
AI Local Recorder
100% Offline AI Voice Recorder
What it does
AI Local Recorder is a fully offline voice recorder with on-device AI. Record audio, auto-transcribe, and ask questions about what was said — all without uploading a byte to the cloud. Supports multiple languages. Privacy-first, no tracking, no account required.
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Phones can often feel cumbersome when quick action is needed. This is where a sleek, accessible, and intelligent solution comes in—an open-source, AI-powered pocket recorder. It syncs recordings locally, provides summaries, and runs on low-power ESP32-S3 hardware with Bluetooth and WebSocket support. The device attaches to a phone via MagSafe or works as a standalone unit, effortlessly capturing conversations, meetings, and spontaneous ideas. The first prototypes were developed in a San Francisco garage, and the project is now being made open-source to let others build and customize it. Your…

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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, May 2026
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Parallel agents, diff reviewer, and multi-model comparisons
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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