Thoughts on AI agents, automation, and building systems that work
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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