Articles
Talks and essays, written out in full.
Our talks, expanded into proper essays you can actually read. Honest, in-depth, and free, like everything else here.
A 5-part primer
How do LLMs work? Start the series →
Spec-driven development: write the plan before the code
At our 5 August session, we explored a simple shift for building with AI: decide what the software must do, write those decisions down, and only then ask the agent to build it.
Read the articleLoop engineering: from prompts to /goal
At our July 9 drop-in, we looked at the work hiding behind a good AI answer: the loop that tries, checks, adjusts, and knows when to stop. Then we put it to work on Oslo's live city-bike data.
Read the articleOur first vibe coding class at Spaces
Our vibe coding drop-in moved to Spaces in central Oslo this week. Here is the whole session for anyone who missed it: what vibe coding is, where AI is heading, how to set up your own tools, and two small apps we built from open Oslo data.
Read the articleWhat cheap AI does to the developer's job
Two old ideas, one from AI research and one from economics, explain why writing code got cheap and what that means for someone learning to build today.
Read the articleI spent $25,507 on AI coding agents in twelve weeks
A twelve-week receipt from real agentic engineering, and what the numbers actually mean for your own spend.
Read the articleHow an LLM is trained: the internet in a box
Pretraining is the first stage of building something like ChatGPT. By the end of this piece you will understand how raw internet text becomes a model that can continue any document, and why that model is not yet an assistant.
Read the articleInside the model: predicting the next token
What actually sits behind the chat box is one giant fixed math function. This piece explains what that function is, how it generates text one token at a time, and why that shape sets a hard limit on what it can do in a single step.
Read the articleFrom base model to assistant
A base model is an internet-document simulator. Post-training turns it into ChatGPT by teaching it to imitate human labelers who write ideal answers. Once you see the model as a simulation of those labelers, its behavior stops feeling magical.
Read the articleWhy LLMs make things up, and what fixes it
A language model always emits a plausible next token, even when it has no idea. Once you see why, you can tell when to trust it, when to hand it a tool, and when to check its work.
Read the articleTeaching models to think: RL and reasoning
After a model has read the internet and copied expert answers, one stage is left. Reinforcement learning lets the model practice, discover its own way to solve problems, and start to think before it answers.
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