ChooseAIModel.com
Stop guessing which AI model to use
What it does
Every time we design a new AI feature, the PM asks the exact same question: "What is this going to cost us at scale?" Building complex spreadsheets to calculate non-linear token costs and multi-turn chat compounding was breaking my brain, so I built a free tool to kill the friction.
Does the same job
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Hey HN! When implementing an AI-powered feature for a project, we—and many people we've talked to—often reach a point where we have to choose an AI model but aren’t sure which one best fits our constraints or where to even start. Unfortunately, the advice to "just use chatgpt" is not always a good one. What if I want an open-source model? What languages does it support? What about context window size or the number of parameters? There are thousands of AI models already out there and many of them are perfect for certain problems. That’s why we’ve carved out this part of our product as a free…
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I use multiple AI tools for work and also my side projects, and the annoying part was to track my costs and token usage across tools. Everytime I had to visit each tool and its respective usage setting to check it and I was losing patience and also was getting hit by surprise limits Now I know that there are already free/open-source trackers for Cursor or Claude usage, and they are useful if that is all you need. My problem is broader as I wanted one small place to see tokens, spend, subscriptions and limits across the AI tools I actually use. I was really tired of switching tabs and…
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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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