stuff like that). However, some tasks are pretty consistent across each application, so let’s talk about a few of those:

Token management

As I said earlier, each LLM has a token limit per interaction and this token limitation includes both the input query and the output response.

The input prompt an interface delivers to a LLM can include a lot of things: the user’s query (obviously), but also extra information relevant to the query, conversation history, instructions on how to deliver its response (such as the tone, style, or ‘persona’ of the response), etc. How much extra information the interface pulls to include in the input prompt depends on the desired length of an output response and what sort of information pulled for the input prompt is prioritized by the application varies depending on what task it was developed for. (For example, a chatbot application would likely allocate more tokens to conversation history and output response length as compared to a program like Sudowrite* which probably prioritizes additional (context) content from the document over previous suggestions and the lengths of the output responses are much more restrained.)

(*Sudowrite is…kind of weird in how they list their program information. I’m 97% sure it’s a writer assistant interface that keys into the GPT series, but uhh…I might be wrong? Please don’t hold it against me if I am lol.)

Anyways, how the interface allocates tokens is generally determined by trial-and-error depending on what sort of end application the developer is aiming for and the token limit(s) their LLM engine(s) have.

tl;dr -- all LLMs have interaction token limits, the AI manages them so the user doesn’t have to.

Simulating short-term memory

LLMs have no memory. As far as they figure, every new query is a brand new start. So if you want to build on previous prompts and responses, you have to deliver the previous conversation to the LLM along with your new prompt.

AI interfaces do this for you by managing what’s called a ‘context window’. A context window is the amount of previous conversation history it saves and passes on to the LLM with a new query. How long a context window is and how it’s managed varies from application to application. Different token limits between different LLMs is the biggest restriction for how many tokens an AI can allocate to the context window. The most basic way of managing a context window is discarding context over the token limit on a first in, first out basis. However, some applications also have ways of stripping out extraneous parts of the context window to condense the conversation history, which lets them simulate a longer context window even if the amount of allocated tokens hasn’t changed.

Augmented context retrieval

Remember how I said earlier that LLMs are really bad at knowledge-intensive tasks? Augmented context retrieval is how people “inject knowledge” into LLMs.

Very basically, the user submits a query to the AI. The AI identifies keywords in that query, then runs those keywords through a secondary knowledge corpus and pulls up additional information relevant to those keywords, then delivers that information along with the user’s query as an input prompt to the LLM. The LLM can then process this extra info with the prompt and deliver a more useful/reliable response.

Also, very importantly: “knowledge-intensive” does not refer to higher level or complex thinking. Knowledge-intensive refers to something that requires a lot of background knowledge or context. Here’s an analogy for how LLMs handle knowledge-intensive tasks:

A friend tells you about a book you haven’t read, then you try to write a synopsis of it based on just what your friend told you about that book (see: every high school literature class). You’re most likely going to struggle to write that summary based solely on what your friend told you, because you don’t actually know what the book is about.

This is an example of a knowledge intensive task: to write a good summary on a book, you need to have actually read the book. In this analogy, augmented context retrieval would be the equivalent of you reading a few book reports and the wikipedia page for the book before writing the summary -- you still don’t know the book, but you have some good sources to reference to help you write a summary for it anyways.

This is also why it’s important to fact check a LLM’s responses, no matter how much the developers have fine-tuned their accuracy.

(*Sidenote, while AI does save previous conversation responses and use those to fine-tune models or sometimes even deliver as a part of a future input query, that’s not…really augmented context retrieval? The secondary knowledge corpus used for augmented context retrieval is…not exactly static, you can update and add to the knowledge corpus, but it’s a relatively fixed set of curated and verified data. The retrieval process for saved past responses isn’t dissimilar to augmented context retrieval, but it’s typically stored and handled separately.)

So, those are a few tasks LLM interfaces can manage to improve LLM responses and user experience. There’s other things they can manage or incorporate into their framework, this is by no means an exhaustive or even thorough list of what they can do. But moving on, let’s talk about ways to fine-tune AI. The exact hows aren't super necessary for our purposes, so very briefly;

Supervised fine-tuning

As a quick reminder, supervised learning means that the training data is labeled. In the case for this stage, the AI is given data with inputs that have specific outputs. The goal here is to coach the AI into delivering responses in specific ways to a specific degree of quality. When the AI starts recognizing the patterns in the training data, it can apply those patterns to future user inputs (AI is really good at pattern recognition, so this is taking advantage of that skill to apply it to native tasks AI is not as good at handling).

As a note, some models stop their training here (for example, Vicuna-13b stopped its training here). However there’s another two steps people can take to refine AI even further (as a note, they are listed separately but they go hand-in-hand);

Reward modeling

To improve the quality of LLM responses, people develop reward models to encourage the AIs to seek higher quality responses and avoid low quality responses during reinforcement learning. This explanation makes the AI sound like it’s a dog being trained with treats -- it’s not like that, don’t fall into AI anthropomorphism. Rating values just are applied to LLM responses and the AI is coded to try to get a high score for future responses.

For a very basic overview of reward modeling: given a specific set of data, the LLM generates a bunch of responses that are then given quality ratings by humans. The AI rates all of those responses on its own as well. Then using the human labeled data as the ‘ground truth’, the developers have the AI compare its ratings to the humans’ ratings using a loss function and adjust its parameters accordingly. Given enough data and training, the AI can begin to identify patterns and rate future responses from the LLM on its own (this process is basically the same way neural networks are trained in the pre-training stage).

On its own, reward modeling is not very useful. However, it becomes very useful for the next stage;

Reinforcement learning

So, the AI now has a reward model. That model is now fixed and will no longer change. Now the AI runs a bunch of prompts and generates a bunch of responses that it then rates based on its new reward model. Pathways that led to higher rated responses are given higher weights, pathways that led to lower rated responses are minimized. Again, I’m kind of breezing through the explanation for this because the exact how doesn’t really matter, but this is another way AI is coached to deliver certain types of responses.

You might’ve heard of the term reinforcement learning from human feedback (or RLHF for short) in regards to reward modeling and reinforcement learning because this is how ChatGPT developed its reward model. Users rated the AI’s responses and (after going through a group of moderators to check for outliers, trolls, and relevancy), these ratings were saved as the ‘ground truth’ data for the AI to adjust its own response ratings to. Part of why this made the news is because this method of developing reward model data worked way better than people expected it to. One of the key benefits was that even beyond checking for knowledge accuracy, this also helped fine-tune how that knowledge is delivered (ie two responses can contain the same information, but one could still be rated over another based on its wording).

As a quick side note, this stage can also be very prone to human bias. For example, the researchers rating ChatGPT’s responses favored lengthier explanations, so ChatGPT is now biased to delivering lengthier responses to queries. Just something to keep in mind.

So, something that’s really important to understand from these fine-tuning stages and for AI in general is how much of the AI’s capabilities are human regulated and monitored. AI is not continuously learning. The models are pre-trained to mimic human language patterns based on a set chunk of data and that learning stops after the pre-training stage is completed and the model is released. Any data incorporated during the fine-tuning stages for AI is humans guiding and coaching it to deliver preferred responses. A finished reward model is just as static as a LLM and its human biases echo through the reinforced learning stage.

People tend to assume that if something is human-like, it must be due to deeper human reasoning. But this AI anthropomorphism is…really bad. Consequences range from the term “AI hallucination” (which is defined as “when the AI says something false but thinks it is true,” except that is an absolute bullshit concept because AI doesn’t know what truth is), all the way to the (usually highly underpaid) human labor maintaining the “human-like” aspects of AI getting ignored and swept under the rug of anthropomorphization. I’m trying not to get into my personal opinions here so I’ll leave this at that, but if there’s any one thing I want people to take away from this monster of a post, it’s that AI’s “human” behavior is not only simulated but very much maintained by humans.

Anyways, to close this section out: The more you fine-tune an AI, the more narrow and specific it becomes in its application. It can still be very versatile in its use, but they are still developed for very specific tasks, and you need to keep that in mind if/when you choose to use it (I’ll return to this point in the final section).

#LLMs for dummies

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sweetchotimochi · 2 years ago

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tips for managing time (ɔ◔‿◔)ɔ ♥

Disclaimer: these tips worked for me and based on my own experiences. It’s okay if this doesn't work for you!

Lets start with some methods;

Method 1 - Bullet Journaling: This is a great way/method to manage your time! I have used this method for a long time and it really has kept me consistent with how I manage my time. It is, however, annoying to bring your bullet journal everywhere or try to write in it all the time. The problem is about how much effort you put into your “journaling”. We all see online how pretty or aesthetic bullet journaling spreads are and when we look at our own journal we stop using it because it is not pretty enough. That doesn’t matter. Instead, look at how well it can help you manage time. If not, this is totally okay. Try switching to an online platform.

Method 2 - Google Calendar/Calendar app: Another tried and true method. This helps in organizing your day. Great way to manage your time, especially because you can edit your tasks and time-block your day. It is also really good because calendars can be implemented in your computer, phone, and other devices so you can check out your tasks on the go. They also have components for tasks, reminders, events and more. I really recommend this for a lot of people because it is a game-changer, especially for people who need something easy to use for managing time. I guess the only problem for me is that I feel guilty, sometimes, if I see an empty part of my day which makes me feel unproductive. That’s totally okay! You don’t have to be busy all the time, take some time off for yourself. Nobody is productive 24/7.

Method 3 - Notion: This is all the rage nowadays, so I checked it out for myself. It is a really great way to implement aesthetics and different parts of a journal. Unlike a calendar app, you can make lists, add other parts, add calendars and many other things. Its also accessible on many sites. Unfortunately, after making everything look very aesthetic, I didn’t use this platform as much except for keeping track of my anime and books. I do also use this to keep track of my classes and planning on-the-go, but it may work for you, so try it out!

Method 4 - Diary with template/Planner: This is a great idea for people who want to use something on the go, but also easy to use and still have room for creativity. Daily, Weekly, and Monthly planners are all great ways to manage your time. The only problem is the template itself for me. Let’s say you might not be doing anything one day or slack off. Personally, seeing that one blank spot makes me feel guilty and then I can’t keep up. If you feel like you might feel like this, that's totally okay! Try managing time at your own pace, or use another method.

There are plenty of other methods out there, but these are the ones I tried. I believe everyone should use something to help manage their time, because I have yet to meet someone who remembers everything they have to do in their head. Here are some quick-fire tips:

Try writing your tasks for every day the day before: Write out your tasks the day before. It helps you plan for the future and gives you a clearer head for the next day.

Use a reward system for completing tasks: Let's say you complete something big, make a reward system for that specific task! For example, if you complete a really hard chapter and do well on the quiz, that task deserves some reward!

Keep some time for yourself each day: Even I have a hard time doing this, but keep some allocated time each day for yourself. Relax, watch some tv, do something fun and wholesome. You deserve it.

Divide up your tasks if they are really big: This is such a repeated method, however it is super effective. Let's say you are trying to study for a couple of chapters in a subject. Try dividing them up along the week into smaller tasks. It makes the whole thing as a whole less daunting.

Try color-blocking your tasks: I use color-coding to divide my tasks on google calendar. For example, things for school/work are in green, and chores are in white. I think, at a glance, it really helps you think about how the day will go.

This was a long one, I hope you’re still here. Again, these methods may not work for you, and that's okay. TLDR; Explore your methods and figure out what works best for you.

#tips for managing time #tips #studyblr #study tips #time management #productivity #studying #productivity tips #notion #google calendar #calendar #bujo #bujo tips #bullet journaling #diary #template #planners #study #studyblog

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casuallivi · 3 years ago

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The Things You Like, The Ones You Don’t

Set: post ACOSF, during Nessian’s Wedding.

Words: 2305

Sneak a peak

Part 1: The Things You Like

The wedding ceremony was intimate per Nesta’s request.

Her family, her Valkyrie friends, some of Cassian’s closest friends, the inner circle, Varian. And that’s about it.

The reception was a different story.

The first time she saw the ballroom in the river house, Elain thought Rhys had gone mad. There was no occasion to require such large space, no party that could possible receive so many guests. Elain was wrong. The place was crowed, barely any space left with all the guests and servants circulating. Apparently, the whole Velaris decided to pay respect and wish good fortune to their favorite cheerful General and his not so jolly bride.

Rhys promised Nesta the greatest party of the century, and Elain did not doubt he was holding up his end of the bargain.

The ballroom had been decorated with shades of silver and red. Big chandeliers with diamonds cascading down. Lavish centerpieces filled with red roses sitting on top of dozens of tables, a mix of round and square ones. All scattered around the perimeter creating an inviting dance floor in the center. On the back, a temporary platform was built, big enough to fit a full-scale orchestra.

Nesta cried when she first saw them.

It was his especial gift for her. A hundred musicians, dressed in elegant black, that played everything. From intricated waltz to bawdy ballads, from drinking songs to romantic hymn, making the dance floor permanently occupied, like a happy void trapping drunk dancer. Not even Elain could escape from it.

The second Mor spotted her chatting with Mr. Thomas, she cried her name with joy, smacking Elain on both cheeks and smearing her with dark lipstick. Mor had forgone her usual red for an off-shoulder blue dress, the bodice peppered with small sapphires, the sheer fabric hugging her body like a second skin, her golden hair styled with two braids morphing in a low ponytail.

“All my partners had two left feet. Come dance with me.”

She glanced the dancers diving in groups of six as a new song began to play.

“I don’t know this one.” Elain admitted sipping her wine.

“I can teach you!

She slipped her arm under Elain's and hauled her forward.

They joined in the second chorus, the melody cheerful and rhythmic. The dance consisting in a pattern of handclaps, twirls and steps strong enough to shake the wooden floor. Mor helped her to find the right pace before spinning her to the male beside her. She changed partners four times and returned Mor, who dipped her in a cheesy maneuver. Elain laughed so hard her ribs hurt.

Once on the dance floor it was hard to leave. Not only Mor was feeling extra energetic, but dance requests never stop coming. Every time she tried to take a break, another fae appeared from thin air. She danced with all the males and females she recognized, mostly neighborhood friends, politely declining some eager young males.

By the time she succeed in returning to the safety of her table, Elain's legs felt like jelly. She massaged her aching calves searching for Nesta on the crowd. She would dance one more time with her sister and be done for the night. Elain located the happy couple on the other side of the parlor. Dozens of guests surrounding them.

She munched on a juicy lamb dish, watching the exchanges, Cassian grinning and howling, Nesta joined in conversation from time to time. To her sister credit, she didn’t roll her eyes once, well, except at Cassian. She even smiled here and there receiving hugs from elderly that showered Cassian with love. But even Nesta’s good sense appeared to have a limit. After an hour of cozy greetings, she spotted Elain. Nesta waved at her frenetically, her eyes glowing with a new hope as mouthed; “Save me.”

Oblivious to his wife’s wishes to run away, Cassian turned around, grabbing a passing Azriel by the sleeve, dragging him to his side, before returning to chat with a male she didn’t know. Azriel was deadly handsome in a tux.

Elain spied him in a trance.

He wore a white boutonniere on his navy jacket. The buttons undone to reveal a grey waistcoat and white shirt, the attire completed by a merlot tie and merlot pocket square. Your lips taste like merlot too? She mused with interest. Probably not. So far, he had drunk bourbon, cognac, whiskey, more bourbon. All neat. No wine.

His aloof mask did little to hide mood. He had that I-am-bored-to-death look on. She would have laugh if he didn't look so sexy with his hair swept back. Azriel cracked his neck, his patience worning out. Whoever that male was, he was clearly absurd, because Azriel forehead creased, his mouth curled in distaste, shadows fidgeting from his torso to his wings. His eyes darted around, calculating the right time to slip into the night.

The most boring man alive slapped Cassian’s shoulder, and Azriel almost scoffed. Almost. This time she did laugh. The whole thing was just too funny to hold back. Cassian in his own world while his wife and brother died on the inside. She was impressed they lasted so long. Her laugh was cut short when churning hazel eyes found their way to her.

The rest of the room blurred.

There was only him

His steady heartbeat.

His unwavering attention.

And that look.

She stopped breathing because she knew that look too. The same one that made her think he wanted to kiss her on solstice. His gaze so intense, the hairs in her arms stood up. Teeth sinking into her lip. Azriel scanned her unhurried, quietly drowning the rest of the amber liquid in his glass. Eyes lingering on her exposed collarbone. Her neck. Her lips.

She saw the image clear as day. His precise fingers unlacing the straps in her shoulder, his tongue tracing the path beneath, wet lip closing over her heated skin, rough finger pads sliding against her jaw, releasing her trapped lip, his teeth replacing hers.

Elain crossed her legs. Her throat dry. She couldn't remember the last time he looked at her like that. Completely unabashed. Her Azriel did it all the time, shameless. Teasing her to acknowledge it, daring her to say something about it. The new Azriel didn’t share his passions. He had only one agenda when it came to Elain, and that was Avoiding her.

Her heart galloped.

Thump. Thump. Thump.

This was not the new, distant, Azriel. No. This was his old self slipping to the surface. Shattering the cold façade he had put on for months. He placed the empty glass in a passing servant' tray, running his thumb across his lips. Inviting. A shiver ran down her spine.

Elain tried to remember that he rejected her, that he wanted nothing to do with her. She really did. But that was a hard thing to do with him burning a hole in her face. His desire bleeding all over her. Had he changed his mind? Was she pathetic for hoping the answer was “yes”? A movement caught her attention.

Elain peeked down, watching the table-cloth shadow move. The skittish thing snaking over her heel, unnoticeable on the black sandal. Sliding under the straps, caressing the arch of her foot, curling around her ankle amorously. Tugging. Calling.

Come.

Come with me.

Her skin prickled.

She fidgeted.

Maybe she should go. Maybe she should go over there and help her poor sister. Not because Azriel was there. Because Nesta needed her. Yes. She would go there and help Nesta. After that she might linger around. In case someone else wanted to escape with her. Someone who was beguiling her for several minutes now. Begging her to come closer.

Thump. Thump. Thump.

Elain smoothed the fabric of her dress, fixing some loose curls back in place.

His eyes following her every move.

Encouraging her.

His mouth shaped into the tinniest smile of world.

Her insides quivered with expectation.

She pushed her chair back. Soundless. Confidence.

‘Elain.’

The world unblurred.

Everything came rushing back.

Hundreds of people buzzing, thousands of sounds returning in a crushing wave. Voices, laughter, cheers, music, choices. So many people. So many fates. Live and death and rebirth. Lovers, enemies, warriors, cowards. Overwhelming. The tides tried to pulled her in. Images flashing like a thunderstorm, too fast to follow.

She gripped her chair. The skittish anklet a comfortable anchor to the present. Breath. Oh, this is a strong one. Elain breathed in and out, shutting the sounds, blinking the unwanted visions away, naming the things around, tying herself in the present.

Not a beach. Not a prison. Not a battlefield. Simple Nesta’s wedding. Breath, breath, breath. Think of the ballroom. And Az. The hard chair. And Az. The crystal chandeliers. And Az. The red roses and silver table cloth. And Az. Az is here. It’s fine. I’m fine.

‘Elain?’

Elain looked at her brother-in-law, having forgot he was there. Sitting beside her. Honestly, she had forgot he existed all together, forgot where she was. Ease prey to her power. Her mistake. The visions weren’t so unruled as long as she anchored herself in the present, as long as she let them pass by without dwelling. If her mind dispersed for too long, others fates flooded her endlessly.

Rhys intrusive insistent inner voice had startled her, almost tossing her in a loop. Almost.

‘Yes?’

‘Can you watch Nyx for a minute? I want to dance your sister.’

‘Actually, I was going–’

‘I’ll be quick! One dance and I come back for him. This song is her favorite.’

Rhys pleaded, cradling a fully awake Nyx with one arm, his free hand holding Feyre’s feet on his lap. She looked back at Nesta. Her sister now alone with her husband. No guests. No Azriel. Her cue, gone. She offered Rhys a yellow smile.

‘Take as long as you need.’

Elain bent, Nyx squealing, more than happy to jump into his aunt’s open arms. Beside him, Feyre eyed the interaction with suspicion.

“Where are you going?”

He placed the flats back on her feet, setting them on the floor.

“We are going to dance,” he corrected.

Rhys throw his jacket on the back of the chair, rolling his sleeves, her sister practically drooling in her sit. Elain covered Nyx eyes. Disgusting. He bowed dramatically, waiting for Feyre to accept his hand.

Elain smiled. Feyre rolled her eyes. Nyx sucked his finger watching his dad.

“I’m tired Rhys. I’ll end up stepping on your feet.”

“Then it will not be different from all of our previous dances, will it Feyre darling.” He said with a smug.

Feyre punched his gut, cursing as he lifted her from the chair, caring her away. Elain waited for him to set her down. He didn’t. Rhys danced with Feyre in his arms, not caring for decorum or etiquette, their foreheads resting together, equal smiles plastered on their faces. She swallowed her jealousy, turning to her nephew.

“This close, Nyx,” she joined her index and thumb, “this close”

“Just you and me now,” she cooed, “I would ask you for a dance but I need to get out for a bit. Care to join me in a stroll, my little lord?

Elain secured the baby in her arms, sneaking between the mass of bodies to the safety of the empty hallway leading to the west wing. The one with big glass windows pointing to the backyard. The voices died down, replaced by the gentle sound of nature, her mind calming. She hugged Nyx tighter, admiring the starlight sky, searching for the moon.

“It’s a new moon night, Nyx. Intended for reflection and new beginnings. This is the perfect time to plant good seed for the future, a good omen for aunt Nest and her married life, but” she smiled down at Nyx, “I think Nesta would rather shove the seed down Cassian’s throat than plant them.”

Nyx giggled, flapping his arms with gusto as Elain kept talking. She talked about nothing in particular, her soothing tone being interrupted by his blabbers and hair tugs from time to time, his only form of participating in conversation.

“…ten months. I was shocked too. Mother only carried me for seven…Ouch, don’t put your hand in my mouth. I do know so, aunt Nesta told me…Yes, it is why aunt Nesta worries for me so much. You are so clever, that’s why you are my favorite nephew…What? You’re the only one? Hmmm, knowing you parents I don’t think that will stand true for much longer.”

Elain’s poor ears were testimony for their attempt to bridge the only child gap. Although, she doubted that a child was the only intuit of their favorite recreational activity. Her cheeks heated. She had chosen the farther room possible, in the opposite wing of the master’s, and some days the distance still wasn’t enough. Are married couples all like this? She missed the quietude of the town house. Lately she thought about the same thing over and over. Moving out. Nyx was bigger now, almost 8 full months, and Feyre was nursed back to health for good, no more sudden weaknesses and bedridden days.

Nyx whimpered grabbing her face, little mouth quaking, a cry ready to burst.

“No, no, no. Don’t cry my handsome boy.” She kissed his head, “I’ll come visit.”

She lifted her head and her body froze. Nyx fist curling around her hair, tugging hard. Elain didn’t move. Taking in his wrinkled cloths, red hair spilled over his shoulder, hands in his pockets, his attention on his muddied boot tapping on the marble floor. The last person she expected to see in a party for Nesta.

Her stare lured him to turn around, his shoulders tensing.

Lucien’s eyes widened at the sight of her.

.........

Rated: 🐥blocking

your girl love to chat, so you can beam me up anytime😺😸👻👻

#elriel fanfic #elain fanfic #elriel #elain archeron #azriel #elain x azriel #ish #the things you like the ones you don't #part 1: the things you like #TTYLTOYD #my writing #acosf #a wild cat marrying a golden retriever? yep that’s nessian #someone save me from myself #and my never ending additions #we all know he is going to 🐥block #and we all act surprised when reading it #lol

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