The Model That Aced Olympiad Math Can't Write a Tweet
The 4 GB series found a 3B that reasons like a giant. So surely it can write? It can't — and how it fails is the interesting part. VibeThinker-3B chokes on a 120-word story while two plain instruct models a fraction as clever nail it, and a language twist decides which one you want.
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You Don't Need to Be Bunnings to Build an AI Assistant
When Bunnings launched 'Buddy', an AI shopping assistant, it looked like something only a national retailer could pull off. We built the same kind of thing for a small networking store in about a week, out of parts most businesses already have. Here is what an AI store assistant is actually made of — and why the language model is the easy part.
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The 4 GB Card You Already Own Can Reason Now
The dispatch experiment ended on a question: route the hard tail to a more capable model — but which one, if you do not want to reach for the cloud? VibeThinker-3B, a 3B reasoning specialist, runs entirely on the 4 GB laptop GPU you probably already own, solves competition math the workhorse cannot, and sips about 50 watts doing it.
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The Small Model's Real Job Is Dispatch, Not Work
We pushed a 4 GB laptop model past coding into reasoning, extraction and writing to find its limit — then measured a better use for it entirely: not doing the work, but routing it. A reasoning-tuned 1.7B model covers 90% of a general workload, and a perfect router is 74% cheaper at equal quality.
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What a Native Tool Call Actually Is
An LLM only ever outputs text — so how does an AI agent run a tool? The answer is the native tool call, a precise, trained, parseable protocol that most people hand-wave over. Here is exactly what it is, what happens on the wire, and why a small model that knows what to do still cannot make one.
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The 4 GB Experiment: What Actually Makes a Small Model a Good Coding Agent
We stopped theorizing and ran ~900 trials on a 4 GB laptop GPU to measure what makes a small local model a good coding agent. The result overturns intuition: the edit format and the editor model decide almost everything, the clever agentic scaffolding mostly hurts, and the simplest setup won.
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How a Coding Agent Actually Writes Code
The first four posts in this series took the telescope view of AI agents. This one takes the microscope: how a coding agent turns an instruction into a working, tested change — how it edits, compiles, runs, tests and debugs — and why Claude Code behaves like a native *nix programmer while others reach for a throwaway script.
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The Coding Agent Shootout and the Best Harness We Could Not Read
After reading the source of five open coding agents — OpenAI's Codex, Pi, OpenCode, Kimi Code and Gemini CLI — here is how they actually stack up, strengths and weaknesses, which one wins, and why the best harness of all belongs to a tool we deliberately left out of the code study: Claude Code.
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Skills and Memory: How AI Agents Actually Learn
Self-improving AI agents sound like marketing — until you read the code. This is how agents capture knowledge as skills and memory, why a human still has to write some of it down, and what self-improving actually means once you strip away the hype: bookkeeping, an LLM review pass, and a human gate.
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What an AI Agent Harness Actually Does
We read the source code of six AI agent harnesses — Pi, Hermes, OpenCode, Kimi Code, OpenAI's Codex CLI and Google's Gemini CLI. This is what the harness really does for the model, why almost every agent failure is a harness failure, and why the engineering lives there, not in the model.
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