Hive vs Document360
Side-by-side trajectory, velocity, and editorial themes.
Hive keeps stacking dashboard and reporting widgets while pushing core work to mobile.
Hive's recent shipping cadence is dominated by dashboard and reporting depth: a Gantt widget, pivot-table conditional formatting, a unified series manager, 100% stacked bars, and project-scoped Goals filters. Alongside the BI work, it extended core surfaces to mobile with Hive Mail and audio messages, and tightened time-tracking hygiene. These are steady, user-visible improvements rather than direction changes.
The arc points at making dashboards a real reporting workspace instead of a summary layer, so PMO and operations teams can build reusable, project-templated views without leaving Hive. In parallel, Hive is closing the desktop-to-mobile gap for communication and email. Time-tracking changes suggest ongoing attention to timesheet accuracy for services teams.
Expect continued dashboard widget and filtering additions plus more mobile parity for existing desktop features. Nothing in these entries signals a pricing or platform pivot.
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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