
Shadow AI
AI spend visibility and control for teams
What it does
Shadow AI gives companies visibility into AI spend, usage, adoption, and ROI across Marketing, Sales, and Engineering. Track tools like ChatGPT and Claude alongside production AI APIs and built in mcp tools. Our Chrome extension captures browser usage, SDK tracks OpenAI and Anthropic calls with zero latency impact, and read-only billing API requires no code changes. Get unified dashboards, guardrails, alerts, policy enforcement, subscription detection, and rightsizing recommendations.
Track AI usage by user, team, and tool. See spend, adoption, duplicate subscriptions, and ROI across Marketing, Sales, and Engineering.
Shadow AI shows every dollar — by user, by team, by tool — in real time. Set budgets, get alerts, cut waste. Your team signed up for ChatGPT, Claude, Copilot, and Perplexity — separately, on different cards. Nobody has the full list. Some are duplicates. Some nobody uses anymore. One engineer ran a script that made 10,000 API calls overnight. You find out 30 days later when the invoice arrives. There's no alert, no dashboard, no way to know until it's too late. Leadership wants to know if the AI spend is worth it. You have no data to show them. You're guessing — and that makes the whole AI initiative look bad. See which tools nobody uses and which subscriptions overlap. Cancel what's not…from shadow-ai-usage.vercel.app
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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.
AI · 16d ago · simedw.com
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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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Launched alongside, August 2026
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Life & fun · 10d ago · louisabraham.github.io


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Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io


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.
AI · 16d ago · simedw.com