AI coding chat got too bloated to trust
Long coding chats collect old attempts, wrong assumptions, stale files, and half-abandoned ideas. More context does not automatically mean better context.
Why this hurts.
The model starts optimizing around yesterdays failed paths. It may keep answering, but the answers carry stale assumptions into fresh code.
How ShardStitch solves it.
Keep the current objective
Record the current objective in the next-session brief before Hivy trims noisy context.
Retain only load-bearing context
Keep current goal, changed files, decisions, failed attempts, verification, and next action.
Reconcile notes with the checkout
Check the handoff against current repo state so stale notes do not quietly become ground truth.
Restart from an actionable brief
Start the next session from a lean continuation packet instead of a transcript dump.
What the next AI receives.
- Lean project slice
- Verified files and diffs
- Important constraints
- Next action without dead branches
What stays out.
- Side conversations
- Abandoned fixes
- Repeated logs
- Explanations that no longer affect the code
Triage an overloaded coding chat
FAQ.
Should I compress the entire transcript before starting again?
Usually the useful task state matters more than a shorter transcript. Preserve decisions, evidence, attempts that affect the next choice, and open questions; omit turns that add no actionable context.
How can I tell whether a short handoff left out something important?
Compare it with the current diff, task requirements, unresolved test failures, and any decisions that exist only in conversation. Mark gaps for review instead of assuming the brief is complete.