Thoughts on AI agents, automation, and building systems that work
You already know how to verify work at scale — tests, CI, pipelines. You're just not applying any of it to the agent. You're checking its output with your eyes, which is the one thing you spent a career learning doesn't work.
Read more →I was convinced that prescriptive skills get in the way of a frontier model, so I measured it: ~640 responses and 147 agent runs. I was wrong about where the damage is — and once the agent has tools, the benefit disappears and only the bill stays.
Read more →I rendered text into images and faded it until I couldn't read it, then asked six vision models and two OCR engines to try. They read text at a contrast of one grey level out of 255 — invisible to me — and some of them obeyed instructions hidden there. The margin they have over me turns out not to be sharper eyes.
Read more →Agents plan around events that never occurred and invent dependencies between things that don't depend on each other. It looks like a missing clock, so I measured it: 572 responses, six models, two domains. The date changes nothing, and one failure gets worse in the strongest models.
Read more →You described what you wanted, an agent built it, and it works. Here's what to learn first — not the whole list, the order. Written for people who never wrote software and don't intend to start.
Read more →You can build software now without writing a line of it. What's left isn't coding — it's a way of working, and a list of things you have to know exist or you'll never think to ask. Here's both.
Read more →A safety filter gave a base64 string a medium self-harm severity. Two judges scoring the same ciphered replies agreed worse than chance. A base model wrote fluent cipher it had never understood. Three systems producing confident verdicts about text they cannot read — and one conclusion of mine that didn't survive the full run.
Read more →A coda to the trilogy: chasing the specialist's blind spot led somewhere I didn't expect. The hardest ball to locate isn't the one flying through the air — it's the one sitting perfectly still.
Read more →I ran the same 45 blinded comparisons past a GPT, a Grok and a Claude judge. Each produced a different ranking, each favoured its own answers, and the effect scales with how subjective the task is.
Read more →Opening up the tiny network that beat the frontier models: ten interpretable geometric numbers recover 92% of it, fancy topology adds nothing, and the model turns out to be overconfident on exactly the cases it gets wrong.
Read more →I argued that a conversational journey added little over a well-designed form. The call went the other way, so I built it — and the client's feedback settled the argument better than I could have.
Read more →A tiny network trained on my laptop solves the hidden-ball cases that frontier VLMs can't — and a two-sport transfer study reveals what, exactly, a model learns about a game.
Read more →Once prompting stops being the hard part, a new question takes its place: which model, at what reasoning effort, for which slice of the work. Choosing well is becoming its own engineering discipline.
Read more →A claimed counterexample to a 30-year-old conjecture emerged from prompts like 'do a breakthrough' and 'just continue.' The craft of writing the perfect prompt is dying — and something more interesting is taking its place.
Read more →I hid the ball from vision-language models and asked them to find it from the players alone. The answer was interesting — but the more interesting part was how easily I almost concluded the wrong thing.
Read more →The next paradigm shift in application development isn't putting an agent inside your app — it's putting your app inside the agent your users already have. MCP is just the current plumbing for it.
Read more →AI agents keep quoting internal feedback, spec codenames, and process notes in client-facing deliverables. Why it happens, a taxonomy of the three failure modes, and the structural fixes that actually work.
Read more →The real process behind an AI-assisted preprint — and why the hardest frontier isn't prose but planning the science, where a more capable model stops being a convenience and becomes the thing that lets the work go further.
Read more →Speak to a language model in a cipher it has never been told the key to, and something surprising happens before any decoding does: some models refuse to play at all. A small experiment on how fast models crack novel codes — and where that runs into a safety wall.
Read more →I set out to reproduce a DeepMind result and instead found a clean way verification can lie to you: a code world model that passes its gate at 100% accuracy, stays 98% accurate on the states a planner visits, and still loses systematically at play.
Read more →Karpathy suggested letting a stuck agent restart instead of grinding on. I built a loop with visible tests and measured it: iterating on feedback beats clean restarts at equal compute — and handing the agent a scoped restart button reveals that knowing *what* to discard is what separates the tiers.
Read more →Say the same thing to a language model far past what any human would tolerate, and the polished ones don't break — they quietly give up. Strip the alignment layer off, though, and the raw model degenerates into loops. A small experiment on what repetition reveals.
Read more →A recent benchmark says coding agents can't collaborate because they lack social intelligence. I test a different hypothesis: they lack structure — and find that making integration one agent's job recovers collaboration, while merge conflicts mostly mask deeper semantic failures.
Read more →Why cloud workspaces and AI coding sessions need repository-owned bootstrap scripts for system packages, tests, and builds.
Read more →When you continually pre-train an LLM into a 'world model', it quietly forgets general knowledge — and a little data mixing buys most of it back. But how much it forgets depends heavily on how you specialize it, and full fine-tuning pays three costs a single benchmark undersells.
Read more →Agents made building almost free, which exposed what you were actually buying all along — not the software, but the maintenance, the de-risking, and your attention. A build-vs-buy calculus rewritten around the one resource that doesn't parallelize.
Read more →Reviewing used to mean reading the implementation. Increasingly I verify what the model produced, not the path it took — and what 'verify the result' means changes wildly depending on whether the result is a number or a behavior.
Read more →I build systems whose business logic I couldn't recite if you woke me up at 3am. The model holds it for me — and that's fine. The hard question isn't how much you delegate, it's whether you can still tell when the answer is wrong.
Read more →Two philosophies of the client demo — mock-first spectacle vs functional proof-of-concept — and why, with AI, the prettier one hides exactly what matters: latency and non-determinism.
Read more →Generative UI lets an agent render interactive widgets — forms, charts, signatures — inside the chat instead of plain text. AG-UI is the protocol standardising it, and I've already been building it by hand.
Read more →Before you write a judge prompt, you owe three decisions: the context, the unit, and the dimension. Here is the framework, applied to evaluating my production DevOps agent.
Read more →How I built an AI agent that investigates infrastructure issues autonomously — with read-only guardrails, output compaction, and infrastructure knowledge baked into the system prompt.
Read more →How a simple question about vitamin D synthesis turned into a Claude artifact, then a full PWA with D3.js visualizations, push notifications, and 6 languages.
Read more →What we write is becoming markdown skills, what users see is becoming generated pixels, and the middle keeps getting thinner. A look at where software is heading in 2026.
Read more →AI can now build almost anything. The hard part is no longer the code — it's managing your own cognitive limits as you orchestrate multiple agents in parallel.
Read more →Why most AI-driven tests should be reproducible scripts, not CLI exploration — and how to use each approach effectively based on real deployment automation experience.
Read more →Lessons from writing a 16-chapter essay with two AI coding tools — why prose is harder than code for AI, and what actually worked.
Read more →How I used Claude Code to build a recording pipeline, edge-tts for voiceover, ffmpeg for assembly, and Gemini to QA the result — without touching a video editor.
Read more →How I built an RLM prototype that processes 71 arXiv papers (~1M tokens) without injecting them into the prompt, using out-of-core document analysis with Azure OpenAI tool calling.
Read more →How to orchestrate multiple AI agents working in parallel using Claude Code's new Agent Teams feature within a tmux session.
Read more →Analyzing the legal and ethical implications of 'Co-Authored-By: Claude' appearing in commits. Tool or author? The future of AI-generated code ownership.
Read more →TypeScript provides essential guardrails for AI coding agents but creates friction with type errors. The solution isn't changing languages—it's changing workflows with specialized sub-agents.
Read more →How I built a Claude Code skill that standardizes and automates the creation of bilingual blog articles, ensuring consistency across EN/ES content.
Read more →A workflow for running multiple Claude Code agents simultaneously on different features, using git worktree to manage branches and automating PR reviews.
Read more →How Azure's content filter blocks legitimate browser automation tools based on word choice, not actual risk—and simple workarounds that expose the flaw.
Read more →How I built a comprehensive MCP server with 58 tools to manage Bitbucket repositories, pull requests, and pipelines through natural language.
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