How I Think About AI Native Software Development
AI Native is not just adding a chat box. It is designing the product, codebase, and feedback loop around what models can reliably do.
Thoughts on modern web development, architecture, and design.
AI Native is not just adding a chat box. It is designing the product, codebase, and feedback loop around what models can reliably do.
A practical way I like to structure Bun and Hono backends: small routes, typed validation, clear errors, and no unnecessary framework weight.
Notes from building and owning a real React Native fintech app: payments, native modules, releases, observability, and the small things that keep users safe.
A practical look at the rendering ideas behind a fast terminal UI engine: layout, framebuffers, dirty regions, text editing, and why Rust fits the core.
Static first websites are still one of the best choices for portfolios, blogs, docs, and content-heavy products because they are fast, simple, and easy to trust.
My practical view on coding agents: prompts help, but the real quality comes from tools, tests, context, permissions, and a good harness loop.
The database layer should keep product code stable when schema, queries, and business rules change. This is how I like to think about it.
Benchmarks are useful, but I judge coding models by how they behave inside real repositories: reading context, editing safely, testing properly, and admitting uncertainty.
The checklist I like before shipping: branch discipline, scoped changes, tests, lint, typecheck, build, smoke testing, and honest release notes.
The work I am most interested in now: AI-native products, serious frontend systems, backend platforms, Rust developer tools, and products with real users.