20 articles exploring engineering
Framework choice is no longer about developer ergonomics. In an AI-driven era, the winners will be frameworks that resist entropy, enforce constraints, and scale safely under continuous AI modification.
For 30 years, language choice was driven by developer productivity. AI changes the equation. When machines generate code, verification matters more than velocity.
Stop over-engineering AI infrastructure. PostgreSQL already has everything you need: pgvector for embeddings, pgai for automation, TimeScaleDB for metrics. Build faster by using what you have.
Production AI systems are 98% harness, 2% model. New research reveals why architecture, permissions, and safety matter more than model capability - and how to build systems that actually work.
AI writes 80% of my code. I still review 100% of these 5 file types. A blast-radius framework ranking what to review line-by-line, and what to trust.
Smaller, constrained AI models force clarity and structure. I build faster with Haiku than Opus because constraints eliminate bad habits. Here's why.
A comprehensive data engineer's comparison of Apache Airflow, Prefect, and Dagster with 20-category feature matrix. Covers ease of use, learning curve, architecture, pricing, extensibility, community, integrations, and why Airflow still dominates for complex production pipelines.
A journey building issue-search-skill: capturing errors once, retrieving solutions forever. Local-first knowledge management that resolves recurring issues 12x faster.
How to build a SaaS metrics stack that produces ARR, MRR, churn, LTV, and CAC you can actually defend - with SQL, Python, and the right source-of-truth hierarchy.
Most AI coding tool comparisons still reward the wrong things. A workflow-first breakdown of Claude Code, Cursor, Copilot, Windsurf, and Antigravity through the lens that actually matters: how teams ship under real constraints.
AI-generated code feels fast, but the maintenance cost appears later. Why AI creates locally correct but globally fragile systems, and the engineering standards that fix it.
Why one-off prompting does not compound, and how to move from isolated prompts to repeatable AI workflows using playbooks, MCP data sources, and action layers.
A practical blueprint for structuring Claude Code projects so they stay predictable as they grow. From folder layout and .claudeignore to prompts, skills, and AI-friendly component patterns.
The MCP servers that matter most for real AI leverage: analytics, email, calendar, GitHub, databases, observability, SEO, social, docs, and file storage. Plus practical playbooks for turning them into repeatable workflows.
The 10 Claude Code skills that now separate developers who merely generate from those who ship differentiated products. From UI taste and frontend structure to brand systems and skill creation.
The uncomfortable truth: faster delivery doesn't come from working harder. It comes from structure. How I went from 6-month delivery cycles to weekly releases by investing in the unglamorous side of engineering - org design, clarity, and ruthless prioritization.
A CTO's honest account of building a personal portfolio site from scratch - the decisions that made sense at the time, the bugs that didn't, and what I'd do differently.
16 concrete strategies to reduce token consumption by 60–90% while keeping Opus and Sonnet actively predicting. From .claudeignore to multi-agent architectures.
From a simple JSON formatter to a 400+ tool developer platform serving 100K+ users - the complete engineering journey covering architecture, zero-backend design, performance, and deployment.
Treat cloud spend like a product, not a bill. Use credits and sponsorships to bring money in, then cut waste with data, commitments, right-sizing, and smarter architectures.