VelocityNote – A tiny Markdown notebook with local AI

Hacker News

Hi HN — I built VelocityNote, a compact, Markdown cross-platform notebook. The desktop app is under 100 MB. It starts quickly and remains responsive under memory pressure. VelocityNote has a built-in, pluggable AI system that can run local models: - It is a local LLM app: you can use a llama.cpp-based local agent to summarize notes and tasks. More features, including translation, are coming. - It is an intelligent screenshot app: built-in OCR extracts text, while vision models can describe your images. - It is a dictation app: with Silero VAD and speech-to-text, you can record and transcribe hours-long meetings entirely on your device. - It is an app for managing documents: it supports importing a variety of file formats, including PDF and Word, and migrating data from other notebook databases. With local models, your data always stays on your device, with no external provider or per-token limits. Under the hood, notes are stored in SQLite. Full-text search makes searching large notebooks fast. Everything remains available offline and can be exported as standard Markdown. VelocityNote also exposes an MCP server and a command-line interface, allowing it to serve as a local second brain for both you and your AI agents. Agents can use ultra-fast full-text search to find notes, then read, create, and update them through explicit tools instead of scraping the UI or directly modifying the database. VelocityNote is cross-platform and currently available for Windows and macOS. Download: https://velocitynote.app/download Releases, issues, and feedback: https://github.com/VelocityNote/velocitynote

Visit product View on Hacker NewsAug 30, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, macos · Missing: cursor, claude, apple

95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: supports, including · Missing: reddit linkedin, podcasting, created

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released

73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface · Missing: plus, intuitive, reviews

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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