AstroFabric
Agentic AI operating system
What it does
AstroFabric is an agentic AI operating system for growth, revenue and digital operations. Specialist AI agents plan and execute complete missions through metered tools, with enforced budgets, approval-gated writes and full run transcripts. Work runs from the console, REST API, hosted MCP server, schedules, email or chat.
Deploy autonomous AI agents to discover, verify, enrich and stream high-fidelity company, person and market intelligence into your existing systems.
The data infrastructure behind prospecting, enrichment, outreach and targeting: autonomous AI agents identify the companies, find and verify the people, attach the buying signals, score the fit and deliver finished lists and audiences into the CRM, outreach tools and ad accounts your team already runs. Work from the AstroFabric console, connect through MCP, or embed every capability through one REST API. Give our AI agents a strategic objective and they automatically identify target entities, discover and verify key people, deeply enrich every record, score behavioral fit, and stream high-fidelity intelligence directly into the systems where your team already works. Inspect a sample dataset…from astrofabric.ai
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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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