Knowledge changelog
What entered the system over time - new building blocks, tutorials and updates, in chronological order.
July 2026
- New
The workbench has its first coherent course: the harness series, nine chapters in which you build your own agent harness. From the LLM call through message history, system prompt, tools & loop, guardrails, memory, channels & triggers and context compaction to tool-call optimization, where a small model handles the tool calls while the big one thinks. Every chapter ships copyable build prompts, and you build against the real API instead of a mock-up. Findings from the joint trial run of the first four parts flowed back into the chapters - among them the case of the model that acknowledges without ever touching the tool, now part of the acceptance checklist.
- Updated
The LLM call gains two interactive surfaces. The Jeopardy-style quiz wall tests what stuck from finish_reason, max_tokens, stop words and model choice - four categories, four point values, an explanatory answer for every question, played solo, full screen, or hot seat with two to four players. In the deep dive, the tokenizer playground now offers a second mode: the BPE run from corpus to vocabulary, merge by merge.
- Updated
Sidecars: a new section on what comes back through the process boundary. Worked through on a real session in the session viewer - three curl calls dump 52,489 characters into the context, 43.5 % of the entire history, and the most expensive single message is 25,400 characters of three error pages. A sidecar returns the result instead of the raw material. Including the honest counter-check: saving context alone is not yet a reason for a sidecar.
- New
New building block in block II: tool calling in production. Tool calls work in the prototype and break in production - not because of the model but because of six translations in between. The problem and the chain arithmetic are free at the top; below it, seven chapters for Pro members: failure catalogue, constrained syntax, toolbox, repair, receipts - and finally FunctionGemma, the 270-million-parameter specialist that stands next to the large model rather than in its place. With five new interactive surfaces - and, at the top of the page, the German-language workshop volume on the subject, 90 pages as a PDF for Pro members.
- New
New building block: benchmarks. Every week a new model "beats" all the others. Benchmarks turn that noise into numbers - useful for rough sorting, dangerous when taken on faith. The block walks through the names you meet everywhere and shows what they measure, and what they do not.
- New
New building block: hardware. Why does everyone talk about graphics cards when the topic is LLMs? Because memory is the hard limit: VRAM decides which models you can pick at all, and what every answer costs. Worth reading even if you never buy a GPU - it explains the prices and the choices.
- New
New building block: inference engines. A model file is just a bag of numbers. Turning it into an API that answers your call is the engine's job - the record player for the record. It produces the answer token by token and keeps that fast with a KV cache and batching.
- New
What's inside a model download
New building block: what is inside a model download. "Running it locally" means, concretely: a folder with a few gigabytes in it and nothing that does anything on its own. The block walks through it file by file - and shows why the smallest one of all, the chat template, decides whether your model answers or stutters.
- New
New building block: the model landscape. There is no such thing as "the LLM" - there are hundreds of models, in every size, open and closed. The first fork is not "which model" but "where does it run": at the provider, or on your own hardware. The good news: your harness stays the same, only the number it dials changes.
- New
New building block: sidecars. What actually sits at the other end of a tool? Who parses the PDF, who transcribes the audio? In almost every serious setup, a sidecar: a service beside the agent that does exactly one thing and keeps nothing once it has answered. Why that is a container of its own and not a function inside the agent process.
- New
New building block: memory. An agent never remembers - it re-sends. The block shows how information gets into the context in the first place, what travels once per session and what travels with every request, and when that calls for a service of its own beside the agent. It is the first step out of the harness.
- Updated
New recap stage on the tools-and-loop building block: chat, wire and code side by side in full screen. The stage replays a complete tool round - the model requests a call, the harness executes, the result goes back - making visible that the loop lives in the harness, not in the model.
- Updated
New recap stage on the system-prompt building block: chat, wire and code side by side in full screen. It shows where the system prompt sits in the request, that it travels along on every turn - and how the same user sentence gets a different answer under a different system prompt.
- Updated
New recap stage on the message-history building block: chat, wire and code side by side in full screen. The stage shows how single calls become a conversation - which messages travel over the wire again on every turn, and what that means for context and cost.
- Updated
The LLM-call building block now has a full-screen stage: the harness recap shows chat, wire and code side by side and replays the first call step by step. Also new in the deep dive: the tokenizer playground - three tokenizers run entirely in the browser (our Qwen3 as byte-level BPE, cl100k_base, GPT-2), so the unit's three claims can be verified in seconds: German costs more tokens than English, every model splits differently, and "Strawberry" trips the tokenizer, not the model.
- Updated
The Guardrails building block no longer treats prompt injection as a warning but as a course: the defence ladder from level 0 to 6 as a table, every layer with the attack it breaks on. Plus the three findings from Lakera's analysis of 279,000 real attacks (layering works, domain restriction is the single strongest measure, defence in the system prompt costs usability), the classification as OWASP LLM01 - and this site's own comment agent as an example from our own house. Finished Arena challenges now get a debrief on top: which layer caught how much, and where the dead ends were.
- Updated
Get an OpenRouter key and make your first call
The introductory tutorial for your first LLM call now runs on OpenRouter instead of a direct Anthropic account: one key, hundreds of models, free variants — and no business verification. Now with screenshots for sign-up and key creation, both a curl and an SDK variant, a comparison of the response fields against the Anthropic dialect, and how to store the key in your own account.
- Updated
English