#context
7 posts
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Subagent Orchestration: Divide Judgment, Not Work
Phases 80-83 and 89 of kodr: splitting planner, implementer and reviewer into isolated model conversations, giving each its own model, shrinking the handoffs, and making sure every stage inherits the real harness contract.
#ai#kodr#local-models#agents#prompt-engineering#context -
Knowing the Model, Shrinking the Conversation
Phases 69-70 of kodr: a model profile registry that treats capability as harness behaviour rather than scattered defaults, and deterministic session compaction that keeps a long conversation usable for a small local model.
#ai#kodr#local-models#agents#context#memory -
Inspection as Tools, and a Plan You Can Read
Phases 62-64 of kodr: structural inspection handed to the model as two narrow tools, the same inspection exposed to the human in CLI and TUI, and a deterministic inspection plan that hands a small model the likely edit path instead of asking it to derive one.
#ai#kodr#local-models#agents#tools#context -
Ranking and Budgeting What the Model Sees
Phases 59 and 61 of kodr: a deterministic ranked repo-map so the likeliest code lands first, and token-budget-aware packing so a small local model gets useful context without starving its own completion room.
#ai#kodr#local-models#agents#context -
Inspection: kodr Learns to Read Code
Phases 51 and 53 of kodr: a zero-dependency structural code index across four languages, then an optional registry that lets real language servers enrich it - so the model reads before it writes, without the harness taking on a parser stack.
#ai#kodr#local-models#agents#context#tools -
Sessions: Write It Down, Pick It Up, Look Back
Phases 42-44 of kodr: a trilogy that turns one-shot runs into conversations - record the full transcript, resume it with --continue, then browse what you built. Plus the one-line bug that was quietly truncating every transcript.
#ai#kodr#local-models#agents#memory#context -
Context Is Just an Input
Phase 06 of kodr: build the workspace context for a prompt deterministically, then let yourself look at it before it ever hits the model.
#ai#local-models#agents#context#prompt-engineering#kodr