AI Inventory Toolkit + Guide
Manage your AI systems, Dashboard for quick access
What it does
The AI Inventory Toolkit is a practical package for documenting and managing AI systems. It includes an Dashboard, Excel Workbook, Implementation Guide, and ISO 42001 Mapping. The dashboard provides clear views of your AI inventory. The workbook provides structured fields for recording AI systems. The Implementation Guide provides practical steps for setting up and maintaining the inventory. The ISO 42001 Mapping explains why each field is collected and how it supports AI governance.
Build a structured, organization-wide inventory of your AI systems.Without a centralized inventory, organizations can struggle to understand what AI systems they use, who owns them, what data they process, how they are deployed, and how they are governed.The AI Inventory Toolkit provides a practical Excel-based framework for identifying, documenting, classifying, and managing AI systems across you
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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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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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