30 articles exploring engineering
The structural signal for splitting CTO and VP Engineering roles isn't headcount — it's a specific week when strategy and delivery both need full attention. A founder's decision framework with real failure modes.
Why AI output depends on how teams assemble and retrieve context - not on prompting. A 7-metric measurement framework with 8 charts for engineering teams.
Most teams adopt a dedicated vector database before the production signals that justify it arrive. A decision framework for when pgvector stops being enough.
Benchmark-driven comparison of the 2026 document parsing landscape - LiteParse, LlamaParse, Unstructured, Docling, PyMuPDF, Google Document AI - with Python code, failure modes, and an async routing architecture.
Three-wave audit that serves Google ranking and LLM citation simultaneously - fix crawl health, add entity disambiguation in structured data, and open AI crawler channels.
A real audit case study - three rounds, 17 mobile score points gained, every fix shown. The systematic PageSpeed Insights approach that catches what visual QA misses.
AI agents create a second bill: invisible to FinOps, landing on the wrong invoice, and breaking every financial model built for linear AI costs.
Expert persona prompting reduces factual accuracy by 3.6 points. Here are the four patterns that actually work - with a decision table and real session data.
Most tools marketed as knowledge graphs for AI coding are dependency graphs. I built three production KGs, ran the experiments on LangChain, and the results were not what the vendors claimed.
Every company says it's AI-first. After 30+ CTO interviews, I can tell you exactly which questions reveal the ones that aren't - and what the real ones say instead.
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.
After running all three in production: 20-criteria breakdown of real migration costs, team overhead, backfill behaviour, and which orchestrator survives 500+ 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 battle-tested blueprint for Claude Code projects that stay predictable at 50+ files - folder layout, .claudeignore, skills architecture, and 3 ready-to-copy templates.
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.
Battle-tested techniques for .claudeignore, context compression, and multi-agent task splitting that cut token usage by 60–90% without losing Sonnet's predictive accuracy.
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.
How to cut cloud spend 30–60% without freezing delivery velocity - using cloud credits, right-sizing, commitments, and platform guardrails that engineering teams actually adopt.