Llm

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. ...

Apple’s Foundation Models framework (macOS 26) provides access to a ~3B parameter LLM that runs on-device, for free, with no API key. But here’s the catch: it only speaks Swift. And not just any Swift — Swift async. Is your tooling in Python? No bindings. Rust? Nope. A shell script? Forget it. The cheapest LLM in the world (literally free) is trapped behind two barriers: the language and the concurrency model. ...

Yesterday I wrote a commit message with Claude Code. The diff was a one-line change: a typo in a comment. Claude Opus read the diff, thought for two seconds, and generated fix: correct typo in auth comment. That consumed about 800 input tokens and 30 output tokens, at $15 and $75 per million respectively. Cost: a fraction of a cent. But multiply that by 40 commits per day, 250 days per year, across a company with 200 developers using coding agents, and the fraction of a cent becomes thousands of dollars spent on the intellectual equivalent of applying band-aids. ...

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. ...

You’re paying anywhere from $20 to $200 a month for access to LLMs. Claude, GPT, Gemini, whatever. And most of the calls you’re making from your scripts and dev tools boil down to something like this: “Classify this bug into one of these five categories” “Name this variable for me” “Tell me if this commit is a fix, feat, or refactor” “Summarize this block of text in two sentences” Meanwhile, your Apple Silicon Mac has a 3-billion-parameter language model baked right in, integrated with the operating system. No cost, no internet needed, no API key, no network latency. And you’re probably not using it at all. ...

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: When your primary consumer is an LLM, XML and JSON waste tokens by repeating structure in every element. A compact positional format reduces consumption by 50%. Turns out this idea already had a name: TOON (Token-Oriented Object Notation). Same selective pressure — expensive tokens and repeated keys — same solution. Anthropic uses XML for everything. Their system prompts are wrapped in <instructions>, their examples in <example>, their tools in <function>. If you work with Claude, you live surrounded by tags. ...

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? ...

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. ...