A playable toy model of frontier AI lab capex decisions
I made a lightweight web game about compute CAPEX tradeoffs: https://darios-dilemma.up.railway.app/ No signup, runs on mobile/desktop. Loop per round: 1. choose compute capacity 2. forecast demand 3. allocate capacity between training and inference 4. random demand shock resolves outcome You can end profitable, cash constrained, or bankrupt depending on allocation + forecast error. Goal was to make the decision surface intuitive in 2–3 minutes per run. It’s a toy model and deliberately omits many real world factors. Note: this is based on what I learned after listening to…
What it does
In the maker’s words, at launch
I made a lightweight web game about compute CAPEX tradeoffs: https://darios-dilemma.up.railway.app/ No signup, runs on mobile/desktop. Loop per round: 1. choose compute capacity 2. forecast demand 3. allocate capacity between training and inference 4. random demand shock resolves outcome You can end profitable, cash constrained, or bankrupt depending on allocation + forecast error. Goal was to make the decision surface intuitive in 2–3 minutes per run. It’s a toy model and deliberately omits many real world factors. Note: this is based on what I learned after listening to Dario on Dwarkesh's podcast - thought it was fascinating.
Does the same job
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the category →
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…
AI · 27d ago · cactuscompute.com
