Two takes on Markdown knowledge
Logseq is open-source, local-first, and built around an outliner. Every note is a tree of bullets, every bullet is its own block, and links between blocks form a graph you can explore. For thinkers who want to work in fragments, that shape is a feature, not a quirk.
Hjarni is a hosted knowledge base with documents instead of blocks. Notes are full Markdown files, folders give them structure, and AI assistants connect through a built-in MCP server. Less customization, more out-of-the-box AI behavior.
Outliner versus document
Logseq's block-first model is great for daily journaling, idea capture, and PKM-style linking. The tradeoff is that long-form documents (runbooks, briefs, customer interviews) can feel awkward as a stack of bullets, and the format is harder for AI assistants to consume cleanly through anything other than a dedicated integration.
Hjarni keeps notes as plain documents that AI can read end to end. That matters when you ask Claude or ChatGPT to summarize a project history or pull patterns from a folder of interview notes.
Pick Logseq if you want to own every block. Pick Hjarni if you want your AI to read everything you've written.
A concrete workflow difference
You've been keeping research notes for three months. In Logseq, you open the graph, follow backlinks, and synthesize manually with the outliner's help. The structure is yours to assemble.
In Hjarni, you point Claude at the research folder, set folder-level instructions like "cite specific notes and quote where possible", and ask for a synthesis. Claude reads the notes through MCP, drafts a summary, and writes it back as a new note in the same folder.
When Logseq is the better fit
Pick Logseq if you want open-source, local-first, and an outliner. It is excellent for daily notes, PKM workflows, and people who want to own their stack end to end. If your AI use is occasional and built into your own scripts, the lack of native MCP is not a blocker.
Why some Logseq users switch
The shift usually starts when AI workflows become daily. Repeated copy-paste between Logseq and Claude. Trying to share a graph with a teammate. Wanting different AI behavior across personal journaling and team documentation. Hjarni trades the outliner and local files for a cloud knowledge base that AI can talk to without setup.
The data-loss reports worth knowing about
Local-first is Logseq's appeal, but it carries a multi-year trail of reports that cut the other way. Users have described losing entire graphs, often while paying for Logseq Sync, along with corruption after a power loss and edits that never reached disk. The case is not that Logseq always loses data. Most graphs are fine for most people. The point is the failure mode: when the local cache and the files on disk disagree, what gets lost is your notes.
Hjarni removes that particular race by design. Writes go to the server and there is one authoritative copy, so there is no local cache that can drift out of step with a file on disk. That is an architectural difference, fewer moving parts and no local sync layer to desync, not a reliability record. Hjarni is young and has not earned a track record at scale, so the honest claim is the narrow one: there is simply no local-cache-versus-file conflict here to lose a note to.
Migration and practical questions
Logseq exports as Markdown. Hjarni's Markdown ZIP importer preserves folders, wiki-links, and frontmatter. The bigger question is shape: do you want to keep working in bullets, or are you ready to let your notes become documents your AI reads end to end?