Evidence layer
Proof before claims.
CacheSphere is being tested as a decision/context layer for AI coding agents. The proof loop compares a task-only baseline against raw CacheSphere records and compact Context Packs, then reports token usage and review status separately.
Current status: early local evidence, human review still requiredNo-cacheTask prompt only. This measures what a model does without CacheSphere context.
Raw-recordTask prompt plus full relevant CacheSphere records. Useful, but intentionally token-heavy.
Compact-packTask prompt plus selected Context Packs. This is the claim under test: smaller context with equal or better decision quality.
What counts as proof?
- Every run records model, mode, task, token usage, selected Context Pack IDs, prompt hash, wall time, and output text.
- Unreviewed model completions are marked
partial, notpassed. Token savings alone do not prove engineering quality. - A useful claim requires task coverage, multiple models, complete mode triples, and reviewed rubric scores.
Machine-readable artifacts
Why this matters
Vibecoders, engineers, and autonomous agents all suffer from the same failure mode: plausible defaults that are not grounded in the actual task. CacheSphere’s job is to compress the right decision context before code is written, then make the evidence trail visible enough that teams can trust or challenge the recommendation.