Les Orchard <me@lmorchard.com>

Awhile back Hidde De Vries identified a pain point around LLM-usage in standards work which leads to something I call an asymmetry of thought. In a conversation where one person is a domain expert and one person is copy-pasting ChatGPT responses, it creates an imbalance of effort in the discussion. A second-hand burdening, like Brandolini’s Law, where debunking inaccuracies and subtly-wrongs takes more effort than creating the inaccuracies. We are, in effect, taxing experts to do quality assurance on the model’s responses. When that work is unpaid I believe this is immoral… or at least it’s a breach in social etiquette.

What do you get when you spend the last decade and a half exploiting the very same people you were supposed to empower?

In fact, it would require about 31 hectares of corn ethanol to produce the same amount of energy generated by one hectare of land covered in solar panels.

A friend once said that if you’ve ever tried to meditate and failed to quiet your jumbled mind, you should take a trip instead.

WebMCP is an open source JavaScript library that allows any website to integrate with the Model Context Protocol. It provides a small blue widget in the bottom right corner of your page that allows users to connect to and interact with your webpage via LLM or agent.

At some point, our Silicon Valley overlords forgot that in order for their vision of the future to be adopted, people had to want it. That’s why NFTs, the metaverse, and the Oculus and Vision Pro never really found their customer base. AI is, admittedly, more useful — it’s good for organizing large swaths of data, for instance. LLMs have had widespread consumer adoption, at least as long as they remain free. But there is only really one customer for LLMs that can justify the massive cash incineration process that was required to build them: the US government.

As we deploy ML more broadly, there will be new kinds of work. I think much of it will take place at the boundary between human and ML systems. Incanters could specialize in prompting models. Process and statistical engineers might control errors in the systems around ML outputs and in the models themselves. A surprising number of people are now employed as model trainers, feeding their human expertise to automated systems. Meat shields may be required to take accountability when ML systems fail, and haruspices could interpret model behavior.

mini cli search engine for your docs, knowledge bases, meeting notes, whatever. Tracking current sota approaches while being all local

Question, Research, Structure, Plan, Implement — an 8-phase workflow for Claude Code that breaks complex coding tasks into focused prompts with clear artifacts between each step.

The idea here is different. Instead of just retrieving from raw documents at query time, the LLM incrementally builds and maintains a persistent wiki — a structured, interlinked collection of markdown files that sits between you and the raw sources. When you add a new source, the LLM doesn't just index it for later retrieval. It reads it, extracts the key information, and integrates it into the existing wiki — updating entity pages, revising topic summaries, noting where new data contradicts old claims, strengthening or challenging the evolving synthesis. The knowledge is compiled once and then kept current, not re-derived on every query.

When you let an LLM author content into a knowledge base, and that content gets indexed alongside the original sources, you’ve just introduced unverifiable information into your source of truth. And not obviously wrong information — that would be easy to catch. I mean plausibly correct, well-structured, confident-sounding prose that happens to be a subtle interpolation of what the model thought the document said.