Essays, announcements, and research from the team behind Falconer.
One knowledge tree gets noisy as a company grows, but separate workspaces create silos. Spaces give each team a focused view while preserving the context, permissions, and decisions that connect everything, with no migration required.
Editable frontmatter and offline-safe editing, Tailwind styling and live HTML embeds in any doc, Knowledge Health triage, curated MCP server presets, and new models including Kimi 3, Claude Opus 5, and Sonnet 5.
Cerebras's post on building their internal knowledge base hit 2.5M views. From someone who led knowledge management at Stripe and Uber: why knowledge bases break around 150 people, what a great one looks like, and how to roll one out.
API access and the Falconer MCP server come to every plan, plus one-click MCP setup, a redesigned version history and Inbox, dedicated chat history, and support for Anthropic's Sonnet 5.
Customer guides, onboarding flows, API references: they live in the codebase, where the people who write them can't easily edit. So they keep a working copy somewhere else and maintain two versions of the same thing, forever.
Company search is hard: the corpus is too large to browse and too small to learn ranking from. Here's how Falconer's search page and answer engine find the right answer.
Inefficient retrieval is burning your token budget. When the first search comes back incomplete, context grows and the bill follows.
Falconer Skill documents let teams create, share, manage, and reuse AI instructions across Falconer and MCP-connected tools like Claude, Cursor, and Codex.
Most teams quietly work around the rough edges of the open source libraries they depend on. We decided to fix them at the source instead, contributing patches upstream to Tiptap, the framework behind Falconer's editor.
Falconer's May 2026 changelog covers six new editor block types, agent memory upgrades, expanded MCP/API write operations, Slack and Granola integration improvements, and dozens of fixes.
Changelogs used to take hours each week to assemble across GitHub, Linear, Slack, and meeting notes. Falconer turns the same week of context into a changelog in seconds — already cited, audience-aware, and ready to send.
The Falconer agent can now answer questions about your code's history: who wrote what, when, and why. It calls git directly against your connected repos to do code archaeology, release diffs, regression hunts, and ownership lookups, all in a chat.
How we gave the Falconer agent git access (log, blame, diff between arbitrary refs) by mounting a shared NFS filesystem backed by S3 Files across our ingest and UI services. A walkthrough of the storage choice, the Pulumi quirks, and how we made the repo sync robust and reliable.
ChatGPT, Claude Code, and Letta have each built production memory systems for AI agents. Looking at the differences shaped how I built agent signals for Falconer.
Every company runs on written knowledge, and almost none of them know how healthy theirs is. Knowledge Health puts a single, live score on it and shows you exactly what's dragging it down: contradictions, stale docs, coverage gaps, and redundancies.
Falconer now tailors every response to who you are. Agent Personalization builds a lightweight, transparent profile of your role, team, and preferences — fully editable, with every attribute traced back to its source.
Everyone has access to the same frontier models. Your competitive advantage is institutional context — the decisions, tradeoffs, and battle scars inside your four walls. Here's why curating that context is now existential.
Falconer Update keeps your documentation in sync with your codebase automatically. Toggle it on for any document and choose Review mode for human-in-the-loop edits, or Full Self-Driving mode to let Falconer handle it entirely.
Falconer Generate turns a connected GitHub repo into a structured documentation set, helping teams get documentation started faster.
Our mission is to capture all of your important context, keep it up to date, and make it easy for you to deploy it wherever you want: your teammates, your customers, your coding agents.
How we reduced data ingestion time from hours to minutes by reimagining our pipeline as a directed acyclic graph. This post covers the architectural shift from async workflows to job queues, the migration strategy we used to preserve behavior, and the observability patterns that helped us identify and isolate bottlenecks at scale.
How we built a multi-agent courtroom simulation to decide when code changes require documentation updates—and why the legal system is humanity's best framework for binary decisions under uncertainty.