
OpenHuman
An open source AI harness built with the human in mind
In plain words
OpenHuman is an open-source AI assistant designed for users frustrated with existing AI tools. It operates locally on users' devices with privacy-first architecture, retaining conversation history and user data across sessions rather than resetting after each use. The platform improves over time through accumulated interactions and offers a unified, user-friendly interface without requiring command-line knowledge. With one-click setup and fully open-source code, it aims to address common barriers that cause users to abandon AI agents.
written from the facts on this page · September 2026
From the sources
90% of people who try AI agents give up. Three reasons: memory that resets every session, your data sitting in someone else's cloud and a terminal just to get started. Real blockers. OpenHuman fixes all of it. Local-first, privacy-first. It remembers everything about you and actually gets smarter the more you use it. Every feature lives in one simple interface. Fully open source. One-click setup. P.S. The product is in beta, so expect bugs, but we're building and shipping fast.
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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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Dev tools · May 2026 · kilo.ai


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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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