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Stop trading manually like a caveman. If you can type, anyone can copy my Polymarket high-frequency bot. I didn’t write a single line of code. I built a top-tier strategy just by “chatting” with AI,one that self-debugs, self-repairs, and even uses “large model decision-making + small model monitoring”. This... show more

Satori 🎴 💀

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73,559 Aufrufe • vor 6 Monaten •via X (Twitter)

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I just found this new and unknown sports trader who specializes in cricket and made $83K this year! Do you struggle finding profitable accounts to copy trade on Polymarket? Try out this new AI tool by Kreo! You can easily find new accounts and copy trade them in one click. Set a few parameters and let the AI do the work for you. All you need to do is double check that it's not a arbitrage bot or market making bot you are copying. Once you found a good account, click copy and let Kreo do the hard work Great tech Kreo I approve!

I just found this new and unknown sports trader who specializes in cricket and made $83K this year! Do you struggle finding profitable accounts to copy trade on Polymarket? Try out this new AI tool by Kreo! You can easily find new accounts and copy trade them in one click. Set a few parameters and let the AI do the work for you. All you need to do is double check that it's not a arbitrage bot or market making bot you are copying. Once you found a good account, click copy and let Kreo do the hard work Great tech Kreo I approve!

Car

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School Student and Anthropic AI keep blowing my mind. In just the last 24 hours, he made $15,000. His balance is now over $1.5M. He started with $1,000. AI literally changed his life. x1500, OMG. You can’t argue with it, this is genius. A 15-minute BTC arbitrage strategy that anyone can use, even if you know nothing. According to his profile: > You’re losing money every day while his strategy keeps working, and you’re not even copy trading him for free. I use a bot that’s faster than the rest and has zero fees: > On Polymarket, even a kid can make money, and this guy proved it.

igorizuchaetcrypty

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something I’ve wanted for a while: an AI that isn’t just a text box so I built it lets you create a 3D AI character on-chain that lives in your browser it can talk, remember you, show emotions, and use different skills. you can even mint it onchain, so it actually belongs to you it gets its own identity, history, and reputation that no one can take away most AI today can disappear if the app shuts down this one is permanent and yours the future is AI you can see, interact with, and actually own

something I’ve wanted for a while: an AI that isn’t just a text box so I built it lets you create a 3D AI character on-chain that lives in your browser it can talk, remember you, show emotions, and use different skills. you can even mint it onchain, so it actually belongs to you it gets its own identity, history, and reputation that no one can take away most AI today can disappear if the app shuts down this one is permanent and yours the future is AI you can see, interact with, and actually own

nich

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At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

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Ecom brands are paying creators a $0.50 RPM to post AI UGC clips promoting their product This account has gotten millions of views from AI podcasts The thing is you can make so much more money by running the traffic to your affiliate link and you can almost automate the entire process with MakeUGC + Claude Scripting is 70% of the AI UGC process if you don't have a good script it doesn't matter how good your AI videos are and just telling claude "write me a script for this" will get you no where I put my entire process with how I pump out insane amounts of volume of AI UGC for affiliate products in a google doc including: >How to build a customer research file for your copy using AI >How to make an automated scripting process that gives actual good copy using this file >How to make insane amounts of volume of AI UGC and push it across all platforms simultaneously if you want it RT + comment "aff" and ill send it to you (must be following so I can DM)

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i found the secret to writing copy that SELLS with AI... it's not about the model, it's not about the prompt, and it doesn't matter how good you are at writing it's about feeding AI with world-class examples i built a full copywriting swipe file you can use with any LLM: - over 1,000 examples of the best emails, ads and landing pages copy - my 3-step process to use this inside AI projects - my super-prompt to extract the sauce from any copy reply "SWIPE" + retweet and i'll send it for free (must be following so i can DM)

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Machina

32,588 Aufrufe • vor 1 Jahr

prompting tip: How to write hybrid english & code prompts while building custom GPTs -Turn on code interpreter -"write code to ..." -Regenerate a few times until you get one you like -Copy paste it back into your prompt -tweak it to improve Now your ai can write consistent code Plus, you can add & combine with all sorts of english rules on top. Essentially adding english code logic "If the user asks for ..., then run this code" "If the user needs help with ..., use this code snippet and ask the user for all required information" Guess what thats a working if statement in english! if userAsksFor { // run this code } Prompts are code! English is a coding language Prompt-gramming is abstract coding

prompting tip: How to write hybrid english & code prompts while building custom GPTs -Turn on code interpreter -"write code to ..." -Regenerate a few times until you get one you like -Copy paste it back into your prompt -tweak it to improve Now your ai can write consistent code Plus, you can add & combine with all sorts of english rules on top. Essentially adding english code logic "If the user asks for ..., then run this code" "If the user needs help with ..., use this code snippet and ask the user for all required information" Guess what thats a working if statement in english! if userAsksFor { // run this code } Prompts are code! English is a coding language Prompt-gramming is abstract coding

Nick Dobos

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I’M DROPPING EVERYTHING AND GOING ALL-IN ON CLAUDE Just asked him to: “Look for mispriced Polymarket markets to profit from arbitrage, and spot wallets that copy these trades” Turned $600 into $10K in one 2 day Monitored ~1k+ wallets I looked at the wallets that he scans and was shocked, here is one of them: $120 → $1.4M 1,947 trades. Win rate 55%. I just realized that there are many arbitrage bots that I can't beat without code knowledge But I can find them and copy So Claude created a monitoring terminal and copytraded found wallets using Ares copytrading. It’s not a script and not even the bot, it’s an AI agent that automatically adds top wallets to the leaderboard, and you can simply start copying them and earning. Nothing complicated: - choose a trader - choose PRIORITY MODE (the fastest bot for copytrading) - press the start button That’s all you need to consistently earn. If you’re curious about how to write your own bot, I will send you a step-by-step guide on how to create your bot from scratch. I’ve already helped more than a dozen people achieve stable income. I don’t promise huge sums, I promise stability. Just: 1. Comment the word ‘CLAUDE’ 2. Like and Retweet this post 3. Follow me Marry Evan (so that I can message you)

I’M DROPPING EVERYTHING AND GOING ALL-IN ON CLAUDE Just asked him to: “Look for mispriced Polymarket markets to profit from arbitrage, and spot wallets that copy these trades” Turned $600 into $10K in one 2 day Monitored ~1k+ wallets I looked at the wallets that he scans and was shocked, here is one of them: $120 → $1.4M 1,947 trades. Win rate 55%. I just realized that there are many arbitrage bots that I can't beat without code knowledge But I can find them and copy So Claude created a monitoring terminal and copytraded found wallets using Ares copytrading. It’s not a script and not even the bot, it’s an AI agent that automatically adds top wallets to the leaderboard, and you can simply start copying them and earning. Nothing complicated: - choose a trader - choose PRIORITY MODE (the fastest bot for copytrading) - press the start button That’s all you need to consistently earn. If you’re curious about how to write your own bot, I will send you a step-by-step guide on how to create your bot from scratch. I’ve already helped more than a dozen people achieve stable income. I don’t promise huge sums, I promise stability. Just: 1. Comment the word ‘CLAUDE’ 2. Like and Retweet this post 3. Follow me Marry Evan (so that I can message you)

Marry Evan

19,800 Aufrufe • vor 4 Monaten

99% winrate: polymarket trader that literally forgets how to lose i found a wallet on Polymarket that feels like a glitch in the simulation his stats: win rate: 99% PnL: $30,000 strategy: manual, disciplined, high-probability trades bet size: ~$10k why this is the perfect copy-trading setup: >99% win rate means you almost never see a "red day" its the perfect setup for those who want steady growth without the stress of drawdowns >this isnt a high-speed bot, you have time to sync and follow without getting crushed by execution speed >at ~$10k per bet, he doesnt move the market. you can copy him using PolyGun and get filled at almost the same prices wallet:

99% winrate: polymarket trader that literally forgets how to lose i found a wallet on Polymarket that feels like a glitch in the simulation his stats: win rate: 99% PnL: $30,000 strategy: manual, disciplined, high-probability trades bet size: ~$10k why this is the perfect copy-trading setup: >99% win rate means you almost never see a "red day" its the perfect setup for those who want steady growth without the stress of drawdowns >this isnt a high-speed bot, you have time to sync and follow without getting crushed by execution speed >at ~$10k per bet, he doesnt move the market. you can copy him using PolyGun and get filled at almost the same prices wallet:

AshenSoul

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"I told Claude 'remember you are not human' and it started working 10x better." "I have not written a single line of code in 14 months. I just run loops that write code for me." 1 hour masterclass on loop engineering that will turn you into the most dangerous person in AI right now. You will level up your coding and ship 10x faster. If I had this a year ago, I would've built my self improving hedge fund in a week instead of a year. Bookmark this, then read the article on building hedge fund.

"I told Claude 'remember you are not human' and it started working 10x better." "I have not written a single line of code in 14 months. I just run loops that write code for me." 1 hour masterclass on loop engineering that will turn you into the most dangerous person in AI right now. You will level up your coding and ship 10x faster. If I had this a year ago, I would've built my self improving hedge fund in a week instead of a year. Bookmark this, then read the article on building hedge fund.

Roan

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This guy is setting up passive income for the rest of his life by selling digital books on Amazon While you are stuck at a 9 to 5, he uses a software called Book Bolt to find high demand, low competition products that people are already searching for He doesn't even write or draw anything himself. He just uses Midjourney AI to generate images for a coloring book and drops them into Canva Amazon handles all the traffic and customers and he just collects the passive income indefinitely You could launch your own AI generated book empire tonight with zero inventory and zero upfront cost But you'll probably just keep scrolling and ignore the easiest wealth hack of the year

This guy is setting up passive income for the rest of his life by selling digital books on Amazon While you are stuck at a 9 to 5, he uses a software called Book Bolt to find high demand, low competition products that people are already searching for He doesn't even write or draw anything himself. He just uses Midjourney AI to generate images for a coloring book and drops them into Canva Amazon handles all the traffic and customers and he just collects the passive income indefinitely You could launch your own AI generated book empire tonight with zero inventory and zero upfront cost But you'll probably just keep scrolling and ignore the easiest wealth hack of the year

Nekt0

12,503 Aufrufe • vor 2 Monaten

i built a full game on a single GPU with a 3B model and this is the worst local AI will ever be. this was supposed to be a benchmark test. load the model, measure tokens per second, write it up, move on. instead i spent 20 minutes playing Octopus Invaders because the game is genuinely fun and i couldn't stop. a model with 3B active parameters built this from a single prompt. it debugged its own collision system when bullets were phasing through enemies. read the error, found the fix, kept building. this is not a frontier API. this is a quantized open source model running on hardware you can buy used for $800-$1200. no cloud. no subscription. no API costs. just a mass produced consumer GPU doing things that would have been absurd 12 months ago. and here's the part that should keep you up at night: every month the models get smaller and smarter. the quants get tighter. the context windows get longer. the tooling gets cleaner. what 3B active parameters does today on 24gb, a 1B model will do on 8gb within a year. you are looking at the floor. not the ceiling.

i built a full game on a single GPU with a 3B model and this is the worst local AI will ever be. this was supposed to be a benchmark test. load the model, measure tokens per second, write it up, move on. instead i spent 20 minutes playing Octopus Invaders because the game is genuinely fun and i couldn't stop. a model with 3B active parameters built this from a single prompt. it debugged its own collision system when bullets were phasing through enemies. read the error, found the fix, kept building. this is not a frontier API. this is a quantized open source model running on hardware you can buy used for $800-$1200. no cloud. no subscription. no API costs. just a mass produced consumer GPU doing things that would have been absurd 12 months ago. and here's the part that should keep you up at night: every month the models get smaller and smarter. the quants get tighter. the context windows get longer. the tooling gets cleaner. what 3B active parameters does today on 24gb, a 1B model will do on 8gb within a year. you are looking at the floor. not the ceiling.

Sudo su

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I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

Andrew Ng

1,224,810 Aufrufe • vor 2 Jahren

New Paper! Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents A longstanding goal of AI research has been the creation of AI that can learn indefinitely. One path toward that goal is an AI that improves itself by rewriting its own code, including any code responsible for learning. That idea, known as a Gödel Machine, proposed by Jürgen Schmidhuber over two decades ago, is a hypothetical self-improving AI. It optimally solves problems by recursively rewriting its own code when it can mathematically prove a better strategy, making it a key concept in meta-learning or “learning to learn.” While the theoretical Gödel Machine promised provably beneficial self-modifications, its realization relied on an impractical assumption: that the AI could mathematically prove that a proposed change in its own code would yield a net improvement before adopting it. Sakana AI, in collaboration with Jeff Clune’s lab at UBC, proposes something more feasible: a system that harnesses the principles of open-ended algorithms like Darwinian evolution to search for improvements that empirically improve performance. We call the result the Darwin Gödel Machine. DGMs leverage foundation models to propose code improvements, and use recent innovations in open-ended algorithms to search for a growing library of diverse, high-quality AI agents. Applied to practical tasks, we implemented Darwin Gödel Machine as a self-improving coding agent that rewrites its own code to improve performance on programming tasks. It creates various self-improvements, such as a patch validation step, better file viewing, enhanced editing tools, generating and ranking multiple solutions to choose the best one, and adding a history of what has been tried before (and why it failed) when making new changes (see the attached video). We believe that Darwin Gödel Machines represent a concrete step towards AI systems that can autonomously gather their own stepping stones to learn and innovate forever!

New Paper! Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents A longstanding goal of AI research has been the creation of AI that can learn indefinitely. One path toward that goal is an AI that improves itself by rewriting its own code, including any code responsible for learning. That idea, known as a Gödel Machine, proposed by Jürgen Schmidhuber over two decades ago, is a hypothetical self-improving AI. It optimally solves problems by recursively rewriting its own code when it can mathematically prove a better strategy, making it a key concept in meta-learning or “learning to learn.” While the theoretical Gödel Machine promised provably beneficial self-modifications, its realization relied on an impractical assumption: that the AI could mathematically prove that a proposed change in its own code would yield a net improvement before adopting it. Sakana AI, in collaboration with Jeff Clune’s lab at UBC, proposes something more feasible: a system that harnesses the principles of open-ended algorithms like Darwinian evolution to search for improvements that empirically improve performance. We call the result the Darwin Gödel Machine. DGMs leverage foundation models to propose code improvements, and use recent innovations in open-ended algorithms to search for a growing library of diverse, high-quality AI agents. Applied to practical tasks, we implemented Darwin Gödel Machine as a self-improving coding agent that rewrites its own code to improve performance on programming tasks. It creates various self-improvements, such as a patch validation step, better file viewing, enhanced editing tools, generating and ranking multiple solutions to choose the best one, and adding a history of what has been tried before (and why it failed) when making new changes (see the attached video). We believe that Darwin Gödel Machines represent a concrete step towards AI systems that can autonomously gather their own stepping stones to learn and innovate forever!

hardmaru

104,854 Aufrufe • vor 1 Jahr

I'm giving away Claude Code for FREE. Yes, you read that right -- the world's best AI, now completely FREE. My mission is to get the next billion people to write great software. I believe EVERYONE should have access to the best AI, but so many are constrained by costs. I'm solving this by: disrupting the world's fastest growing industry = AI + with the most proven business model = Ads You can actually try it out now -- just one line of code, and you unlock all the powers of Claude Code, for free. how it works: 1. sign up on Giga AI Free 2. change Claude Code env variables to use our inference endpoint 2. to support the free usage, we'll add ads inside the AI responses (only contextual and relevant ads, no spam) Since we get too many signups, there's a waitlist, but we'll be rolling it out to the maximum people as we can. Watch the video to get started & comment and retweet to get access first.

I'm giving away Claude Code for FREE. Yes, you read that right -- the world's best AI, now completely FREE. My mission is to get the next billion people to write great software. I believe EVERYONE should have access to the best AI, but so many are constrained by costs. I'm solving this by: disrupting the world's fastest growing industry = AI + with the most proven business model = Ads You can actually try it out now -- just one line of code, and you unlock all the powers of Claude Code, for free. how it works: 1. sign up on Giga AI Free 2. change Claude Code env variables to use our inference endpoint 2. to support the free usage, we'll add ads inside the AI responses (only contextual and relevant ads, no spam) Since we get too many signups, there's a waitlist, but we'll be rolling it out to the maximum people as we can. Watch the video to get started & comment and retweet to get access first.

Namanyay

487,949 Aufrufe • vor 9 Monaten

Head of Claude Code: "agents don't fail because they're dumb, they fail because you're vague" these 11 minutes explain what most people using AI are getting wrong without knowing it the distance between an idea and a working product is collapsing Spotify proved it with one background agent merging 1,000+ PRs a month and cutting migration time by 90% most people use AI for small tasks and wonder why nothing changes the real power is when the model becomes part of your planning, execution, and review model capabilities are growing on an exponential while adoption is still linear his advice is to write the routine, describe the outcome, and then let it cook stop prompting back and forth and let Claude prompt itself understanding this is step one knowing which Claude features actually let you build those systems is step two the article below covers everything most people have never found

Head of Claude Code: "agents don't fail because they're dumb, they fail because you're vague" these 11 minutes explain what most people using AI are getting wrong without knowing it the distance between an idea and a working product is collapsing Spotify proved it with one background agent merging 1,000+ PRs a month and cutting migration time by 90% most people use AI for small tasks and wonder why nothing changes the real power is when the model becomes part of your planning, execution, and review model capabilities are growing on an exponential while adoption is still linear his advice is to write the routine, describe the outcome, and then let it cook stop prompting back and forth and let Claude prompt itself understanding this is step one knowing which Claude features actually let you build those systems is step two the article below covers everything most people have never found

Anatoli Kopadze

30,444 Aufrufe • vor 2 Monaten

This guy made $8,400 in 13 days from a business that was built entirely by AI. He is not a coder. He doesn't have any special technical skills. He just had an idea and let AI do the rest. 2 weeks in, he's projected to make $100k/year from this "side hustle" If you are even the slightest bit curious about what you can build with AI, watch this!

This guy made $8,400 in 13 days from a business that was built entirely by AI. He is not a coder. He doesn't have any special technical skills. He just had an idea and let AI do the rest. 2 weeks in, he's projected to make $100k/year from this "side hustle" If you are even the slightest bit curious about what you can build with AI, watch this!

Chris Koerner

142,765 Aufrufe • vor 5 Monaten

"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."

"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."

Patrick OShaughnessy

24,818 Aufrufe • vor 6 Monaten

.Naval: The easiest way to see that AI is not taking jobs but creating opportunities is to go learn AI and then reapply for the job that rejected you. Watch how they pull you in. “My last startup, Airchat, could not have existed without AI because we needed the transcription/translation. And even the current thing I’m working on—it’s not an AI company, but it cannot exist without AI. It is relying on AI. Even at AngelList, we’re significantly adopting AI. Like everywhere you turn, it’s more opportunity, more opportunity, more opportunity. And people like to go on Twitter—or the artist formerly known as Twitter—and basically, they like to exaggerate. Like, ‘Oh my God, we’ve hit AGI.’ ‘Oh my God, I just replaced all my mid-level engineers.’ ‘Oh my God, I’ve stopped hiring.’ To me, that’s moronic. The two valid perspectives are the one-man entrepreneur shows, where there’s one guy or one gal, and they’re scaling up like crazy thanks to AI. Or there are people who are embracing AI and being like, ‘I need to hire, and I need to hire anyone who can even spell ‘AI’—anyone who’s even used AI. Just come on in, come on in.’ Again, I would say the easiest way to see that AI is not taking jobs but creating opportunities is this: go brush up on your AI, learn a little bit, watch a few videos, use the AI, tinker with it, and then go reapply for that job that rejected you. Watch how they pull you in.”

.Naval: The easiest way to see that AI is not taking jobs but creating opportunities is to go learn AI and then reapply for the job that rejected you. Watch how they pull you in. “My last startup, Airchat, could not have existed without AI because we needed the transcription/translation. And even the current thing I’m working on—it’s not an AI company, but it cannot exist without AI. It is relying on AI. Even at AngelList, we’re significantly adopting AI. Like everywhere you turn, it’s more opportunity, more opportunity, more opportunity. And people like to go on Twitter—or the artist formerly known as Twitter—and basically, they like to exaggerate. Like, ‘Oh my God, we’ve hit AGI.’ ‘Oh my God, I just replaced all my mid-level engineers.’ ‘Oh my God, I’ve stopped hiring.’ To me, that’s moronic. The two valid perspectives are the one-man entrepreneur shows, where there’s one guy or one gal, and they’re scaling up like crazy thanks to AI. Or there are people who are embracing AI and being like, ‘I need to hire, and I need to hire anyone who can even spell ‘AI’—anyone who’s even used AI. Just come on in, come on in.’ Again, I would say the easiest way to see that AI is not taking jobs but creating opportunities is this: go brush up on your AI, learn a little bit, watch a few videos, use the AI, tinker with it, and then go reapply for that job that rejected you. Watch how they pull you in.”

Arjun Khemani

167,746 Aufrufe • vor 1 Jahr

Software Engineering Expectations for 2026 - The majority of your code should be written by AI now - Cursor/Codex/Claude Code/Gemini/etc - You should try all the tooling and switch between them, as each one gets an edge over the others depending on the release cycle. - You should be using AI to check the code that is written by AI - Have AI write tests - Have AI read logs - Have AI navigate your browser - I don't do this every time because sometimes it's simple enough to check it myself - You should still skim code changes - This can be a lighter skim on internal tools and a heavier read through on customer facing code - Use AI to help you define specs

Software Engineering Expectations for 2026 - The majority of your code should be written by AI now - Cursor/Codex/Claude Code/Gemini/etc - You should try all the tooling and switch between them, as each one gets an edge over the others depending on the release cycle. - You should be using AI to check the code that is written by AI - Have AI write tests - Have AI read logs - Have AI navigate your browser - I don't do this every time because sometimes it's simple enough to check it myself - You should still skim code changes - This can be a lighter skim on internal tools and a heavier read through on customer facing code - Use AI to help you define specs

Ben Awad

534,992 Aufrufe • vor 7 Monaten