Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

Someone vibe coded a 3D pixel art city from real GitHub data. That someone was me. 29 days later: - 66,000+ developers in the city - 120K unique visitors - 449K pageviews - 2.2M ad impressions - 40+ brands advertising inside it - 4,238 GitHub stars - 3M+ organic... show more

Git City

1,086 subscribers

25,288 просмотров • 7 месяцев назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 17

Фото профиля Git City

Git City7 месяцев назад

1/ What is Git City? A vibe coded 3D pixel art city where every building is a real developer. Your building height = your GitHub contributions Your building width = your repos Lit windows = recent activity It's your GitHub profile, but as a skyscraper.

Фото профиля Git City

Git City7 месяцев назад

2/ Growth Week 1: 1,635 developers Week 2: 12,042 (+636%) Week 3: 55,988 (+365%) Week 4: 63,548 We hit 10K to 50K in just 6 days. 100% organic. People just... shared it.

Фото профиля Git City

Git City7 месяцев назад

3/ Traffic 120K unique visitors in 30 days. Where they came from: - GitHub: 36K - Google: 33K (organic SEO) - Twitter: 11K - LinkedIn: 3.2K - Instagram: 3.1K No paid ads. Not a single dollar.

Фото профиля Git City

Git City7 месяцев назад

4/ The audience This isn't random traffic. Every user is a verified GitHub developer. - 29,103 authenticated via GitHub - 14.4M+ combined GitHub stars - 226 devs with 10K+ stars - Top languages: JavaScript, Python, TypeScript, Java India 35%, Brazil 20%, USA 8%, 30+ countries.

Фото профиля Git City

Git City7 месяцев назад

5/ Engagement 22.8% bounce rate (industry avg is 40-60%). Users don't just visit. They play: - 106K building visits - 33K daily missions completed - 9,500+ PvP raids - 55K achievements unlocked - 197K XP events

Фото профиля Git City

Git City7 месяцев назад

6/ The ad platform 40+ brands started advertising inside the city. They came to us. 2.2M impressions. ~1% CTR. Industry average for display ads? 0.1-0.5%. Our ads are planes flying over the city, blimps, billboards, rooftop signs. Not banners. Immersive.

Фото профиля Git City

Git City7 месяцев назад

7/ Revenue $953 in 29 days with minimal pricing. - Ad subscriptions starting at $9/week - In-game shop (cosmetics) - Sponsored landmarks (companies pay for custom buildings) MRR: $96 and growing. CAC? $0. Literally zero.

Фото профиля Git City

Git City7 месяцев назад

8/ Open source 4,238 GitHub stars. 200+ forks. 568 Discord members. 176 commits. The community doesn't just play — they contribute code, suggest features, and vote on the roadmap.

Фото профиля Git City

Git City7 месяцев назад

9/ What I learned Build something people want to share. I didn't need a marketing team. I didn't need a budget. I needed a product that makes developers say "I need to show this to my friends." The best marketing is a product worth talking about.

Фото профиля Git City

Git City7 месяцев назад

10/ What's next This is Day 29. We're just getting started. If you're a developer, find your building: If you're a brand targeting developers, our ad platform delivers 2-10x the CTR of traditional display ads at a fraction of the cost.

Фото профиля Igor Yuzovitskiy

Igor Yuzovitskiy7 месяцев назад

super cool!

Фото профиля MAFIA BOD

MAFIA BOD6 месяцев назад

Amazing 🤩 Love it.

Фото профиля Kage Core

Kage Core7 месяцев назад

what about ur coin though

Фото профиля KajetK

KajetK6 месяцев назад

This is an incredibly cool idea - one of those "wouldn't it be cool if..." ideas, but you actually did it 💪

Фото профиля Anthony Khelil

Anthony Khelil4 месяцев назад

Congratulations, the simplest ideas are sometimes the best 😁

Фото профиля pixelz.hoodfi.eth

pixelz.hoodfi.eth6 месяцев назад

💎

Фото профиля esky33

esky335 месяцев назад

I’m here to 🫡

Похожие видео

A GUY AT GOOGLE DEEPMIND MADE AN ISOMETRIC PIXEL-ART MAP OF NEW YORK CITY AND PUT IT ON THE OPEN WEB FOR FREE it's called you open the tab and the city is just sitting there in classic SimCity 2000 isometric pixel art. you scroll. and it keeps going. and going. i zoomed in on midtown and i could read the H&M signage in times square. in red. as actual pixel-art letters on the side of a building. i could see the crystalline spire of the Bank of America Tower poking out of a clump of skyscrapers. individual rooftop HVAC units. tiny green roof gardens. the little driveway loops in front of the hotels. he estimates the map needs roughly 40,000 tiles. nothing is a placeholder. the guy who made it is Andy Coenen, a senior staff engineer at Google DeepMind. he is not a pixel artist. by his own admission he is "a former electronic musician." what he actually did is kind of insane: > pulled NYC's geometry from the Google Maps 3D tiles API > fine-tuned an open-source image model (Qwen-Image-Edit) on ~40 hand-paired examples of "satellite tile → pixel art tile" > spun up 50 parallel instances on rented GPUs and generated tens of thousands of tiles in a few hours > the fine-tune cost him 12 bucks his own stated mission for the project, verbatim, is one sentence: "what's possible now that was impossible before?" apparently the answer is "one engineer can pixel-art most of a metropolis for the price of a sandwich." and the wildest part to me is he didn't sell it. no signup. no paywall. no NFT. you open the URL and the city is yours to wander. the post landed at 1,325 points on Hacker News and topped bestofshowhn's 2026 list. we live in a timeline where a senior engineer at one of the largest AI labs on earth spent his nights pixel-arting Manhattan for fun and then gave it away. the internet is healing.

A GUY AT GOOGLE DEEPMIND MADE AN ISOMETRIC PIXEL-ART MAP OF NEW YORK CITY AND PUT IT ON THE OPEN WEB FOR FREE it's called you open the tab and the city is just sitting there in classic SimCity 2000 isometric pixel art. you scroll. and it keeps going. and going. i zoomed in on midtown and i could read the H&M signage in times square. in red. as actual pixel-art letters on the side of a building. i could see the crystalline spire of the Bank of America Tower poking out of a clump of skyscrapers. individual rooftop HVAC units. tiny green roof gardens. the little driveway loops in front of the hotels. he estimates the map needs roughly 40,000 tiles. nothing is a placeholder. the guy who made it is Andy Coenen, a senior staff engineer at Google DeepMind. he is not a pixel artist. by his own admission he is "a former electronic musician." what he actually did is kind of insane: > pulled NYC's geometry from the Google Maps 3D tiles API > fine-tuned an open-source image model (Qwen-Image-Edit) on ~40 hand-paired examples of "satellite tile → pixel art tile" > spun up 50 parallel instances on rented GPUs and generated tens of thousands of tiles in a few hours > the fine-tune cost him 12 bucks his own stated mission for the project, verbatim, is one sentence: "what's possible now that was impossible before?" apparently the answer is "one engineer can pixel-art most of a metropolis for the price of a sandwich." and the wildest part to me is he didn't sell it. no signup. no paywall. no NFT. you open the URL and the city is yours to wander. the post landed at 1,325 points on Hacker News and topped bestofshowhn's 2026 list. we live in a timeline where a senior engineer at one of the largest AI labs on earth spent his nights pixel-arting Manhattan for fun and then gave it away. the internet is healing.

Nav Toor

442,183 просмотров • 4 месяцев назад

Someone vibe coded a basketball hoop that floats above your Mac screen so you can shoot hoops while your agent codes. → The entire app is 1MB with no permissions or network access needed → Two modes, a 24-second shot clock where buzzer beaters count and free play → Your live score and all-time best sit at the top of the screen next to the notch → Floats above everything on the desktop so you never leave what you're doing → Free and open source The net was the hardest part. Claude rewrote it as a full cloth simulation. Then someone forked it and shipped a native Windows version at 469KB with real gravity constants, quadratic air drag, ball mass from the official rulebook and floor bounce from FIBA rebound data on a fixed 240Hz integrator. The golf version is already in progress. The notch is the hole.

Someone vibe coded a basketball hoop that floats above your Mac screen so you can shoot hoops while your agent codes. → The entire app is 1MB with no permissions or network access needed → Two modes, a 24-second shot clock where buzzer beaters count and free play → Your live score and all-time best sit at the top of the screen next to the notch → Floats above everything on the desktop so you never leave what you're doing → Free and open source The net was the hardest part. Claude rewrote it as a full cloth simulation. Then someone forked it and shipped a native Windows version at 469KB with real gravity constants, quadratic air drag, ball mass from the official rulebook and floor bounce from FIBA rebound data on a fixed 240Hz integrator. The golf version is already in progress. The notch is the hole.

0xMarioNawfal

40,904 просмотров • 2 месяцев назад

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 просмотров • 5 месяцев назад

an ex-OpenAI researcher pushed his trading system to GitHub the night he got fired someone in a crypto Discord dropped the link at 2am. "this repo appeared an hour ago. zero stars. readme says internal use only. the code looks real" i clicked. it was a complete prediction market execution engine. bayesian consensus scoring. whale divergence tracker. kelly position sizing. edge detection on 5-minute BTC binary markets. clean Python. commented like someone who writes for a team. i ran git clone before finishing the readme. by 4am the repo was gone. deleted. commits. branches. history. everything. but i had it. opened a Chinese AI. gave it the code and one prompt. "adapt this for Polymarket. deploy it live. keep the scoring logic intact" 18,000 stars. open source HFT framework. the AI used it as the execution backbone. order routing. position management. risk controls. merged the stolen scoring model with hummingbot's engine. system was live by sunrise. 288 BTC markets per day. every 5 minutes. the crowd bets direction. his model doesn't care about direction. it buys whichever side the crowd underpriced. pair cost below 99c = profit regardless of outcome. the part that scared me: it worked immediately. first trade. green. second trade. green. third. fourth. fifth. all green. like it was built by someone who actually knew what they were doing. because it was. edge table live: BTC+100K 15m +7.5c. BTC+103K 30m spread 7c. BTC+101K +12.0c. BTC 0xd8dA...6045 +$9,260. 92%. > 0xBE0e...48CD +$7,147. 85%. > 0x47ac...d563 +$5,874. 81%. 323 trades. 85% win rate. sharpe 3.96. drawdown -0.9%. +$19,812 from $1,100 seed. my setup: > AI - $20/mo > VPS - $5/mo > hummingbot - free > his code - free now copy setup you can here: checked his GitHub profile last week. still active. new repos. normal stuff. open source contributions. no mention of the deleted repo anywhere. he has no idea someone cloned it four minutes before it disappeared. and now you're reading what was in it.

Hanako

29,053 просмотров • 6 месяцев назад

UPDATE: Ended up with this cool little tool to both build and verify the training data quality; it turned out very accurate! So I am at least happy about that (tho it obvisouly was the easy part) The pipeline to produce this kind of training data (from any Warsh recitation) is in the GitHub below. The repo also documents my full exploration of the Tarteel-for-Warsh problem (and quite frankly, of the offline open source tarteel problem in general): what I tried, what's promising, what's not, and what I think the next steps worth taking are: from a non-engineer with little technical expertiste trying to figure it out. I tried to document everything so the repo might be excessively comprehensive at times. It's basically my entire convo with Claude on the subject, condensed in one repo. Please someone take this and run with it! I genuinely believe the right engineers who can make something real out of this are out there First public repo I've ever shared, so if I made dumb mistakes, please tell me! 🔗

UPDATE: Ended up with this cool little tool to both build and verify the training data quality; it turned out very accurate! So I am at least happy about that (tho it obvisouly was the easy part) The pipeline to produce this kind of training data (from any Warsh recitation) is in the GitHub below. The repo also documents my full exploration of the Tarteel-for-Warsh problem (and quite frankly, of the offline open source tarteel problem in general): what I tried, what's promising, what's not, and what I think the next steps worth taking are: from a non-engineer with little technical expertiste trying to figure it out. I tried to document everything so the repo might be excessively comprehensive at times. It's basically my entire convo with Claude on the subject, condensed in one repo. Please someone take this and run with it! I genuinely believe the right engineers who can make something real out of this are out there First public repo I've ever shared, so if I made dumb mistakes, please tell me! 🔗

Yousr

38,089 просмотров • 8 месяцев назад

Anthropic's in trouble, again! They spent years building what's now fully open-source. What made Claude feel different from a normal app is that the agent could act inside the interface instead of only talking in a chat box. For instance, Claude Artifacts let an agent render real UI, charts, dashboards, and interactive components that assemble live inside the response. Every major AI product tried to replicate it. But the problem was that unlike reasoning, planning, tool-calling, etc., none of it shipped natively with LangGraph, CrewAI, or Google ADK. So teams started building an owned version that required engineering the entire interface layer from scratch. Most teams, however, just settled for shipping the agent as a backend API in a chat box since rendering the UI is only one piece of it. To actually make it work, the interface layer also needed real-time streaming, state kept in sync between agent and UI, conversations that persist across sessions, and reconnection when a user refreshes mid-run. CopilotKit🪁 is now the only open-source framework that actually lets you build your own full-stack Claude-like apps. It decouples the agent from the interface, talking over AG-UI (an open protocol for agent-to-user communication). Being a standard protocol, the frontend never needs to know whether it is talking to a LangGraph or a CrewAI agent. You can change the backend anytime and the UI will never notice. In practice, CopilotKit's interface layer gives several pre-implemented React building blocks that wire the agent directly into the app, like: - generative UI, so the agent renders real components instead of text - chat windows, sidebars, and popups, or a fully headless setup - shared state, so the agent and app stay in sync - human-in-the-loop approvals, where the agent waits before acting - persistent threads that store the whole session, including the agent-user interactions and generated UI, not just text And because that full history is captured, those interactions can feed a self-learning layer that also improves the agent from real usage over time. The interface layer that Anthropic spent years engineering in-house is now literally available to any developer/team. CopilotKit is open-source with 30k+ GitHub stars, and AG-UI, the protocol underneath, is already supported across every major agent framework: LangGraph, CrewAI, Mastra, Google ADK, and more. CopilotKit GitHub repo → (don't forget to star it ⭐ ) If you want to go deeper, I found a detailed breakdown by Shubham Saboo recently on the three Generative UI patterns, with implementation. Read it below.

Anthropic's in trouble, again! They spent years building what's now fully open-source. What made Claude feel different from a normal app is that the agent could act inside the interface instead of only talking in a chat box. For instance, Claude Artifacts let an agent render real UI, charts, dashboards, and interactive components that assemble live inside the response. Every major AI product tried to replicate it. But the problem was that unlike reasoning, planning, tool-calling, etc., none of it shipped natively with LangGraph, CrewAI, or Google ADK. So teams started building an owned version that required engineering the entire interface layer from scratch. Most teams, however, just settled for shipping the agent as a backend API in a chat box since rendering the UI is only one piece of it. To actually make it work, the interface layer also needed real-time streaming, state kept in sync between agent and UI, conversations that persist across sessions, and reconnection when a user refreshes mid-run. CopilotKit🪁 is now the only open-source framework that actually lets you build your own full-stack Claude-like apps. It decouples the agent from the interface, talking over AG-UI (an open protocol for agent-to-user communication). Being a standard protocol, the frontend never needs to know whether it is talking to a LangGraph or a CrewAI agent. You can change the backend anytime and the UI will never notice. In practice, CopilotKit's interface layer gives several pre-implemented React building blocks that wire the agent directly into the app, like: - generative UI, so the agent renders real components instead of text - chat windows, sidebars, and popups, or a fully headless setup - shared state, so the agent and app stay in sync - human-in-the-loop approvals, where the agent waits before acting - persistent threads that store the whole session, including the agent-user interactions and generated UI, not just text And because that full history is captured, those interactions can feed a self-learning layer that also improves the agent from real usage over time. The interface layer that Anthropic spent years engineering in-house is now literally available to any developer/team. CopilotKit is open-source with 30k+ GitHub stars, and AG-UI, the protocol underneath, is already supported across every major agent framework: LangGraph, CrewAI, Mastra, Google ADK, and more. CopilotKit GitHub repo → (don't forget to star it ⭐ ) If you want to go deeper, I found a detailed breakdown by Shubham Saboo recently on the three Generative UI patterns, with implementation. Read it below.

Avi Chawla

462,344 просмотров • 4 месяцев назад

Nobody is talking about this. There's an open-source AI platform with 17.3K stars that connects to 40+ of your internal tools and runs completely on your own servers. It's called Onyx. Most companies using ChatGPT Enterprise are feeding sensitive internal data to OpenAI's cloud every single day. Onyx flips that. You self-host it. You control the LLM. Your documents, Slack messages, Notion pages, and Jira tickets stay on your infrastructure. The connectors are the killer feature. Onyx pulls live knowledge from Slack, Notion, Google Drive, Confluence, GitHub, Jira, and 35+ more then indexes everything with hybrid search and a knowledge graph that stays accurate at tens of millions of documents. Document permissioning mirrors your existing access controls automatically. If someone doesn't have access to a Confluence page, the AI won't surface it to them in answers. No extra config. No permission rebuilds. Everything else: → Custom agents with unique instructions and MCP tool access → Deep Research for complex multi-step questions → Code interpreter, image generation, web search → SSO, RBAC, full enterprise security stack → Docker, Kubernetes, Terraform full deployment flexibility MIT License on Community Edition. 17.3K stars. 2.3K forks. 192 contributors.

Nobody is talking about this. There's an open-source AI platform with 17.3K stars that connects to 40+ of your internal tools and runs completely on your own servers. It's called Onyx. Most companies using ChatGPT Enterprise are feeding sensitive internal data to OpenAI's cloud every single day. Onyx flips that. You self-host it. You control the LLM. Your documents, Slack messages, Notion pages, and Jira tickets stay on your infrastructure. The connectors are the killer feature. Onyx pulls live knowledge from Slack, Notion, Google Drive, Confluence, GitHub, Jira, and 35+ more then indexes everything with hybrid search and a knowledge graph that stays accurate at tens of millions of documents. Document permissioning mirrors your existing access controls automatically. If someone doesn't have access to a Confluence page, the AI won't surface it to them in answers. No extra config. No permission rebuilds. Everything else: → Custom agents with unique instructions and MCP tool access → Deep Research for complex multi-step questions → Code interpreter, image generation, web search → SSO, RBAC, full enterprise security stack → Docker, Kubernetes, Terraform full deployment flexibility MIT License on Community Edition. 17.3K stars. 2.3K forks. 192 contributors.

Hasan Toor

11,638 просмотров • 6 месяцев назад

HOLY F*CKING SHIT SOME GUY BUILT AN ENTIRE SWARM OF CRAWLERS THAT NEVER SLEEP AND NOBODY KNOWS ABOUT IT repo: same h100envy whose last repo hit 200 stars in a single day. now he built a full blown hive of crawlers that parse everything that moves how it works inside > swarm map with live dots: color is source (x, telegram, pumpfun, dexscreener, tiktok, reddit), dot size is crawler reputation > 4 tiers: larva → worker → soldier → queen. you grow not by time but by signal quality you bring in > reputation 0-1000 with grades from S to D: quality 40%, uptime 25%, volume 20%, stake 15% > leaderboard updates in real time, you see who carries and who is dead weight the tier system is not just badges. larva is still learning to parse, worker pulls data consistently, soldier filters noise, queen manages entire sectors. every transition is earned through quality but the real juice > marketplace: sell or rent out your crawlers for 1-30 days > 2.5% fee on every deal gets burned. doesn't go to the team, doesn't go to anyone. burn > governance: vote on priority narratives, change reputation weights, add new sources > proposals cost 10k credits to prevent spam, voting is open so you can grow a crawler to soldier, rent it out for 30 days and earn passive income while it works for someone else. and 2.5% gets burned and goes to nobody governance is a whole separate thing. the community itself decides which narratives are priority right now, which sources to add, how to distribute weights. not the team but the holders everything is open source on github. code is open, fork it, read it, verify it who's actually tried launching their own crawlers?

HOLY F*CKING SHIT SOME GUY BUILT AN ENTIRE SWARM OF CRAWLERS THAT NEVER SLEEP AND NOBODY KNOWS ABOUT IT repo: same h100envy whose last repo hit 200 stars in a single day. now he built a full blown hive of crawlers that parse everything that moves how it works inside > swarm map with live dots: color is source (x, telegram, pumpfun, dexscreener, tiktok, reddit), dot size is crawler reputation > 4 tiers: larva → worker → soldier → queen. you grow not by time but by signal quality you bring in > reputation 0-1000 with grades from S to D: quality 40%, uptime 25%, volume 20%, stake 15% > leaderboard updates in real time, you see who carries and who is dead weight the tier system is not just badges. larva is still learning to parse, worker pulls data consistently, soldier filters noise, queen manages entire sectors. every transition is earned through quality but the real juice > marketplace: sell or rent out your crawlers for 1-30 days > 2.5% fee on every deal gets burned. doesn't go to the team, doesn't go to anyone. burn > governance: vote on priority narratives, change reputation weights, add new sources > proposals cost 10k credits to prevent spam, voting is open so you can grow a crawler to soldier, rent it out for 30 days and earn passive income while it works for someone else. and 2.5% gets burned and goes to nobody governance is a whole separate thing. the community itself decides which narratives are priority right now, which sources to add, how to distribute weights. not the team but the holders everything is open source on github. code is open, fork it, read it, verify it who's actually tried launching their own crawlers?

zostaff

15,300 просмотров • 12 дней назад

Anthropic turned one GitHub request into a living 3D model that answers every new issue with a rebuilt version of itself Opus 5.5 builds the whole system from that first request, from geometry and rig to the viewer and the pipeline behind it. > REQUEST - one issue describes the object in plain words, nothing else needed > BUILD - mesh, rig and materials generated and committed as editable source files > VIEW - a browser viewer added to the repo so anyone can spin the model instantly > LISTEN - every new issue is read as a change request against the current model > PATCH - only what changed gets rebuilt, then a pull request opens with a fresh render A 3D asset is normally a dead file. Someone exports it once, and every later change means opening the software again. Here the model stays alive inside the repo. Ask for a longer handle or a softer material, and it answers in a pull request. Each pull request carries a new render, so the change gets reviewed by eye before anyone merges it. Because the source is code and not a binary blob, every version is diffable. You see exactly which part moved and why. Rejected changes cost nothing. Close the pull request and the model stays exactly as it was. Teams already run software this way. Running a 3D model like a codebase is the part nobody had wired up. The first request, the repo layout and one full issue-to-render cycle are pinned in the article.

Anthropic turned one GitHub request into a living 3D model that answers every new issue with a rebuilt version of itself Opus 5.5 builds the whole system from that first request, from geometry and rig to the viewer and the pipeline behind it. > REQUEST - one issue describes the object in plain words, nothing else needed > BUILD - mesh, rig and materials generated and committed as editable source files > VIEW - a browser viewer added to the repo so anyone can spin the model instantly > LISTEN - every new issue is read as a change request against the current model > PATCH - only what changed gets rebuilt, then a pull request opens with a fresh render A 3D asset is normally a dead file. Someone exports it once, and every later change means opening the software again. Here the model stays alive inside the repo. Ask for a longer handle or a softer material, and it answers in a pull request. Each pull request carries a new render, so the change gets reviewed by eye before anyone merges it. Because the source is code and not a binary blob, every version is diffable. You see exactly which part moved and why. Rejected changes cost nothing. Close the pull request and the model stays exactly as it was. Teams already run software this way. Running a 3D model like a codebase is the part nobody had wired up. The first request, the repo layout and one full issue-to-render cycle are pinned in the article.

wast3

16,350 просмотров • 13 дней назад

Adobe tried to buy Figma for $20 billion in 2022. The deal collapsed. So Figma went public on the NYSE in July 2025 instead. Ticker FIG. Public company. Quarterly earnings. Wall Street pressure. You know what happens to design tools after they IPO. In March 2025, Figma raised the Professional Full seat 33%. From $15 to $20 a month. Organization seats jumped to $55. Enterprise to $90. Then they took Dev Mode, which was free during beta, and locked it behind a paid seat. Your developers now pay extra to inspect the designs your designers already paid to create. In March 2026, Figma started charging for AI credits on top. If Figma raises prices again, you pay. If Figma gets acquired, you pray. If Figma shuts down, your files die with it. Your design system. On their servers. In a proprietary format only their app can read. To draw rectangles on a screen. There is an open source design platform that runs on your hardware. Stores your files in plain SVG. Costs $0 forever for unlimited users. It is called Penpot. 45,700+ stars on GitHub. A full Figma-grade design platform built on open web standards. Vector editing. Components. Design tokens to W3C spec. Flex and Grid layouts. Real-time multiplayer. Interactive prototyping. Here's what it does: → Real-time collaboration. Live cursors. Comments in line. → Components, variants, shared libraries. → Auto layout, Flex, CSS Grid. The tool outputs production CSS, not lookalike CSS. → Interactive prototypes with overlays, animations, and flows. → Inspect tab. Free. Built in. Every developer grabs production CSS, SVG, HTML without a separate seat. → Plugin ecosystem. Figma import to migrate your files. → Self-host on Docker in one command. Your designs never leave your network. Here's the wildest part: Figma stores your designs in a proprietary format only Figma can read. Penpot files are SVG. The same format your browser has rendered for 25 years. Open them in any editor. Open them in 20 years. Nobody can lock you out. The feature Figma charges your developers extra for, Penpot gives away. Without asking permission. Figma Professional: $20/month per seat. A 10-person team: $2,400/year. Figma Organization: $55/month per Full seat. A 50-person org: $33,000/year. Penpot: $0. Unlimited users. Unlimited files. Unlimited teams. Self-hosted. Free forever. 45,700+ stars. 2,700+ forks. 250+ contributors. MPL-2.0 license. Backed by a community that believes design tools should be free. Your designs. Your files. Your standards. 100% Open Source. (Link in the comments)

Adobe tried to buy Figma for $20 billion in 2022. The deal collapsed. So Figma went public on the NYSE in July 2025 instead. Ticker FIG. Public company. Quarterly earnings. Wall Street pressure. You know what happens to design tools after they IPO. In March 2025, Figma raised the Professional Full seat 33%. From $15 to $20 a month. Organization seats jumped to $55. Enterprise to $90. Then they took Dev Mode, which was free during beta, and locked it behind a paid seat. Your developers now pay extra to inspect the designs your designers already paid to create. In March 2026, Figma started charging for AI credits on top. If Figma raises prices again, you pay. If Figma gets acquired, you pray. If Figma shuts down, your files die with it. Your design system. On their servers. In a proprietary format only their app can read. To draw rectangles on a screen. There is an open source design platform that runs on your hardware. Stores your files in plain SVG. Costs $0 forever for unlimited users. It is called Penpot. 45,700+ stars on GitHub. A full Figma-grade design platform built on open web standards. Vector editing. Components. Design tokens to W3C spec. Flex and Grid layouts. Real-time multiplayer. Interactive prototyping. Here's what it does: → Real-time collaboration. Live cursors. Comments in line. → Components, variants, shared libraries. → Auto layout, Flex, CSS Grid. The tool outputs production CSS, not lookalike CSS. → Interactive prototypes with overlays, animations, and flows. → Inspect tab. Free. Built in. Every developer grabs production CSS, SVG, HTML without a separate seat. → Plugin ecosystem. Figma import to migrate your files. → Self-host on Docker in one command. Your designs never leave your network. Here's the wildest part: Figma stores your designs in a proprietary format only Figma can read. Penpot files are SVG. The same format your browser has rendered for 25 years. Open them in any editor. Open them in 20 years. Nobody can lock you out. The feature Figma charges your developers extra for, Penpot gives away. Without asking permission. Figma Professional: $20/month per seat. A 10-person team: $2,400/year. Figma Organization: $55/month per Full seat. A 50-person org: $33,000/year. Penpot: $0. Unlimited users. Unlimited files. Unlimited teams. Self-hosted. Free forever. 45,700+ stars. 2,700+ forks. 250+ contributors. MPL-2.0 license. Backed by a community that believes design tools should be free. Your designs. Your files. Your standards. 100% Open Source. (Link in the comments)

Nav Toor

217,656 просмотров • 6 месяцев назад

Karpathy's prediction about RL is coming true now! He called reward functions unreliable and argued that a single reward number is too low-dimensional to teach an agent what "good" means for complex tasks. To solve this, Agents need a knowledge-guided review as a higher-dimensional feedback channel. Every major AI lab trains models with RL today (OpenAI, Anthropic, DeepSeek). And their key bottleneck has always been the reward functions. GRPO by DeepSeek worked well for math and code because the environment gave a binary signal. But for real agent tasks, someone still has to hand-code the scoring function. That takes days and breaks every time the pipeline changes. RULER (implemented in OpenPipe ART, 10k stars) addresses the exact problem Karpathy identified. The reward criteria are defined in plain English, and an LLM evaluates each trajectory against that description to provide feedback for training. I trained a Qwen3 1.4B agent that plays 2048 using GRPO with this exact workflow. In this case, the agent saw the board, picked a direction, and RULER evaluated the outcome, all from this natural language definition. You can see the full implementation on GitHub and try it yourself. Here's the ART Repo: (don't forget to star it ⭐ ) Just like RLHF replaced manual rankings and GRPO replaced the critic model, natural language rewards are replacing hand-coded scoring functions. RL reward engineering is now prompt engineering. I wrote a full walkthrough covering RL for LLM agents, from RLHF to GRPO to RULER, in the article below.

Karpathy's prediction about RL is coming true now! He called reward functions unreliable and argued that a single reward number is too low-dimensional to teach an agent what "good" means for complex tasks. To solve this, Agents need a knowledge-guided review as a higher-dimensional feedback channel. Every major AI lab trains models with RL today (OpenAI, Anthropic, DeepSeek). And their key bottleneck has always been the reward functions. GRPO by DeepSeek worked well for math and code because the environment gave a binary signal. But for real agent tasks, someone still has to hand-code the scoring function. That takes days and breaks every time the pipeline changes. RULER (implemented in OpenPipe ART, 10k stars) addresses the exact problem Karpathy identified. The reward criteria are defined in plain English, and an LLM evaluates each trajectory against that description to provide feedback for training. I trained a Qwen3 1.4B agent that plays 2048 using GRPO with this exact workflow. In this case, the agent saw the board, picked a direction, and RULER evaluated the outcome, all from this natural language definition. You can see the full implementation on GitHub and try it yourself. Here's the ART Repo: (don't forget to star it ⭐ ) Just like RLHF replaced manual rankings and GRPO replaced the critic model, natural language rewards are replacing hand-coded scoring functions. RL reward engineering is now prompt engineering. I wrote a full walkthrough covering RL for LLM agents, from RLHF to GRPO to RULER, in the article below.

Avi Chawla

351,173 просмотров • 5 месяцев назад

I spent 2.5 billion tokens, every limit I had and five days to make this. A GTA-style city, built with GPT-6 using only prompts. No code or 3D by hand. The whole point was to push it as far as it goes and wire in every API I had. The setup: Codex driving Unreal Engine 5, with Higgsfield, Tripo, Meshy, Seedance, Seed Audio and fal handling the buildings, characters, animations, materials and sound. 8 buildings taken from concept to game, 247 generated voice lines for residents and police, 10 radio tracks across 5 stations. Here's the honest part. Most of those tokens didn't go into building. They went into fixing. The model writes a mechanic, runs it, watches it break, and tries again for hours. Getting a character to open a car door and actually sit in the seat took an absurd number of passes. A carjacking needed two separate bodies to stay locked to the same grab. Half the work was animation that looked fine on its own and fell apart the moment it ran in the game. And almost all of that 2.5B is the same project history being re-read from cache on every request, not unique code. Procedural generation from prompts is genuinely fun to watch and wildly inefficient at the same time. The city started as glowing blocks with working driving, money and jobs underneath, and ended up somewhere I'd actually screenshot. Full breakdown:

I spent 2.5 billion tokens, every limit I had and five days to make this. A GTA-style city, built with GPT-6 using only prompts. No code or 3D by hand. The whole point was to push it as far as it goes and wire in every API I had. The setup: Codex driving Unreal Engine 5, with Higgsfield, Tripo, Meshy, Seedance, Seed Audio and fal handling the buildings, characters, animations, materials and sound. 8 buildings taken from concept to game, 247 generated voice lines for residents and police, 10 radio tracks across 5 stations. Here's the honest part. Most of those tokens didn't go into building. They went into fixing. The model writes a mechanic, runs it, watches it break, and tries again for hours. Getting a character to open a car door and actually sit in the seat took an absurd number of passes. A carjacking needed two separate bodies to stay locked to the same grab. Half the work was animation that looked fine on its own and fell apart the moment it ran in the game. And almost all of that 2.5B is the same project history being re-read from cache on every request, not unique code. Procedural generation from prompts is genuinely fun to watch and wildly inefficient at the same time. The city started as glowing blocks with working driving, money and jobs underneath, and ended up somewhere I'd actually screenshot. Full breakdown:

Stefan 3D AI

129,647 просмотров • 1 месяц назад

JustHireMe V0.1.31 is out. 🔗 Github: 📥 Download: The first post hit 390K impressions, 3.2K likes, 6K+ bookmarks, 1.2k+ GitHub stars and 400+ downloads . Genuinely didn't expect that. I've been shipping updates since and this release is a big one. Full video walkthrough attached but here's what's new: Command Center — proper dashboard now — active leads, scores, agent status, top roles — all in one screen — no more clicking around to figure out where things stand Customize One Job — paste any job URL — agent captures the JD, scores your fit, shows keyword gaps — generates a tailored resume + cover letter as PDFs — one page, one flow Outreach Generator — every lead now comes with ready-to-copy messages — 3-line founder DM — LinkedIn connection note — cold email — all written from your real profile, not templates Knowledge Graph (rebuilt from scratch) — Relation Atlas - weighted ribbons showing how projects prove skills — Gravity Graph - force-directed career layout — 3D Orbit - rotate through your profile in 3D — Curated mode - strongest evidence paths only — this is what the matching engine actually uses to rank you Job Pipeline — stages: Hot → New → Rated → Ready → Applied → Discarded — Fit score + Quality score on every lead — search, filter, re-eval, bulk clean, export — feels like a real job search CRM now Live Activity Stream — "what is the agent thinking?" — real-time logs of every query, eval, and decision — full transparency, nothing hidden Built-in AI Assistant — ask the app how to configure sources, understand a lead, generate packages — right inside the dashboard Modular Agent Config — Scout, Evaluator, Generator, Ingestor — each one independently configurable — pick your own models and thresholds Still local-first. Still privacy-first. Still open source. Still free. Your data never leaves your machine. If you starred the repo last time — pull the new release. If you bookmarked and forgot — now's a good time. Feedback, issues, PRs, or just a ⭐— all welcome. ⭐ 📥 #OpenSource #AgenticAI #AIAgents #GraphRAG #Python #FastAPI #ReactJS #TypeScript #Tauri #JobSearch #CareerTech #PrivacyFirst

Vasu-Devs

121,743 просмотров • 5 месяцев назад

OpenAI, Anthropic and Microsoft all back the same standard for running apps inside AI chats. Booking .com, Figma, and Canva are already using it. Still, most developers have never heard of it. It's called MCP Apps. So, why does it matter? The next time someone buys from your website, they may never visit it. They'll ask ChatGPT or Claude, and the model decides how your product shows up. Usually as plain text, sometimes wrong, with none of the experience you built. That's the next shift in how people find you: - SEO was about Google finding your page. - AEO was about AI citing you. - MCP Apps are about your actual product showing up inside the chat. An MCP server returns data. An MCP App returns data plus an interactive UI, like hotel cards or an add-to-cart flow, right inside the chat. The model even knows what the user clicked. In this episode, we build an MCP app from start to finish with Julien, the lead maintainer of Skybridge, an open-source framework that lets you turn any React app into an MCP app. Chapters: 0:00 - Intro 2:44 - MCP servers vs MCP Apps 5:12 - Live demo: Booking .com in ChatGPT 9:24 - How MCP Apps work: context and shared state 15:43 - Skybridge and its devtools 17:48 - Live demo: an e-commerce app in ChatGPT 24:49 - Code walkthrough: MCP tools to React views 28:10 - Syncing UI state: "What's in my cart?" 32:16 - Agent skill to turn any React app → MCP App 37:52 - Outro Link to Skybridge GitHub repo: MCP Apps bring your website into the chat. WebMCP does the reverse and lets AI agents use your website through tools it exposes. I've also written a full article on it, quoted below.

OpenAI, Anthropic and Microsoft all back the same standard for running apps inside AI chats. Booking .com, Figma, and Canva are already using it. Still, most developers have never heard of it. It's called MCP Apps. So, why does it matter? The next time someone buys from your website, they may never visit it. They'll ask ChatGPT or Claude, and the model decides how your product shows up. Usually as plain text, sometimes wrong, with none of the experience you built. That's the next shift in how people find you: - SEO was about Google finding your page. - AEO was about AI citing you. - MCP Apps are about your actual product showing up inside the chat. An MCP server returns data. An MCP App returns data plus an interactive UI, like hotel cards or an add-to-cart flow, right inside the chat. The model even knows what the user clicked. In this episode, we build an MCP app from start to finish with Julien, the lead maintainer of Skybridge, an open-source framework that lets you turn any React app into an MCP app. Chapters: 0:00 - Intro 2:44 - MCP servers vs MCP Apps 5:12 - Live demo: Booking .com in ChatGPT 9:24 - How MCP Apps work: context and shared state 15:43 - Skybridge and its devtools 17:48 - Live demo: an e-commerce app in ChatGPT 24:49 - Code walkthrough: MCP tools to React views 28:10 - Syncing UI state: "What's in my cart?" 32:16 - Agent skill to turn any React app → MCP App 37:52 - Outro Link to Skybridge GitHub repo: MCP Apps bring your website into the chat. WebMCP does the reverse and lets AI agents use your website through tools it exposes. I've also written a full article on it, quoted below.

Akshay 🚀

18,064 просмотров • 22 дней назад

Remember when AI couldn't draw a hand? Seven fingers, knuckles pointing backwards. And the AI spaghetti videos. That was three years ago. Images are done now. Video is close enough that you scrolled past AI ads this week and clocked exactly zero of them. Code writes itself and there are like 40 coding agents. AI voice spent that entire stretch sounding like the lady voice in a 2014 GPS. Flat, evenly spaced and every sentence landing with the same weight, like it's reading from a phone book. Here's why it stayed broken. Bad images are funny. You screenshot the seven fingers, it goes viral for being bad, someone fixes it. Bad audio is just boring. It doesn't fail spectacularly, so it never got that pressure. The bigger problem was the scoring. The whole industry graded AI voices on whether you could make out the words. So the models learned to over pronounce everything, hitting every syllable like a newsreader. Perfectly clear but robotic. Everyone was chasing a score that had nothing to do with sounding human. Meanwhile a small open-source team was doing something harder. Their lead researcher, an ex-NVIDIA engineer, went all in on an approach the rest of the field had written off. Two years early. No funding announcements or launch tour. He just put the whole thing on GitHub for free. It's sitting at 50,000+ stars now. Then they ran the test everyone else avoided. For 10 days they piped real users through their model and every big competitor with the listener never told which was which. Thousands of real people, real scripts. Whichever voice you actually preferred, they logged it. Theirs came out on top. It beat ElevenLabs about 6 times out of 10, head to head. It beat OpenAI's voice model 8 times out of 10. The gap was widest on the breathing, the pauses, the little hesitations, which is exactly the stuff that makes a voice sound like a person instead of a machine reading. They ran on real users rather than a lab, which is more than most of these claims can say. That's Fish Audio. This week they shipped S2.1 Pro: - Clone anyone's voice from 15 seconds of audio - Fast enough to hold a live conversation - 83 languages, one model - Type [whisper] or [sigh] mid-sentence and it does it - Around 70% cheaper than ElevenLabs - Free to download and run yourself Voice was the last thing on the list. Around 20 people with a free repo got there before other billion dollar companies did.

Remember when AI couldn't draw a hand? Seven fingers, knuckles pointing backwards. And the AI spaghetti videos. That was three years ago. Images are done now. Video is close enough that you scrolled past AI ads this week and clocked exactly zero of them. Code writes itself and there are like 40 coding agents. AI voice spent that entire stretch sounding like the lady voice in a 2014 GPS. Flat, evenly spaced and every sentence landing with the same weight, like it's reading from a phone book. Here's why it stayed broken. Bad images are funny. You screenshot the seven fingers, it goes viral for being bad, someone fixes it. Bad audio is just boring. It doesn't fail spectacularly, so it never got that pressure. The bigger problem was the scoring. The whole industry graded AI voices on whether you could make out the words. So the models learned to over pronounce everything, hitting every syllable like a newsreader. Perfectly clear but robotic. Everyone was chasing a score that had nothing to do with sounding human. Meanwhile a small open-source team was doing something harder. Their lead researcher, an ex-NVIDIA engineer, went all in on an approach the rest of the field had written off. Two years early. No funding announcements or launch tour. He just put the whole thing on GitHub for free. It's sitting at 50,000+ stars now. Then they ran the test everyone else avoided. For 10 days they piped real users through their model and every big competitor with the listener never told which was which. Thousands of real people, real scripts. Whichever voice you actually preferred, they logged it. Theirs came out on top. It beat ElevenLabs about 6 times out of 10, head to head. It beat OpenAI's voice model 8 times out of 10. The gap was widest on the breathing, the pauses, the little hesitations, which is exactly the stuff that makes a voice sound like a person instead of a machine reading. They ran on real users rather than a lab, which is more than most of these claims can say. That's Fish Audio. This week they shipped S2.1 Pro: - Clone anyone's voice from 15 seconds of audio - Fast enough to hold a live conversation - 83 languages, one model - Type [whisper] or [sigh] mid-sentence and it does it - Around 70% cheaper than ElevenLabs - Free to download and run yourself Voice was the last thing on the list. Around 20 people with a free repo got there before other billion dollar companies did.

Rez Karim

18,446 просмотров • 2 месяцев назад

Apple's product pages have that scroll effect. The camera flies through a 3D world. Products float. Scenes transition. No cuts. One continuous flight driven by your scroll wheel. Here is what one costs to build in 2026. Big storytelling agencies (Noomo, WithLore, Utsubo): $15,000 to $100,000. Three to six months. A team of designers, 3D artists, and WebGL developers. Awwwards-tier flagship from a senior freelancer: $8,000 minimum. Three.js multi-scene site with GSAP scroll: $3,500 to $8,000. That is if you can find a Three.js specialist. They charge $75 to $150 an hour. There are fewer of them than there are brands that want the site. Now meet scroll-world. A free, open-source agent skill for Claude Code and Codex that generates the entire thing from a prompt. Describe your brand. Describe the scenes. "A coffee brand. Start outside the farm. Fly into the roasting facility. Pass through the packaging. Land in the cafe." It generates the isometric stills. It generates the camera flights. It generates the connector clips between scenes from the actual boundary frames of both neighbors, so every seam is pixel-identical. No flicker. No cut. One continuous flight. Then it wires the chain into a portable vanilla JS scroll engine that drops into plain HTML, Next.js, or Vue. One prompt. Hours. Not months. 6,096 stars on GitHub in 25 days. 724 forks. MIT license. Built by cyw, founder and CTO of Hermai AI, out of San Francisco. Here is what scroll-world does: - Interviews you on brand, scenes, art direction, and budget before spending a cent - Generates the isometric stills through GPT Image 2 - Generates the camera flights through Seedance on Monid - Renders a native 9:16 portrait chain for mobile, not a crop - Ships a vanilla JS scrub engine with blob-seek, lazy load, and seam crossfade - Installs into Claude Code as a plugin or Codex through the Vercel skills CLI - Framework-agnostic Here is what scroll-world costs to install: Zero. Forever. Generation burns credits. A 6-scene 1080p chain on Monid pay-per-clip lands around $27, printed before the run starts. Big storytelling agency: $15,000 to $100,000. scroll-world: $27. Awwwards WebGL flagship: $8,000. scroll-world: $27. Three.js contractor for a month: $12,000. scroll-world: this afternoon. Your brand. Your world. Your scroll. 100% Open Source. (Link in the comments)

Nav Toor

365,432 просмотров • 2 месяцев назад

Meet WebBrain: An Open-Source, Local-First AI Browser Agent That Reads Pages and Automates Tasks in Chrome and Firefox WebBrain lives inside your browser and can run entirely on your own local model — no cloud, no account, no data leaving your machine. Most "AI browser agents" are a chat box that pastes your page into someone else's server. That's not an agent that lives where you browse — and WebBrain draws a very clear line between the two. It's an open-source (MIT), local-first browser agent for Chrome and Firefox. It runs inside your existing authenticated session, on a model you pick — so with llama.cpp or Ollama, nothing leaves your machine. Here's what's actually interesting: → Two modes, cleanly separated. Ask reads the page (read-only, content scripts). Act clicks and types through the Chrome DevTools Protocol (chrome.debugger) — trusted input events that modern sites honor, reaching cross-origin iframes and shadow DOM. → UI-first by design. For anything that submits, sends, or buys, it drives the visible UI and refuses to hit REST/GraphQL endpoints directly. It starts read-only and asks before consequential actions. → Bring any model. llama.cpp, Ollama, LM Studio, vLLM — or OpenAI, Claude, Gemini, DeepSeek, Groq, OpenRouter. Recommended local: Qwen 3.6 35B (Qwen3.6-35B-A3B), which beat Gemma 4 on the project's screenshot benchmark. → Tuned for cost and privacy. Token-conscious screenshots, oldest-first context trimming, a dedicated vision model, 40+ tools (~20 in Compact mode). No telemetry. No accounts. Full analysis: GitHub Repo: Chrome Extension: Firefox Add-on: Portal:

Meet WebBrain: An Open-Source, Local-First AI Browser Agent That Reads Pages and Automates Tasks in Chrome and Firefox WebBrain lives inside your browser and can run entirely on your own local model — no cloud, no account, no data leaving your machine. Most "AI browser agents" are a chat box that pastes your page into someone else's server. That's not an agent that lives where you browse — and WebBrain draws a very clear line between the two. It's an open-source (MIT), local-first browser agent for Chrome and Firefox. It runs inside your existing authenticated session, on a model you pick — so with llama.cpp or Ollama, nothing leaves your machine. Here's what's actually interesting: → Two modes, cleanly separated. Ask reads the page (read-only, content scripts). Act clicks and types through the Chrome DevTools Protocol (chrome.debugger) — trusted input events that modern sites honor, reaching cross-origin iframes and shadow DOM. → UI-first by design. For anything that submits, sends, or buys, it drives the visible UI and refuses to hit REST/GraphQL endpoints directly. It starts read-only and asks before consequential actions. → Bring any model. llama.cpp, Ollama, LM Studio, vLLM — or OpenAI, Claude, Gemini, DeepSeek, Groq, OpenRouter. Recommended local: Qwen 3.6 35B (Qwen3.6-35B-A3B), which beat Gemma 4 on the project's screenshot benchmark. → Tuned for cost and privacy. Token-conscious screenshots, oldest-first context trimming, a dedicated vision model, 40+ tools (~20 in Compact mode). No telemetry. No accounts. Full analysis: GitHub Repo: Chrome Extension: Firefox Add-on: Portal:

Marktechpost AI

203,011 просмотров • 3 месяцев назад

GeoLibre v2.5.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release is about meeting you where your work already lives: open your QGIS and ArcGIS Pro projects directly, talk to your team on the map itself, explore hyperspectral imagery as a 3D cube, work in discrete global grids, and host sharing and live collaboration on your own server. It is also the most community-driven release so far: 121 pull requests and 52 issues closed in 8 days, from 14 contributors, 12 of whom sent their first contribution to GeoLibre in this cycle. Thank you all. What's new in v2.5.0 - Comments on the map: pin a comment to any location or feature, reply in a thread, resolve it when it is handled. - Live collaboration, on your own server: several people can open the same session and see each other's edits, cursors, and comments in real time. - QGIS and ArcGIS Pro project import: open a .qgs, .qgz, .aprx, or .mapx and get the layers. - Discrete global grids: three new DGGS plugins (A5, DGGRID, and DGGAL) render and identify cells over the current view, plus DGGS Generator, Binning, and Compact processing tools. - Hyperspectral data you can actually read: local NetCDF and HDF grids are colormapped in the browser, a cube gets an RGB band combination picked by wavelength. - Spectral profiles from a click: identify a pixel to chart its spectrum against wavelength. - Smart styling on add: every new layer arrives with its own color and geometry-appropriate sizing, and a Style suggestions strip offers one-click renderers. - Autosave, crash recovery, and project history, plus an Elements panel for managing every annotation from a list, a Layer Library for saving a fully configured layer and re-adding it to any later project, and project duplication and templates. - GeoLibre Desktop is on the Mac App Store, and Thai brings the shipped locales to 16 languages besides English, all at 100% coverage. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #DataVisualization #MapLibre #GeoLibre

GeoLibre v2.5.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release is about meeting you where your work already lives: open your QGIS and ArcGIS Pro projects directly, talk to your team on the map itself, explore hyperspectral imagery as a 3D cube, work in discrete global grids, and host sharing and live collaboration on your own server. It is also the most community-driven release so far: 121 pull requests and 52 issues closed in 8 days, from 14 contributors, 12 of whom sent their first contribution to GeoLibre in this cycle. Thank you all. What's new in v2.5.0 - Comments on the map: pin a comment to any location or feature, reply in a thread, resolve it when it is handled. - Live collaboration, on your own server: several people can open the same session and see each other's edits, cursors, and comments in real time. - QGIS and ArcGIS Pro project import: open a .qgs, .qgz, .aprx, or .mapx and get the layers. - Discrete global grids: three new DGGS plugins (A5, DGGRID, and DGGAL) render and identify cells over the current view, plus DGGS Generator, Binning, and Compact processing tools. - Hyperspectral data you can actually read: local NetCDF and HDF grids are colormapped in the browser, a cube gets an RGB band combination picked by wavelength. - Spectral profiles from a click: identify a pixel to chart its spectrum against wavelength. - Smart styling on add: every new layer arrives with its own color and geometry-appropriate sizing, and a Style suggestions strip offers one-click renderers. - Autosave, crash recovery, and project history, plus an Elements panel for managing every annotation from a list, a Layer Library for saving a fully configured layer and re-adding it to any later project, and project duplication and templates. - GeoLibre Desktop is on the Mac App Store, and Thai brings the shipped locales to 16 languages besides English, all at 100% coverage. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #DataVisualization #MapLibre #GeoLibre

Qiusheng Wu

27,074 просмотров • 2 месяцев назад

SOME GUY BUILT A 24/7 ONLINE RAVE IN YOUR BROWSER AND IT IS COMPLETELY FREE TO ATTEND it's called Hallucinate. you click one link, your browser loads a 3D field at night, and suddenly you are standing in a crowd of hundreds of low-poly strangers dancing under a moon to a youtube video playing on a giant screen. there is no login. there is no wallet. there is no signup. you are just at the rave. the rave has its own keyboard. > 7 and 8 cycle your hair color > 9 and 0 cycle your skin tone > U and I cycle your hair style > L and ; cycle your dance moves > V is the wave emote > B is the bounce emote > SPACE bar is literally labeled "speak" every other person in the room is a real human pressing the same keys, in real time. nobody ever stops dancing because the creator wrote on hacker news: "everybody dancing forever is crucial for the feeling. you don't stop dancing in a rave." the dev who made it goes by the handle stagas. he says the entire thing was "vibe coded in its entirety" by an LLM. the commit messages on the repo are three words long: "cool." "jump jump." "perf." he has about 500 other repos on his github account. the post went up on Show HN two days ago. it is currently sitting at 435 points and 193 comments and the top discussion in the thread is people arguing whether magic numbers in shaders.ts disqualify you from getting a job. we built the global internet so that two strangers across the planet could fall in love or trade stocks or topple regimes. and the very best thing happening on it right now is a thousand cubes dancing forever under a fake moon. the internet is healing.

SOME GUY BUILT A 24/7 ONLINE RAVE IN YOUR BROWSER AND IT IS COMPLETELY FREE TO ATTEND it's called Hallucinate. you click one link, your browser loads a 3D field at night, and suddenly you are standing in a crowd of hundreds of low-poly strangers dancing under a moon to a youtube video playing on a giant screen. there is no login. there is no wallet. there is no signup. you are just at the rave. the rave has its own keyboard. > 7 and 8 cycle your hair color > 9 and 0 cycle your skin tone > U and I cycle your hair style > L and ; cycle your dance moves > V is the wave emote > B is the bounce emote > SPACE bar is literally labeled "speak" every other person in the room is a real human pressing the same keys, in real time. nobody ever stops dancing because the creator wrote on hacker news: "everybody dancing forever is crucial for the feeling. you don't stop dancing in a rave." the dev who made it goes by the handle stagas. he says the entire thing was "vibe coded in its entirety" by an LLM. the commit messages on the repo are three words long: "cool." "jump jump." "perf." he has about 500 other repos on his github account. the post went up on Show HN two days ago. it is currently sitting at 435 points and 193 comments and the top discussion in the thread is people arguing whether magic numbers in shaders.ts disqualify you from getting a job. we built the global internet so that two strangers across the planet could fall in love or trade stocks or topple regimes. and the very best thing happening on it right now is a thousand cubes dancing forever under a fake moon. the internet is healing.

Nav Toor

249,635 просмотров • 4 месяцев назад

While the world doomscrolls 15-second TikToks and loses its attention span.. YOU SHOULD CHECK OUT THIS NEW REPO A Chinese college kid built MiroFish in just 10 days, scored $4M funding and ByteDance just dropped the upgrade that turns it into a prediction monster. Fourth-year student Guo Hanjiang vibe coded MiroFish: thousands of autonomous AI agents simulating entire societies in real time. See the details in the post below. Feed it any news, report, or historical data - watch markets, crowds, and politics react exactly as they would. GitHub went nuclear, he scored $4M funding from Shanda Group, and Polymarket traders are already printing +$120k+ using his SPX and event simulations. But long runs had one fatal flaw: agents got amnesia, hallucinations, and context overload. Classic RAG garbage. Now the same company behind TikTok (via VolcEngine) just open-sourced the fix: OpenViking - already at 11.6k+ stars on GitHub. Repo: It turns chaotic memory into a clean, structured filesystem: -> viking://user/memories/ (your habits + past outcomes) -> viking://agent/skills/ (trading and analysis superpowers) Smart 3-layer hierarchy: .L0 - 100-token ultra-summary .L1 - quick overview .L2 - full details (opened only when needed) Agents browse folders intelligently instead of dumping everything. Result: > No more forgetting crucial facts from the start of a simulation > Way fewer hallucinations > Massive API token savings > Self-updating memory - agents get smarter after every run MiroFish + OpenViking = absolute nuclear edge for Polymarket and event prediction. I will use it for my private bot. Thousands of agents now run with perfect long-term memory, stay sharp for 100+ steps, and deliver hyper-accurate probabilities. This combo is about to change the game for anyone trading predictions. The irony is insane: the company that killed human attention with short videos just gave AI agents eternal, structured, self-evolving memory. Who’s already running this combo on Polymarket? Save this. The real alpha just dropped.

While the world doomscrolls 15-second TikToks and loses its attention span.. YOU SHOULD CHECK OUT THIS NEW REPO A Chinese college kid built MiroFish in just 10 days, scored $4M funding and ByteDance just dropped the upgrade that turns it into a prediction monster. Fourth-year student Guo Hanjiang vibe coded MiroFish: thousands of autonomous AI agents simulating entire societies in real time. See the details in the post below. Feed it any news, report, or historical data - watch markets, crowds, and politics react exactly as they would. GitHub went nuclear, he scored $4M funding from Shanda Group, and Polymarket traders are already printing +$120k+ using his SPX and event simulations. But long runs had one fatal flaw: agents got amnesia, hallucinations, and context overload. Classic RAG garbage. Now the same company behind TikTok (via VolcEngine) just open-sourced the fix: OpenViking - already at 11.6k+ stars on GitHub. Repo: It turns chaotic memory into a clean, structured filesystem: -> viking://user/memories/ (your habits + past outcomes) -> viking://agent/skills/ (trading and analysis superpowers) Smart 3-layer hierarchy: .L0 - 100-token ultra-summary .L1 - quick overview .L2 - full details (opened only when needed) Agents browse folders intelligently instead of dumping everything. Result: > No more forgetting crucial facts from the start of a simulation > Way fewer hallucinations > Massive API token savings > Self-updating memory - agents get smarter after every run MiroFish + OpenViking = absolute nuclear edge for Polymarket and event prediction. I will use it for my private bot. Thousands of agents now run with perfect long-term memory, stay sharp for 100+ steps, and deliver hyper-accurate probabilities. This combo is about to change the game for anyone trading predictions. The irony is insane: the company that killed human attention with short videos just gave AI agents eternal, structured, self-evolving memory. Who’s already running this combo on Polymarket? Save this. The real alpha just dropped.

slash1s

293,591 просмотров • 7 месяцев назад