Aamir21

5 ms·

1.
by Aamir21 6mo ago
@aayushkumar, please give us a star on github if you like the work.
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Show HN: OQP – A verification protocol for AI agents
4 points by Aamir21 6mo ago|0 comments
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by Aamir21 6mo ago
A few questions.. 1. Does the /verification/assess-risk endpoint capture what you'd actually need in a CI/CD gate? What's missing from the request/response schema? 2. The Knowledge Graph as the source of busine
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Show HN: OQP – A verification protocol for AI agents(github.com)
8 points by Aamir21 6mo ago|2 comments
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by Aamir21 7mo ago
if that alone give you 100% coverage, 0 critical bugs leaking into production, that is sufficient for the size of app you may have, however in an enterprise setting where thousands of B2B customers with nuances in usage and customizations a
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by Aamir21 7mo ago
And the other thing is tacit or tribal knowledge. Ai system is good when data is structured and available. Not so much when data is scattered and largely the connect the dots information is in dev or testers head. My recipe is memory + con
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by Aamir21 7mo ago
lets connect if you like to see some lessons learned?
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by Aamir21 7mo ago
i agree, but i wwant to add that perhaps just specs might not give you full testing coverage, have to add other artifacts too, like prod logss and incidents and using some layer of ontology + KG to produce meaningful data connectins and und
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by Aamir21 7mo ago
I tried claude code, and it did write some good quality e2e tests but my biggest worry was the full coverage. Its really difficult to quantify e2e test coverage the way developers do unit test coverage. its really impossible. specs is j
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Generate tests from GitHub pull requests
8 points by Aamir21 7mo ago|8 comments