VQV Signal
According to a post on X by @waldekm, Waldek Mastykarz's team ran 210 tasks using Claude Code, GitHub Copilot CLI and Copilot Chat. In 76% of the 140 runs that had to handle API failures, the agent built the failure itself with stubs or a throwaway server. In 61 of 105 runs, the agent also added a base-URL switch to production code so it could point at its fake.
According to a post on X by @waldekm, Waldek Mastykarz's team ran 210 tasks using Claude Code, GitHub Copilot CLI and Copilot Chat. In 76% of the 140 runs that had to handle API failures, the agent built the failure itself with stubs or a throwaway server. In 61 of 105 runs, the agent also added a base-URL switch to production code so it could point at its fake.
AI-assisted summary based on the listed source.
Waldek Mastykarz's team ran 210 tasks with Claude Code, GitHub Copilot CLI and Copilot Chat; in 76% of the 140 runs that had to handle API failures, the agent built the failure by hand with stubs or a throwaway server, and in 61 of 105 runs it added a base-URL switch to production code to point at its fake. It ran...
For teams using AI coding agents, passing tests may not show that error handling works against real API failures, because the tests can rely on fakes the agent wrote. Developers reviewing agent output may need to check how error paths are tested and whether production code has been changed to reach test doubles. The available snippet is cut off, so the full reasoning should be checked in the original post.
VQV organizes public signals from inspectable sources. It does not independently verify the underlying report.
Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
VQV surfaced this signal because it is recent, relevant to AI Coding Tools, connected to Scobleizer News Pulse (X).
No login, cookies, social SDKs, or automatic posting.
