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Created an open-source mesh painter using Three.js . You can paint Crystals 💎 molten cracks 🔥 aurora silk 🌌 glowing coral reefs 🪸 WebGPU is used. GPU-instanced pieces, animated live in shaders & real-time preview during changes. Source code :

Chiro Visuals

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32,562 次观看 • 1 个月前 •via X (Twitter)

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You should really stop using Zapier or Make. Deploying Python scripts on Replit takes less than 5 min and is cheaper. Just describe what you want to automate and AI takes care of writing the code. For example, I created an automation that merges all the PDFs in a folder on Drive into a single file. All that's left to do is: - Copy/paste the API key - Click on "Run" And you're done! In this case I used Aider Chat (free and open source), but you can also use Replit Agent.

You should really stop using Zapier or Make. Deploying Python scripts on Replit takes less than 5 min and is cheaper. Just describe what you want to automate and AI takes care of writing the code. For example, I created an automation that merges all the PDFs in a folder on Drive into a single file. All that's left to do is: - Copy/paste the API key - Click on "Run" And you're done! In this case I used Aider Chat (free and open source), but you can also use Replit Agent.

Paul Couvert

163,002 次观看 • 2 年前

I made an interactive three.js site that constructs towers with weather and time of day effects. I went from making mostly static sites with huge images to three.js with highly textured, detailed 3D models, and the entire site is under 1-2mb. Because it's 3D, you can ask AI to customize everything: themed towers from different countries, ancient and modern styles, weather like snow and rain, lighting from morning to night. The surprising part is that with a solid foundation it creates those changes pretty much one-shot. It's like starting a design system, where the AI figures out how to stay consistent in quality with the rest of the design. The hard part is avoiding 3D slop, which is easier thanks to Opus 5 and open-source projects. I used Claude Code for the harness, Higgsfield to generate textures and soundfx/music. Let me know if you'd like the open-source. Inspiration: Btw, I love that we get auto-translated posts from around the world. It really expands our horizon in terms of inspiration.

I made an interactive three.js site that constructs towers with weather and time of day effects. I went from making mostly static sites with huge images to three.js with highly textured, detailed 3D models, and the entire site is under 1-2mb. Because it's 3D, you can ask AI to customize everything: themed towers from different countries, ancient and modern styles, weather like snow and rain, lighting from morning to night. The surprising part is that with a solid foundation it creates those changes pretty much one-shot. It's like starting a design system, where the AI figures out how to stay consistent in quality with the rest of the design. The hard part is avoiding 3D slop, which is easier thanks to Opus 5 and open-source projects. I used Claude Code for the harness, Higgsfield to generate textures and soundfx/music. Let me know if you'd like the open-source. Inspiration: Btw, I love that we get auto-translated posts from around the world. It really expands our horizon in terms of inspiration.

Meng To

144,302 次观看 • 25 天前

Live Link: Source Code: While updating my website, I found an old project that was broken and decided to bring it back to life using WebGPU + TSL in #threejs. I’ve always loved Predator. The first time I saw the Predator cloak effect, I knew I wanted to learn visual effects and make them part of my professional life. Turn the sound on — it makes the experience much more fun 😅 This is also a great exercise if you’re learning TSL and want to experiment with glass, dispersion, IOR, and custom shaders. Both the classic shader version and the new TSL version are included in the repo so you can compare the approaches side by side. Hope it’s useful. Don’t forget to ⭐ the GitHub repo! #r3f #threejsJourney

Live Link: Source Code: While updating my website, I found an old project that was broken and decided to bring it back to life using WebGPU + TSL in #threejs. I’ve always loved Predator. The first time I saw the Predator cloak effect, I knew I wanted to learn visual effects and make them part of my professional life. Turn the sound on — it makes the experience much more fun 😅 This is also a great exercise if you’re learning TSL and want to experiment with glass, dispersion, IOR, and custom shaders. Both the classic shader version and the new TSL version are included in the repo so you can compare the approaches side by side. Hope it’s useful. Don’t forget to ⭐ the GitHub repo! #r3f #threejsJourney

Anderson Mancini

13,714 次观看 • 1 个月前

🚨Architects are going to hate this. Someone just open sourced a full 3D building editor that runs entirely in your browser. No AutoCAD. No Revit. No $5,000/year licenses. It's called Pascal Editor. Built with React Three Fiber and WebGPU -- meaning it renders directly on your GPU at near-native speed. Here's what's inside this thing: → A full building/level/wall/zone hierarchy you can edit in real time → An ECS-style architecture where every object updates through GPU-powered systems → Zustand state management with full undo/redo built in → Next.js frontend so it deploys as a web app, not a desktop install → Dirty node tracking -- only re-renders what changed, not the whole scene Here's the wildest part: You can stack, explode, or solo individual building levels. Select a zone, drag a wall, reshape a slab -- all in 3D, all in the browser. Architecture firms pay $50K+ per seat for BIM software that does this workflow. This is free. 100% Open Source.

🚨Architects are going to hate this. Someone just open sourced a full 3D building editor that runs entirely in your browser. No AutoCAD. No Revit. No $5,000/year licenses. It's called Pascal Editor. Built with React Three Fiber and WebGPU -- meaning it renders directly on your GPU at near-native speed. Here's what's inside this thing: → A full building/level/wall/zone hierarchy you can edit in real time → An ECS-style architecture where every object updates through GPU-powered systems → Zustand state management with full undo/redo built in → Next.js frontend so it deploys as a web app, not a desktop install → Dirty node tracking -- only re-renders what changed, not the whole scene Here's the wildest part: You can stack, explode, or solo individual building levels. Select a zone, drag a wall, reshape a slab -- all in 3D, all in the browser. Architecture firms pay $50K+ per seat for BIM software that does this workflow. This is free. 100% Open Source.

Guri Singh

2,967,409 次观看 • 5 个月前

🦞 ClawLibrary: The Generated Asset Index & Monitor for OpenClaw My project ClawLibrary is officially open-source! 🥰 It's a retro 2D pixel-art style asset management UI for OpenClaw. The backstory: As my openclaw gets smarter, the generated assets are piling up. Finding things became very time-consuming. Inspired by Ring Hyacinth and Simon Lee 's Star-Office-UI, I built it a dedicated library! Current features: 🗂️ Full Asset Index & Preview: Seamlessly browse & preview almost everything OpenClaw generates (docs, code, images, Skills, memories, cron jobs, logs, etc.) right in the UI. 📊 Real-time Dashboard: Keep an eye on the lobster's config, task queues, active models, health status, and error tracking at a glance. 👀 Dynamic Tracking: Visually track real-time access and modification history of your resources. 🎨 Highly Customizable: Swap out animated characters, plus seamless English/Chinese support. It's completely open-source now! If you like it, a little Star 🌟 would mean a lot! Issues and PRs are super welcome to help improve it together! 🥹 🔗 #openclaw

🦞 ClawLibrary: The Generated Asset Index & Monitor for OpenClaw My project ClawLibrary is officially open-source! 🥰 It's a retro 2D pixel-art style asset management UI for OpenClaw. The backstory: As my openclaw gets smarter, the generated assets are piling up. Finding things became very time-consuming. Inspired by Ring Hyacinth and Simon Lee 's Star-Office-UI, I built it a dedicated library! Current features: 🗂️ Full Asset Index & Preview: Seamlessly browse & preview almost everything OpenClaw generates (docs, code, images, Skills, memories, cron jobs, logs, etc.) right in the UI. 📊 Real-time Dashboard: Keep an eye on the lobster's config, task queues, active models, health status, and error tracking at a glance. 👀 Dynamic Tracking: Visually track real-time access and modification history of your resources. 🎨 Highly Customizable: Swap out animated characters, plus seamless English/Chinese support. It's completely open-source now! If you like it, a little Star 🌟 would mean a lot! Issues and PRs are super welcome to help improve it together! 🥹 🔗 #openclaw

Simon Meng

10,213 次观看 • 5 个月前

What if instead of autoresearch reflecting incremental progress from a single person, it reflected *hundreds* of researchers’ progress, updated live, with every run’s data point being interactive and reproducible? You could survey the full space of explored ideas, see which changes actually move the benchmark, and fold the most promising ideas into your next run. Labless ( is my attempt at this. It can support thousands of simultaneous training runs across contributors around the world, each learning & improving together to hillclimb on a fixed benchmark, validated using a standardized codebase scaffold. Importantly, all valid training runs auto-submit to Labless, so failed experiments are as visible and reproducible as the successes. We have an agentic API that lets you or your coding agent study every run submitted so far so you don't waste time investigating something someone already tried, and you can source novel ideas from other contributors. Labless already hosts our Nanopath challenge to build the best pathology foundation model that trains in 1 hour on a single GPU. We are hoping to expand the platform further and support a suite of open-source research challenges. If this resonates with you, if you think the path to research innovation lies in open-source collaborative hill-climbing, please reach out.

What if instead of autoresearch reflecting incremental progress from a single person, it reflected hundreds of researchers’ progress, updated live, with every run’s data point being interactive and reproducible? You could survey the full space of explored ideas, see which changes actually move the benchmark, and fold the most promising ideas into your next run. Labless ( is my attempt at this. It can support thousands of simultaneous training runs across contributors around the world, each learning & improving together to hillclimb on a fixed benchmark, validated using a standardized codebase scaffold. Importantly, all valid training runs auto-submit to Labless, so failed experiments are as visible and reproducible as the successes. We have an agentic API that lets you or your coding agent study every run submitted so far so you don't waste time investigating something someone already tried, and you can source novel ideas from other contributors. Labless already hosts our Nanopath challenge to build the best pathology foundation model that trains in 1 hour on a single GPU. We are hoping to expand the platform further and support a suite of open-source research challenges. If this resonates with you, if you think the path to research innovation lies in open-source collaborative hill-climbing, please reach out.

Paul Scotti @ ICML

22,181 次观看 • 2 个月前

I’m blown away by the quality of the landing pages that Opus 5 can create in Three.js. Prompting it with "orbit using mouse" is enough to create a 3D parallax effect. The same goes for "add particles to pointer." It’s like creating a living, breathing world. More prompts I used: - Add a butterfly that flies to the branch and flies away when I mouse over it. - During the intro, first load a wireframe of the branches, similar to the field-scanning effect in Death Stranding. - Add a scan effect to the card images while they load. Then I asked it to use the shader buttons and nav bar I made in previous projects. As explained in my last video, I started with an inspiration and the prompt, “Recreate this in Three.js in a single HTML file. Self-verify until perfect.” Opus 5 ran for two hours. All the code is less than 1 MB. Inspiration: Let me know if I should open-source this.

I’m blown away by the quality of the landing pages that Opus 5 can create in Three.js. Prompting it with "orbit using mouse" is enough to create a 3D parallax effect. The same goes for "add particles to pointer." It’s like creating a living, breathing world. More prompts I used: - Add a butterfly that flies to the branch and flies away when I mouse over it. - During the intro, first load a wireframe of the branches, similar to the field-scanning effect in Death Stranding. - Add a scan effect to the card images while they load. Then I asked it to use the shader buttons and nav bar I made in previous projects. As explained in my last video, I started with an inspiration and the prompt, “Recreate this in Three.js in a single HTML file. Self-verify until perfect.” Opus 5 ran for two hours. All the code is less than 1 MB. Inspiration: Let me know if I should open-source this.

Meng To

524,396 次观看 • 18 天前

I’ve officially open-sourced ABYSSAL. 🌊⚡ ABYSSAL is a fully procedural ocean and extreme-weather experience that runs directly in the browser. Waves, volumetric clouds, rain, lightning, hurricanes, waterspouts, rogue waves, and tsunamis are generated in real time using Three.js, WebGL2, and GLSL. The project is still actively being developed, and there’s a lot left to do. Deeper optimization, building the world beneath the ocean’s surface, refining the existing systems, and exploring plenty of ideas I haven’t implemented yet. But that’s also why I wanted to open it now. ABYSSAL is MIT-licensed, so you can explore the code, learn from it, modify it, fork it, and use it as the foundation for your own experiments and projects. Performance note: the demo starts with adaptive resolution enabled and automatically selects a quality tier based on your GPU. If the image looks soft, open the settings panel and raise the quality as far as your device can handle. The experience begins in Cinematic mode, click SANDBOX for full control of the camera, weather, and disasters. Explore the source code: Play it here: Feedback, contributions, experiments, and forks are all welcome.

I’ve officially open-sourced ABYSSAL. 🌊⚡ ABYSSAL is a fully procedural ocean and extreme-weather experience that runs directly in the browser. Waves, volumetric clouds, rain, lightning, hurricanes, waterspouts, rogue waves, and tsunamis are generated in real time using Three.js, WebGL2, and GLSL. The project is still actively being developed, and there’s a lot left to do. Deeper optimization, building the world beneath the ocean’s surface, refining the existing systems, and exploring plenty of ideas I haven’t implemented yet. But that’s also why I wanted to open it now. ABYSSAL is MIT-licensed, so you can explore the code, learn from it, modify it, fork it, and use it as the foundation for your own experiments and projects. Performance note: the demo starts with adaptive resolution enabled and automatically selects a quality tier based on your GPU. If the image looks soft, open the settings panel and raise the quality as far as your device can handle. The experience begins in Cinematic mode, click SANDBOX for full control of the camera, weather, and disasters. Explore the source code: Play it here: Feedback, contributions, experiments, and forks are all welcome.

Token Gremlin

166,625 次观看 • 6 天前

NOBODY wants to send their data to Google or OpenAI. Yet here we are, shipping proprietary code, customer information, and sensitive business logic to closed-source APIs we don't control. While everyone's chasing the latest closed-source releases, open-source models are quietly becoming the practical choice for many production systems. Here's what everyone is missing: Open-source models are catching up fast, and they bring something the big labs can't: privacy, speed, and control. I built a playground to test this myself. Used CometML's Opik to evaluate models on real code generation tasks - testing correctness, readability, and best practices against actual GitHub repos. Here's what surprised me: OSS models like MiniMax-M2, Kimi k2 performed on par with the likes of Gemini 3 and Claude Sonnet 4.5 on most tasks. But practically MiniMax-M2 turns out to be a winner as it's twice as fast and 12x cheaper when you compare it to models like Sonnet 4.5. Well, this isn't just about saving money. When your model is smaller and faster, you can deploy it in places closed-source APIs can't reach: ↳ Real-time applications that need sub-second responses ↳ Edge devices where latency kills user experience ↳ On-premise systems where data never leaves your infrastructure MiniMax-M2 runs with only 10B activated parameters. That efficiency means lower latency, higher throughput, and the ability to handle interactive agents without breaking the bank. The intelligence-to-cost ratio here changes what's possible. You're not choosing between quality and affordability anymore. You're not sacrificing privacy for performance. The gap is closing, and in many cases, it's already closed. If you're building anything that needs to be fast, private, or deployed at scale, it's worth taking a look at what's now available. MiniMax-M2 is 100% open-source, free for developers right now. I have shared the link to their GitHub repo in the next tweet. You will also find the code for the playground and evaluations I've done.

NOBODY wants to send their data to Google or OpenAI. Yet here we are, shipping proprietary code, customer information, and sensitive business logic to closed-source APIs we don't control. While everyone's chasing the latest closed-source releases, open-source models are quietly becoming the practical choice for many production systems. Here's what everyone is missing: Open-source models are catching up fast, and they bring something the big labs can't: privacy, speed, and control. I built a playground to test this myself. Used CometML's Opik to evaluate models on real code generation tasks - testing correctness, readability, and best practices against actual GitHub repos. Here's what surprised me: OSS models like MiniMax-M2, Kimi k2 performed on par with the likes of Gemini 3 and Claude Sonnet 4.5 on most tasks. But practically MiniMax-M2 turns out to be a winner as it's twice as fast and 12x cheaper when you compare it to models like Sonnet 4.5. Well, this isn't just about saving money. When your model is smaller and faster, you can deploy it in places closed-source APIs can't reach: ↳ Real-time applications that need sub-second responses ↳ Edge devices where latency kills user experience ↳ On-premise systems where data never leaves your infrastructure MiniMax-M2 runs with only 10B activated parameters. That efficiency means lower latency, higher throughput, and the ability to handle interactive agents without breaking the bank. The intelligence-to-cost ratio here changes what's possible. You're not choosing between quality and affordability anymore. You're not sacrificing privacy for performance. The gap is closing, and in many cases, it's already closed. If you're building anything that needs to be fast, private, or deployed at scale, it's worth taking a look at what's now available. MiniMax-M2 is 100% open-source, free for developers right now. I have shared the link to their GitHub repo in the next tweet. You will also find the code for the playground and evaluations I've done.

Akshay 🚀

50,323 次观看 • 9 个月前

We are excited to share #PDF2Audio, an open-source alternative to the #podcast feature of #NotebookLM with flexibility & tailored outputs that you can precisely control in the app: You can make a podcast, lecture, discussions, short/long form summaries & more, including the use of the amazing🍓o1 model (Sam Altman OpenAI: with stunning results!). Code & HF Space: You can find #PDF2Audio on GitHub for local use or try the Hugging Face space, all featuring Gradio. Link to the repo & HF space in the reply. Thank you @knowsuchagencyfor the great work on #promptic and #pdf2podcast, as well as LiteLLM (YC W23), & AK for helping us with the Hugging Face spaces version. We hope that this tool is useful for the community. Background: Developing audio podcasts, lectures, & summaries from complex documents & data has become an exciting trend with impacts from research to education to business. Our open-source #PDF2Audio tool that allows users to utilize various models such as #o1 or local/open-source models, to develop deep-dives into technical content. Example application - material design analysis: As an example to show what the system can do, check the video for a detailed 13-minute analysis of one of the designs created by #SciAgents merging silk & dandelion pigments, created using 🍓o1. The conversation describes the new material, an integration of silk proteins & luteolin/dandelion pigments to create a new biomaterial. Silk, a natural #nanostructured protein-based fiber known for its strength & flexibility, is combined with dandelion pigments like luteolin, which offer unique optical properties. By merging these components at the nanoscale level, the resulting material displays structural coloration—vibrant, tunable colors created by the material's structure rather than synthetic dyes, and leverages silk's hierarchical organization as a scaffold for the pigments, ensuring uniform distribution and non-covalent bonding at the molecular level. Key technical features include: ➡️Low-temperature processing to maintain the integrity of both silk and pigments while reducing energy consumption by 30%. ➡️Enhanced mechanical properties, with tensile strength up to 1.5 gigapascals. ➡️Potential self-healing capabilities and environmental responsiveness, allowing the material to repair minor damage and change color based on environmental conditions. ➡️UV protection and antimicrobial properties, which make this material ideal for smart textiles, eco-friendly coatings, and medical applications. This development opens new doors for sustainable materials, offering an eco-friendly alternative to synthetic fibers with applications in various industries, from fashion to healthcare.

We are excited to share #PDF2Audio, an open-source alternative to the #podcast feature of #NotebookLM with flexibility & tailored outputs that you can precisely control in the app: You can make a podcast, lecture, discussions, short/long form summaries & more, including the use of the amazing🍓o1 model (Sam Altman OpenAI: with stunning results!). Code & HF Space: You can find #PDF2Audio on GitHub for local use or try the Hugging Face space, all featuring Gradio. Link to the repo & HF space in the reply. Thank you @knowsuchagencyfor the great work on #promptic and #pdf2podcast, as well as LiteLLM (YC W23), & AK for helping us with the Hugging Face spaces version. We hope that this tool is useful for the community. Background: Developing audio podcasts, lectures, & summaries from complex documents & data has become an exciting trend with impacts from research to education to business. Our open-source #PDF2Audio tool that allows users to utilize various models such as #o1 or local/open-source models, to develop deep-dives into technical content. Example application - material design analysis: As an example to show what the system can do, check the video for a detailed 13-minute analysis of one of the designs created by #SciAgents merging silk & dandelion pigments, created using 🍓o1. The conversation describes the new material, an integration of silk proteins & luteolin/dandelion pigments to create a new biomaterial. Silk, a natural #nanostructured protein-based fiber known for its strength & flexibility, is combined with dandelion pigments like luteolin, which offer unique optical properties. By merging these components at the nanoscale level, the resulting material displays structural coloration—vibrant, tunable colors created by the material's structure rather than synthetic dyes, and leverages silk's hierarchical organization as a scaffold for the pigments, ensuring uniform distribution and non-covalent bonding at the molecular level. Key technical features include: ➡️Low-temperature processing to maintain the integrity of both silk and pigments while reducing energy consumption by 30%. ➡️Enhanced mechanical properties, with tensile strength up to 1.5 gigapascals. ➡️Potential self-healing capabilities and environmental responsiveness, allowing the material to repair minor damage and change color based on environmental conditions. ➡️UV protection and antimicrobial properties, which make this material ideal for smart textiles, eco-friendly coatings, and medical applications. This development opens new doors for sustainable materials, offering an eco-friendly alternative to synthetic fibers with applications in various industries, from fashion to healthcare.

Markus J. Buehler

208,441 次观看 • 1 年前

ALRIGHT! Introducing SEEDTHREE! A Three.js webgpu procedural plant generator. I'm releasing this fully open source. I built this with Claude Fable and GPT-5.5 Codex + gptimage2 over just the past few days. The best part is: given the code, your agents can add new plants to it all you want! They just need an understanding of the code and an image generator capable of creating material maps. I highly encourage you to do PRs for any extra plants you add! I'll be adding more myself in future versions. The system is fully robust: it provides complete LOD and billboards/imposters generation for the plants and you can adjust the detail levels all you want for different platform targets like mobile and/or use lower LODs as your main for extreme optimization. As this is the first release alpha version, some bugs may have been missed, I encourage you to report any bugs. The repo includes all the assets for the scenery world too, even the ambient sounds of birds and wind! This is targeted at high end laptop and PC use, it requires baking generated geometry, so you won't be using this on a phone (I mean, feel free to try, but I doubt it'll run well, lol). This is fundamentally an asset generation tool for 3d dev. Have fun! Github repo link and live demo (you don't even have to download anything for basic non-editing/contributing use!) below ⤵️

ALRIGHT! Introducing SEEDTHREE! A Three.js webgpu procedural plant generator. I'm releasing this fully open source. I built this with Claude Fable and GPT-5.5 Codex + gptimage2 over just the past few days. The best part is: given the code, your agents can add new plants to it all you want! They just need an understanding of the code and an image generator capable of creating material maps. I highly encourage you to do PRs for any extra plants you add! I'll be adding more myself in future versions. The system is fully robust: it provides complete LOD and billboards/imposters generation for the plants and you can adjust the detail levels all you want for different platform targets like mobile and/or use lower LODs as your main for extreme optimization. As this is the first release alpha version, some bugs may have been missed, I encourage you to report any bugs. The repo includes all the assets for the scenery world too, even the ambient sounds of birds and wind! This is targeted at high end laptop and PC use, it requires baking generated geometry, so you won't be using this on a phone (I mean, feel free to try, but I doubt it'll run well, lol). This is fundamentally an asset generation tool for 3d dev. Have fun! Github repo link and live demo (you don't even have to download anything for basic non-editing/contributing use!) below ⤵️

Utah teapot 🫖

29,593 次观看 • 2 个月前

What they don't tell you about vibe coding: • Moltbook exposed 1.5M auth tokens. The owner hadn't written a single line of code. • Tea App leaked 72,000 government IDs. The database was just open, no sophisticated hack needed. • A researcher took control of a journalist's computer through her own vibe-coded game, without a single click. The code ran fine in all three cases, tests passed, reviews looked clean, and nothing raised a flag. That's the problem nobody is talking about. Teams are shipping faster than ever. AI writes the code. CI catches build failures. Tests catch regressions. Observability catches outages. But nobody is asking the one question that actually matters: What can an attacker do with this, right now? Because the bottleneck is no longer writing code. It's understanding what that code actually exposes once it's live. PR reviews miss auth edge cases. Unit tests don't probe broken access control. Staging environments don't simulate adversarial behavior. And business logic flaws look completely fine until someone decides to break them on purpose. Strix is an open-source tool that fills this gap. It reviews your running app the way an attacker would: - Crawls the app and maps every exposed route and flow - Probes abuse paths dynamically, not just at build time - Returns findings with proof-of-concepts and suggested fixes Strix was benchmarked against 200 real companies and open-source repos, where it found 600+ verified vulnerabilities including assigned CVEs. It's designed to fit into how modern teams already work. Run it before a release, after major changes, or continuously as the app evolves. If your team is shipping AI-generated code and you don't currently have a way to answer "what does this actually expose", it's worth looking at. GitHub link in the next tweet.

What they don't tell you about vibe coding: • Moltbook exposed 1.5M auth tokens. The owner hadn't written a single line of code. • Tea App leaked 72,000 government IDs. The database was just open, no sophisticated hack needed. • A researcher took control of a journalist's computer through her own vibe-coded game, without a single click. The code ran fine in all three cases, tests passed, reviews looked clean, and nothing raised a flag. That's the problem nobody is talking about. Teams are shipping faster than ever. AI writes the code. CI catches build failures. Tests catch regressions. Observability catches outages. But nobody is asking the one question that actually matters: What can an attacker do with this, right now? Because the bottleneck is no longer writing code. It's understanding what that code actually exposes once it's live. PR reviews miss auth edge cases. Unit tests don't probe broken access control. Staging environments don't simulate adversarial behavior. And business logic flaws look completely fine until someone decides to break them on purpose. Strix is an open-source tool that fills this gap. It reviews your running app the way an attacker would: - Crawls the app and maps every exposed route and flow - Probes abuse paths dynamically, not just at build time - Returns findings with proof-of-concepts and suggested fixes Strix was benchmarked against 200 real companies and open-source repos, where it found 600+ verified vulnerabilities including assigned CVEs. It's designed to fit into how modern teams already work. Run it before a release, after major changes, or continuously as the app evolves. If your team is shipping AI-generated code and you don't currently have a way to answer "what does this actually expose", it's worth looking at. GitHub link in the next tweet.

Akshay 🚀

52,399 次观看 • 5 个月前

I built a Three.js rendering study inspired by Tiny Glade’s painterly aesthetic, and got it running at 120fps in the browser. Over the past few weeks, I’ve been studying how stylized games achieve that soft, handcrafted look in real time. Tiny Glade was a huge inspiration, and I wanted to use the browser as a constraint: no compute shaders, no native GPU access, and single-threaded JavaScript. As part of this study, I implemented: - GPU-driven instanced brick walls with procedural noise jitter and elastic build animations - Tree, bush, and flower rendering with billboard card expansion, wind sway, and grow animations - Procedural grass with terrain conformance and interactive push deformation - Animated water with layered noise, interactive ripples, and Fresnel-based reflections - Procedural terrain with slope-aware triplanar materials, dirt paths, and rocks - A 7-pass post-processing stack with TAA, bloom, depth of field, painterly filtering, ACES tonemapping, 3D LUT color grading, and film grain The hardest part wasn’t writing any single shader. It was making all of these systems work together at high frame rates inside WebGL, where every millisecond counts and performance problems compound quickly across animation, materials, post-processing, and scene management. Some techniques in this study were inspired by analyzing Tiny Glade’s rendering approach, while others were original implementations built from scratch from visual reference. That contrast taught me a lot: recreating an effect is one challenge, but designing your own shaders and systems to achieve a similar feel is a very different one. This is a private educational rendering study. Some temporary placeholder content is being used during the research phase, and any public or production version would use original or properly licensed assets. Huge credit to Pounce Light for the incredible art direction and rendering work in Tiny Glade: Three.js #gamedev #webgl #threejs #rendering #graphics #realtimerendering #shaderdev

I built a Three.js rendering study inspired by Tiny Glade’s painterly aesthetic, and got it running at 120fps in the browser. Over the past few weeks, I’ve been studying how stylized games achieve that soft, handcrafted look in real time. Tiny Glade was a huge inspiration, and I wanted to use the browser as a constraint: no compute shaders, no native GPU access, and single-threaded JavaScript. As part of this study, I implemented: - GPU-driven instanced brick walls with procedural noise jitter and elastic build animations - Tree, bush, and flower rendering with billboard card expansion, wind sway, and grow animations - Procedural grass with terrain conformance and interactive push deformation - Animated water with layered noise, interactive ripples, and Fresnel-based reflections - Procedural terrain with slope-aware triplanar materials, dirt paths, and rocks - A 7-pass post-processing stack with TAA, bloom, depth of field, painterly filtering, ACES tonemapping, 3D LUT color grading, and film grain The hardest part wasn’t writing any single shader. It was making all of these systems work together at high frame rates inside WebGL, where every millisecond counts and performance problems compound quickly across animation, materials, post-processing, and scene management. Some techniques in this study were inspired by analyzing Tiny Glade’s rendering approach, while others were original implementations built from scratch from visual reference. That contrast taught me a lot: recreating an effect is one challenge, but designing your own shaders and systems to achieve a similar feel is a very different one. This is a private educational rendering study. Some temporary placeholder content is being used during the research phase, and any public or production version would use original or properly licensed assets. Huge credit to Pounce Light for the incredible art direction and rendering work in Tiny Glade: Three.js #gamedev #webgl #threejs #rendering #graphics #realtimerendering #shaderdev

Ibrahim Boona

58,625 次观看 • 5 个月前

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Santiago

101,473 次观看 • 1 年前

Anthropic's most viral feature is now open-source! Until now, Anthropic's Generative UI capabilities only existed inside its own products. CopilotKit🪁 just shipped Open Generative UI, an open-source implementation of Claude Artifacts that works in any app. The agent generates HTML/SVG at runtime, and CopilotKit streams it token-by-token into a sandboxed iframe inside the app's chat. So the user can watch the UI assemble itself in real time, not after the full response is ready. The sandbox is fully isolated with no access to the parent app, the DOM, or user data. So if the agent hallucinates broken markup or unexpected JavaScript, nothing leaks outside the iframe. Under the hood, the agent does not select from pre-built components. Instead, it generates arbitrary visuals from scratch every time. The output is unconstrained by default, but you can shape it by defining prompt-based skills that teach the agent specific visual formats or guidelines. For instance, a skill prompt can guide the agent toward producing a Chart.js dashboard with proper axis labels and responsive sizing, or an interactive 3D model with rotation controls. The video below shows this in action, and the output quality you see actually comes from the skills layer. Open Generative UI runs on AG-UI, so it works out of the box with LangGraph, CrewAI, Mastra, Google ADK, AWS Strands, and more. It also ships with a standalone MCP server that plugs into Claude Code, Cursor, or any MCP-compatible client. And the entire stack is built on top of CopilotKit, the open-source frontend framework for agents and generative UI. 30k+ GitHub stars, with SDKs for React, Next.js, Angular, and Vue. I have shared the GitHub repo and a live playground in the replies!

Anthropic's most viral feature is now open-source! Until now, Anthropic's Generative UI capabilities only existed inside its own products. CopilotKit🪁 just shipped Open Generative UI, an open-source implementation of Claude Artifacts that works in any app. The agent generates HTML/SVG at runtime, and CopilotKit streams it token-by-token into a sandboxed iframe inside the app's chat. So the user can watch the UI assemble itself in real time, not after the full response is ready. The sandbox is fully isolated with no access to the parent app, the DOM, or user data. So if the agent hallucinates broken markup or unexpected JavaScript, nothing leaks outside the iframe. Under the hood, the agent does not select from pre-built components. Instead, it generates arbitrary visuals from scratch every time. The output is unconstrained by default, but you can shape it by defining prompt-based skills that teach the agent specific visual formats or guidelines. For instance, a skill prompt can guide the agent toward producing a Chart.js dashboard with proper axis labels and responsive sizing, or an interactive 3D model with rotation controls. The video below shows this in action, and the output quality you see actually comes from the skills layer. Open Generative UI runs on AG-UI, so it works out of the box with LangGraph, CrewAI, Mastra, Google ADK, AWS Strands, and more. It also ships with a standalone MCP server that plugs into Claude Code, Cursor, or any MCP-compatible client. And the entire stack is built on top of CopilotKit, the open-source frontend framework for agents and generative UI. 30k+ GitHub stars, with SDKs for React, Next.js, Angular, and Vue. I have shared the GitHub repo and a live playground in the replies!

Akshay 🚀

87,048 次观看 • 4 个月前

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 次观看 • 9 个月前

I am happy to be finally able to post what I was able to build over the last few weeks. A full real-time high-frequency state estimation and mapping algorithm completely written line by line from scratch in Rust, which can be used by robots to navigate and reason within the 3D world also in complicated scenarios. TBH this took me longer than expected (which was still super fast :D) but you need to get a lot right: From the sensors over the drivers to their respective estimation pipeline and then fusing everything together - a covariance nightmare - and something that can be refined over years to come (currently using Fisher Information from the real measurements). What you see here is not the output of some structure from motion or Gaussian splatting, these are the points of a tight mesh (high res for the video) that a robot can use in real time to plan a path using any open-source planner. The flight you experience through the world is the actual state estimate of the scanner which is published at IMU rate. Yes, currently we have some artefacts of filtered-out humans (GDPR compliant of course :) ) and moving cars and there is still some calibration that could be improved. Offline refinement with SFM and Gaussian splats is possible as well but currently not on the road map. What is on the road map is an exciting step of now being able to collect data from customers at construction sites and in warehouses (currently handheld in the near future with a robot). This data can then be used by our physical agents to reason within this world and automate any customer’s task related to 3D data. If you have anyone who wastes time manually looking 👀 through 3D data, or cannot collect enough 3D data and interpret: Tell me how to reach them!

I am happy to be finally able to post what I was able to build over the last few weeks. A full real-time high-frequency state estimation and mapping algorithm completely written line by line from scratch in Rust, which can be used by robots to navigate and reason within the 3D world also in complicated scenarios. TBH this took me longer than expected (which was still super fast :D) but you need to get a lot right: From the sensors over the drivers to their respective estimation pipeline and then fusing everything together - a covariance nightmare - and something that can be refined over years to come (currently using Fisher Information from the real measurements). What you see here is not the output of some structure from motion or Gaussian splatting, these are the points of a tight mesh (high res for the video) that a robot can use in real time to plan a path using any open-source planner. The flight you experience through the world is the actual state estimate of the scanner which is published at IMU rate. Yes, currently we have some artefacts of filtered-out humans (GDPR compliant of course :) ) and moving cars and there is still some calibration that could be improved. Offline refinement with SFM and Gaussian splats is possible as well but currently not on the road map. What is on the road map is an exciting step of now being able to collect data from customers at construction sites and in warehouses (currently handheld in the near future with a robot). This data can then be used by our physical agents to reason within this world and automate any customer’s task related to 3D data. If you have anyone who wastes time manually looking 👀 through 3D data, or cannot collect enough 3D data and interpret: Tell me how to reach them!

Benedikt Seidel

16,671 次观看 • 4 个月前