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Turn documents and datasets into linked knowledge with a portable Markdown wiki, a native graph, and answers with source citations. Runs locally on your Mac with local inference. Open source under MIT. Currently in alpha for macOS Apple Silicon. Soon cross-platform.
Hi Product Hunt! 👋 I’m Federico, the designer and full-stack developer behind Knemora.
About six months ago, I started thinking about how to turn scattered documents and datasets into connected knowledge that I could explore and use on my own computer.
That idea became Knemora: an open-source, local-first AI (SI) Second Brain, built during evenings and late nights alongside my regular work.
Here’s what you can do with it:
📄 **Bring your own material** — Upload PDFs, documents, Markdown files, spreadsheets, and CSV datasets.
🔗 **Build connected knowledge** — Compile your sources into a portable Markdown wiki, with linked pages and references back to the original material.
🧠 **Explore a native graph** — Navigate your knowledge through global and local graph views, directly inside Knemora.
💬 **Ask questions with citations** — Search and chat with your knowledge using local inference, with source references you can inspect.
📊 **Analyze datasets** — Use deterministic calculations for structured data analysis.
💾 **Keep your knowledge portable** — Markdown files, SQLite, and backup/restore workflows help you retain control of what you build.
As a designer, I’ve put care into the interface and the small details. As a developer, I’ve focused on traceability, portability, and making setup easier.
**This is an early alpha for macOS Apple Silicon.** Node.js 24 with npm is currently required; the launcher manages the remaining runtimes and model downloads. After initial setup, the core workflow runs locally without a cloud inference service.
Knemora is free and MIT-licensed. Open-source software has helped me throughout my career, and I’m proud to contribute something of my own.
Next, I’m working toward simpler standalone distribution, a browser clipper, broader platform support, and better performance.
Thanks for taking a look. If Knemora is useful to you, give it a try. Contributions, ideas, and bug reports are welcome, and I’ll be here to answer questions about the project and its architecture.
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About Knemora on Product Hunt
“Open-source, Local-first AI (SI) Second Brain”
Knemora was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #159 on the daily leaderboard. Turn documents and datasets into linked knowledge with a portable Markdown wiki, a native graph, and answers with source citations. Runs locally on your Mac with local inference. Open source under MIT. Currently in alpha for macOS Apple Silicon. Soon cross-platform.
Knemora was featured in Productivity (663.7k followers), Open Source (69k followers), Artificial Intelligence (481.7k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 351.4k products, making this a competitive space to launch in.
Who hunted Knemora?
Knemora was hunted by Federico La Terra. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Knemora stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Federico La Terra