AI for Innovators:
A Prompt Library for Methodology-Driven Innovation
What it does
If you run innovation projects, you’ve likely used AI and seen it produce fluent but generic answers that fail under real scrutiny. The issue isn’t the AI, it’s the prompt. AI for Innovators is a library of 82 structured prompts built on real methods (design thinking, JTBD, lean experimentation, business models, EU grant writing, and facilitatio) designed with constraints, falsification criteria, and confidence flags to make outputs actually useful.
A prompt library for people who already know what they're doing.If you run innovation projects, as a founder, researcher, consultant, project lead, or facilitator, you've probably tried using AI to speed up the work. And you've probably noticed the same thing everyone else has: it gives you fluent, plausible, generic answers. The kind of answers that read well in a draft and fall apart the moment
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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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Source: Product Hunt launch ↗
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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🌱 Private, quiet space for thinking. Simple app for .md files. - zakirullin/files.md
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