Gruvle Radar
Know what changed before it breaks your business
What it does
Most tools stop at "something changed." Gruvle Radar goes further: every change runs through deterministic diffing, AI-backed impact mapping, risk scoring, and a recommended action -- tied to a real dependency graph, not a flat alert list. Detection is code, not an AI call, so you're not burning tokens to notice a timestamp changed. Bring your own AI provider: Groq, local Ollama, or any OpenAI-compatible endpoint. Read-only by default. Try the real pipeline on the homepage, no signup.
Know what changed outside your business before it breaks something inside. Gruvle Radar watches the APIs, vendors, AI models, and systems you depend on, then turns meaningful changes into clear actions.
Gruvle Radar watches the APIs, vendors, AI models, cloud services, policies and systems your company depends on — then tells you what changed, what matters, and what to do. Step through a real detected change — the same data model, risk scoring, and impact mapping that runs when you connect your own sources. This is real product UI, not a mockup — connect your own sources in minutes. Every API, vendor, model, and policy you rely on keeps shipping changes — most quietly, some silently breaking. Nobody has time to read every changelog. Individually, easy to miss. Together, the kind of thing that causes an outage, a compliance gap, or a surprise bill. A required parameter became mandatory on…from gruvleradar.space
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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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