Thoughts, insights, and guides on software development and technology
AI is changing how we build software: faster prototyping, better docs, and smarter debugging — but also new risks like prompt injection, data leakage, and silent failures. In this post, I share a practical workflow for shipping AI-assisted features with guardrails: clear requirements, strong tests, secure-by-default APIs, and lightweight evaluation so you can move fast without breaking trust.
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Accessibility isn't a checkbox — it’s how you make your product usable for everyone. This guide covers practical WCAG techniques you can apply immediately: semantic HTML, keyboard navigation, focus states, ARIA only when necessary, and a repeatable testing routine with Lighthouse + real screen readers.
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React performance work should be evidence-based. This post walks through a simple workflow: measure first (Profiler + Web Vitals), remove avoidable re-renders, split bundles intentionally, virtualize long lists, and keep state local. Bonus: how to use AI assistants to suggest optimizations — and how to verify them safely.
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GraphQL has revolutionized API development by providing more flexibility and efficiency. This article walks through setting up a robust GraphQL API and integrating it with various front-end frameworks.
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Serverless computing is transforming how we deploy and scale applications. This comprehensive guide covers practical patterns for implementing serverless solutions across AWS, Azure, and Google Cloud.
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Not every product needs a chatbot. This post breaks down high-ROI AI features that users actually love: smart search, summarization, form autofill, and content QA. I also cover the basics of safety (PII handling), latency/cost, and how to evaluate quality before you ship.
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AI can generate code quickly — which makes review even more important. This post shares a practical review checklist: security first, test coverage, edge cases, readability, and long-term maintainability. I also show how to use AI for review summaries without letting it replace engineering judgment.
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AI assistants are amazing for scaffolding, refactors, and explaining unfamiliar code. The key is keeping quality high. In this post I share a simple workflow: small diffs, strong tests, typed interfaces, and PR checklists that turn “AI speed” into production-ready results.
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If you’re adding AI to a product, “RAG vs fine-tuning” comes up fast. This guide explains what each approach is, when to use it, and how to keep the system reliable: retrieval quality, caching, guardrails, and evaluation you can automate.
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