SiYuan vs Document360
Side-by-side trajectory, velocity, and editorial themes.
SiYuan rides its 3.7 overhaul with rapid alpha polish on mobile and cross-platform
SiYuan is an open-source, local-first knowledge base. Its 3.7 line was a throughout renewal — a redesigned UI, a kernel plugin system, and a CLI — and the feed since has been a fast cadence of alpha releases (3.7.1, 3.7.2) grinding on mobile editing and cross-platform quirks across Android, HarmonyOS, iOS, and the Microsoft Store build.
The work is consolidating the 3.7 overhaul along three lines: extensibility (kernel plugins), cross-platform parity (HarmonyOS now treated as a first-class target alongside Android and iOS), and mobile editing polish. The 'step toward intelligence' language hints at AI features, but the concrete shipping is platform breadth and extensibility.
Expect 3.7.2 to stabilize out of alpha with continued mobile and HarmonyOS fixes; the kernel plugin system points toward a growing third-party plugin ecosystem as the next surface.
Document360 is rebuilding the knowledge base around AI agents — readable by them and operable through them.
Document360 is a knowledge-base and documentation platform shipping monthly point releases. The recent arc is heavily AI-shaped: an MCP server connects ChatGPT, Claude, and Copilot to the KB, then expands to manage the full content lifecycle — search, create, update, assign reviewers, and publish — from inside an AI assistant. The June release adds auto-generated llms.txt so AI agents can discover and cite docs accurately, plus native Mermaid diagrams. Enterprise plumbing (SCIM, multiple JWT configs, CSP controls) rounds out the cadence.
The product is positioning the knowledge base for the AI-agent era on two fronts: making docs machine-readable and citable (llms.txt, MCP search), and making content operations agent-driven (publish/workflow via MCP). Around that core bet, Document360 keeps hardening multilingual, security, and analytics for enterprise buyers.
Expect continued deepening of the MCP and AI-discoverability surface — more lifecycle actions exposed to assistants and richer agent analytics — alongside the steady enterprise security and localization work.
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