AI Agent Automation Platform
Local-first AI agents with full execution control
What it does
A privacy-focused, local-first AI automation platform for building powerful agent workflows. Design and run AI agents with full execution control, document chat, and workflow orchestration - all without relying on cloud APIs. Built for developers who care about control, transparency, and extensibility. Fully open-source and designed to run locally.
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- ASAgenticSeek – Self-hosted alternative to cloud-based AI tools2025 · github.com · ▲122
I’ve spent the last two months building AgenticSeek, a privacy-focused alternative to cloud-based AI tools like ManusAI. It runs entirely on your machine—no API calls, no data leaks. Why AgenticSeek? Optimized for local LLMs (developed mostly on an RTX 3060 running deepseek r1 14b). Truly private: All components (TTS, STT, planner) run locally. More responsive than alternatives (we respond fast to issues + active Discord). Designed to be fun—think JARVIS-like voice control, multi-agent workflows, and a slick web UI. Current Features: Web browsing (research + form filling), code…
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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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AI · 19d ago · company-app.joinastute.com
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…
AI · 27d ago · cactuscompute.com
