Skill documents: shared team instructions for every AI tool
Falconer Skill documents let teams create, share, manage, and reuse AI instructions across Falconer and MCP-connected tools like Claude, Cursor, and Codex.
Notes from Falconer
Essays, announcements, and research from the team behind Falconer.
Stop freeloading off open source
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 May 2026 changelog
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.
How to generate a changelog in 10 seconds
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.
Falconer agent now speaks git
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.
Ditching the agent sandbox for AWS S3 Files
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.
How others build agent memory, and what I took from each
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.
Knowledge Health: observability for your knowledge base
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.
Agent Personalization: an agent that knows you
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.
Your context is more than training data
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: Full self-driving docs
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: from repo to doc set in minutes
Falconer Generate turns a connected GitHub repo into a structured documentation set, helping teams get documentation started faster.
The source of truth for high-speed teams
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.
Rethinking data ingestion as a DAG
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.
LLM-as-a-Courtroom
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.