Evidence layer
Proof before claims.
CacheSphere is being tested as a decision and context layer for AI coding agents. The proof loop compares a task-only baseline against raw catalogue records and compact agent context, then reports token usage and review status separately.
Loading live benchmark evidence…No-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 agent context. This is the claim under test: smaller context with equal or better decision quality.
Measured benchmark summary
Loading benchmark data… If none is available, the proof loop is still collecting runs.
What counts as proof?
- Every run records model, mode, task, token usage, selected context 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.