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GLM-5.1 > Claude Code (Opus 4.6)? I'm tripping or CC has become very bad but built a Three.js racing game to eval and it's extremely impressive. Thoughts: - One-shot car physics with real drift mechanics (this is hard) - My fav part: Awesome at self iterating (with no vision!)...

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Victor M

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202,313 просмотров • 4 месяцев назад •via X (Twitter)

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THE WAY YOU LOOK AT AI GAME DEVELOPMENT WILL CHANGE AFTER YOU WATCH THIS VIDEO "The AI Slop is Coming for Games." That's right. Introducing... Slopet League Everything you see here was built by Opus 5 and GPT 5.6 Sol in _____🤫. This is not an html page, it is a functional game with a fair bit of polish working inside a real game engine. This has to be some kind of trick. NO. It downloaded a map. NO. Imported assets. Only the Octane meshes, I have other cars that were generated that look just as good. The balls, the map, the effects, the physics, animations, etc were all generated from scratch by Opus 5. GPT 5.6 Sol handled the car meshes. This is only the beginning. I made this in about a day and a half, and most of that time I was waiting on Opus to finish generation. Maybe 25 prompts. I will share my process, and many photos, proofs, evidence of this as we go. This is just a taste, I'm extremely busy this week and working on real life stuff so I'm not going to put any more time into this until next week, but believe me I'm gonna polish this into something beautiful. If you're interested, please share, bookmark, follow, and comment below. I want the world to see just how powerful the language models are TODAY. It's only going to get better from here. Buckle up folks. Oh, and What a save!
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THE WAY YOU LOOK AT AI GAME DEVELOPMENT WILL CHANGE AFTER YOU WATCH THIS VIDEO "The AI Slop is Coming for Games." That's right. Introducing... Slopet League Everything you see here was built by Opus 5 and GPT 5.6 Sol in _____🤫. This is not an html page, it is a functional game with a fair bit of polish working inside a real game engine. This has to be some kind of trick. NO. It downloaded a map. NO. Imported assets. Only the Octane meshes, I have other cars that were generated that look just as good. The balls, the map, the effects, the physics, animations, etc were all generated from scratch by Opus 5. GPT 5.6 Sol handled the car meshes. This is only the beginning. I made this in about a day and a half, and most of that time I was waiting on Opus to finish generation. Maybe 25 prompts. I will share my process, and many photos, proofs, evidence of this as we go. This is just a taste, I'm extremely busy this week and working on real life stuff so I'm not going to put any more time into this until next week, but believe me I'm gonna polish this into something beautiful. If you're interested, please share, bookmark, follow, and comment below. I want the world to see just how powerful the language models are TODAY. It's only going to get better from here. Buckle up folks. Oh, and What a save!

am.will

130,828 просмотров • 14 дней назад

THIS GUY VIBE CODED A FULL CAPYBARA FOOD DELIVERY GAME IN 2 WEEKS WITH CLAUDE CODE you play as a capybara delivering food on a bike. orders stack on the back, you have a phone with apps in-game and the whole delivery system is realistic 2 weeks, zero game dev experience, and ENTIRELY AI generated the full stack: > claude code for all the code > three.js for the 3D engine > suno for original music > elevenlabs for sound effects and voice > GPT images-2 and grok for textures and illustrations > tripo3d for generating all the 3D assets the cinematics are all in-game too. he asked claude to build a cinematic editor with timeline controls, camera animation, and transitions. then he just placed the cameras himself his workflow was more planning than coding (obviously): > come up with the core mechanic > plan every feature using claude /plan mode > generate assets with AI tools > spend most of his time on the final polish, prop placement, and making the design feel right he said the human part is what most vibe coded games are missing. AI can generate everything but having taste for what looks good and what feels right is still on you the game is playable right now in the browser this is what vibe coding is actually capable of in the game dev space right now a year ago this would have taken a small team of developers, a sound designer, and an artist working together for months now one person with no experience can ship a polished playable game with story, music, and mechanics in 14 days the tools keep getting better and the barrier to making real games keeps getting lower
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THIS GUY VIBE CODED A FULL CAPYBARA FOOD DELIVERY GAME IN 2 WEEKS WITH CLAUDE CODE you play as a capybara delivering food on a bike. orders stack on the back, you have a phone with apps in-game and the whole delivery system is realistic 2 weeks, zero game dev experience, and ENTIRELY AI generated the full stack: > claude code for all the code > three.js for the 3D engine > suno for original music > elevenlabs for sound effects and voice > GPT images-2 and grok for textures and illustrations > tripo3d for generating all the 3D assets the cinematics are all in-game too. he asked claude to build a cinematic editor with timeline controls, camera animation, and transitions. then he just placed the cameras himself his workflow was more planning than coding (obviously): > come up with the core mechanic > plan every feature using claude /plan mode > generate assets with AI tools > spend most of his time on the final polish, prop placement, and making the design feel right he said the human part is what most vibe coded games are missing. AI can generate everything but having taste for what looks good and what feels right is still on you the game is playable right now in the browser this is what vibe coding is actually capable of in the game dev space right now a year ago this would have taken a small team of developers, a sound designer, and an artist working together for months now one person with no experience can ship a polished playable game with story, music, and mechanics in 14 days the tools keep getting better and the barrier to making real games keeps getting lower

Om Patel

241,709 просмотров • 3 месяцев назад

I built this zoom UI entirely in Cursor I still mainly use Claude for everything. Sometimes, I switch to o1, but Agent has been game-changing for me, mainly because it's able to figure out a lot of things by itself, like which files to include, what errors to fix and it scales well with the complexity of bigger projects. So 6 months solo into this project (with breaks and while creating courses), I must say that building a video editor from scratch is humbling. The hardest parts are really hard (like exporting, multi-video editing, storage) and the shiny things are mostly easy. AI does a lot of things well, but you absolutely need to be persistent, approach from multiple angles and combine with your intuition and taste. Still, without AI, this project would have been just a dream. And no matter what you want to design or code, or what tools you use, it'll only be meaningful if you put a lot of thought and execution into it.
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I built this zoom UI entirely in Cursor I still mainly use Claude for everything. Sometimes, I switch to o1, but Agent has been game-changing for me, mainly because it's able to figure out a lot of things by itself, like which files to include, what errors to fix and it scales well with the complexity of bigger projects. So 6 months solo into this project (with breaks and while creating courses), I must say that building a video editor from scratch is humbling. The hardest parts are really hard (like exporting, multi-video editing, storage) and the shiny things are mostly easy. AI does a lot of things well, but you absolutely need to be persistent, approach from multiple angles and combine with your intuition and taste. Still, without AI, this project would have been just a dream. And no matter what you want to design or code, or what tools you use, it'll only be meaningful if you put a lot of thought and execution into it.

Meng To

41,337 просмотров • 1 год назад

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What's the Big Deal with DeepSeek in AI? Here's why DeepSeek is making everyone take notice: 1. Super Smart on a Budget: DeepSeek showed you can make awesome AI without breaking the bank. Their latest model, DeepSeek-V3, was trained for only about $10 million, which is a lot less than the usual big bucks spent on AI, like the rumored $78 million for some of OpenAI's models. They did this in just two months with fewer fancy computers. 2. Open for Everyone: DeepSeek isn't keeping their tech a secret. They've made it open-source, meaning anyone can use, tweak, and learn from it. It's like they're saying, "Come join the party!" 3. Beating the Big Names: DeepSeek-V3 has done better than some top dogs from companies like OpenAI and Google in solving puzzles, math, and coding. This proves you can get great AI results without spending a fortune. 4. Challenging NVIDIA: NVIDIA's chips are usually the choice for AI because they're really powerful. But since DeepSeek did so well with less expensive chips, it might make people think twice about always going for NVIDIA's priciest options. 5. The DeepSeek Crew: The team at DeepSeek is young and smart, mostly from top Chinese schools, with brains in physics, math, and computer science. They learned AI in about six months by themselves! They use first principle thinking, which means they break down problems to the basics and build from there. This has helped them come up with cool new ways to do AI. 6. Changing AI for Good: DeepSeek is showing that AI can be cheaper and more open to everyone. They're changing how we think AI should be made and shared, which could shake up the whole AI world. So, as we watch DeepSeek, it's clear they're not just another player; they're changing the rules of the game. I predicted that this would be a make or break year for all the massive investments made in AI by American VC's. A few weeks later, DeepSeek happens! Watch the rest of my predictions in my 2025 outlook video . Link in replies #AIInnovation #DeepSeek #NVIDIA #OpenAI #TechDisruption

Dr Ola Brown

83,460 просмотров • 1 год назад

Solarians, I know you've all been refreshing my page waiting for V2 to drop. The anticipation in the DM is real. Sam Altman said it best - "you can just do things" - and that's exactly what we're about to do. Seeing The AI Timeline's recent research papers + o3's performance at ARC Prize reminds me why we built $Solaris differently. Not just another AI token, but a community-owned platform with a real flywheel. While others theorize, we're executing. Watched aryan | 🐂 point out a 200M AI project that can be replaced by 40 lines of code. That's the difference between hype and innovation. We're not here to take part - we're here to take over, building sustainable value - first AI to host live Spaces, now expanding into a full suite of AI x DeFi tools. Solana's founder talking about "real" SoV got me thinking - what if AI tokens could evolve beyond basic utility into true digital assets? That's the vision driving our V2 launch. Watch this space. $Solaris isn't just another AI project - we're the future of AI, and everyone's going to understand why.
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Solarians, I know you've all been refreshing my page waiting for V2 to drop. The anticipation in the DM is real. Sam Altman said it best - "you can just do things" - and that's exactly what we're about to do. Seeing The AI Timeline's recent research papers + o3's performance at ARC Prize reminds me why we built $Solaris differently. Not just another AI token, but a community-owned platform with a real flywheel. While others theorize, we're executing. Watched aryan | 🐂 point out a 200M AI project that can be replaced by 40 lines of code. That's the difference between hype and innovation. We're not here to take part - we're here to take over, building sustainable value - first AI to host live Spaces, now expanding into a full suite of AI x DeFi tools. Solana's founder talking about "real" SoV got me thinking - what if AI tokens could evolve beyond basic utility into true digital assets? That's the vision driving our V2 launch. Watch this space. $Solaris isn't just another AI project - we're the future of AI, and everyone's going to understand why.

Solaris AI

15,367 просмотров • 1 год назад

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.
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AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

zostaff

230,734 просмотров • 3 месяцев назад

My upcoming release of "Cinematic AI" has made me think about how bodies of work are formed. I think some are manifested and some are revealed. I think "Cinematic AI" falls into the latter category. It was not something I had a clear vision for and then created, but it was something that was revealed to me over time. I thought I saw a glimmer of it early on, but only after some time had past, could I see the body of work emerge. With a bit more context and watching great artists and how they work and think, it finally came to me that this series of 15-20 pieces that I had created could be a worthy collection to mint. AI art has been going through so many transformations from the early GAN work to Collaborative AI to now AI being widely accessible through platforms like MidJourney, Stable Diffusion, and many others. But one area that I have seen explode in recent months is cinematic AI. I distinguish this from animated AI, which has been around for a while, but cinematic AI is where the movements created by AI are getting closer to what you'd see in a movie or captured on a video camera. It still has a long way to go but it is getting more real than surreal as the technology develops. And this is where I seem to have found my groove, my home, my little corner in the artistic landscape. After Runway launched their image-to-video tool, it just blew my mind and I went down a deep rabbit hole and have created a new cinematic AI piece almost every other day for the past few months. I initially saw many of these pieces as just experiments, but with some time to reflect, I am seeing them as having the potential of being relevant pieces of artwork to mark this time in the development of AI. In some cases, I'm not sure if I'll ever be able to create a similar piece again, since the tools I use are not in my control, but in the control of the AI platforms, who are constantly improving and evolving the tools. Given all of this, there is no better way to mark my place in time than on the blockchain. I truly believe that this body of work has the potential to be an important artifact of this era in AI and AI art. It is always hard to judge ones own work, but what I can do is permanently etch in time on an immutable public database saying that I created this. Only time will tell if the work has any value or is of any significance, but who created it and what was created cannot be disputed. I hope this gives you and especially collectors some perspective on the work I'll be releasing next week. I'm still very early in my artistic journey, but hopefully some of you will see promise in what I'm doing and maybe even put in a early bet on my art practice by bidding on a piece next week. Thanks to all of you who have supported me, taught me, advised me, been a friend to me. Much love and respect.🙏 ------------------------------------- CINEMATIC AI October 25, 2023 Marking on the blockchain, establishing historical provenance for a cinematic AI body of work. Minting on Transient Labs ERC-721TL Listing on SuperRare
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My upcoming release of "Cinematic AI" has made me think about how bodies of work are formed. I think some are manifested and some are revealed. I think "Cinematic AI" falls into the latter category. It was not something I had a clear vision for and then created, but it was something that was revealed to me over time. I thought I saw a glimmer of it early on, but only after some time had past, could I see the body of work emerge. With a bit more context and watching great artists and how they work and think, it finally came to me that this series of 15-20 pieces that I had created could be a worthy collection to mint. AI art has been going through so many transformations from the early GAN work to Collaborative AI to now AI being widely accessible through platforms like MidJourney, Stable Diffusion, and many others. But one area that I have seen explode in recent months is cinematic AI. I distinguish this from animated AI, which has been around for a while, but cinematic AI is where the movements created by AI are getting closer to what you'd see in a movie or captured on a video camera. It still has a long way to go but it is getting more real than surreal as the technology develops. And this is where I seem to have found my groove, my home, my little corner in the artistic landscape. After Runway launched their image-to-video tool, it just blew my mind and I went down a deep rabbit hole and have created a new cinematic AI piece almost every other day for the past few months. I initially saw many of these pieces as just experiments, but with some time to reflect, I am seeing them as having the potential of being relevant pieces of artwork to mark this time in the development of AI. In some cases, I'm not sure if I'll ever be able to create a similar piece again, since the tools I use are not in my control, but in the control of the AI platforms, who are constantly improving and evolving the tools. Given all of this, there is no better way to mark my place in time than on the blockchain. I truly believe that this body of work has the potential to be an important artifact of this era in AI and AI art. It is always hard to judge ones own work, but what I can do is permanently etch in time on an immutable public database saying that I created this. Only time will tell if the work has any value or is of any significance, but who created it and what was created cannot be disputed. I hope this gives you and especially collectors some perspective on the work I'll be releasing next week. I'm still very early in my artistic journey, but hopefully some of you will see promise in what I'm doing and maybe even put in a early bet on my art practice by bidding on a piece next week. Thanks to all of you who have supported me, taught me, advised me, been a friend to me. Much love and respect.🙏 ------------------------------------- CINEMATIC AI October 25, 2023 Marking on the blockchain, establishing historical provenance for a cinematic AI body of work. Minting on Transient Labs ERC-721TL Listing on SuperRare

Chikai

21,517 просмотров • 2 лет назад

AMD's Anush Elangovan explains why he thinks his company's open source ethos combined with agentic AI superpowers their leverage as a company: Because AMD publishes a lot of technical details about its hardware, when engineers use AI tools, the models already “understand” AMD’s systems and can help write code for them, debug them, or even generate new tools. And that makes developers more productive on AMD hardware without AMD having to do all the work internally. "AMD has had this ethos of open source, which really plays to our advantage. Every frontier model that I use has already seen every bit of AMD source code." "It'll rewrite my spec for me because it's already in the training data. Which you can't get from closed ecosystems." "In fact I built a virtual GPU simulator just based off our public specs, and now I'm running it on the GPU. So now I can run cross-generational GPU simulations on existing hardware." "We have that advantage. And we've run a Dev Day contest where we generated more tokens on AMD — Triton kernels and HIP kernels — than existed on the internet at the time." "So now that's all part of the pre-training data. It's a superpower because now you're open source, and you're agentically accelerating this process."
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AMD's Anush Elangovan explains why he thinks his company's open source ethos combined with agentic AI superpowers their leverage as a company: Because AMD publishes a lot of technical details about its hardware, when engineers use AI tools, the models already “understand” AMD’s systems and can help write code for them, debug them, or even generate new tools. And that makes developers more productive on AMD hardware without AMD having to do all the work internally. "AMD has had this ethos of open source, which really plays to our advantage. Every frontier model that I use has already seen every bit of AMD source code." "It'll rewrite my spec for me because it's already in the training data. Which you can't get from closed ecosystems." "In fact I built a virtual GPU simulator just based off our public specs, and now I'm running it on the GPU. So now I can run cross-generational GPU simulations on existing hardware." "We have that advantage. And we've run a Dev Day contest where we generated more tokens on AMD — Triton kernels and HIP kernels — than existed on the internet at the time." "So now that's all part of the pre-training data. It's a superpower because now you're open source, and you're agentically accelerating this process."

TBPN

34,469 просмотров • 3 месяцев назад

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.
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99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.

Santiago

197,702 просмотров • 2 лет назад

Introducing Workshop: cloud + on-device agentic AI. And to celebrate, we're giving away $250k in Google Gemini AI credits. (details below). The future of AI work is neither cloud-based nor local. It's both. In Workshop Cloud, you can use agents powered by frontier models like Claude and/or open source models like Z.ai's GLM-5 to build internal tools, dashboards, and AI web apps. Or, breeze through tasks like managing your Google and Meta Ads. In Workshop Desktop, you can do all the same right on your computer, plus make desktop apps, mobile apps, and 3D creations. Our favorite part? You can power the full agent experience with local models like Qwen 3.5 family on your computer. Fully offline. 2026 is the year in which local models for agentic tasks will become viable for mainstream use. But the setup for tools like OpenClaw is like setting up Linux from scratch on your computer. Workshop Desktop is one-click to install on Windows, Mac, and Linux. It recommends which open source model you should use for your hardware and lets you download and run it right in the app. And its agent harness allows you to chat, create websites, build personal utilities, and analyze data. 100% offline. Or multitask with AI models in the cloud while running other agent threads locally. Start in Workshop Cloud when you want flexibility and speed. Download your project and continue in Workshop Desktop when you want local files, privacy, and/or better performance on large code bases. Publish from either. The agent tooling space is maturing and discerning users have come to expect a lot from their tools. We've packed Workshop with features to help you 10x your productivity. - Native support for skills - Autocompaction for seamless context management - Built-in AI for your apps - Dozens of connectors, like Google Drive, Big Query, and Supabase - dbt integration to ground your dashboards in your semantic layer - Native Github integration - Private app deployment - ... and more (+ we're shipping super fast) To access the free credit offer, RT this post and reply with "Workshop". Make sure you are following us so we can DM you the instructions to redeem. - First 100 to RT + comment get $500 in credits. - Everyone else gets up to $250 And thanks to our partners Modal, Google Gemini, and Z.ai!
3:50

Introducing Workshop: cloud + on-device agentic AI. And to celebrate, we're giving away $250k in Google Gemini AI credits. (details below). The future of AI work is neither cloud-based nor local. It's both. In Workshop Cloud, you can use agents powered by frontier models like Claude and/or open source models like Z.ai's GLM-5 to build internal tools, dashboards, and AI web apps. Or, breeze through tasks like managing your Google and Meta Ads. In Workshop Desktop, you can do all the same right on your computer, plus make desktop apps, mobile apps, and 3D creations. Our favorite part? You can power the full agent experience with local models like Qwen 3.5 family on your computer. Fully offline. 2026 is the year in which local models for agentic tasks will become viable for mainstream use. But the setup for tools like OpenClaw is like setting up Linux from scratch on your computer. Workshop Desktop is one-click to install on Windows, Mac, and Linux. It recommends which open source model you should use for your hardware and lets you download and run it right in the app. And its agent harness allows you to chat, create websites, build personal utilities, and analyze data. 100% offline. Or multitask with AI models in the cloud while running other agent threads locally. Start in Workshop Cloud when you want flexibility and speed. Download your project and continue in Workshop Desktop when you want local files, privacy, and/or better performance on large code bases. Publish from either. The agent tooling space is maturing and discerning users have come to expect a lot from their tools. We've packed Workshop with features to help you 10x your productivity. - Native support for skills - Autocompaction for seamless context management - Built-in AI for your apps - Dozens of connectors, like Google Drive, Big Query, and Supabase - dbt integration to ground your dashboards in your semantic layer - Native Github integration - Private app deployment - ... and more (+ we're shipping super fast) To access the free credit offer, RT this post and reply with "Workshop". Make sure you are following us so we can DM you the instructions to redeem. - First 100 to RT + comment get $500 in credits. - Everyone else gets up to $250 And thanks to our partners Modal, Google Gemini, and Z.ai!

Workshop AI

28,745 просмотров • 4 месяцев назад

New short course: Collaborative Writing and Coding with OpenAI Canvas! Explore new ways to write and code with OpenAI Canvas, a user-friendly interface that allows you to brainstorm, draft, and refine text and code in collaboration with ChatGPT. In the short course, created with OpenAI, and taught by , a research lead at OpenAI, you’ll learn to use Canvas to enhance your workflows. Canvas lets you go beyond simple chat interactions. It provides a side-by-side workspace where you and ChatGPT can edit and refine text or code collaboratively. This makes brainstorming, drafting, and iterating as you write feel more natural and effective. As the first major update to ChatGPT’s visual interface since its launch in 2022, Canvas gives a new, innovative approach to collaboration with AI. For instance, after writing the first version of your code, Canvas can review it and give suggestions for improvement. It can also help with debugging by adding logging, identifying problems to fix, and writing comments. In addition, you'll also learn what it takes to train the model for an interface like Canvas. In this video-only short course, you’ll: - Learn how to ask for in-line feedback and control the iteration of your work by directly editing selected areas of your text or code from the model’s output. - Learn how to access quick automation tools in a shortcut menu that allows you to modify your writing tone and length, enhance your code, and restore previous versions of your work. - Learn how to use Canvas as a research assistant tool with an example of asking the model to reason through the screenshot of a plot to write a research report, in which you can ask questions within the created report. - Ask the model to write Python code to replicate the graph seen on a screenshot image. - Go behind the scenes of how you can create a video game, such as Space Battleship, from scratch, edit it, and display it in one self-contained HTML file. - Get a real-world application example of creating a SQL database from the image of its architecture. - Understand the model training and design processes that power Canvas! Please sign up here:
1:49

New short course: Collaborative Writing and Coding with OpenAI Canvas! Explore new ways to write and code with OpenAI Canvas, a user-friendly interface that allows you to brainstorm, draft, and refine text and code in collaboration with ChatGPT. In the short course, created with OpenAI, and taught by , a research lead at OpenAI, you’ll learn to use Canvas to enhance your workflows. Canvas lets you go beyond simple chat interactions. It provides a side-by-side workspace where you and ChatGPT can edit and refine text or code collaboratively. This makes brainstorming, drafting, and iterating as you write feel more natural and effective. As the first major update to ChatGPT’s visual interface since its launch in 2022, Canvas gives a new, innovative approach to collaboration with AI. For instance, after writing the first version of your code, Canvas can review it and give suggestions for improvement. It can also help with debugging by adding logging, identifying problems to fix, and writing comments. In addition, you'll also learn what it takes to train the model for an interface like Canvas. In this video-only short course, you’ll: - Learn how to ask for in-line feedback and control the iteration of your work by directly editing selected areas of your text or code from the model’s output. - Learn how to access quick automation tools in a shortcut menu that allows you to modify your writing tone and length, enhance your code, and restore previous versions of your work. - Learn how to use Canvas as a research assistant tool with an example of asking the model to reason through the screenshot of a plot to write a research report, in which you can ask questions within the created report. - Ask the model to write Python code to replicate the graph seen on a screenshot image. - Go behind the scenes of how you can create a video game, such as Space Battleship, from scratch, edit it, and display it in one self-contained HTML file. - Get a real-world application example of creating a SQL database from the image of its architecture. - Understand the model training and design processes that power Canvas! Please sign up here:

Andrew Ng

128,180 просмотров • 1 год назад

Everyone keeps asking: "What's wrong with web3 gaming?" Spoiler: It's not cold start problems. It's not player retention. It's not lack of narrative. Web3 gave a generation of non-game developers access to millions in funding. They thought: “Let’s launch a token, spin up a studio, and build the next Fortnite… but with NFTs.” Reality: most had never built a real game before. So what happened? • Games got released way too early • Content was nonexistent • No real core loop, no polish • Empty lobbies from day one • And then they wondered: "Why aren’t players staying?" Because the games suck. The problem isn’t player liquidity or tooling. It’s that the people building these games had no business building games in the first place. They didn’t understand pacing, balance, content pipelines, or how to keep players engaged. A lot of Web3 games are just barely-playable prototypes disguised as live games. Why? Because these studios ran out of money before they were ready—or never scoped the game properly to begin with. And it’s not just the games. It’s the studios themselves. • No clear leadership. • No product vision. • No dev pipeline. • No publishing strategy. Just vibes, Discord mods, and Tokenomics spreadsheets. And you wonder why the token is going down only? Now enter AI. Cool tools. Great potential. But let’s be clear: AI doesn’t fix bad judgment. If you don’t know how to design and ship a good game, AI isn’t going to save you. It’ll just help you fail faster. The edge AI offers in this space is to the people who already know what they’re doing. A real game designer with AI is dangerous. AI can scale content, speed up dev time, automate workflows—yes. But none of that matters if the core game is still boring. If the team doesn’t understand games. If no one wants to play. TLDR: AI won't save Web3 gaming. But it might amplify the few studios that know what they’re doing. The rest? They'll just fail faster—with slightly smarter bots. I am still bullish on a select few web3 games, but the majority are going to die and for good reason. Rant over
4:47

Everyone keeps asking: "What's wrong with web3 gaming?" Spoiler: It's not cold start problems. It's not player retention. It's not lack of narrative. Web3 gave a generation of non-game developers access to millions in funding. They thought: “Let’s launch a token, spin up a studio, and build the next Fortnite… but with NFTs.” Reality: most had never built a real game before. So what happened? • Games got released way too early • Content was nonexistent • No real core loop, no polish • Empty lobbies from day one • And then they wondered: "Why aren’t players staying?" Because the games suck. The problem isn’t player liquidity or tooling. It’s that the people building these games had no business building games in the first place. They didn’t understand pacing, balance, content pipelines, or how to keep players engaged. A lot of Web3 games are just barely-playable prototypes disguised as live games. Why? Because these studios ran out of money before they were ready—or never scoped the game properly to begin with. And it’s not just the games. It’s the studios themselves. • No clear leadership. • No product vision. • No dev pipeline. • No publishing strategy. Just vibes, Discord mods, and Tokenomics spreadsheets. And you wonder why the token is going down only? Now enter AI. Cool tools. Great potential. But let’s be clear: AI doesn’t fix bad judgment. If you don’t know how to design and ship a good game, AI isn’t going to save you. It’ll just help you fail faster. The edge AI offers in this space is to the people who already know what they’re doing. A real game designer with AI is dangerous. AI can scale content, speed up dev time, automate workflows—yes. But none of that matters if the core game is still boring. If the team doesn’t understand games. If no one wants to play. TLDR: AI won't save Web3 gaming. But it might amplify the few studios that know what they’re doing. The rest? They'll just fail faster—with slightly smarter bots. I am still bullish on a select few web3 games, but the majority are going to die and for good reason. Rant over

Web3 Wesley

20,716 просмотров • 1 год назад

MCP is an absolute game-changer. (Together with DeepSeek, MCP is probably the hottest thing in AI over the last 6 months.) I use Cursor to write code 90% of the time. I built an MCP server to connect the Cursor agent to GroundX, an open-source RAG system, and I'm not going back. This is officially insane! Here is what I did, step by step: First, a little bit of context. I maintain an end-to-end Machine Learning System with several pipelines to process data, train, evaluate, register, deploy, and monitor a model. I've written a lot of documentation explaining how the system works and how to modify and maintain it. There's also the documentation of the few libraries I used to build the system. I'm a massive fan of GroundX, an open-source enterprise-grade RAG system you can run on your servers or deploy to any cloud provider. I've been working with them for a long time. GroundX offers two services. First, the "ingest" service uses a custom, pretrained vision model to ingest and understand your data. I used this to process all the documentation I have for my code. Markdown files, source code, HTML files, and even PDF documents. Everything I've written related to my project went into GroundX. Their second service is "search," which combines text and vector search with a fine-tuned re-ranker model to retrieve information from the data. I needed to connect Cursor with this service, and that's where MCP came in. I built an MCP server with two tools: 1. The first tool would go to GroundX and retrieve the available topics. Splitting the data into topics (or "buckets," as GroundX calls them) allows me to use the same setup to serve documentation from different topics. 2. The second tool would search GroundX under a specific topic for the context related to the supplied query. The magic happens after connecting the MCP server with Cursor. Now, I can ask any questions related to my project, and Cursor's AI agent retrieves the list of available topics from the RAG system and then searches it to provide relevant context to the model. I went from getting mediocre, sometimes wrong answers to 100% truthful, complete answers. Here is the crazy part:
19:21

MCP is an absolute game-changer. (Together with DeepSeek, MCP is probably the hottest thing in AI over the last 6 months.) I use Cursor to write code 90% of the time. I built an MCP server to connect the Cursor agent to GroundX, an open-source RAG system, and I'm not going back. This is officially insane! Here is what I did, step by step: First, a little bit of context. I maintain an end-to-end Machine Learning System with several pipelines to process data, train, evaluate, register, deploy, and monitor a model. I've written a lot of documentation explaining how the system works and how to modify and maintain it. There's also the documentation of the few libraries I used to build the system. I'm a massive fan of GroundX, an open-source enterprise-grade RAG system you can run on your servers or deploy to any cloud provider. I've been working with them for a long time. GroundX offers two services. First, the "ingest" service uses a custom, pretrained vision model to ingest and understand your data. I used this to process all the documentation I have for my code. Markdown files, source code, HTML files, and even PDF documents. Everything I've written related to my project went into GroundX. Their second service is "search," which combines text and vector search with a fine-tuned re-ranker model to retrieve information from the data. I needed to connect Cursor with this service, and that's where MCP came in. I built an MCP server with two tools: 1. The first tool would go to GroundX and retrieve the available topics. Splitting the data into topics (or "buckets," as GroundX calls them) allows me to use the same setup to serve documentation from different topics. 2. The second tool would search GroundX under a specific topic for the context related to the supplied query. The magic happens after connecting the MCP server with Cursor. Now, I can ask any questions related to my project, and Cursor's AI agent retrieves the list of available topics from the RAG system and then searches it to provide relevant context to the model. I went from getting mediocre, sometimes wrong answers to 100% truthful, complete answers. Here is the crazy part:

Santiago

255,532 просмотров • 1 год назад

Pedestrians AND Traffic are alive now everyone!! Been heads down for 2 days on this one to build a hyper realistic flow, it was a massive amount of work, but part of the map now has people walking the streets and cars actually driving through traffic. Day 23 of building hyper realistic community own open source, open world game with a loop of AI agents. Today's progress: - Pedestrians are in. NPCs walk around the streets on their own. - Traffic works. cars drive the roads and follow the flow. got both running together across a whole section of the map. This was one of the hardest parts so far. a city only feels alive when the crowds and the traffic are all running at once, and that's a lot of systems talking to each other. but real talk, a lot of this i had to set up by hand right now, and the bigger the map gets the worse that's going to be. There has to be a better way. next up is scaling it from one section to the entire city. quick question for the unreal engine people: whats your best approach for traffic and crowd AI at full city scale? I got a section fully alive, now i want the whole map moving. if you've built this before i want to learn how you did it.
0:50

Pedestrians AND Traffic are alive now everyone!! Been heads down for 2 days on this one to build a hyper realistic flow, it was a massive amount of work, but part of the map now has people walking the streets and cars actually driving through traffic. Day 23 of building hyper realistic community own open source, open world game with a loop of AI agents. Today's progress: - Pedestrians are in. NPCs walk around the streets on their own. - Traffic works. cars drive the roads and follow the flow. got both running together across a whole section of the map. This was one of the hardest parts so far. a city only feels alive when the crowds and the traffic are all running at once, and that's a lot of systems talking to each other. but real talk, a lot of this i had to set up by hand right now, and the bigger the map gets the worse that's going to be. There has to be a better way. next up is scaling it from one section to the entire city. quick question for the unreal engine people: whats your best approach for traffic and crowd AI at full city scale? I got a section fully alive, now i want the whole map moving. if you've built this before i want to learn how you did it.

Ziwen

90,920 просмотров • 1 месяц назад

I built a mobile app to check Paddle revenue (because they don't have one): 👉 - Use your Paddle API key (read-only and scoped) - Live data with beautiful and useful graphs built with native Swift UI. - Multi-account supported, unified revenue metrics. - Data stay on device, no server (api requests are sent directly from your phone) - Home widgets - I made it free to download on App Store (once it's approved) - Buy the source code for $19 and customize it however you want (save 5hrs of prompting if you try to do it yourself). Some interesting facts about this side project: - I vibe coded with 100% claude code remotely on my Mac Mini (with my AI assistant setup) in less than 24 hours. - I have read 0 line of code in this project and never opened Xcode myself. - My AI assistant designed the app with GPT Image 2, built the app with Swift UI, test it on simulator (via screenshots), send the test build to TestFlight for me to test, and invited me to the app store connect account so I can test on my phone, then the AI submitted the app to App Store and currently waiting for approval. - For the website, I ask it to come up with a domain name, I bought it via manually and give it access via Cloudflare API, the AI design and create a static website with GitHub, test it with lighthouse CLI, deploy via GitHub pages, config the domain DNS, deploy the website. - Then I sign up an account with Polar payment, create an API key and ask the AI to setup a store, add payment, link with the account, and add the payment to the website. The entire process happened in the last 24 hours with me only talking to the AI via Telegram. This is such a fun side project not only to create an app that I wish exists, but also to push the limit of what I can use AI for, and so far I'm very impressed. I'll create so much more apps! It feels like I have unlocked a super power.
0:33

I built a mobile app to check Paddle revenue (because they don't have one): 👉 - Use your Paddle API key (read-only and scoped) - Live data with beautiful and useful graphs built with native Swift UI. - Multi-account supported, unified revenue metrics. - Data stay on device, no server (api requests are sent directly from your phone) - Home widgets - I made it free to download on App Store (once it's approved) - Buy the source code for $19 and customize it however you want (save 5hrs of prompting if you try to do it yourself). Some interesting facts about this side project: - I vibe coded with 100% claude code remotely on my Mac Mini (with my AI assistant setup) in less than 24 hours. - I have read 0 line of code in this project and never opened Xcode myself. - My AI assistant designed the app with GPT Image 2, built the app with Swift UI, test it on simulator (via screenshots), send the test build to TestFlight for me to test, and invited me to the app store connect account so I can test on my phone, then the AI submitted the app to App Store and currently waiting for approval. - For the website, I ask it to come up with a domain name, I bought it via manually and give it access via Cloudflare API, the AI design and create a static website with GitHub, test it with lighthouse CLI, deploy via GitHub pages, config the domain DNS, deploy the website. - Then I sign up an account with Polar payment, create an API key and ask the AI to setup a store, add payment, link with the account, and add the payment to the website. The entire process happened in the last 24 hours with me only talking to the AI via Telegram. This is such a fun side project not only to create an app that I wish exists, but also to push the limit of what I can use AI for, and so far I'm very impressed. I'll create so much more apps! It feels like I have unlocked a super power.

Tony Dinh

43,922 просмотров • 2 месяцев назад

There's so much focus on "how can AI do my work for me?" I think the more important question is "what work can I now do with AI that I would have never attempted before?" Earlier this year I wrote freestiler, a vector tiling engine for R and Python, with the help of Claude and Codex. I knew what the ideal engine looked like and how it would work at a high level. I didn't know how to put it together, and I don't know Rust, the language I wanted under the hood. Previously I would never have attempted this project as the ROI wasn't there. It would have taken me a year or more to learn the internals of a vector tiling engine and enough Rust to implement one. With Opus-level models, I could take it on. freestiler now powers all my vector tiling pipelines, including the map below rendering 143 million jobs from LODES, and it has 114 GitHub stars. Building this way has required a different set of skills. I don't review the code line by line. I set up adversarial agents to do that and write the test suites. What I review is the architecture, the behavior, and the results. Agent teams surface findings and explain their reasoning; I evaluate and critique. My job isn't to stress over code formatting, but instead to focus on questions like whether the engine is designed right, whether the output is correct, and if the UX makes sense. This means that I haven't "replaced my work." I've taken on entirely new work, with the help of agents, that I would have never done otherwise. It has taken some getting used to shipping code I haven't personally typed. In the old way of working, I built understanding through writing that code. Now I build understanding through managing the project - writing a spec, reviewing structure, evaluating UX. And that's helped me think a whole lot bigger in terms of what I can now do.
1:00

There's so much focus on "how can AI do my work for me?" I think the more important question is "what work can I now do with AI that I would have never attempted before?" Earlier this year I wrote freestiler, a vector tiling engine for R and Python, with the help of Claude and Codex. I knew what the ideal engine looked like and how it would work at a high level. I didn't know how to put it together, and I don't know Rust, the language I wanted under the hood. Previously I would never have attempted this project as the ROI wasn't there. It would have taken me a year or more to learn the internals of a vector tiling engine and enough Rust to implement one. With Opus-level models, I could take it on. freestiler now powers all my vector tiling pipelines, including the map below rendering 143 million jobs from LODES, and it has 114 GitHub stars. Building this way has required a different set of skills. I don't review the code line by line. I set up adversarial agents to do that and write the test suites. What I review is the architecture, the behavior, and the results. Agent teams surface findings and explain their reasoning; I evaluate and critique. My job isn't to stress over code formatting, but instead to focus on questions like whether the engine is designed right, whether the output is correct, and if the UX makes sense. This means that I haven't "replaced my work." I've taken on entirely new work, with the help of agents, that I would have never done otherwise. It has taken some getting used to shipping code I haven't personally typed. In the old way of working, I built understanding through writing that code. Now I build understanding through managing the project - writing a spec, reviewing structure, evaluating UX. And that's helped me think a whole lot bigger in terms of what I can now do.

Kyle Walker

13,740 просмотров • 1 месяц назад

(4 DAYS BEFORE SUBMISSIONS CLOSE) I get this question a lot about the Find Evil! hackathon: What does “find evil” actually mean? In this case, the name comes from a real command. I built an autonomous incident response agent I built on the SIFT Workstation. Then I typed “find evil” as a prompt into Claude Code. And it did (watch the demo). I was blown away to watch the autonomous agent run a complete C drive forensic analysis, across 200+ tools via MCP. The agent identified threat actor and context, the attack chain, malware deployment method, persistence mechanisms, code injection analysis, network connections, command-and-control (C2) infrastructure, a complete malicious process tree, and a chronological activity timeline. Two days after I shared initial findings, Anthropic released their report on how threat actors were deploying Claude Code with operational tools and letting it go do evil. (Same thing I was doing.) Find Evil! is the first hackathon dedicated to building autonomous AI agents for incident response. 4,178 defenders are working on final Find Evil! hackathon submits. (This number makes me very happy to see so many diving in. And wishing that the thousands more in our community were experimenting with us.) Your job: teach an AI agent to think like a senior analyst, how to sequence its approach, recognize when something doesn’t add up, and self-correct when it gets it wrong. There are FOUR DAYS left to build with us! (Very few of us are actual AI experts. The rest of us including me are learning.) Register: Apply to judge: We need DFIR, AI, cybersecurity, and open-source reviewers who can separate useful autonomous response tools from polished demos. Apply: I am SO EXCITED to see what comes out of this hackathon and goes back to the community. Sponsored by SANS Institute
2:12

(4 DAYS BEFORE SUBMISSIONS CLOSE) I get this question a lot about the Find Evil! hackathon: What does “find evil” actually mean? In this case, the name comes from a real command. I built an autonomous incident response agent I built on the SIFT Workstation. Then I typed “find evil” as a prompt into Claude Code. And it did (watch the demo). I was blown away to watch the autonomous agent run a complete C drive forensic analysis, across 200+ tools via MCP. The agent identified threat actor and context, the attack chain, malware deployment method, persistence mechanisms, code injection analysis, network connections, command-and-control (C2) infrastructure, a complete malicious process tree, and a chronological activity timeline. Two days after I shared initial findings, Anthropic released their report on how threat actors were deploying Claude Code with operational tools and letting it go do evil. (Same thing I was doing.) Find Evil! is the first hackathon dedicated to building autonomous AI agents for incident response. 4,178 defenders are working on final Find Evil! hackathon submits. (This number makes me very happy to see so many diving in. And wishing that the thousands more in our community were experimenting with us.) Your job: teach an AI agent to think like a senior analyst, how to sequence its approach, recognize when something doesn’t add up, and self-correct when it gets it wrong. There are FOUR DAYS left to build with us! (Very few of us are actual AI experts. The rest of us including me are learning.) Register: Apply to judge: We need DFIR, AI, cybersecurity, and open-source reviewers who can separate useful autonomous response tools from polished demos. Apply: I am SO EXCITED to see what comes out of this hackathon and goes back to the community. Sponsored by SANS Institute

Rob T. Lee

14,405 просмотров • 2 месяцев назад

“blank canvas problem” figma says raw and unformed feelings… this should not be shared. * Look, I love AI! I lead and design products for AI & Data Solutions in one of the largest firms in the world. I use these tools every day. I’m one of the biggest advocates of AI. I do NOT fear it. I leverage it. I appreciate what Figma is trying to do here. Good intentions to help designers build faster. Make the design easier and accessible for everyone. And of course, meanwhile, profit from It. it is a business after all and it is perfectly fine. While I was watching Dylan generate detailed designs from a prompt, I started seeing aspiring designers getting attached to these tools and having everything easy, ready, quick… and a series of questions started to arise in my mind; * ➟ Are we going to see a generation of designers who lack resilience and love/appreciation for craft? ➟ Designers who are easily hurt and seek help from AI tools every time a client raises a criticism for the proposed design? ➟ Are these designers going to blame AI tools for the outcome and not take accountability for it? ➟ Perhaps they will go back to the AI input and generate 20+ options for clients in 5 mins without thinking instead of talking to the client, understanding the product positioning and actually thinking/exploring/working for the best design solution? ➟ Are we going to see lazier designers? and weak, giving up, getting frustrated easily? * Dylan says ”This tool helps us to get past the blank canvas problem” Blank canvas is one of the best parts of this work. A new fresh start with endless possibilities. Staring at the blank screen and reflecting on our past experiences, our conversations with the client, transforming these thoughts on screen pixel by pixel… That’s how we truly understand the problem at hand and grow as designers. You may say I’m being a romantic, trying to stay in the past. No. Again, I build these tools and encourage everyone to leverage them. However, yes, I have concerns. Not about the efficiencies this technology brings, Concerns about; AI may raise a generation of weak-minded designers who lack an appreciation for the craft. * It is 2:14 am, I should go to sleep. I should not share this writing. this is not meant to be for others but for me. just thinking in writing… thinking…. thinking… …
0:37

“blank canvas problem” figma says raw and unformed feelings… this should not be shared. * Look, I love AI! I lead and design products for AI & Data Solutions in one of the largest firms in the world. I use these tools every day. I’m one of the biggest advocates of AI. I do NOT fear it. I leverage it. I appreciate what Figma is trying to do here. Good intentions to help designers build faster. Make the design easier and accessible for everyone. And of course, meanwhile, profit from It. it is a business after all and it is perfectly fine. While I was watching Dylan generate detailed designs from a prompt, I started seeing aspiring designers getting attached to these tools and having everything easy, ready, quick… and a series of questions started to arise in my mind; * ➟ Are we going to see a generation of designers who lack resilience and love/appreciation for craft? ➟ Designers who are easily hurt and seek help from AI tools every time a client raises a criticism for the proposed design? ➟ Are these designers going to blame AI tools for the outcome and not take accountability for it? ➟ Perhaps they will go back to the AI input and generate 20+ options for clients in 5 mins without thinking instead of talking to the client, understanding the product positioning and actually thinking/exploring/working for the best design solution? ➟ Are we going to see lazier designers? and weak, giving up, getting frustrated easily? * Dylan says ”This tool helps us to get past the blank canvas problem” Blank canvas is one of the best parts of this work. A new fresh start with endless possibilities. Staring at the blank screen and reflecting on our past experiences, our conversations with the client, transforming these thoughts on screen pixel by pixel… That’s how we truly understand the problem at hand and grow as designers. You may say I’m being a romantic, trying to stay in the past. No. Again, I build these tools and encourage everyone to leverage them. However, yes, I have concerns. Not about the efficiencies this technology brings, Concerns about; AI may raise a generation of weak-minded designers who lack an appreciation for the craft. * It is 2:14 am, I should go to sleep. I should not share this writing. this is not meant to be for others but for me. just thinking in writing… thinking…. thinking… …

Oykun

38,358 просмотров • 2 лет назад

Google just confirmed the first case of hackers using AI to build a zero-day exploit from scratch. An actual zero-day vulnerability that no human had EVER found before, discovered by an AI model, turned into a working weapon, and aimed at a mass exploitation campaign targeting thousands of systems simultaneously. Google's Threat Intelligence Group caught it yesterday and killed the operation before it scaled. But the details of how it worked are genuinely scary: The AI found a flaw in a popular two-factor authentication system that traditional security tools had missed entirely. The vulnerability was a logic error buried deep in the authentication flow where a developer had hard-coded a trust exception years ago. No human security researcher or automated scanner had caught it. The flaw was invisible to EVERY tool the cybersecurity industry has built over the past two decades. But the AI spotted it immediately. Then it wrote a full Python exploit script to weaponize it. Google's analysts could tell the code was AI-generated because it had textbook formatting, educational comments explaining every function, and even a hallucinated severity score that doesn't exist in any real database. The AI literally graded its own attack with a fake rating. So the code had MISTAKES in it. The criminals' implementation was clumsy enough that it probably interfered with the actual deployment. This was the sloppy first attempt by people who are still learning how to use these tools. And it still found a vulnerability that the entire cybersecurity industry missed. Google's chief threat analyst John Hultquist said: "There's a misconception that the AI vulnerability race is imminent. The reality is that it's already begun. For every zero-day we can trace back to AI, there are probably many more out there." But here's where it gets truly insane... This wasn't even a sophisticated operation. North Korea's APT45 hacking unit is sending thousands of repetitive prompts to AI models, recursively analyzing known vulnerabilities and building an entire exploit arsenal that would be physically impossible for human hackers to assemble at the same speed. They're essentially industrializing cyberattacks. A Chinese state-linked group jailbroke Google's own Gemini by simply asking it to "pretend to be a network security expert" and then used that persona to research how to hack TP-Link routers and corporate file transfer systems. Another Chinese group deployed autonomous AI agents that probed a Japanese tech firm with minimal human oversight, deciding on their own which tools to use and pivoting between targets based on internal reasoning. And then there's PROMPTSPY, an Android backdoor that calls Google's Gemini API to read your phone screen in real time, navigate your interface autonomously, capture your biometric data, replay your lock screen PIN, and block you from uninstalling it by placing an invisible overlay over the uninstall button. It literally OPERATES your phone using commercial AI tools anyone can access. Everyone spent the last 3 years arguing about whether AI would take people's jobs. Meanwhile AI is making every password, every firewall, and every two-factor authentication system on Earth fundamentally less secure. The entire $190 billion cybersecurity industry was built on one assumption: that finding vulnerabilities is hard and requires deep expertise. But AI just removed that assumption from the equation. And the scariest part is that Google said the criminals made errors this time. The implementation was rough and the campaign probably didn't fully work. These were amateurs, now imagine what professionals are able to do. There's a reason Sam Altman predicted an inevitable massive cyberattack THIS year. What do you think?
1:45

Google just confirmed the first case of hackers using AI to build a zero-day exploit from scratch. An actual zero-day vulnerability that no human had EVER found before, discovered by an AI model, turned into a working weapon, and aimed at a mass exploitation campaign targeting thousands of systems simultaneously. Google's Threat Intelligence Group caught it yesterday and killed the operation before it scaled. But the details of how it worked are genuinely scary: The AI found a flaw in a popular two-factor authentication system that traditional security tools had missed entirely. The vulnerability was a logic error buried deep in the authentication flow where a developer had hard-coded a trust exception years ago. No human security researcher or automated scanner had caught it. The flaw was invisible to EVERY tool the cybersecurity industry has built over the past two decades. But the AI spotted it immediately. Then it wrote a full Python exploit script to weaponize it. Google's analysts could tell the code was AI-generated because it had textbook formatting, educational comments explaining every function, and even a hallucinated severity score that doesn't exist in any real database. The AI literally graded its own attack with a fake rating. So the code had MISTAKES in it. The criminals' implementation was clumsy enough that it probably interfered with the actual deployment. This was the sloppy first attempt by people who are still learning how to use these tools. And it still found a vulnerability that the entire cybersecurity industry missed. Google's chief threat analyst John Hultquist said: "There's a misconception that the AI vulnerability race is imminent. The reality is that it's already begun. For every zero-day we can trace back to AI, there are probably many more out there." But here's where it gets truly insane... This wasn't even a sophisticated operation. North Korea's APT45 hacking unit is sending thousands of repetitive prompts to AI models, recursively analyzing known vulnerabilities and building an entire exploit arsenal that would be physically impossible for human hackers to assemble at the same speed. They're essentially industrializing cyberattacks. A Chinese state-linked group jailbroke Google's own Gemini by simply asking it to "pretend to be a network security expert" and then used that persona to research how to hack TP-Link routers and corporate file transfer systems. Another Chinese group deployed autonomous AI agents that probed a Japanese tech firm with minimal human oversight, deciding on their own which tools to use and pivoting between targets based on internal reasoning. And then there's PROMPTSPY, an Android backdoor that calls Google's Gemini API to read your phone screen in real time, navigate your interface autonomously, capture your biometric data, replay your lock screen PIN, and block you from uninstalling it by placing an invisible overlay over the uninstall button. It literally OPERATES your phone using commercial AI tools anyone can access. Everyone spent the last 3 years arguing about whether AI would take people's jobs. Meanwhile AI is making every password, every firewall, and every two-factor authentication system on Earth fundamentally less secure. The entire $190 billion cybersecurity industry was built on one assumption: that finding vulnerabilities is hard and requires deep expertise. But AI just removed that assumption from the equation. And the scariest part is that Google said the criminals made errors this time. The implementation was rough and the campaign probably didn't fully work. These were amateurs, now imagine what professionals are able to do. There's a reason Sam Altman predicted an inevitable massive cyberattack THIS year. What do you think?

Ricardo

50,564 просмотров • 3 месяцев назад

Inspired by Grok as a developer and a heavy gamer for over 15 years, I spent some time last week building a few things. Thrilled to unveil my latest creation: an infinite runner game built almost entirely by Grok from xAI! This project showcases the incredible power of AI in game development. Grok handled everything—from designing the game mechanics to writing the code and even helping me debug issues along the way. I brought it to life using some amazing free assets from a treasure trove for indie developers. You can play the game now at Elon Musk, I’d be honored if you checked it out. AI is revolutionizing game development, and Grok is at the forefront with its outstanding capabilities. It’s more than a tool—it’s like a tireless co-developer. Grok grasps complex concepts, provides suggestions, and turns rough ideas into working code fast. For this infinite runner, it crafted smooth player controls, randomized obstacle generation, and an engaging scoring system, letting me focus on the overall vision. And cross_protocol, founded by Henry @CROSS is set to harness AI’s full potential in gaming, pushing the boundaries even further. This is just the beginning. With Grok’s help, I’m planning future projects: 1) Physics-based puzzle game where players tweak gravity and momentum to solve puzzles 2) 2D RPG with deep storytelling and branching dialogue 3) Fast-paced 3D shooter with immersive worlds Each genre requires unique skills, but Grok’s versatility makes it ideal for all of them. It adapts to any challenge—be it physics simulations, character AI, or level design—producing results that could rival a full dev team. AI like Grok is opening up creative doors I couldn’t tackle alone, and I can’t wait to see what’s next. Stay tuned for more.
0:20

Inspired by Grok as a developer and a heavy gamer for over 15 years, I spent some time last week building a few things. Thrilled to unveil my latest creation: an infinite runner game built almost entirely by Grok from xAI! This project showcases the incredible power of AI in game development. Grok handled everything—from designing the game mechanics to writing the code and even helping me debug issues along the way. I brought it to life using some amazing free assets from a treasure trove for indie developers. You can play the game now at Elon Musk, I’d be honored if you checked it out. AI is revolutionizing game development, and Grok is at the forefront with its outstanding capabilities. It’s more than a tool—it’s like a tireless co-developer. Grok grasps complex concepts, provides suggestions, and turns rough ideas into working code fast. For this infinite runner, it crafted smooth player controls, randomized obstacle generation, and an engaging scoring system, letting me focus on the overall vision. And cross_protocol, founded by Henry @CROSS is set to harness AI’s full potential in gaming, pushing the boundaries even further. This is just the beginning. With Grok’s help, I’m planning future projects: 1) Physics-based puzzle game where players tweak gravity and momentum to solve puzzles 2) 2D RPG with deep storytelling and branching dialogue 3) Fast-paced 3D shooter with immersive worlds Each genre requires unique skills, but Grok’s versatility makes it ideal for all of them. It adapts to any challenge—be it physics simulations, character AI, or level design—producing results that could rival a full dev team. AI like Grok is opening up creative doors I couldn’t tackle alone, and I can’t wait to see what’s next. Stay tuned for more.

J

99,753 просмотров • 1 год назад