GLM-5.2: The Frontier Coding Model You Can Actually Download
A deep, builder-focused breakdown of Z.ai's GLM-5.2: a roughly 744B mixture-of-experts model with a 1M-token context, released open-weights under MIT. What it is, what the benchmarks say (and the asterisk nobody mentions), what it costs on an API versus your own hardware, and when to reach for the open model nobody can ban.
Read the pieceWhy LLMs Hallucinate (and What Actually Reduces It)
A technical, no-hand-waving explanation of why large language models make things up: how next-token prediction works, why confidence is not correctness, and the techniques that genuinely reduce hallucination in production.
Why Your AI App Feels Slow (and the Latency Budget That Fixes It)
A deeply technical walkthrough of AI app latency: the real cost of TLS handshakes, cross-region round trips, cold starts, vector search, and LLM time-to-first-token, plus the budget framework that makes a product feel instant.
More writing
Automated Development: Soon You Won't Write Code, You'll Build the Machine That Does
The job is shifting from writing code to building the pipeline that writes and ships it. A field guide to automated development: the history, the mechanism, and what comes next.
India's Startup Story Isn't Being Written in Mumbai
Nandan Nilekani's digital public infrastructure bet is paying off — and the dividends are flowing to Tier-2 founders, not just metros.
Loop Engineering in 2026, Part 2: The Outer Loop, Shared Memory, and How Loops Compound
Part 1 built a single agent loop. Part 2 is the systems layer: the outer loop that decides what to work on next, and the shared artifact store that makes independent loops learn from each other and compound into an operating system for continuous improvement.
Learn Anything Faster: The Science of Rapid Skill Acquisition
A practical playbook for rapid skill acquisition: deconstruct any skill, drill the high-leverage 20 percent, and reach useful proficiency in weeks.
Loop Engineering in 2026, Part 1: What an Agent Loop Is and How to Build One
The practical version of loop engineering: what an agent loop actually is, the six parts every loop is assembled from, how Claude Code's /goal works, a PR-babysitter you can build today, why iterations not tokens are the real cost, and when not to loop at all.
The EU AI Act Is a Startup Catalyst, Not a Startup Killer
The EU AI Act entered force August 2024 with obligations phasing through 2027. Here's why the compliance burden is also the biggest go-to-market opening in European AI.
Human Capital, Token Capital, and the Climbing Machine: Why the Next Moat Is Owning Your Learning Loop
Frontier AI models are becoming a commodity. The durable advantage is owning the learning loop that turns your workflows and judgment into AI that compounds over time.
Running Claude Agents in Parallel: A Practical Guide to Doing More Than One Thing at Once Without Creating Chaos
A detailed, practical guide to parallel agent work in Claude Code: when to use Agent View, Agent Teams, and Dynamic Workflows, how worktrees and /batch keep parallel edits from colliding, how to monitor and control running agents, and the operating rules that keep it all sane.