Practical engineering insights on frontend architecture, legacy modernization and building products with dedicated teams.
A grounded look at where AI-assisted and no-code tools genuinely replace engineer-led development today, and where professional engineering judgment remains the deciding factor in project outcomes.
A practical React architecture for separating API clients, server state, and UI behavior before async code turns components into untestable modules.
A practical React architecture for feature flags that keeps rollout logic isolated, testable, type-safe, and removable when an experiment ends.
A practical React form architecture that keeps field state, validation, submission, and server errors predictable as forms grow.
A practical guide to React Query cache invalidation: update the right data, avoid stale screens, and prevent unnecessary network requests.
What actually changes when a team adopts AI coding agents day to day — where they genuinely help, where they introduce new risk, and the review discipline that makes the difference.
A practical guide to React error boundaries: where to place them, what they catch, how to recover, and why one global fallback is not enough.
A practical guide to optimistic React interfaces: temporary state, failed requests, overlapping mutations, rollback, and reliable cache synchronization.
The security problems that actually show up in commercial web projects — from the OWASP Top 10 to frontend-specific mistakes — and the practical fixes that prevent most of them.
TypeScript protects your code at compile time, but API data is untrusted at runtime. Learn how to validate boundaries without spreading defensive checks.
A practical guide to React Server Components: what belongs on the server, what must stay interactive, and how to avoid accidental complexity.
Learn how explicit state machines make React interfaces easier to reason about, test, and extend than collections of interacting boolean flags.
A practical guide to test automation — the testing pyramid, which tests earn their maintenance cost and which do not, and how to decide what actually deserves automated coverage.
Beyond the tech stack: the business context, product intent, and working agreements that separate a development team that ships the right thing from one that just ships code.
The recurring, mostly non-technical problems that derail commercial software projects — scope creep, unclear ownership, misaligned incentives — and what actually prevents each one.
The architecture problems that show up most often in real frontend and full-stack projects — tight coupling, premature abstraction, state sprawl, and more — with practical ways to prevent or fix each one.
A practical map of the main debugging techniques — from print debugging to breakpoints, bisection, and production observability — and a clear guide to which one fits which kind of bug.
A hands-on playbook for making your content findable and citable by AI answer engines — ChatGPT, Perplexity, Google AI Overviews — beyond the basics of what SEO is and why it matters.
A practical guide to web performance — what slow performance actually costs a business, the metrics that matter (Core Web Vitals), where the problems usually come from, and the concrete practices that move them.
A clear guide to what SEO actually is and why it matters for your business — plus the extra work required so that AI answer engines like Google’s AI Overviews, ChatGPT and Perplexity can find, understand and cite you.
A practical, vendor-neutral checklist for evaluating and choosing a dedicated development team — covering team composition, sizing, communication, ownership, vetting, contracts and the red flags that predict a bad engagement.
A practical 2026 comparison of the three major Git platforms — GitHub, GitLab and Bitbucket — across CI/CD, ecosystem, self-hosting and AI tooling, plus the concrete reasons WebMriya standardizes on GitHub.
A practical guide for estimating the true cost of migrating a legacy frontend — AngularJS, jQuery or an outdated React — to a modern stack, including the real cost drivers and the budgeting mistakes teams make most often.