Mocking and Deploying an AI Chatbot in Minutes
summary
Claims five concrete "cheat codes": use AI-driven API specs to auto-generate and maintain OpenAPI contracts, derive mock servers directly from those specs to enable parallel work, and convert specs into workflow tests (ARZO-style) that chain calls for end-to-end validation. Cites surprising metrics: 71% of internet traffic is API calls while 85% report REST but under half publish OpenAPI specs, and predicts MCP discovery for 80% of enterprise AI agents by 2028. Presents Blackbird (IDE extension, CLI, shared remote dev environments) to run production-like tests before committing.
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chapters (14)
- 0:00API development challenges
- 1:07API specs importance
- 3:15What API specs are
- 5:41Missing specs risks
- 6:01Inner and outer dev loops
- 9:55AI-driven API specs
- 12:02Mocks from API specs
- 14:15Workflow testing with Arazzo
- 18:08Dev productivity trade-offs
- 19:33Local dev remote test
- 20:56Remote production-like envs
- 22:21Specs and loops for AI
- 23:04Blackbird API tool
- 24:51Q&A
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