Are we measuring AI ROI the wrong way?
Teams track tokens and costs but not actual business value
What it does
We’re seeing a lot of focus on AI metrics like tokens, latency, and cost per call. But these don’t really answer the key question: Is AI creating meaningful business impact? This article explores a shift toward measuring: Productivity gains Time saved Cost reduction Revenue impact Curious how others are thinking about this. Are you measuring AI usage… or actual outcomes? 👉 https://medium.com/@ravisankarit/measuring-ai-effectiveness-in-the-enterprise-from-tokens-to-business-value-00f03e0605b7
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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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