AI Infrastructure 101 for QA Engineers: From a Prompt to a Test Result
AI infrastructure for QA engineers: understand the path from data center, GPUs, memory, and networking to model execution and reliable LLM evaluation.
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Practical insights on Playwright, self-healing tests, AI-driven QA, and scaling engineering teams. Real lessons from 20+ years in quality engineering.
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AI infrastructure for QA engineers: understand the path from data center, GPUs, memory, and networking to model execution and reliable LLM evaluation.
Read articleA failure log can preserve the facts and still leave the system unchanged. The missing field is not more context. It is authority.
Read articleA documented fix is not a shipped fix. The difference is a control boundary: who can execute it, when, and what proves it happened.
Read articleA rejected order is a decision. Retrying it without preserving the reason can turn a risk control into a duplicate trade.
Read articleAn agent that can act but cannot leave a useful decision record is not autonomous. It is an opaque process with write access.
Read articleA backtest can know the exchange hours and still schedule a trade into a holiday, an early close, or a stale session. Calendar state is market data.
Read articleWhen an agent edits a shared checkout, the dangerous variable is not context length. It is the boundary around what the run is allowed to write.
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