
Your books finally have a witty narrator
AI narrates books + comments, 4 langs
What it does
Problem: Audible is costly, limited, monotonous. Reading is slow. Solution: Book v2 Audio – free open-source desktop app (Win/Linux). Takes FB2/EPUB, splits into sentences, inserts AI commentary between them (DeepSeek, ChatGPT, Grok, Qwen), synthesizes to speech (Edge TTS or Piper), saves as MP3. Uniqueness: AI comments with personality. Choose: Porfiry Petrovich (sarcastic detective), Strict Critic, Enthusiastic Fan, Scientific Expert, or custom prompt.
Does the same job
all alternatives →- IGI generated 70k audiobooks with OpenAI Text-to-Speech2024 · listenly.io · ▲140
Hey HN. I’m Ivan, hacker from Ukraine. For about a year, I was working on Listenly — an app to listen to text content with OpenAI's natural-sounding text-to-speech model. At some moment, I realized that it would be cool to take all the public domain e-books and create audio versions for them. So I did it... kind-of. It would cost an immense amount of money to generate all the audio right away (OpenAI TTS costs approximately $0.84/hour of audio; 11labs, for comparison, is 10 times more expensive). So, I took a more gradual approach. I took all the metadata from the Project Gutenberg…




- 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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- 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…
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