A few times during the WordCamp US keynote, I asked for a show of hands and got mostly blank stares back, especially on the AI questions. That’s not a criticism — it’s a reading list. If the future is going to be built on this stuff, the WordPress community should be fluent in it. Here’s the homework.
How models are actually trained
When I asked who knew about the reinforcement learning models go through, Robert looked out and said “I see no yeses.” Base models are trained to predict the next word across huge amounts of text; the friendly, helpful behavior comes after, in post-training, where human/AI feedback reinforces some responses over others. If you use these tools every day, understanding that pipeline changes how you prompt them and what you expect from them.
Start with Hugging Face’s Illustrating RLHF, a visual explainer. Chip Huyen’s RLHF deep dive is the best longer read, and Anthropic’s Constitutional AI paper shows where post-training went next. If you only have time for one thing, watch Andrej Karpathy’s Deep Dive into LLMs — pretraining, fine-tuning, reinforcement learning, and why models behave the way they do, all in one sitting.
Uncensored models
Almost nobody had tried an OBLITERATED model. Since refusal behavior is added in post-training, researchers found it can also be removed — it turns out to be, roughly, a single direction inside the model that you can subtract out. Maxime Labonne’s Uncensor any LLM with abliteration explains the technique, and the paper behind it, Refusal in Language Models Is Mediated by a Single Direction, is short and genuinely fascinating. Whatever you think of the applications, it’s worth understanding that open weights mean the community can modify these models, the same way the GPL means you can modify WordPress.
The open-weight wave
Only a few hands went up for Qwen 3.8. The open-weight models coming out of Chinese labs — Qwen, DeepSeek, Kimi, GLM — are at or near the frontier at a fraction of the cost, and the smaller ones run on a laptop. I demoed one from this MacBook on stage. Token costs have dropped about 20x, and that curve isn’t slowing down.
The easiest way to feel this for yourself is to run one locally tonight: install Ollama or LM Studio, pull a small Qwen model, and talk to an intelligence that lives entirely on your own machine, no cloud required. For a community built on the four freedoms, that should feel familiar — and exciting.
Read one, watch one, run one. Then bring what you learned to your next meetup.
It is really important to me that WordPress community members ride this AI wave.
Errata: I said Automattic has 32 or 33 data centers; the actual number is 29, all co-located.
