AI Boost vs Black

Capture what took years to learn. Inject it in seconds. Share it when you're ready. AI Boost is your expertise layer for LLM agents, works with Claude Code, Cursor, and...

A black-box evaluation of how AI-generated tests find functional bugs in live APIs.

About AI Boost

Capture what took years to learn. Inject it in seconds. Share it when you're ready. AI Boost is your expertise layer for LLM agents, works with Claude Code, Cursor, and any MCP client. Ask anyone shipping real work with AI agents and you will hear the same complaint: the agent gets a convention right once, then forgets it exists by the next session. Multiply that across a team and the same rules, gotchas, and fixes get re-explained constantly, this is a known pain across the agent-building ecosystem, not a one-off annoyance.

About Black

A black-box evaluation of how AI-generated tests find functional bugs in live APIs. The harder question is whether those tests find bugs. Each system receives only a JSON schema and one valid sample payload, then must generate API test cases that expose failures in a live reference API. The evaluation uses APIEval-20 v1.0, a black-box benchmark contributed by KushoAI. Because KushoAI is also one of the evaluated systems, this report includes the methodology, workflow definitions, repeated-run setup, and robustness checks so readers can understand where the performance difference comes from.