Two new security audits of Obsidian Sync by @cure53berlin and @trailofbits are now available on our Security page. All findings have been addressed via remediations and disclosures validated by the respective auditors. Read more:

I am building Obsidian Reader because I wonder how the web would feel if it was designed solely around the reading experience. There is so much cognitive burden that comes with every site having different layouts, fonts, ads, popups, cookie banners, engagement traps, etc.

Obsidian Web Clipper now lets you manage highlights, and stays in Reader mode when you click links. It's such a pleasant way to browse the web. You can control the colors, fonts, and easily copy anything to Markdown.

Obsidian is weird: - 7 full-time employees - ~1 million users per employee - fully remote - 1 in-person meetup per year - no scheduled meetings - no stand-ups - deep focus is prioritized - our manifesto guides our product What works for us may not work for you.

New in Obsidian Reader: - Themes and typography settings - Highlighting - Save options Also added a beautiful new reading experience for deeply nested comments on Reddit and Hacker News. Available with Obsidian Web Clipper 1.3 on all browsers.

I like @karpathy's Obsidian setup as a way to mitigate contamination risks. Keep your personal vault clean and create a messy vault for your agents. I prefer my personal Obsidian vault to be high signal:noise, and for all the content to have known origins. Keeping a separation between your personally-created artifacts and agent-created artifacts prevents contaminating your primary vault with ideas you can't source. If you let the two mix too much it will likely make Obsidian harder to use as a representation of *your* thoughts. Search, bases, quick switcher, backlinks, graph, etc, will no longer be scoped to your knowledge. Only once your agent-facing workflow produces useful artifacts would I bring those into the primary vault.

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

I have been working on Obsidian Reader for a over a year. I didn't want to share it until I felt it was good enough. It's finally there. Consistent formatting for any article. Outline, syntax highlighting, nice footnotes, adjustable typography. Runs locally. Just rules, no AI.