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.
The team-context problem nobody has solved yet
Every AI tool solves context for one person. The team-context problem, one shared, current context across every tool and teammate, is the gap nobody filled.
When a flat .cursorrules file isn't enough for a team
A .cursorrules file is great for one developer. Here are the four moments it breaks down for a team, and what to use when a flat file stops being enough.
Notion AI vs BaseThread: a wiki your AI reads vs context your tools read
Notion AI vs BaseThread: Notion AI answers inside your Notion docs. BaseThread feeds your team's context to every AI tool. Here is the honest difference.
MCP for teams
Using the Model Context Protocol to give a whole team's AI tools one shared context layer, local or remote.
Best MCP servers for engineering teams (2026)
The best MCP servers for engineering teams in 2026: GitHub, issue trackers, databases, observability, and a shared context server, with what each is good for.
How to set up a shared MCP context server for your team
Set up a shared MCP context server your whole team's AI tools read: curate the context, choose local or remote, connect tools, scope access, and let it update.
What is a remote MCP server (and when teams need one)?
A remote MCP server is a hosted endpoint any AI tool can connect to over the network. Here is how it differs from a local server and when a team should use it.
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.
What is context rot, and how to avoid it
Context rot is when an AI's answers get worse as its context window fills with stale or irrelevant information. Here is why it happens and how to keep context clean.
What is context engineering for teams?
Context engineering is the discipline of getting the right information in front of a model. For teams it means doing it once, shared, so every tool answers well.
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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