by humanlayer-team
Systematic context management—through frequent intentional compaction and a Research-Plan-Implement workflow—enables productive AI-assisted development in complex production codebases.
by u-similar-bid1784
Production-quality vibe coding requires strict CI/CD pipelines, git discipline, extensive planning, and expert oversight—code is 30% of the work, deployment and performance are the real challenges.
The Pragmatic Engineer Podcast
Chip Huyen explains how AI engineering differs from ML engineering, walking through the practical developmental path from prompts to RAG to fine-tuning.
by chip-huyen
A practitioner's guide to building applications on foundation models, covering prompt engineering, RAG, finetuning, agents, and evaluation.
by sarah-drasner
A practical guide to engineering management focused on empathy, trust, and supporting your team—written for those who didn't plan to become managers.
by steve-fenton
A comprehensive guide to GitHub Actions covering architecture, core components, best practices, and practical implementation patterns.
Patterns and tools for building GraphQL APIs—from schema design to Meta's fragment philosophy
by rami-mccarthy, shay-berkovich
A comprehensive security guide covering organization-level configuration, workflow hardening, and self-hosted runner protection for GitHub Actions.
by gergely-orosz, martin-fowler
Martin Fowler argues AI represents the biggest shift in software engineering since assembly to high-level languages—not because of abstraction level, but because we now work with non-deterministic systems.
by marcus-felling
Practical techniques to speed up GitHub Actions workflows through caching, parallelization, matrix builds, and conditional execution.
by chip-huyen
A comprehensive guide explaining the three-phase process (pretraining, supervised fine-tuning, RLHF) used to train models like ChatGPT.
by mohammad-bagher-abiyat
Vitest Browser Mode runs tests in real browsers instead of jsdom simulations, eliminating false positives from Node.js APIs leaking into the test environment.
by jessica-sachs
Vitest browser mode runs component tests in real browsers instead of JSDOM, providing actual rendering confidence while maintaining Vitest's speed and developer experience.
by cian-clarke
BMAD's spec-driven methodology beats pure prompting for production-ready AI development because it forces clarity before code and catches requirements gaps before they become technical debt.
by matt-mahoney
Meta's @async directive solves the problem of paying server compute costs for data that may never be displayed, using a persist-time transform that defers fragments until explicitly requested.
by fernando-rojo
Replace boolean prop sprawl with compound components. Lift state to context providers and compose distinct component trees instead of branching with conditionals.
by janette-cheng
GraphQL fragments describe data requirements for components, not reduce code duplication. Reusing fragments causes overfetching. Use fragment models, collocate GraphQL with code, and write GraphQL that mirrors your component structure.
by dex-horthy
A practical framework for getting AI coding agents to work reliably in brownfield codebases through context engineering, intentional compaction, and the Research-Plan-Implement workflow.
by martin-fowler
The definitive guide to improving code structure without changing behavior. Fowler catalogs refactoring techniques with clear examples, making messy code maintainable.
by kent-beck
A compact guide to code tidying—small, behavior-preserving changes that make code easier to understand and modify before making structural changes.
by michael-thiessen
A curated collection of 12 Vue-specific design patterns addressing common problems every app encounters, from state management to component organization.
by michael-thiessen
Practical patterns for writing maintainable Vue composables, covering state management, reactivity handling, and code organization.
Patterns and practices for building autonomous AI agents with prompt chaining, routing, and reflection
by maham-codes
A practical guide to building AI agents using prompt chaining and basic primitives instead of heavy frameworks.
by erik-schluntz, barry-zhang
Anthropic's guide to building agentic LLM systems, advocating for simple composable patterns over complex frameworks.
by pnpm
pnpm v10 introduces built-in protections against npm supply chain attacks through script blocking, dependency restrictions, and release delays.
by tsvetan-tsvetanov
Quality foundations—testing, strict types, clean APIs—enable rapid AI-assisted development because features emerge naturally once verification loops exist.
by kent-c-dodds
Prioritize integration tests over unit tests for maximum confidence with minimal maintenance burden—aim for ~70% coverage, not 100%.
by sean-goedecke
Effective software design requires hands-on codebase knowledge—architects who don't implement their designs lack accountability and produce solutions that ignore concrete system constraints.
by alexander-opalic
A comprehensive style guide for writing clean, testable, and maintainable Vue 3 composables by applying time-tested software design principles like single responsibility, separation of concerns, and functional core/imperative shell.
A comprehensive guide to building maintainable, scalable Vue 3 applications
by den-delimarsky
Spec Kit provides a structured four-phase workflow for AI coding agents, replacing vague prompting with specification-driven development.
by zara-cooper
Technical specs force engineers to think through problems before coding, align teams on deliverables, and prevent scope creep through eight essential components.
by kent-c-dodds
Nested describe blocks with beforeEach hooks create cognitive burden by requiring developers to track mutable state across scope levels—prefer inline setup or factory functions instead.
by ron-jeffries
Refactoring should happen continuously during feature work, not as separate backlog items. Deferring code cleanup creates a vicious cycle that slows velocity and rarely gets approved.