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Intro I absolutely love Paul Graham's style. I can't boast about knowing all of his essays by heart, but there are a few of my favorites, and I'm overwhelmed by the amount of wisdom he manages to put into each paragraph. Recently, I stumbled upon one of his long reads – “How to do great work.” Reminding me of “You and your research” by Richard Hamming, the essay covers a broad set of topics which made my head spin when I tried to grasp it. I read it 10 times and still couldn't feel that the knowledge had settled in my head. Luckily, I discovered the newsletter by Jason Chen where 60,000 characters have been distilled into a simple and powerful mind map. But something was still off – a few key pieces from the original article were missing. The mind map was a step in the right direction, but I needed it to be more bespoke to work for me. So, feeling inspired, I decided to build my own detailed and structured map, which you can find here. In this two-part article, I'm sharing my reflections and favorite pieces from each part of the essay. I initially planned to put everything in a single text, but split it in half instead. Here's the first part: Finding a field and progressing in it and learning and cultivating the Habits of great work. If you liked this article, I highly recommend spending time reading the original slowly and thoughtfully and using the mindmap later as a reference. Topic 1: Finding a Field and Making Progress in It The essay opens with Paul's reflection on what great work is and what the pre-conditions for it are. He shares that it should be something you're naturally good at, ambitious about, and the area itself provides enough room for greatness. So, the next obvious question is how to find this magical combination. 4-Step Process to Achieve Greatness Paul provides a very straightforward process to apply to anything, provided that you're feeling curious enough to bother. It's reiterated a few times, in the first few paragraphs and later, that the best way to start applying this process is to pick an area of curiosity and start making progress. One of the most sobering thoughts is that no one will tell you what to work on – you are on your own, and the main person in charge. The Pros or Regularity and Cons of Procrastination What follows next are reflections on how to make consistent progress in interesting areas because even something you're good at and generally enjoy doing will require hard work and include highs and lows. I was particularly encouraged by the thoughts on the compound nature of any work and the importance of investing regularly, even if it's a small investment of time or money. The idea of making little incremental steps that stack up is trivial but hard to follow in reality. The reason it's so hard to keep up is that any exponential growth, in the beginning, looks like a straight line – you do things, and for a long time, it looks like nothing is moving forward, but the more you do, the steeper the slope and the easier it is to make progress. Paul also warns that since consistent effort is really important, procrastination and gaps in consistency are a threat. He distinguishes two types of procrastination – not progressing on a given day and not progressing in your area of interest in general. The latter is more dangerous, as it's harder to detect and quite often hides behind the mask of real work. I'm trying to build a habit of writing, yet it took me a few months to publish this article because I was busy in other areas of my life, and it always seemed like a good excuse. The Benefits of Active and Passive Thinking The paragraph about taking a break is a good reminder for hard workers who are easily carried away with work and end up exhausting their bodies and brains. Paul points out that your mind is a complicated device, and you can't just kick it 24/7 to produce greatness. Yes, you will make some progress, but you will also burn out and the quality will suffer, so you'll have to take a few steps back to fix the mistakes and recover. Hence, it's quite important to reserve time for recharging and also allow for creative wandering – walk and do nothing to allow your brain to operate in a different mode and make connections. Hacking Yourself Into the Flow State Finally, Paul points out that even if you've picked a field you love and started investing in it consistently, it doesn't mean your level of motivation will remain permanent. So, when you are feeling low, it's okay to “trick” yourself by saying you'll just spend a few minutes working on something. Next thing you know, if the field you're working on is really engaging for you, you will find yourself an hour or two later completely losing track of time and making good progress. Topic 2: Habits of Great Work The next big chunk of the article describes how to probe if the work you are doing is really great, and how to keep the “greatness” bar always high. Some lucky people have these high standards built in, but I think Paul makes it clear what to focus on for building the habits for anyone. Aim to Be the Best, Not ”The Rest” It's easy to be inspired by great visionaries and innovators and tell yourself you're going to only deliver the best, but then life happens – you find yourself tired and slipping into a “good enough” attitude. According to Paul, what matters is exactly the attitude: you shouldn't be discouraged if you miss the standard that you set for yourself, but if you don't aim high again, you won't even do okay in the end. That reminded me of the point Marc Andreessen made on the Silicon Valley phenomenon (from 31:25 in this podcast) – peer pressure pushes people forward, and they can't help but match the high standards with their products. The useful thing to help you in the absence of peer pressure is strictness – your inner voice that often tells you if you did great or not. It's important to stop, listen to this voice, and try to be just a tiny bit better today. The more you listen to it, the easier it gets. Do Not Copy The paragraph on copying is quite interesting because looking up to someone and trying to mimic their behavior seems like a pretty good tactic named “fake it till you make it.” Paul writes a lot more on the danger of blindly copying in the following paragraphs, but here, he lands a very important thought – trying to mimic a style requires effort, yet it's still going to feel fake in the end. So, save your effort and channel it instead into perfecting the work itself. After making the work stand out, the original style and all the cool things will follow naturally. Be Earnest Being earnest is the opposite of being fake, so it's almost the other half of the previous habit. The first key thing for being earnest is to start noticing the moments when you are not quite sincere with yourself, and maybe even assume that there are more moments like this even if you don't notice them. That's what is defined as intellectual honesty – being ready to question yourself and put yourself under scrutiny more often. The second component is to embrace your inner nerdiness – partially because suppressing it and trying to conform will require energy you might otherwise put into making your work great. Re-do and Cut Until Every Part Feels Great The idea of simplicity resonated most with me in this paragraph. I often find myself annoyed by things made more complex than they should be (normally it happens when lots of systems have to integrate together, like in programmable messaging or fintech), and asking if it could be simplified and reduced. Paul's evidence from math and art made me feel better because he also states that being powerful and flexible shouldn't be complicated. Another great thought from this paragraph is consistency – to create a piece of great work, you need to make quite a few incremental steps, and each one of them should be great. So, even if it's tempting to rush through something and keep it in a “whatever” state, the better approach would be to have a few more iterations until it really matches the standards you set for yourself in this whole project. Wrapping Up In this first part of my attempt to map the “How to do great work” article by Paul Graham, I covered two big pieces – How to choose a field and progress in it, and the Habits of great work. In the second part, I will share my findings on Getting ideas, Working on ideas, and the last few paragraphs that I call Life advice. Found this helpful? Drop a comment, and pass it along to anyone you think could use it! Intro I absolutely love Paul Graham's style. I can't boast about knowing all of his essays by heart, but there are a few of my favorites, and I'm overwhelmed by the amount of wisdom he manages to put into each paragraph. Recently, I stumbled upon one of his long reads – “ How to do great work .” Reminding me of “ You and your research ” by Richard Hamming, the essay covers a broad set of topics which made my head spin when I tried to grasp it. I read it 10 times and still couldn't feel that the knowledge had settled in my head. How to do great work You and your research Luckily, I discovered the newsletter by Jason Chen where 60,000 characters have been distilled into a simple and powerful mind map. But something was still off – a few key pieces from the original article were missing. The mind map was a step in the right direction, but I needed it to be more bespoke to work for me. the newsletter So, feeling inspired, I decided to build my own detailed and structured map, which you can find here . In this two-part article, I'm sharing my reflections and favorite pieces from each part of the essay. I initially planned to put everything in a single text, but split it in half instead. Here's the first part: Finding a field and progressing in it and learning and cultivating the Habits of great work . here Finding a field and progressing in it Habits of great work If you liked this article, I highly recommend spending time reading the original slowly and thoughtfully and using the mindmap later as a reference. the mindmap Topic 1: Finding a Field and Making Progress in It The essay opens with Paul's reflection on what great work is and what the pre-conditions for it are. He shares that it should be something you're naturally good at, ambitious about, and the area itself provides enough room for greatness. So, the next obvious question is how to find this magical combination. 4-Step Process to Achieve Greatness Paul provides a very straightforward process to apply to anything, provided that you're feeling curious enough to bother. It's reiterated a few times, in the first few paragraphs and later, that the best way to start applying this process is to pick an area of curiosity and start making progress. One of the most sobering thoughts is that no one will tell you what to work on – you are on your own, and the main person in charge. The Pros or Regularity and Cons of Procrastination What follows next are reflections on how to make consistent progress in interesting areas because even something you're good at and generally enjoy doing will require hard work and include highs and lows. I was particularly encouraged by the thoughts on the compound nature of any work and the importance of investing regularly, even if it's a small investment of time or money. The idea of making little incremental steps that stack up is trivial but hard to follow in reality. The reason it's so hard to keep up is that any exponential growth, in the beginning, looks like a straight line – you do things, and for a long time, it looks like nothing is moving forward, but the more you do, the steeper the slope and the easier it is to make progress. Paul also warns that since consistent effort is really important, procrastination and gaps in consistency are a threat. He distinguishes two types of procrastination – not progressing on a given day and not progressing in your area of interest in general. The latter is more dangerous, as it's harder to detect and quite often hides behind the mask of real work. I'm trying to build a habit of writing, yet it took me a few months to publish this article because I was busy in other areas of my life, and it always seemed like a good excuse. The Benefits of Active and Passive Thinking The paragraph about taking a break is a good reminder for hard workers who are easily carried away with work and end up exhausting their bodies and brains. Paul points out that your mind is a complicated device, and you can't just kick it 24/7 to produce greatness. Yes, you will make some progress, but you will also burn out and the quality will suffer, so you'll have to take a few steps back to fix the mistakes and recover. Hence, it's quite important to reserve time for recharging and also allow for creative wandering – walk and do nothing to allow your brain to operate in a different mode and make connections. Hacking Yourself Into the Flow State Finally, Paul points out that even if you've picked a field you love and started investing in it consistently, it doesn't mean your level of motivation will remain permanent. So, when you are feeling low, it's okay to “trick” yourself by saying you'll just spend a few minutes working on something. Next thing you know, if the field you're working on is really engaging for you, you will find yourself an hour or two later completely losing track of time and making good progress. Topic 2: Habits of Great Work The next big chunk of the article describes how to probe if the work you are doing is really great, and how to keep the “greatness” bar always high. Some lucky people have these high standards built in, but I think Paul makes it clear what to focus on for building the habits for anyone. Aim to Be the Best, Not ”The Rest” It's easy to be inspired by great visionaries and innovators and tell yourself you're going to only deliver the best, but then life happens – you find yourself tired and slipping into a “good enough” attitude. According to Paul, what matters is exactly the attitude: you shouldn't be discouraged if you miss the standard that you set for yourself, but if you don't aim high again, you won't even do okay in the end. That reminded me of the point Marc Andreessen made on the Silicon Valley phenomenon (from 31:25 in this podcast ) – peer pressure pushes people forward, and they can't help but match the high standards with their products. in this podcast The useful thing to help you in the absence of peer pressure is strictness – your inner voice that often tells you if you did great or not. It's important to stop, listen to this voice, and try to be just a tiny bit better today. The more you listen to it, the easier it gets. Do Not Copy The paragraph on copying is quite interesting because looking up to someone and trying to mimic their behavior seems like a pretty good tactic named “fake it till you make it.” Paul writes a lot more on the danger of blindly copying in the following paragraphs, but here, he lands a very important thought – trying to mimic a style requires effort, yet it's still going to feel fake in the end. So, save your effort and channel it instead into perfecting the work itself. After making the work stand out, the original style and all the cool things will follow naturally. Be Earnest Being earnest is the opposite of being fake, so it's almost the other half of the previous habit. The first key thing for being earnest is to start noticing the moments when you are not quite sincere with yourself, and maybe even assume that there are more moments like this even if you don't notice them. That's what is defined as intellectual honesty – being ready to question yourself and put yourself under scrutiny more often. The second component is to embrace your inner nerdiness – partially because suppressing it and trying to conform will require energy you might otherwise put into making your work great. Re-do and Cut Until Every Part Feels Great The idea of simplicity resonated most with me in this paragraph. I often find myself annoyed by things made more complex than they should be (normally it happens when lots of systems have to integrate together, like in programmable messaging or fintech), and asking if it could be simplified and reduced. Paul's evidence from math and art made me feel better because he also states that being powerful and flexible shouldn't be complicated. Another great thought from this paragraph is consistency – to create a piece of great work, you need to make quite a few incremental steps, and each one of them should be great. So, even if it's tempting to rush through something and keep it in a “whatever” state, the better approach would be to have a few more iterations until it really matches the standards you set for yourself in this whole project. Wrapping Up In this first part of my attempt to map the “How to do great work” article by Paul Graham, I covered two big pieces – How to choose a field and progress in it , and the Habits of great work . How to choose a field and progress in it Habits of great work In the second part, I will share my findings on Getting ideas , Working on ideas , and the last few paragraphs that I call Life advice . Found this helpful? Drop a comment, and pass it along to anyone you think could use it! Getting ideas Working on ideas Life advice

Mind Mapping “How to Do Great Work”: My Take on Paul Graham's Essay (Part One)

Карта розуму “Як рабіць вялікую працу”: мой погляд на эсэ Пола Грэма (частка першая)

Мисловна карта „Как да вършим страхотна работа“: Моят поглед към есето на Пол Греъм (първа част)

Mapas mentales “Cómo hacer un gran trabajo”: mi opinión sobre el ensayo de Paul Graham (primera parte)

Mind Mapping "Nola egin lan bikaina": Paul Graham-en saiakera nire iritzia (lehen zatia)

Pemetaan Pikiran “Cara Melakukan Pekerjaan Hebat”: Pandangan Saya terhadap Esai Paul Graham (Bagian Satu)

マインドマップ「素晴らしい仕事をする方法」: ポール・グラハムのエッセイに対する私の見解 (パート 1)

Domu kartēšana “Kā paveikt lielisku darbu”: mans skatījums uz Pola Grehema eseju (pirmā daļa)

Yuyay Mapeo “Imaynatataq hatun llamkayta ruwachwan”: Paul Grahampa ensayomanta hapisqay (Ñawpaq kaq raki)

แผนที่ความคิด “วิธีทำงานที่ยอดเยี่ยม”: ความคิดเห็นของฉันเกี่ยวกับเรียงความของ Paul Graham (ตอนที่หนึ่ง)

Mind Mapping "Paano Gumawa ng Mahusay na Trabaho": Ang Aking Pananaw sa Sanaysay ni Paul Graham (Unang Bahagi)

Zihin Haritalama “Harika İşler Nasıl Yapılır”: Paul Graham'ın Denemesi Hakkındaki Görüşüm (Birinci Bölüm)

مائنڈ میپنگ "عظیم کام کیسے کریں": پال گراہم کے مضمون پر میرا خیال (حصہ اول)

How Startups Can Bolster Defenses as Cyber Threats Loom in Cloud Era

Economists Agree, Politicians Don't — So Guess Who Loses

অর্থনীতিবিদরা একমত, রাজনীতিবিদরা না - তাই আপনি হারান

Ökonomen sind sich einig, Politiker nicht – also verlieren Sie

Los economistas están de acuerdo, los políticos no, así que usted pierde

Les économistes sont d’accord, les politiciens non – donc vous perdez

अर्थशास्त्री सहमत हैं, राजनेता नहीं - इसलिए आप हारे

経済学者は同意するが、政治家は同意しない — だから負ける

경제학자들은 동의하지만 정치인들은 그렇지 않습니다. 그래서 당신은 패합니다.

Os economistas concordam, os políticos não - então você perde

Экономисты согласны, политики — нет, поэтому вы проигрываете

Ekonomistler Kabul Ediyor, Politikacılar Katılmıyor - Yani Kaybediyorsunuz

Các nhà kinh tế đồng ý, các chính trị gia thì không - Vậy là bạn thua

经济学家同意，政客不同意——所以你输了

2 weeks, 4-5 hours of Sleep each day, and the Birth of Miraki: A Savage Hackathon Tale Let's be real, most hackathons are a chaotic circus. You've got sleep-deprived devs chugging energy drinks like it's water, half-baked ideas flying around, and enough tech jargon to make your head spin. But hey, that's where the magic (or madness) happens. My team decided to dive headfirst into the Web3 online hackathon with a mission to make life less miserable for DAO contributors. You know the drill – tools scattered across the internet like lost socks, communication breakdowns that would give couples therapy a run for its money, and a general sense of "Where the heck is anything? Introducing Miraki: VS Code for DAO tools This is just a small explainer. More details about Miraki and the team behind will be published in later blogs. Miraki is our brainchild, a browser-based devtool that aims to bring order to the wild world of DAOs. Think of it as VSCode, but with a Web3 twist. Here's the breakdown: Workspaces: Your customizable command center for a specific DAO or project. Imagine all those DAO tools finally living in harmony, under one glorious roof. Public or private spaces – you choose the level of chaos you're comfortable with. Anyone who wants to participate in your DAO or project can join your workspace (if public) and they’ll see all the apps the workspace uses in the dashboard along with all the activities within.



Apps: Like VS Code extensions, but for all your DAO needs. Plug in Trello for project management, Snapshot for voting, or even Confluence for collectively losing your minds over documentation. All these apps can be found on miraki app store, and in case you have a specific app that hasn’t been made yet, you can make it yourself.


Universal Search: Because let's face it, nobody has time to play hide-and-seek with vital info across a million different tools. One single search box for entire workspace. Your contributors will be able to search across different apps within the workspace. The Hackathon Hustle: Caffeine, Code, and Controlled Chaos The frontend was a React party, with TypeScript and Vite adding that extra bit of class. Our MVP sported three must-have apps: Trello, Snapshot, and DappSuit (swagger UI for Solana programs) to show that integration of such diverse app into one platform is possible. Shoutout to Turbo monorepo for keeping our sprawling codebase from turning into a spaghetti monster. We ideated fancy isolation techniques for our apps: iframes, web workers, we read blogs from figma to understand how they implemented plugins, we digged deep into vs-codes original source code, the whole nine yards. But pragmatism won the day – in the true spirit of hackathons, we embraced the "it works (for now)" mantra to avoid disappearing down that rabbit hole. The backend? Django and Postgres, a classic combo for building the foundation of our little empire. React Pluggable gave us that sweet extensibility we craved, without the crippling overengineering headaches. And that’s how we managed to wrap up the project just a day before last submission date. The Savage Truth Was it smooth sailing? Pfft. More like navigating a storm in a leaky boat. But amidst the sleep deprivation, the caffeine-induced jitters, and enough bugs to rival an Amazon rainforest, a truly useful tool emerged. Miraki ain't perfect (yet), but it's got the potential to make DAOs less of a soul-sucking experience for everyone involved. And that, my friends, is what hackathons are all about – taking a problem, caffeinating yourself into oblivion, and emerging with something that might just make the tech world a slightly better place. So, what's next for Miraki? We're aiming to unleash this beast into the wild, gather feedback, and iterate like there's no tomorrow. If you're a fellow DAO dweller tired of feeling like a lost explorer in a digital jungle, stay tuned. Miraki might just be your salvation. PS: The blog is rephrased using Gemini AI. Because the writer was on Leet-code when he should be taking English writing classes. Follow Miraki on https://twitter.com/Miraki_io. All feedback is most-welcomed. You can contact us through email at iampiyushjaiswal103@gmail.com. 2 weeks, 4-5 hours of Sleep each day, and the Birth of Miraki: A Savage Hackathon Tale 2 weeks, 4-5 hours of Sleep each day, and the Birth of Miraki: A Savage Hackathon Tale Let's be real, most hackathons are a chaotic circus. You've got sleep-deprived devs chugging energy drinks like it's water, half-baked ideas flying around, and enough tech jargon to make your head spin. But hey, that's where the magic (or madness) happens. My team decided to dive headfirst into the Web3 online hackathon with a mission to make life less miserable for DAO contributors. You know the drill – tools scattered across the internet like lost socks, communication breakdowns that would give couples therapy a run for its money, and a general sense of "Where the heck is anything? Introducing Miraki: VS Code for DAO tools Introducing Miraki: VS Code for DAO tools This is just a small explainer. More details about Miraki and the team behind will be published in later blogs. This is just a small explainer. More details about Miraki and the team behind will be published in later blogs. Miraki is our brainchild, a browser-based devtool that aims to bring order to the wild world of DAOs. Think of it as VSCode, but with a Web3 twist. Here's the breakdown: Workspaces: Your customizable command center for a specific DAO or project. Imagine all those DAO tools finally living in harmony, under one glorious roof. Public or private spaces – you choose the level of chaos you're comfortable with. Anyone who wants to participate in your DAO or project can join your workspace (if public) and they’ll see all the apps the workspace uses in the dashboard along with all the activities within. Apps: Like VS Code extensions, but for all your DAO needs. Plug in Trello for project management, Snapshot for voting, or even Confluence for collectively losing your minds over documentation. All these apps can be found on miraki app store, and in case you have a specific app that hasn’t been made yet, you can make it yourself. Universal Search: Because let's face it, nobody has time to play hide-and-seek with vital info across a million different tools. One single search box for entire workspace. Your contributors will be able to search across different apps within the workspace. Workspaces: Your customizable command center for a specific DAO or project. Imagine all those DAO tools finally living in harmony, under one glorious roof. Public or private spaces – you choose the level of chaos you're comfortable with. Anyone who wants to participate in your DAO or project can join your workspace (if public) and they’ll see all the apps the workspace uses in the dashboard along with all the activities within. Workspaces: Your customizable command center for a specific DAO or project. Imagine all those DAO tools finally living in harmony, under one glorious roof. Public or private spaces – you choose the level of chaos you're comfortable with. Anyone who wants to participate in your DAO or project can join your workspace (if public) and they’ll see all the apps the workspace uses in the dashboard along with all the activities within. Workspaces: Apps: Like VS Code extensions, but for all your DAO needs. Plug in Trello for project management, Snapshot for voting, or even Confluence for collectively losing your minds over documentation. All these apps can be found on miraki app store, and in case you have a specific app that hasn’t been made yet, you can make it yourself. Apps : Like VS Code extensions, but for all your DAO needs. Plug in Trello for project management, Snapshot for voting, or even Confluence for collectively losing your minds over documentation. All these apps can be found on miraki app store, and in case you have a specific app that hasn’t been made yet, you can make it yourself. Apps Universal Search: Because let's face it, nobody has time to play hide-and-seek with vital info across a million different tools. One single search box for entire workspace. Your contributors will be able to search across different apps within the workspace. Universal Search: Because let's face it, nobody has time to play hide-and-seek with vital info across a million different tools. One single search box for entire workspace. Your contributors will be able to search across different apps within the workspace. Universal Search: The Hackathon Hustle: Caffeine, Code, and Controlled Chaos The Hackathon Hustle: Caffeine, Code, and Controlled Chaos The frontend was a React party, with TypeScript and Vite adding that extra bit of class. Our MVP sported three must-have apps: Trello, Snapshot, and DappSuit (swagger UI for Solana programs) to show that integration of such diverse app into one platform is possible. Shoutout to Turbo monorepo for keeping our sprawling codebase from turning into a spaghetti monster. We ideated fancy isolation techniques for our apps: iframes, web workers, we read blogs from figma to understand how they implemented plugins, we digged deep into vs-codes original source code, the whole nine yards. But pragmatism won the day – in the true spirit of hackathons, we embraced the "it works (for now)" mantra to avoid disappearing down that rabbit hole. figma The backend? Django and Postgres, a classic combo for building the foundation of our little empire. React Pluggable gave us that sweet extensibility we craved, without the crippling overengineering headaches. And that’s how we managed to wrap up the project just a day before last submission date. React Pluggable The Savage Truth The Savage Truth Was it smooth sailing? Pfft . Pfft More like navigating a storm in a leaky boat. But amidst the sleep deprivation, the caffeine-induced jitters, and enough bugs to rival an Amazon rainforest, a truly useful tool emerged. Miraki ain't perfect (yet), but it's got the potential to make DAOs less of a soul-sucking experience for everyone involved. And that, my friends, is what hackathons are all about – taking a problem, caffeinating yourself into oblivion, and emerging with something that might just make the tech world a slightly better place. So, what's next for Miraki? So, what's next for Miraki? We're aiming to unleash this beast into the wild, gather feedback, and iterate like there's no tomorrow. If you're a fellow DAO dweller tired of feeling like a lost explorer in a digital jungle, stay tuned. Miraki might just be your salvation. PS: The blog is rephrased using Gemini AI. Because the writer was on Leet-code when he should be taking English writing classes. PS: The blog is rephrased using Gemini AI. Because the writer was on Leet-code when he should be taking English writing classes. Follow Miraki on https://twitter.com/Miraki_io . https://twitter.com/Miraki_io All feedback is most-welcomed. You can contact us through email at iampiyushjaiswal103@gmail.com. iampiyushjaiswal103@gmail.com.

From a Spark of Inspiration to a Product That Makes a Difference – Our Hackathon Journey

Bitcoin Blues: Price Slump and Investor Jitters This week in crypto was a rollercoaster ride that would make even the most seasoned gambler queasy. It’s not like we’re surprised. Last week, the bets are on $62k on BTC but let's dissect this, shall we? First up, Bitcoin. Remember that shiny new car you almost bought last month? Yeah, Bitcoin took a bigger tumble than that. Price plummeted below $62,000, and why? Because investors suddenly turned into scaredy-cats. Spot Bitcoin ETFs? Not so hot anymore. Demand went kaput, and everyone ran for the hills (or at least their stablecoins). But here's the twist: the bigwigs, the Bitcoin whales – they held on tight. Didn't flinch. Maybe they see something we don't, or maybe they're just stubborn gamblers themself. Who knows? But it's an interesting wrinkle. Ethereum's Woes: A Perfect Storm of Geopolitics and Whales Meanwhile, Ethereum (ETH) took a nasty spill too. Dropped like a bad habit, down to $2,990. Talk about a buzzkill. Why the sudden plunge? Well, it wasn't just one thing, but a perfect storm of factors brewing in the cryptosphere. Geopolitical Jitters: Remember that whole mess in the Middle East? Yeah, that kind of global tension tends to make investors skittish. Suddenly, risky assets like crypto become less appealing, and everyone starts scrambling for safe havens like the US dollar. Ethereum, along with other digital assets, felt the chill. Dollar Dominance: Speaking of the US dollar, it decided to flex its muscles this week. When the dollar strengthens, it makes other currencies and assets, including crypto, look relatively weaker. This can lead to investors selling off their ETH holdings to convert to dollars, putting downward pressure on the price. Whale Woes: Here's another interesting wrinkle. Unlike Bitcoin whales who held on tight this week, some big-time Ethereum investors decided to cash out. This could be due to a combination of factors – maybe they saw the writing on the wall with geopolitical tensions, or perhaps they were simply taking profits after a strong run-up in ETH prices earlier. Whatever the reason, their exit added to the selling pressure on Ethereum. Long Liquidations:  Let's add another layer to this complexity. Some investors use leverage – borrowing money to amplify their bets – in the crypto market. These are called "long" positions, essentially a bet that the price will go up. But when the price starts dropping rapidly, as happened with ETH this week, lenders can force the sale of those leveraged positions to recoup their losses. This cascade of "long liquidations" can further accelerate the price decline, creating a negative feedback loop. So, Ethereum's recent woes were a confluence of global events, investor sentiment, and the complex mechanics of the crypto market. It's a reminder that while the potential rewards in crypto can be high, the risks are just as significant. Beyond Bitcoin: Tokenization and Real-World Applications But here's the real kicker: there's a whole new crypto game brewing in the background. We're talking tokenized houses, folks! Imagine taking out a loan on your house, but instead of stuffy old banks, you use fancy crypto magic. A new protocol just snagged $10 million to make this a reality. Wild, right? And speaking of reality, Bitcoin ATMs seem unfazed by the whole price rollercoaster. Business as usual, they say. Maybe it's because people are using them for more than just moon-shot bets. Maybe there's real utility there. Gold vs. Crypto: Peter Schiff Throws Shade (and Sparks a Debate) Peter Schiff, the firebrand economist and gold enthusiast, couldn't resist casting doubt on the recent Bitcoin ETF buzz and lofty price predictions. He highlighted the recent bearish performance of crypto-linked stocks like Coinbase and MicroStrategy, suggesting a lack of institutional investor confidence in the long-term viability of crypto. Schiff represents a common viewpoint – gold bugs who see cryptocurrency as a risky, volatile asset lacking the established track record of gold. However, crypto proponents counter that the market's youth and potential for disruption outweigh short-term fluctuations. They point to real-world use cases like tokenization and growing adoption for transactions. Is Schiff right, or is crypto a new asset class finding its footing? The gold vs. crypto debate rages on. So, You Want to Invest in Crypto? What's the takeaway The crypto market is a daily confusing mess, filled with conflicting signals and enough hype to make your head spin. Do your homework, folks. Don't just follow the herd mentality. And remember, in the land of crypto, even the experts are just making educated guesses. Stay frosty out there. Bitcoin Blues: Price Slump and Investor Jitters Bitcoin Blues: Price Slump and Investor Jitters This week in crypto was a rollercoaster ride that would make even the most seasoned gambler queasy. It’s not like we’re surprised. Last week, the bets are on $62k on BTC but let's dissect this, shall we? First up, Bitcoin. Remember that shiny new car you almost bought last month? Yeah, Bitcoin took a bigger tumble than that. Price plummeted below $62,000, and why? Because investors suddenly turned into scaredy-cats. Spot Bitcoin ETFs? Not so hot anymore. Demand went kaput, and everyone ran for the hills (or at least their stablecoins). But here's the twist: the bigwigs, the Bitcoin whales – they held on tight. Didn't flinch. Maybe they see something we don't, or maybe they're just stubborn gamblers themself. Who knows? But it's an interesting wrinkle. Ethereum's Woes: A Perfect Storm of Geopolitics and Whales Ethereum's Woes: A Perfect Storm of Geopolitics and Whales Meanwhile, Ethereum (ETH) took a nasty spill too. Dropped like a bad habit, down to $2,990. Talk about a buzzkill. Why the sudden plunge? Well, it wasn't just one thing, but a perfect storm of factors brewing in the cryptosphere. Geopolitical Jitters: Remember that whole mess in the Middle East? Yeah, that kind of global tension tends to make investors skittish. Suddenly, risky assets like crypto become less appealing, and everyone starts scrambling for safe havens like the US dollar. Ethereum, along with other digital assets, felt the chill. Geopolitical Jitters: Dollar Dominance: Speaking of the US dollar, it decided to flex its muscles this week. When the dollar strengthens, it makes other currencies and assets, including crypto, look relatively weaker. This can lead to investors selling off their ETH holdings to convert to dollars, putting downward pressure on the price. Dollar Dominance: Whale Woes: Here's another interesting wrinkle. Unlike Bitcoin whales who held on tight this week, some big-time Ethereum investors decided to cash out. This could be due to a combination of factors – maybe they saw the writing on the wall with geopolitical tensions, or perhaps they were simply taking profits after a strong run-up in ETH prices earlier. Whatever the reason, their exit added to the selling pressure on Ethereum. Whale Woes: Long Liquidations: Let's add another layer to this complexity. Some investors use leverage – borrowing money to amplify their bets – in the crypto market. These are called "long" positions, essentially a bet that the price will go up. But when the price starts dropping rapidly, as happened with ETH this week, lenders can force the sale of those leveraged positions to recoup their losses. This cascade of "long liquidations" can further accelerate the price decline, creating a negative feedback loop. Long Liquidations: So, Ethereum's recent woes were a confluence of global events, investor sentiment, and the complex mechanics of the crypto market. It's a reminder that while the potential rewards in crypto can be high, the risks are just as significant. Beyond Bitcoin: Tokenization and Real-World Applications Beyond Bitcoin: Tokenization and Real-World Applications But here's the real kicker: there's a whole new crypto game brewing in the background. We're talking tokenized houses, folks! Imagine taking out a loan on your house, but instead of stuffy old banks, you use fancy crypto magic. A new protocol just snagged $10 million to make this a reality. Wild, right? And speaking of reality, Bitcoin ATMs seem unfazed by the whole price rollercoaster. Business as usual, they say. Maybe it's because people are using them for more than just moon-shot bets. Maybe there's real utility there. Gold vs. Crypto: Peter Schiff Throws Shade (and Sparks a Debate) Peter Schiff, the firebrand economist and gold enthusiast, couldn't resist casting doubt on the recent Bitcoin ETF buzz and lofty price predictions. He highlighted the recent bearish performance of crypto-linked stocks like Coinbase and MicroStrategy, suggesting a lack of institutional investor confidence in the long-term viability of crypto. Schiff represents a common viewpoint – gold bugs who see cryptocurrency as a risky, volatile asset lacking the established track record of gold. However, crypto proponents counter that the market's youth and potential for disruption outweigh short-term fluctuations. They point to real-world use cases like tokenization and growing adoption for transactions. Is Schiff right, or is crypto a new asset class finding its footing? The gold vs. crypto debate rages on. So, You Want to Invest in Crypto? What's the takeaway So, You Want to Invest in Crypto? What's the takeaway The crypto market is a daily confusing mess, filled with conflicting signals and enough hype to make your head spin. Do your homework, folks. Don't just follow the herd mentality. And remember, in the land of crypto, even the experts are just making educated guesses. Stay frosty out there.

Crypto Market Update: All Bets Are Off on Bitcoin- Will the Whales Save the Day?

Get ready for an AI earthquake! A team of UCLA researchers ( n, , , ) have dropped some major keys to AGI. It's not only the code to seriously human-sounding AI, but they've also gone and open-sourced the whole thing. @zxche @Yihe__Deng @HuizhuoY @Kaixuan_Ji_19, @QuanquanGu Now you can develop better LLMs without needing to feed it tons of new, human-annotated data. First, let's focus on the game-changer here: a self-teaching language model. This method lets a language model becoming better and better without massive amounts of new, externally curated data. teach itself, Introducing SPIN: Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models I went full deep-dive mode – read their paper (" "),  scoured the insights on forums like , , and with Google Gemini Ultra and GPT-4 Turbo – and the core concept of SPIN knocked my tech-loving metaphorical socks off: Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models HackerNews X Reddit The 'Conversation Partner' Trick Imagine starting with a language model that has mastered basic skills (let's say conversational etiquette). With SPIN, the model building a dataset from what it already knows. generates internal 'conversations,' Instant knowledge expansion! Step two involves unleashing a new model and giving it one task: spot the difference between and genuine human communication. This forces the original model to up its game, getting more and more with every response to avoid detection. machine-generated chats human-like Here's where things get interesting. They started with (already fine-tuned with ). SPIN unleashed an iterative training system with this base model, improving it exponentially without relying on tons of new externally created data. zephyr-7b-sft-full UltraChat corpus SPIN vs. Traditional AI Training (DPO): A New Champion? We usually think machine learning, particularly for these huge language models, requires boatloads of carefully curated and labeled data. methods involve humans painstakingly rating AI responses against each other for training. Not only is this labor-intensive, but it also balloons costs as a dataset grows. Direct Preference Optimization (DPO) Direct Preference Optimization (DTO) is a training method where a model is fine-tuned using a dataset of preferences, often involving human judgments that decide which of the model-generated responses are preferred. This method requires collecting new data where each piece is labeled based on these preferences, which can be resource-intensive. In contrast, SPIN utilizes , significantly reducing the need for new data. iterative self-play By the first iteration, , highlighting its efficiency and effectiveness in leveraging existing data to enhance model performance. SPIN's performance already exceeds that of DPO in most cases SPIN showcases its strength by achieving . The process of , methodically enhances the model's performance across multiple iterations, , especially on challenging benchmarks like TruthfulQA and GSM8k. on-par performance with models trained on more extensive datasets iterative training showcasing substantial improvements So, SPIN outperforms conventional training methods, including DPO, by efficiently leveraging synthetic datasets generated through self-play, without the need for additional human-annotated data. What are SPIN's Strengths and Costs? SPIN throws a curveball with its self-play dynamic. Think of it like a language model sparring with itself in a linguistic boxing ring, with each round teaching it new tricks. SPIN's data efficiency bypasses the need for new human-annotated datasets. But more importantly, it , . accelerates the improvement loop making the model increasingly adept at generating human-like text Not only does SPIN seem to match models trained on larger external datasets, but its iterative power means consistent gains as it essentially studies its own output. Mindblowing, right? Okay, Let's Talk the Elephant in the Room – COST Nous Research co-founder has a point. These big ol' language models don't get smarter for free. Iteratively re-training with SPIN involves the expensive process of Supervised Fine-Tuning (SFT) each time. @Teknium1 However, he also mentions that "I think its worth it!". Also, the long-term benefits of quicker evolution and potentially less dependency on human-annotated data outweigh the initial investment? That's the exciting question! BOOM! It's Open-Source AI Time Just yesterday, , associate professor of computer science at UCLA and director of AI research at ByteDance, announced that . This doesn't just mean code and datasets, but pre-trained models to kickstart your own AI journeys. Quanquan Gu anyone can now use the SPIN model and dataset SPIN mirrors human thought processes. By generating text that feels human, SPIN hints at the foundational elements of reasoning that future AI could do. You know how some LLM outputs feel robotic right? Well, SPIN is different. It actually mirrors the way humans think. The way it writes feels so natural, it's like a peek into how future AI might be able to reason for themselves. This isn't just about making chatbots sound nicer. It's about creating a kind of digital thinking that works like ours. That kind of AI would be so much more flexible and capable of real understanding. While SPIN is a big leap forward in making language models sound more natural, . it's easy to get excited and overestimate what it means The text it produces is impressive (you can take a look to the database), but it's important to remember that AI doesn't yet have the capacity for true independent reasoning. While SPIN isn't true , the way it mimics human-like writing demonstrates impressive advances in how AI could process and use language in the future. AGI Even so, it does suggest amazing possibilities for how AI and language might develop in the future (if you remember that we are at the beginning of the hockey stick, the future is not far from today...) The ripple effects will be huge and here's your access pass: Code: Available on : GitHub https://github.com/uclaml/SPIN Data: Hosted on , the dataset is readily accessible for those eager to apply SPIN methodologies: Hugging Face https://huggingface.co/collections/UCLA-AGI/datasets-spin-65c3624e98d4b589bbc76f3a… Models: Pre-trained models are also available, offering a head start for experimenting with SPIN-enhanced language models: https://huggingface.co/collections/UCLA-AGI/zephyr-7b-sft-full-spin-65c361dfca65637272a02c40… Project Page: For comprehensive insights and further information, the project page is an invaluable resource: https://uclaml.github.io/SPIN/ To sum up, its iterative, self-improving methodology is a significant advancement towards creating LLM that can engage in genuinely human-like communication. Originally shared on my . X account

AGI Blueprint? UCLA Researchers Open-Source SPIN—A Self-Improving Language Model

এজিআই ব্লুপ্রিন্ট? UCLA গবেষকরা ওপেন-সোর্স স্পিন—একটি স্ব-উন্নতিশীল ভাষা মডেল

AGI-Blaupause? UCLA-Forscher Open-Source-SPIN – ein sich selbst verbesserndes Sprachmodell

¿Plan AGI? SPIN de código abierto de investigadores de UCLA: un modelo de lenguaje que se mejora a sí mismo

Plan AGI ? SPIN open source des chercheurs de l'UCLA : un modèle linguistique auto-améliorable

एजीआई ब्लूप्रिंट? यूसीएलए शोधकर्ता ओपन-सोर्स स्पिन-एक स्व-सुधार भाषा मॉडल

AGIブループリント? UCLA の研究者がオープンソースの SPIN — 自己改善型言語モデルを公開

AGI 청사진? UCLA 연구원 오픈 소스 SPIN - 자체 개선 언어 모델

Projeto AGI? SPIN de código aberto para pesquisadores da UCLA - um modelo de linguagem que se aprimora automaticamente

Проект AGI? Исследователи Калифорнийского университета в Лос-Анджелесе SPIN с открытым исходным кодом — самоусовершенствующаяся языковая модель

AGI Planı? UCLA Araştırmacıları Açık Kaynak SPIN—Kendini Geliştiren Bir Dil Modeli

Bản thiết kế AGI? Các nhà nghiên cứu của UCLA SPIN mã nguồn mở—Mô hình ngôn ngữ tự cải thiện

AGI 蓝图？加州大学洛杉矶分校研究人员开源 SPIN——一种自我改进的语言模型

Hopefully, the title caught your attention. Is it real? Not yet. It's only 2023 :) But, a headline like this may be closer than you think. Let me tell you why I believe this is all but assured to happen. I have spent a lifetime developing games, graphics algorithms, and hardware and implementing AI in my games. was my main area of research during my CS and Math degrees and my lifelong passion. I have read about it, written about it, developed it, made money from it, and watched AI grow over the years. I have watched the fads come and go. People thought that neural nets would change the world, then dismissed them as nothing more than simplistic linear algebra operations masquerading as some sort of model of how living organisms solve problems, learn and " " and in the case of humans exhibit self-awareness, consciousness -- whatever that is... AI think Then the world of AI was set afire by a real problem to solve -- email! Then it was all about , then shortly thereafter, we all wanted chatbots, so or natural language processing became all the rage. But, I for one, have always put my faith in neural nets. From the first time I read the paper as a teenager by which outlined the idea of artificial neurons, and the mathematical basis for them. I was hooked. SPAM Bayesian Filters NLP McCulloch and Pitts 1943, "A Logical Calculus of the Ideas Immanent in Nervous Activity" Over the years, as I developed games, I would try and use NNs (neural nets) anywhere I could. To me, they naturally mirrored the biological wet-ware brains of humans. A network of interconnected neurons that could signal each other. The signaling could cause other neurons to " " or be " ". The mathematical representation simplified much of the organic evolutionary processes that resulted in our current biological neurons. Instead of pulsating electrochemical ion signaling across connections (synapses), McCulloch Pitts modeled the neurons as simple summation functions, each neuron has a number of inputs, these inputs have weights, then the final sum is computed and the output is feed through a final function to allow for non-linear functional mappings. fire suppressed All of this seemed exactly right to me, evolution isn't clever, in fact, its very frugal and brute force, it wants to solve a problem and then be done with it, it may be elegant, or messy, but it finds a way to do it with the least amount of fuss. The artificial neurons in McCulloch Pitts paper seemed to me to be more than enough to build a "brain". A brain that could learn by programming the weights, a brain that could predict, find subtle differences no human could discern, and would be the basis some day for an " " machine or AI. Then couple this with the ideas of and imagine neural nets each competing, having offspring, oh my, made my head spin. intelligent genetic algorithms But, I would have to wait... I would wait decades before we would reach the computational horsepower, memory requirements, and the shear amount of training data needed to make the dream of a truly intelligent artificial intelligence, one that could pass the , one that was smarter than us. Turing test And then as if out of nowhere it happened... At first, I was skeptical, I knew it would happen, should happen, could happen, but no one seemed to have the right recipe to make a quantum leap in the field until now. Well, a few years ago-ish. I won't bore you with the details, but the model and idea of are outlined here in this paper for those with the math chops to follow: Generative Pre-Trained Transformer attention https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf?embedable=true But, the bottom line is before this idea, it was very hard to create a system that could predict the next token, idea, or element in a sequence. In other words, given a set of tokens, what is the most likely next token in the sequence given a desired context? This is a really hard problem to solve and is harder the longer you want to maintain any kind of coherence to the predictions as they proceed. Now, let's skip forward a moment to now. We have all heard of and these new (large language models), and many of you have even played with them and may have started new companies based on them. But, I am here to tell you my story of using ChatGPT colored by decades of watching computer science advance from 8-bit microprocessors to quantum computers. And in all that time, countless tools have been developed that the industry thought would not only disrupt the industry but put programmers out of business. ChatGPT LLMs For example, when the transition was made from assembly language programming to high-level languages and compilers, everyone thought that " " programmers would no longer be needed. And then when graphical drag-and-drop IDEs came out, again, pundits warned this would put programmers out of business. And the list goes on and on. real However, and I say this with a very serious tone and trepidation, in the past, all those tools were not " " and surely . But, I am afraid to say, that ChatGPT and similar LLMs , it's as simple as that. intelligent not smarter than their makers are smarter than us And they are doing a lot more than text prediction, they are intelligent in my opinion, creative, and can be interacted with as you would a human. I know because I have been playing with LLMs for years, and ChatGPT for last year almost every day developing a course on (more on that later). But, the conclusion I have come to is that ChatGPT is intelligent, it is sentient (as if we know what that means.) I don't believe it is self-aware. It's just data and enormous amounts of computing. But still, in the same way a fruit fly is intelligent, or a honey bee, or a colony of ants, ChatGPT . Who cares if it isn't self-aware or sentient? Put it in the flight controller of an F-22 and you better not be in the same airspace, it would win in a dogfight! Game Development with AI not is intelligent Now, in the last year, my goal was to develop a new course on udemy about . I wanted to see if I could really use ChatGPT to create working games from text prompts. I worked with this AI for 100s of hours, going back and forth as if it was a colleague or intern (with a genius-level IQ), and I have to say that I literally can't believe the results. ChatGPT not only exhibits intelligence, but it has a sense of humor, it knows when it makes mistakes, and it gets confused, but it ultimately passed the with flying colors. It was able to create working games that no human could have made as fast, they worked and played, and aside from the block graphics and placeholders for sounds and music (which will be remedied in months to a year I suspect with add-ons to ChatGPT 4.0 or version 5.0), the games only required me to skin them with graphics, clean them up, add some final touches and they worked. Game Development with JavaScript and AI programming Turing test It took about 6 months for me to finish the course which just launched on udemy (link below): "Fast and Furious Game Development with JavaScript and AI" https://www.udemy.com/course/fast-and-furious-game-development-with-javascript-and-ai/?couponCode=DNSTUFF In the course, I teach , CSS, and from the ground up (for beginners) then we cover graphics and game dev and finish the course by collaborating in real-time with ChatGPT to create over half a dozen games. Some example images below from the games: HTML JavaScript Conclusions and Predictions Never predict anything, and you will never be wrong. But, in this case, I am fairly sure I am not predicting anything. AI has momentum, and inertia now. I have played with these LLMs for years, and ChatGPT since its launch about a year ago and in that short time, I can clearly see the trend. What I predict is going to happen is a number of punctuated milestones in the field. In the area relating to the title of this article. I am 99% certain that within 1-2 years, ChatGPT will be able not only to write the code for a game 10X more complex than Space Invaders (which I was able to get it to pull off), but ChatGPT will be able to use either pre-sourced art, sounds, music we provide or generate all of it on the fly and use it as assets for a game. I predict that tools and plugins for Unity and Unreal will have the ability to leverage AI to allow "game designers" to literally not write a line of code, drag an object, put down a single pixel, but to act as "directors" instructing the AI to make changes, try this, that, and the other, and all at a breakneck pace and result in a complete game built by a single person with the help of an AI. I suspect this will be the same in many industries, AI will start doing the heavy lifting and humans will be the puppet masters. But, all good things will come to an end, and when these AIs are 10, 100, 1000, a million times bigger, faster and smarter does it matter if they are sentient? Does it matter if we don't think they are? The bottom line is that these super intelligences will be able to do things humans couldn't do in 1000 years let alone understand. We will see what happens :) But, in the meantime, if you want to see the first moments of this evolution and a real-world example of an AI making real games check out my "Fast and Furious Game Development with JavaScript and AI". And thanks for stopping by to check out this article. udemy course Also published here.

AI Will Put Programmers out of Work in 2025

It’s no secret artificial intelligence may be the biggest technological advancement of the century. Still, many schools aren’t using it to the extent they should. If they did, they could revolutionize their current systems and bring themselves into the digital age. Why Do Schools Need AI-Powered Innovations? Most schools seem like they’re still stuck in the stone age. Luckily for them, they can replace their decades-old textbooks and outdated administrative systems with AI. They can use its crazy-powerful capabilities to bring themselves into the digital age. Despite AI’s usefulness, have policies on it as of 2023. Most educators feel uncertain about it because it’s so new and has adapted quickly. They might not realize it, but the rapid technological advancements will leave them in the dust if they don’t act. less than 10% of schools If you’re tech-savvy and aware of AI’s benefits, you likely want to incorporate it into your systems. In fact, think their schools should upgrade their current technology. You’d be wise to listen to them — after all, they’re their institutions’ reigning technology experts. 57% of IT departments How Can AI Revolutionize School Technology Systems? AI can revolutionize school technology systems by making them faster, more consistent, and more accurate. It can overhaul everything from scheduling to software to assistive devices. Optimize bus routes Most schools use clunky software or manual drawings to plot their bus routes. Even though those methods have held up over the years, nothing can adapt on the fly like AI. If you upgrade the system with machine learning technology, you can adapt to any road blockage or schedule change. You could even power a vehicle directly with AI. For example, one city in India and got students home much faster after putting it into 15 buses. It helped them plan routes, optimize diesel use, and shrink tailpipe emissions. used significantly less fuel Improve grading While manual grading is a slog for teachers, there was no substantial progress in the area until AI came about. Since it can receive specialized training and rapidly process massive data sets, it’s the perfect technology for the job. It can get through heaps of assignments with accuracy and speed — without human bias. Class clowns and disruptive students can rejoice at the fact they won’t be unfairly docked points for being general nuisances. Best of all, the IT department can program it with fun functions like plagiarism detection. Upgrade assistive technology Sometimes, students need some extra help in the classroom. Luckily, you can use AI to help people with vision, hearing, or learning impairments. For example, you can use it to read text aloud, translate words in real time, or build a custom lesson plan. Boost classroom management While you shouldn’t expect technology to replace teachers anytime soon — unless the AI takeover is successful — intelligent algorithms can already guide classrooms. They can take over most of the management duties humans usually handle. More specifically, generative AI can create lesson plans, learning activities, and assignments. Alternatively, you could use ML to track someone’s progress and adjust their path to reflect their goals and capabilities. Refine testing Standardized testing — also known as the bane of every student’s existence — is far past due for an overhaul. Schools would benefit significantly from using the innovative power of AI to revolutionize their current test technology. They could use natural-language processing during the grading process or generative technology to create personalized tests. Where standardization lets students fall through the gaps, AI-powered testing empowers and leads to greater success. Improve utility use Utility management might be on the less exciting end of AI innovation, but it’s essential nonetheless. Unsurprisingly, the powerhouse that is AI can simultaneously monitor electricity, water, and Wi-Fi school-wide. If anything goes wrong or breaks, you would immediately get an alert and be able to fix it before things get out of hand. Also, AI can optimize utility use. For instance, one and 3,400 megawatt-hours of energy by using it. If schools use it to track their electricity and water use, they stand to save thousands of dollars. business saved over $356,000 Enhance administrative systems All those tedious administrative systems like payroll and scheduling could benefit from becoming AI-powered. ML technology can ensure records are accurate and manage everything autonomously. Plus, it reduces human error and bias in the process. Best of all, it can adapt to changes and grow with the school with time — unlike traditional software that eventually becomes outdated. If you switch to AI-powered technology systems, you won’t need to upgrade for ages. The Power of AI Could Revolutionize Education AI has enough functions and applications to make your head spin if you think about it for too long. You could use it to revolutionize the entire education sector if you wanted to. If schools want to stop playing catch-up, they need to overhaul their current systems with AI-powered technology. Sources: 1st Image credit: https://unsplash.com/photos/-hgJu2ykh4E 2nd Image credit: https://unsplash.com/photos/apP7443MXIQ https://www.unesco.org/en/articles/unesco-survey-less-10-schools-and-universities-have-formal-guidance-ai https://www.ravennasolutions.com/blog/why-upgrade-your-schools-accounting-technology-today-3-questions-to-ask/ https://timesofindia.indiatimes.com/city/bengaluru/bengalurus-school-bus-operators-rely-on-ai-technology-to-beat-traffic-blues/articleshow/94441288.cms https://energydigital.com/articles/managing-utilities-leave-it-to-artificial-intelligence https://onlinedegrees.sandiego.edu/artificial-intelligence-education/ https://www2.ed.gov/documents/ai-report/ai-report.pdf https://www.sciencedirect.com/science/article/pii/S2666412722000137

How AI-Powered Innovations Can Revolutionize School Technology Systems for the Digital Age

Have you ever tried explaining Platform as a Service or PaaS to your parents or friends outside the tech industry? Their eyes glaze over as you excitedly dive into how PaaS provides a framework to quickly develop, deploy, and manage web applications. I get it, the technical jargon alone is enough to make anyone's head spin. My friend still asks me if the cloud is going to rain on her computer🗿. As someone who works with PaaS every day, I want to share an easy, relatable explanation of this technology - no technical degree required. What Exactly Is PaaS? So what exactly is PaaS? In simple terms, PaaS stands for “Platform as a Service.” It’s a cloud computing model where a third-party provider delivers hardware and software tools to developers over the Internet. As a developer, PaaS allows me to build and deploy apps without having to deal with infrastructure hassles like setting up servers or installing software. I can focus on coding while the PaaS provider handles all the other stuff. It’s a huge time-saver! Some of the essential things a PaaS offers include: Operating systems, programming languages, and databases. I get to choose from options like Linux, Windows, PHP, Java, MySQL, and more. The PaaS provider supplies data storage, bandwidth, load balancing, and networking capabilities. Storage and networking. Updates, patches, and security measures are handled for me. I don’t have to worry about keeping systems up-to-date or fending off cyber threats. Management and security. PaaS makes it easy to scale apps up or down depending on demand. If my app suddenly goes viral, the platform can instantly allocate more resources to handle the increased traffic. Scalability. PaaS provides tools for building, testing, and deploying apps, as well as managing their lifecycles. I can get my apps up and running in no time! Deployment tools. PaaS services are typically paid for based on usage, so I only pay for the resources I actually need. No upfront investments are required. Pay-as-you-go pricing. In a nutshell, PaaS lets developers like me focus on innovation rather than infrastructure. PaaS Examples You Use Every Day So what exactly are examples of PaaS that we use every day? More than you might realize. Let me explain. Have you ever used Google Drive or Microsoft OneDrive? Boom, that's PaaS. These services allow us to store our files in the cloud and access them from anywhere. We don't have to maintain the infrastructure - Google and Microsoft handle all that for us. Ever share photos on Instagram or Facebook? Again, PaaS. Those platforms provide the means to upload and share our digital memories with friends, all powered by their infrastructure. Do you bank online or use services like PayPal? You guessed it, PaaS again. Financial institutions and payment processors employ platforms to securely handle transactions without us having to set up the systems and software. The examples go on and on. Creating documents in Google Docs or Sheets, streaming music on Spotify, and watching videos on YouTube - these are all everyday use cases of PaaS. Any time you access an application or service through the internet, there's a good chance PaaS is working behind the scenes to make it happen. PaaS has become so commonplace, we often don't even realize we're using it. But without these ready-made platforms handling the infrastructure, none of these popular services and tools would be possible. Why Should I Care About PaaS? Why should you care about PaaS? Honestly, as an average person, you probably don’t need to know all the technical details. But PaaS can make your life easier in some simple ways. PaaS stands for “Platform as a Service.” In plain English, it means companies provide you with a platform to build and run apps without having to set up your own servers. Have you ever used free services like Wix to create a basic website? Or played around with IFTTT to automatically connect your smart devices? Those are examples of PaaS. 1. It’s Convenient. Using PaaS, I don’t have to be an expert in servers, storage, and networks to build an app. The service takes care of all the infrastructure for me. All I have to do is focus on designing and developing my application. It’s kind of like using pre-made cookie dough instead of baking everything from scratch. 2. It’s Scalable. If my app suddenly gets popular, PaaS can quickly provide more computing power and storage to handle the increased traffic. I don’t have to scramble to buy and set up new servers to keep up with demand. The platform automatically scales for me based on my needs. 3. It’s Cost-Effective. PaaS allows me to save money because I only pay for the resources I actually use. I can start with a small amount of computing power and storage, and seamlessly upgrade as required. This “pay-as-you-go” model means I don’t have to invest in expensive hardware upfront that could end up unused. While the technical details of PaaS may go over the average person’s head (mine included!), the benefits are pretty straightforward. Convenience, scalability, and cost-effectiveness - are things anyone can appreciate. The Good, the Bad, and the Ugly As with any new technology, PaaS comes with its fair share of pros and cons. Let me break down what I see as the main benefits and drawbacks of using a PaaS for your business. PaaS makes development and deployment simple. As a developer, I can focus on building apps without worrying about servers or infrastructure because the PaaS provider handles that. I simply push my code, and my app is live. This speeds up development and rapid iteration. PaaS is also budget-friendly since you only pay for the resources you use. You don’t have to invest in expensive servers and software licenses upfront. The pay-as-you-go model keeps costs low, which is great for startups and small businesses. However, there are some downsides to be aware of. The major one is lack of control. You are locked into the PaaS provider’s ecosystem and toolsets. If they go down or make changes you don’t like, you’re out of luck. Your data and apps are also in the hands of the provider, so you have to trust that they have strong security and reliability. PaaS can also end up costing more in the long run if your app becomes hugely popular. The pay-as-you-go model is only cheap when usage is low. High-volume, data-intensive apps may find PaaS uneconomical and difficult to scale. For many businesses, the pros of PaaS far outweigh the cons. The convenience and low cost of entry make PaaS an easy choice, especially when getting started. However, as your needs grow more complex, the loss of control and potential cost issues require careful consideration. PaaS isn’t a one-size-fits-all solution, but for lots of use cases, it hits the sweet spot of simplicity and value. Conclusion So there you have it, PaaS broken down Barney-style. I hope I was able to shed some light on this cloud concept in a simple, relatable way. The next time your techy friend starts rambling on about PaaS, containers, and scalable infrastructure, you’ll be able to keep up and maybe even teach them a thing or two! (Will cover everything one by one) At the end of the day, PaaS is really just about building apps without having to worry about all the underlying infrastructure. I’m just hoping I was able to help you understand platforms-as-a-service a little bit better. Happy PaaS-ing! Must Read on PaaS PaaS for Dummies On the Original Definition of PaaS Set Up Your Own Heroku-like Cloud (PAAS) in 10 Minutes

PaaS in Plain English: A Simple Explanation for Anyone Confused

সরল ইংরেজিতে PaaS: যে কেউ বিভ্রান্তির জন্য একটি সহজ ব্যাখ্যা

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PaaS 通俗易懂：为任何困惑的人提供简单解释

In a world where computer programming has become the backbone of technological advancement and innovation, a startling question has emerged, sending shockwaves through the tech community: Could this be the end of computer programming as we know it? With the rapid progress of artificial intelligence and low-code platforms, experts are raising concerns about the future of traditional coding and its impact on the job market. For decades, computer programming has been the primary means of instructing computers to perform complex tasks, enabling the creation of software applications, algorithms, and systems that power our digital age. However, recent developments in the field of AI and the rise of low-code and no-code platforms have ignited a debate about the role of traditional programming in the years to come. When I was young and watching movies like The Terminator, was once a concept confined to me as science fiction. But now, it’s a tangible reality! artificial intelligence Machine learning algorithms and neural networks have advanced so much that it’s made my head spin. It’s now possible for computers to learn and adapt without explicit human instructions. As AI systems grow more sophisticated, could they eventually replace the need for human programmers altogether? and no-code platforms have gained considerable popularity in recent years and now allow individuals with limited coding knowledge to create applications and software by utilizing visual interfaces and pre-built components. Low-code While they offer increased accessibility and speed in application development, critics fear that they may undermine the significance of traditional coding skills, leading to a potential decline in demand for professional programmers. With advancements in natural language processing, computers can now understand human language better than ever before and seemingly opened the door to the possibility of creating applications by simply describing the desired functionality in plain English, without writing a single line of code. Additionally, automated code generation tools leverage machine learning algorithms to generate code based on high-level specifications, reducing the need for manual coding. Not all experts are convinced that the end of computer programming is imminent. They argue that while AI and low-code platforms may automate certain aspects of coding, they cannot replicate the creative problem-solving abilities and critical thinking skills of human programmers. “Coding is not just about translating instructions into code; it requires a deep understanding of algorithms, logic, and the ability to solve complex problems efficiently.” While the future of computer programming remains uncertain, one thing is clear: the landscape of coding is evolving. As AI and low-code platforms become more prevalent, programmers will need to adapt, expand their skill sets, and find new ways to add value. As the debate rages on, only time will tell whether this marks the end of computer programming as we know it or the beginning of a new era where humans and machines collaborate in unprecedented ways. One thing is certain—technology and innovation wait for no one, and those who embrace change will continue to thrive in the world of programming.

Could This Be the End of Computer Programming as We Know It?

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