Blog
Notes on shared context for AI tools: team AI memory, MCP for teams, the activity, decisions, and tasks ledger, and what we're learning building BaseThread.
Shared context for AI tools
What shared context is, why every tool and teammate needs the same source, and how to set it up across Claude Code, Cursor, and the rest.
How to build a team second brain (step by step)
A team second brain is one curated context every teammate's AI reads. Here is how to build one in five steps, starting small and letting your AI keep it current.
A second brain for engineering teams
Scattered CLAUDE.md and .cursorrules files give each engineer's AI a private, partial brain. A second brain for the whole engineering team gives every tool one shared context to read and write.
Team second brain vs personal second brain
A personal second brain (Notion, Obsidian, Roam) is built for one human to read. A team second brain is built for your whole team's AI to read. Here is the difference and when each one matters.
MCP for teams
Using the Model Context Protocol to give a whole team's AI tools one shared context layer, local or remote.
RAG vs MCP: when to retrieve, when to share context
RAG retrieves chunks from documents; MCP connects tools to live context and actions. Here is the real difference, when to use each, and how they work together.
How to use MCP across all your AI tools
A practical guide to connecting Claude Code, Cursor, ChatGPT, and more over MCP so every AI tool reads the same context, with the steps that actually matter.
Local vs remote MCP servers: which your team needs
Local MCP servers run on your machine, remote ones are hosted at a URL. Here is how they differ, which tools each suits, and why teams usually want both.
Works with your tools
Your AI already reads Notion, HubSpot, and the tools your work lives in. BaseThread is where it writes the signal so every AI tool on your team reads it back.
Connect GitHub to your team's AI
Your PRs and issues already tell the story. Your AI reads GitHub and writes the signal into shared context every AI tool on your team reads over MCP.
Give your AI the customer context in HubSpot
Your AI drafts blind without customer context. Your AI reads HubSpot and writes the signal into shared context every AI tool reads over MCP.
Jira and Confluence context for your AI tools
Give your AI the why behind the work. Your AI reads Jira and Confluence and writes the signal into shared context every tool reads over MCP.
AI memory vs shared context
Where per-user AI memory stops and team-wide shared context begins, and why personal memory never compounds into team knowledge.
Glean alternative for small technical teams
Looking for a Glean alternative for a small technical team? Glean is enterprise search built for large orgs. Here is the lighter, AI-tool-native option for smaller teams.
Best AI memory and context tools for teams (2026)
The best AI memory and context tools for teams in 2026: Mem0, Zep, built-in memory, wikis, and shared context layers, with the job each one is actually for.
Per-user AI memory doesn't compound into team knowledge
Per-user AI memory can't add up to team knowledge. Here is the structural reason ten people's personal memories never become one shared team brain, and what does.
Context engineering for teams
Getting the right context in front of every model, done once for the whole team instead of ad hoc in every prompt.
Bigger context windows won't fix team knowledge
Every model ships a bigger context window, but team knowledge is not a capacity problem. Here is why more tokens won't fix what shared context solves.
Semantic, episodic, procedural: the types of AI memory
AI memory comes in three kinds borrowed from cognitive science: semantic, episodic, and procedural. Here is what each one is and why teams need all three.
Just-in-time context: give your AI the right slice, not everything
Stuffing the whole window hurts answers. Just-in-time context delivers the right slice at the right moment. Here is how to do it across your team's tools.
The activity, decisions & tasks ledger
The AI-written record of what shipped, what the team decided and why, and what is next, so nothing important is lost between sessions.
Manual context-logging is dead: your AI should witness the work
Manual context-logging never holds, because writing it is a chore separate from the work. The fix: let the AI that did the work witness and record it.
What is an AI activity and decisions ledger?
An AI activity and decisions ledger is a running record your AI writes as work happens, what shipped and what the team decided, so nothing important is lost.
Get your team's AI tools on the same page
BaseThread is the shared context-graph that Claude Code, Cursor, and every AI tool your team uses can read, so no one re-explains the same context twice.
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