Writing
Faster prototyping and smarter debugging come with new failure modes: prompt injection, quiet data leakage, plausible-but-wrong output. A working set of guardrails — tight requirements, real tests, secure defaults, cheap evals.
Semantic HTML, keyboard paths, visible focus, and ARIA only where it earns its place. Plus a testing routine you will actually repeat, using Lighthouse alongside a real screen reader.
Measure first, then act. Profiler and Web Vitals before optimisation; kill avoidable re-renders, split bundles on purpose, virtualise long lists, keep state local. Verify every AI-suggested fix.
Not every product needs a chatbot. Smart search, summarisation, form autofill and content QA earn their keep — if you handle PII, latency and cost, and measure quality before shipping.
When code arrives faster, review is the bottleneck that protects you. A checklist that holds up: security first, coverage, edge cases, readability, and what it costs to maintain in a year.
Small diffs, strong tests, typed interfaces and a PR checklist. That is the whole trick for turning AI speed into work you would put your name on.
What each one is actually for, when the other is a mistake, and the unglamorous parts that decide whether it works — retrieval quality, caching, guardrails, and evaluation you can automate.
Setting up a GraphQL API that stays fast under real load, and wiring it into front ends without recreating every REST problem you were trying to leave behind.
The handful of patterns that hold up across AWS, Azure and Google Cloud — and the ones that quietly cost you money at scale.
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Build logs, breakdowns and the occasional thing that went wrong — on YouTube and TikTok as BuiltByBlessing.
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