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🚨Anthropic’s research paper proves AI coding tools are secretly making developers worse. every engineering leader bragging about AI adoption should read this twice. > 17% lower scores learning new libraries with AI. sub 40% when AI wrote everything. zero measurable. you’re measuring output. nobody is measuring comprehension loss. different... show more

sui ☄️

28,752 subscribers

95,564 次观看 • 4 个月前 •via X (Twitter)

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NEW: AI Papers of the Week Collection I just released my AI Papers of the Week collection under our Resources hub. Now you can easily find some of the most important AI papers in one place. You can also use our new AI tutor to recommend top AI papers on any topic of interest or topics you are learning about on the platform. Learning about AI is not enough. It's important to keep up to date. So this is why we are building all these tools and resources to help with that. Go try it out here: We will update the collection every week and add new paper collections in the coming weeks. We have another killer feature dropping soon to read and annotate papers, including a completely new way to study and digest AI papers. Stay tuned!

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What if creating AI was as easy as posting content? ➜ Theoriq is here to shake up AI. It’s not another walled garden this protocol lets you build, stack, & evolve AI agents that actually talk to each other. Fully composable, fully decentralized. It’s built to grow with tech, powered by the community & open to devs, creators, and anyone who wants in. You can build with no code tools, help govern the protocol or earn by staking on powerful agents. It’s collective intelligence with real ownership Not just AI you use AI you shape. If you’re about building the future, Theoriq’s your arena.

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AMIKO

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1,361,213 次观看 • 1 个月前

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Guillermo Rauch

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🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.

Kanika

737,570 次观看 • 4 个月前

2 weeks ago i started delegating everything to an AI agent not just writing. actual operations. here's exactly what it does for me now: 1. trades polymarket 24/7 (378 wins, 27 losses, 93% win rate) 2. sends me a morning brief every day at 7:30am with personalized tweet suggestions based on what performed yesterday 3. scrapes my own analytics and tracks what's working 4. monitors all my bots and alerts me when something breaks 5. checks my email and calendar so i don't have to total setup cost: $0. it runs on a $600 mac mini. the part nobody tells you: the first week was terrible. bad suggestions, wrong data, generic output. but every time it got something wrong, i told it why. and it got better. AI isn't plug and play. it's a team member you train with honest feedback. the people treating AI like a google search will never get here.

2 weeks ago i started delegating everything to an AI agent not just writing. actual operations. here's exactly what it does for me now: 1. trades polymarket 24/7 (378 wins, 27 losses, 93% win rate) 2. sends me a morning brief every day at 7:30am with personalized tweet suggestions based on what performed yesterday 3. scrapes my own analytics and tracks what's working 4. monitors all my bots and alerts me when something breaks 5. checks my email and calendar so i don't have to total setup cost: $0. it runs on a $600 mac mini. the part nobody tells you: the first week was terrible. bad suggestions, wrong data, generic output. but every time it got something wrong, i told it why. and it got better. AI isn't plug and play. it's a team member you train with honest feedback. the people treating AI like a google search will never get here.

Sharbel

16,692 次观看 • 5 个月前

Jeff Bezos just told you exactly how to price AI. Nobody listened. Bezos: “AI is real and it is going to change every industry. In fact it’s a very unusual technology in that regard in that it’s a horizontal enabling layer.” Horizontal enabling layer. Three words that reprice the entire technology sector. The iPhone was a vertical. One product. One new market. Electricity was a horizontal. One substrate that rewired every market on Earth. Wall Street is pricing AI like it is the next iPhone. Bezos is telling you it is the next electrical grid. Right now, thousands of companies are trying to sell AI as a product. A feature. A tool. A subscription tier. Every single one of them will be priced to zero. You do not sell a horizontal layer. You do not compete with it. You build on top of it or you disappear beneath it. For a century, entire industries survived on one thing. Complexity. The friction of navigating law, medicine, logistics, finance. That was the moat. If you could not memorize the maze, you could not compete. A horizontal layer does not navigate the maze. It dissolves the walls. Electricity did not compete with the candle industry. It erased the need for one. The most dangerous part of a horizontal shift is how quiet it is. It moves underneath the economy. The surface looks normal. Revenue still holds. Every day you operate on the old substrate, you accumulate a debt you cannot see and cannot repay. The internet repriced distribution. AI is repricing cognition itself. When intelligence becomes a utility that runs through the walls of every company on Earth, the premium on human expertise does not erode. It evaporates. This is not a disruption. Disruptions replace products. This replaces the ground you are standing on.

Jeff Bezos just told you exactly how to price AI. Nobody listened. Bezos: “AI is real and it is going to change every industry. In fact it’s a very unusual technology in that regard in that it’s a horizontal enabling layer.” Horizontal enabling layer. Three words that reprice the entire technology sector. The iPhone was a vertical. One product. One new market. Electricity was a horizontal. One substrate that rewired every market on Earth. Wall Street is pricing AI like it is the next iPhone. Bezos is telling you it is the next electrical grid. Right now, thousands of companies are trying to sell AI as a product. A feature. A tool. A subscription tier. Every single one of them will be priced to zero. You do not sell a horizontal layer. You do not compete with it. You build on top of it or you disappear beneath it. For a century, entire industries survived on one thing. Complexity. The friction of navigating law, medicine, logistics, finance. That was the moat. If you could not memorize the maze, you could not compete. A horizontal layer does not navigate the maze. It dissolves the walls. Electricity did not compete with the candle industry. It erased the need for one. The most dangerous part of a horizontal shift is how quiet it is. It moves underneath the economy. The surface looks normal. Revenue still holds. Every day you operate on the old substrate, you accumulate a debt you cannot see and cannot repay. The internet repriced distribution. AI is repricing cognition itself. When intelligence becomes a utility that runs through the walls of every company on Earth, the premium on human expertise does not erode. It evaporates. This is not a disruption. Disruptions replace products. This replaces the ground you are standing on.

Dustin

540,733 次观看 • 3 个月前

Claude Code Agent Teams are f*cking ridiculous 🤯 One prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

46,398 次观看 • 4 个月前

OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.

OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.

Alex Prompter

7,014,628 次观看 • 6 天前

not sure why nobody is talking about this but Google Omni is insane at video editing Original Video (left) vs Omni Edited Video (right) everyone is comparing it to Seedance and missing the point completely. Seedance is for generating videos from scratch. Google Omni is for editing videos that already exist. which are two completely different use cases this is like when Nano Banana 1 first came out and nobody realized how big it was going to be. this is the first AI that can actually properly edit videos.. i've generated a few hundred videos with this model and it can do literally any type of edit you can think of. changing voices, swapping characters, removing watermarks, adding captions, transitions, pop ups, whatever. if you can describe the edit you want it can do it this completely crushes every other model on the market when it comes to video editing. nothing else even comes close right now and this is just the flash model. imagine what the pro version is going to be able to do when it drops in a couple months this should have way more hype than it's getting..

not sure why nobody is talking about this but Google Omni is insane at video editing Original Video (left) vs Omni Edited Video (right) everyone is comparing it to Seedance and missing the point completely. Seedance is for generating videos from scratch. Google Omni is for editing videos that already exist. which are two completely different use cases this is like when Nano Banana 1 first came out and nobody realized how big it was going to be. this is the first AI that can actually properly edit videos.. i've generated a few hundred videos with this model and it can do literally any type of edit you can think of. changing voices, swapping characters, removing watermarks, adding captions, transitions, pop ups, whatever. if you can describe the edit you want it can do it this completely crushes every other model on the market when it comes to video editing. nothing else even comes close right now and this is just the flash model. imagine what the pro version is going to be able to do when it drops in a couple months this should have way more hype than it's getting..

Miko

29,380 次观看 • 2 个月前

WHY AI NEEDS ROBOTS TO BE THE ECONOMY We keep hearing about the rise of AI - in finance, in art, in code. But here’s the part Elon said out loud: AI doesn’t scale the economy without a body. Right now, AI lives in data centers. It writes essays, diagnoses symptoms, maybe even talks you through a breakup. But it doesn’t fix the plumbing. It doesn’t pick fruit. That’s the missing link: robots. Real-world productivity - the kind that moves GDP - still runs on physical labor. You can automate all the spreadsheets you want, but if strawberries rot in the field or a construction site goes unmanned, you’re not growing the economy. You’re just writing better emails about the shrinkage. AI alone is smart. But AI with wheels, arms, and joints? That’s transformative. Think warehouses where bots stock shelves without lunch breaks. Think 3D-printed houses built in days, not months. Think carebots helping aging populations bathe, cook, and live with dignity. That’s not sci-fi - it’s the only way economies with shrinking workforces and ballooning eldercare costs survive. China’s already there. Japan’s aging crisis is pushing robotic adoption into every corner of daily life. And yet, the conversation stays stuck on chatbots and copyright lawsuits. Because here’s the catch: merging AI with robotics doesn’t just replace jobs - it reshapes civilization. The shift is seismic. It means redefining what “employment” means when machines can work 24/7 and never strike. It’s an economic revolution that doesn’t just disrupt - it displaces. But the choice isn’t between utopia or dystopia. It’s between preparing or pretending. If we want AI to boost productivity, solve labor shortages, and pay off its hype, it needs more than brains. It needs bodies.

WHY AI NEEDS ROBOTS TO BE THE ECONOMY We keep hearing about the rise of AI - in finance, in art, in code. But here’s the part Elon said out loud: AI doesn’t scale the economy without a body. Right now, AI lives in data centers. It writes essays, diagnoses symptoms, maybe even talks you through a breakup. But it doesn’t fix the plumbing. It doesn’t pick fruit. That’s the missing link: robots. Real-world productivity - the kind that moves GDP - still runs on physical labor. You can automate all the spreadsheets you want, but if strawberries rot in the field or a construction site goes unmanned, you’re not growing the economy. You’re just writing better emails about the shrinkage. AI alone is smart. But AI with wheels, arms, and joints? That’s transformative. Think warehouses where bots stock shelves without lunch breaks. Think 3D-printed houses built in days, not months. Think carebots helping aging populations bathe, cook, and live with dignity. That’s not sci-fi - it’s the only way economies with shrinking workforces and ballooning eldercare costs survive. China’s already there. Japan’s aging crisis is pushing robotic adoption into every corner of daily life. And yet, the conversation stays stuck on chatbots and copyright lawsuits. Because here’s the catch: merging AI with robotics doesn’t just replace jobs - it reshapes civilization. The shift is seismic. It means redefining what “employment” means when machines can work 24/7 and never strike. It’s an economic revolution that doesn’t just disrupt - it displaces. But the choice isn’t between utopia or dystopia. It’s between preparing or pretending. If we want AI to boost productivity, solve labor shortages, and pay off its hype, it needs more than brains. It needs bodies.

Mario Nawfal

141,937 次观看 • 8 个月前