Teaching a Dinosaur to Jump: Rust, WebAssembly, and Neural Evolution
What began as a lightweight programming experiment quickly transformed into a deeply engaging technical exploration into the realms of neuroevolution, game physics, and browser-based simulation. My original goal was modest: replicate the "Chrome Dino" game using a performant and modern web stack. However, the project evolved far beyond expectations. The twist? I wanted the dinosaur to teach itself to jump.
Does the same job
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- IMI made the Chrome Dino Game editable by your AI promptsJul 2026 · vibedino.com · ▲7
In 2024 Google Labs released GenDino: a version of the Chrome Dinosaur Game that let users turn prompts into skins. GenDino is dead. The Dino Game happens to be OS, so I forked it and created Vibedino to let your prompts change the actual game. Make the dino front-flip every time it jumps. Add a hard mode after 1000m. Break my backend by implementing the full PageRank algorithm. Give it the dumbest feature idea you can think of and then approve it yourself to deploy to the main site!
- RNRecurrent neural net plays 'neural slime volleyball' in JavaScript2015 · otoro.net · ▲147
- PTPlay the Chrome T-Rex game by physically jumping2020 · github.com · ▲39
- ABA browser-based evolutionary simulation with emergent behaviorJan 2026 · soupof.life · ▲6
I’ve been working on a browser-based evolutionary simulation as a personal experiment. Organisms adapt to environmental pressure over time, and there are no explicit goals or scoring, the system is open-ended and runs continuously. I built this mainly to challenge myself and to explore how to surface simulation behavior and statistics in a way that stays readable rather than overwhelming. As a side effect, it’s also something my kids enjoys watching run. Curious what resonates and what doesn’t, and happy to answer questions about the design or tradeoffs.
More ai this month
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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.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
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…
AI · 26d ago · cactuscompute.com
Launched alongside, April 2025
the whole month →- IB
Hi everyone, I built PyXL — a hardware processor that executes a custom assembly generated from Python programs, without using a traditional interpreter or virtual machine. It compiles Python -> CPython Bytecode -> Instruction set designed for direct hardware execution. I’m sharing an early benchmark: a GPIO test where PyXL achieves a 480ns round-trip toggle — compared to 14-25 micro seconds on a MicroPython Pyboard - even though PyXL runs at a lower clock (100MHz vs. 168MHz). The design is stack-based, fully pipelined, and preserves Python's dynamic typing without static type restrictions.…
Dev tools · 2025 · runpyxl.com
- UC
Life & fun · 2025 · filiph.github.io
- IB
https://the-pocket.github.io/Tutorial-Codebase-Knowledge/
AI · 2025 · github.com
