In an agentic loop, the model can keep calling multiple tools for each specialized artifact (like how claude webapp renders HTML/SVG artifacts within a single turn). Models are already trained for this (tested this approach with qwen 3.5 27B and it was able to follow claude's lead from the previous turns).
There is nothing wrong with big money backing, often is necessary for long term bets, but rug pulling is a serious threat. VC funded open source has become a pattern/playbook.
The author doesn't explain (or is ignorant about) why this happens. These are special tokens that the model is trained on, and are part of its vocab. For example, here are the <think> and </think> tokens defined in the [Qwen3 tokenizer config](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507/bl...).
The model runtime recognizes these as special tokens. It can be configured using a chat template to replace these token with something else. This is how one provider is modifying the xml namespace, while llama.cpp and vllm would move the content between <think> and </think> tags to a separate field in the response JSON called `reasoning_content`.