Agent Draw: An agent draws while you talk, built on TLDraw
We recently built 2draw, a Drawful-style game where players draw on a shared canvas and race to guess each other's drawings, on tldraw, an infinite-canvas SDK for React. We started wondering what it would take to put an agent in that loop, as an opponent or a rival guesser, and dug into how an agent could read and draw on a tldraw canvas. That research turned into: Agent draw, a tool that lets an agent draw to the canvas for you while you present. You can try it right now, or grab the source: - Live demo: https://tldraw-agent-draw-demo.james-664.workers.dev - Source:…
In plain words
Agent Draw is a tool built on tldraw that enables an AI agent to draw on a shared canvas while you present. The agent interprets your spoken descriptions and creates visual representations in real time, assisting with live presentations and demonstrations. It works by integrating AI capabilities with tldraw's infinite-canvas SDK for React, allowing the agent to both read and draw on the canvas alongside human users.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
We recently built 2draw, a Drawful-style game where players draw on a shared canvas and race to guess each other's drawings, on tldraw, an infinite-canvas SDK for React. We started wondering what it would take to put an agent in that loop, as an opponent or a rival guesser, and dug into how an agent could read and draw on a tldraw canvas. That research turned into: Agent draw, a tool that lets an agent draw to the canvas for you while you present. You can try it right now, or grab the source: - Live demo: https://tldraw-agent-draw-demo.james-664.workers.dev - Source: https://github.com/ritza-co/tldraw-agent-draw-demo Here you can see the agent assisting me in my demo presentation of a third grade chemistry class: - https://www.youtube.com/watch?v=iIH2hJAxxm8
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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I might be the only SRE on Earth with his own bowling center. It's a more in-depth gig than you'd think. My family and I bought an abandoned 8-lane bowling center in the rural mid-west. In our small town there weren't many recreation options for families. You've heard of a food desert? This is an R&R desert. It had been abandoned for a good reason. The roof leaks, the electrical system was constantly surging, and my 70-year-old bowling equipment (still) doesn't work perfectly. The system that keeps your score is particularly interesting to me. It's the thing you watch during your game, but…
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Over the past few months, our team has been building more and more slidedecks using web frontend technologies with coding harnesses like Claude Code, but a common complaint is to make even small edits we need to edit the code either manually or via the harness. To avoid this loop, I ended up creating Bento, a single HTML file with everything you need in a slide tool including animations and shared editing. There's no install or cloud login, everything works offline. The default deck is around 560 KB and it doesn't need to fetch anything once you got it. Open it in a browser and then you can…
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A few days ago I found myself trying out GLM 5.2 and was really positively impressed. The capabilities and security I was getting from this LLM are similar to those I've gotten from models like Claude or GPT, and this really surprised me. But then I thought, "I wonder how it would work on a normal computer like mine," and above all, "I wonder if it would work without going into OOM on a computer like mine." So I started working with the help of agents to test this possibility. I started converting the model to int4, understanding MTP usage, and if possible implementing DSA for long context.…
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