AI

I have a code agent—Claude Code—that interacts with Linear, my task management tool, about 800 times a month: listing tasks, creating issues, changing states, leaving comments. I reviewed 165 of its sessions and counted more than 500 errors and over 370 retries. None of these were caused by issues in Linear’s API. All were interface errors: the agent communicated with the command line, and the command line didn’t understand it. ...

Last week, auditing my Cloudflare API tokens, I found one I never created: “Cloudflare Agent Token - 2026-04-28”, created by the dashboard’s AI assistant (“Ask AI”). Cloudflare’s tooltip says it exists so the AI can “understand your environment and take actions on your behalf.” Its actual grant, from the token’s own summary page: read access scoped to All accounts, All zones, and All users — more than 160 permissions. Every one is :Read: it cannot change anything. But “read-only” undersells it. The list includes Secrets Store:Read, Access: Keys:Read, Access: Service Tokens:Read, Zero Trust: PII:Read, Logs:Read, Account Audit Logs:Read, Billing:Read, API Tokens:Read, and every DNS, Access and identity-provider config you have. ...

Google published TurboQuant on March 24th. Within 48 hours the paper had 575 points on Hacker News, Micron’s stock dropped $900 million, and TechCrunch compared it to Pied Piper’s algorithm from Silicon Valley. One month later, the hype fog has cleared enough to answer the only questions that matter: Does it work? Can I use it today? And the one nobody wants to ask: Is it actually new? What TurboQuant promises (30-second recap) If you already read my previous article on the math, skip this section. ...

There comes a time in every programmer’s life, when using coding copilots, where you look at your monthly bill and think: “This is great and all, but I have more subscriptions than my local gym membership.” That moment came for me with quite a lineup: Claude Max 5, Codex Plus, and the temptation to throw in Z.AI with OpenCode for the grunt work. The idea sounded fantastic. The problem? If you throw three agents into your workflow with no ground rules, you’ll end up like a chaotic construction site boss—everyone scrambling around while no one holds the blueprints. ...

TL;DR: After 165 sessions with Claude Code and 27 with Codex CLI, one thing is clear: use Claude for interactive, exploratory work and Codex for autonomous tasks you can delegate and forget about. It’s not about which is better — it’s about when to use each. My data shows the combination outperforms either one alone. It’s 9:00 a.m., coffee in hand, and I have a vague idea about restructuring a module. I don’t know exactly what I want yet — I just know the current setup isn’t right. ...

TL;DR: My AI agent had a 246-line instruction file for managing issues in Linear. 150 of those lines were workarounds: hardcoded UUIDs, curl fallbacks, notes like “the CLI doesn’t support X.” I didn’t rewrite them — I built a tool that made them unnecessary. Now those 150 lines are gone. Have you ever written a set of instructions so long that its sheer length proves something is fundamentally wrong? I’m not talking about legitimate documentation. I mean those files that start with “use tool X” and then spend 80% of the text explaining when tool X doesn’t work and what to do instead. Instructions that are, effectively, a list of apologies for the tool you should have built in the first place. ...

TL;DR: An LLM is like Don Quijote—you can’t cure his madness, it’s stochastic by nature. The solution isn’t to fix the madman but to assign him a deterministic Sancho Panza as a sidekick. MDD consists of two layers: first, you study the errors it makes to design tools that absorb those mistakes, and then you let it loose with those tools to verify you’ve closed any gaps. Design for madness, not against it. ...

TL;DR: Your AI will invent API fields that sound perfect but don’t exist. The solution isn’t hoping it gets it right: download the real schema before writing code, capture real responses as fixtures, and separate fetch from processing so you can test without the network. Adversarial programming: code assuming your copilot lies. Have you ever written code against an API where everything compiled, tests passed, the logic made sense… and when you connected to the real API, nothing worked? ...

TL;DR: Linear just launched an integrated AI agent. Cool, but it doesn’t address the problem developers face when working with coding agents in the terminal. What we actually need isn’t another AI agent but a rock-solid CLI that our existing agents can use seamlessly. And if we’re going to build one, it should be in Rust — which is why lql exists: a CLI for Linear, purpose-built for agents. Yesterday, Linear launched their AI agent. It’s an integrated chatbot that gets your roadmap, your issues, and even your code. You can chat with it on Slack, mention it in a comment, and it’ll synthesize context, suggest actions, and even create issues for you. ...

Multiplication is hard. Addition is easy. Any elementary school kid knows this. What they don’t know is that logarithms exist precisely to exploit this asymmetry: you convert multiplication into addition, operate in the simple world, then undo the transformation. The result is correct. The effort, a fraction. This pattern — transform the problem to a space where solving it is trivial, solve it, then transform back — is one of the most powerful in all of engineering. FFT does this with signals. Logarithms do it with products. And now Google just published a paper that does it with language model compression. ...