Your async tests pass 9 times out of 10 and fail in CI for no reason. The real bug is in how you test time-dependent code. Here is how to replace setTimeout with deterministic clocks, control promise ordering, and eliminate flakiness from every async test in your suite.
Your service takes six seconds to start, and Kubernetes keeps killing it before the health check fires. Here is how to profile module loading, understand the resolution algorithm, and cut startup time by 80% with lazy imports and barrel-file elimination.
Most TypeScript projects barely scratch the surface of what the type system can enforce at compile time. This post walks through four real-world patterns using conditional types, mapped types, and the infer keyword that eliminate entire categories of runtime bugs.
No built-in ESLint rule enforces your team-specific convention. Here is how to write a custom plugin with AST visitor setup, RuleTester tests, and auto-fix, using the ESLint 9 flat config format.
Prisma is beloved for DX, but its generated client, cold start overhead, and proxy layer cause real pain at scale. Drizzle takes the opposite approach: you write SQL and TypeScript validates it at compile time. Here is the migration, schema design, query patterns, and production gotchas from shipping Drizzle in a real Node.js service.
Most Node.js codebases either hardwire every dependency or import a heavyweight DI framework and regret it. This post shows three practical DI patterns with working code: a manual wiring function, a lightweight factory-based container, and function-scoped injection. Pick the pattern that fits your project size.
JavaScript Date is broken. Timezone bugs, daylight saving time surprises, and ambiguous date math cost teams hours every sprint. Here is how to use the Temporal API with the production-ready polyfill today, with patterns for timezone-aware scheduling, duration math, and calendar-safe date handling.
You reach for Babel, ts-node, or an entire build pipeline just to import a .ts file, mock a dependency in tests, or add request tracing. Node.js has built-in hooks for all of this since v18, and most teams do not know they exist. This post builds three loaders from scratch and shows you exactly when to use them.
ESLint covers the common cases. For everything else there is the TypeScript Compiler API. This post walks through building real custom analysis tools: enforcing domain boundaries between packages, detecting leaked internals, and writing refactoring scripts that understand your codebase.
REST and GraphQL both leak a type gap between server and client that costs you a production bug every few sprints. tRPC eliminates that gap entirely. Here is how to build a production tRPC API with authentication, error handling, middleware, and rate limiting, without a code generator or a spec file.
ESLint v9 makes flat config the default and deprecates .eslintrc. This is a breaking change in how you configure linting. Here is the migration path with a working config that covers TypeScript, React, imports, and Prettier integration.
Your package.json `exports` field is either missing, wrong, or silently ignored by some consumers and not others. Here is the complete guide to conditional exports, subpath patterns, TypeScript resolution, and the migration path from the old `main` field.
One thrown exception in a hot path can deoptimize your entire function in V8. Here is the benchmark data that proves it, the Result type pattern that avoids the tax, and the 50-line implementation you can drop into your project today.
Node.js has shipped a production-ready test runner since v20 that needs zero config, runs TypeScript natively, and handles 90% of what you reach for Jest or Mocha to do. Here is the migration path, the API patterns that matter, and the edge cases where you still need a third-party tool.
TypeScript will happily let you pass a UserId where you meant OrderId, and the bug slips into production. Branded types add nominal typing with zero runtime cost, catching mismatched IDs, raw strings, and domain violations before they hit your database.
Your seed.sql is three months out of date, your integration tests pass on your machine but fail on CI because the assumed data does not exist, and nobody knows how to add a new entity to the test dataset without breaking six other tests. Here is the factory-based seeding pattern that makes test data declarative, reproducible, and easy to maintain.
You wrote 60 unit tests and deployed confident. The bug shipped anyway because nobody thought to try an empty array with a negative offset. Property-based testing generates hundreds of edge cases automatically and surfaces the scenario you forgot to imagine. Here is how to use Fast-Check in real Node.js projects to catch the bugs example-based tests miss.
Stop hand-writing fetch wrappers that drift from the spec. Here is how to generate fully typed API clients from OpenAPI specs, catch breaking changes in CI, and never debug a "but the API returned field X" production issue again.
The billing module has 4,000 lines of untested JavaScript, nobody on the team wrote it, and every deploy triggers the "please do not break billing" prayer. Here is the three-phase strategy (characterization tests, strangler fig extraction, and feature flag cutover) that rewrites legacy code incrementally without a freeze or a big bang.
tsc is slow, but dropping it entirely means shipping bugs that only the type checker catches. Here is a dual-pipeline approach that uses esbuild for speed and tsc for safety, with production build times cut by 80% and zero type regressions.
Your monorepo takes 3 minutes to type-check on every CI run. The build gets slower every time you add a package. TypeScript project references with composite mode and incremental builds can cut that to 15 seconds without changing your code.
A malformed request slipped past JSON parsing and wrote a null into a required column, causing a cascade of 500s that took two hours to clean up. Here is the Zod validation layer that stops bad input at the API boundary, with the TypeScript integration, custom refinements, and error formatting that makes client integrations painless.
Adding "type": "module" to your package.json breaks imports, mocks, and dynamic requires in ways that are hard to predict. This guide walks through the practical migration path: incremental adoption, dual-package patterns, testing compatibility, and the traps that catch every team the first time.
Most TypeScript users have heard of template literal types and don't use them. They are the feature that turns "string with a specific shape" into a checked type. Here are the four practical patterns (typed routes, prefixed keys, builder methods, validation) that show up in every real codebase.
Most TypeScript domain models are a single interface with twenty optional fields, half of which are mutually exclusive. Discriminated unions are the feature that turns that into the actual shape of the data, and lets the compiler narrow precisely. Here is the pattern, applied to four real domain shapes.
A default ESLint config has 80 rules and most of them are noise. The ones worth their CI cost catch real bugs: async without await, exhaustive switches, no-floating-promises. Here are the twelve I turn on for every TypeScript project, and the four I turn off because they cause more harm than good.
Flipping `strict: true` on a 200-file TypeScript codebase produces 8000 errors and a team revolt. The right migration is incremental, file-by-file, with the seven flags enabled in the right order. Here is the plan that has worked on three real codebases.