Software Architecture
API Design Principles That Age Well
The design decisions that make APIs a pleasure to work with years later - consistency, clear contracts, thoughtful versioning, and the mistakes that are expensive to undo.
All posts
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Data Engineering
Stream Processing vs Batch Processing: How to Choose
What stream and batch processing actually are, the real tradeoffs between them, and how to decide which approach fits your data pipeline requirements.
AI & Machine Learning
Building a RAG System: Retrieval-Augmented Generation Explained
How RAG works under the hood, why it solves the hallucination and knowledge cutoff problems, and what you need to build one that actually works in production.
Programming Languages
Type Systems: Static, Dynamic, Strong, Weak - What These Actually Mean
The four axes of type systems explained precisely, with real examples of what each combination means in practice and why the distinctions matter for everyday programming.
Engineering Craft
Technical Debt: How to Measure It and How to Argue for Fixing It
What technical debt actually is, how to distinguish debt worth paying from debt worth ignoring, and how to make the case for cleanup work to a non-technical stakeholder.
Backend
Rate Limiting: Why, What, and How
Why every public API needs rate limiting, the common algorithms and their tradeoffs, and how to implement it without reinventing the wheel.
Databases
The N+1 Problem: How It Happens and How to Fix It
Why N+1 queries are so common in ORM-based code, how to detect them, and the three patterns that eliminate them without rewriting your data layer.
Frontend
TypeScript for JavaScript Developers: What Actually Changes
The mental model shifts that make TypeScript click, which features earn their complexity, and how to use the type system to catch real bugs rather than add ceremony.
Testing
Test Doubles: Mock, Stub, Spy, Fake - the Real Differences
What each type of test double actually is, when to use each one, and how to avoid the trap of over-mocking tests into uselessness.
AI & Machine Learning
Prompt Engineering for Developers
The practical techniques that actually improve LLM output quality - structured prompts, few-shot examples, chain-of-thought, and how to build reliable AI-powered features.
Performance
Database Query Performance: EXPLAIN and What It Tells You
How to read a Postgres EXPLAIN ANALYZE output, what sequential scans and index scans mean, and how to identify and fix slow queries before they become incidents.
Networking
WebSockets: When Request-Response Is Not Enough
How WebSockets work, what they are actually good for, and the patterns for building real-time features without the common pitfalls.