TheFalse v0.2.0
Read, track, and share books — with an AI that knows you
What it does
Goodreads hasn't changed since 2013. We got tired of waiting. TheFalse v0.2.0 is a reading companion built for how people share books today. What's new: 📚 Built-in ebook reader — highlights sync to your feed automatically 🤖 Library Agent — AI that knows your reading history. Ask it "why do I keep abandoning books?" or "what should I read after Dostoevsky?" Plus: micro-reviews, session tracking, 15 languages, custom themes. Free at app.thefalse.net
Does the same job
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- AOAn open-source e-book reader for conversational reading with an LLM2025 · github.com · ▲86
Hi HN! I've been working on BookWith, an open-source e-book reader that integrates AI as your reading companion. The problem: Traditional e-readers are passive. When you encounter something unclear, you have to context-switch to search for it. Your highlights and notes remain isolated, and you can't easily connect ideas across different books. My solution: BookWith embeds an AI that maintains full context of what you're reading. It features: - Context-aware AI chat: Ask questions about the current page/chapter and get instant answers - AI podcast generation: Automatically converts book…
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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…
AI · 27d ago · cactuscompute.com
Launched alongside, May 2026
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Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
- NW
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…
Life & fun · May 2026 · github.com
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Dev tools · May 2026 · github.com