Most Tech Writing Explains What. This Is About Why.
I write about what actually happens inside engineering orgs. Not the polished version.
All 24 posts
Building API Dev Utils: A 400+ Tool Developer Platform
From a simple JSON formatter to a 400+ tool developer platform serving 100K+ monthly users — the complete engineering journey covering architecture, zero-backend design, performance, and deployment.
Read article →How to Build a Systematic, AI-Assisted Personal Content Strategy from Scratch
A platform-agnostic how-to for building a disciplined personal content system with voice definition, pillar tracking, research libraries, and AI discoverability built in from day one.
Read article →Airflow vs Prefect vs Dagster: Which Orchestrator Wins in 2026
A comprehensive data engineer's comparison with 20-category feature matrix. Covers ease of use, learning curve, features, community, pricing, integrations, and why Airflow still dominates for complex production pipelines.
Read article →Building Your Personal Stack Overflow: A Knowledge Management Journey
A journey building issue-search-skill: capturing errors once, retrieving solutions forever. Local-first knowledge management that resolves recurring issues 12x faster.
Read article →The SaaS Metrics Stack: ARR, MRR, Churn and LTV You Can Actually Trust
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.
Read article →The Data Room That Helped Close Our Series B
How to build investor-grade revenue data infrastructure before a Series B raise - the stack, the metrics, the entity resolution problem nobody talks about.
Read article →The 10 Most Valuable MCP Servers for Modern AI Workflows
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.
Read article →The Hidden Cost of AI-Generated Code (and How to Fix It)
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.
Read article →From Prompt to System: Building AI Workflows That Actually Run
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.
Read article →The Ideal Claude Code Project Structure That Actually Scales
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.
Read article →The Most Important Claude Code Skills for Modern Web Development
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.
Read article →The Only AI Coding Tool Comparison That Matters in 2026
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.
Read article →How I Increased Delivery Speed by Doing Less, Not More
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.
Read article →Interactive Geospatial Intelligence: Where Real-Time Earth Meets Decision Systems
How six companies are building the future of geospatial systems—from real-time Earth monitoring to predictive intelligence. The stack replacing static maps with decision engines.
Read article →CTO First 90 Days: A Practical Framework for New Technical Leaders
A step-by-step playbook for the first 90 days as CTO or VP Engineering. How to listen, diagnose, align, and deliver quick wins without breaking the org.
Read article →When Do You Need a CTO? A Founder's Decision Framework
The inflection point where you graduate from VP Engineering to full-time CTO. How to know when, why full-time vs fractional matters, and what to expect in the first 90 days.
Read article →I Built My Own Portfolio From Scratch (Here's What Bit Me)
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.
Read article →Top 15 AI Voices I Actually Check on X in 2026
The 15 AI researchers, builders, and thinkers worth following on X in 2026. Cut through hype with voices from OpenAI, Meta, Stanford, and the venture ecosystem.
Read article →How to Hyper-Optimise Claude Code: The Complete Engineering Guide
16 concrete strategies to reduce token consumption by 60–90% while keeping Opus and Sonnet actively predicting. From .claudeignore to multi-agent architectures.
Read article →AI Unlocks Economics: How Founders Are Reshaping What's Fundable
AI fundamentally changed the unit economics of software development. Discover how the most successful Series A founders are architecting for this shift to win at better valuations.
Read article →Why Most AI Strategies Fail to Produce ROI
After auditing dozens of AI programmes, the pattern is identical: companies optimise for technical metrics that boards don't care about. Here's how to fix the framing.
Read article →We Automated 75% of Reporting. Three People's Jobs Changed Overnight.
The tech worked perfectly. The people side broke. How we moved from "automate and forget" to "automate and elevate" — and why that distinction matters for every leader automating work.
Read article →Engineering Passive Discoverability on LinkedIn
A systematic framework for optimising your LinkedIn profile so executive search recruiters find you — without a single cold message.
Read article →How We Hyper-Optimised Cloud Costs Without Slowing Delivery
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.
Read article →Breaking into the Data Engineering Market
A practical framework for entering the field: programming foundations, SQL, cloud platforms, side projects, and how to build a portfolio that gets you hired.
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