Simon WillisonModify

2025-12-14 23:59:23

I recently came across JustHTML, a new Python library for parsing HTML released by Emil Stenström. It's a very interesting piece of software, both as a useful library and as a case study in sophisticated AI-assisted programming.

First impressions of JustHTML

I didn't initially know that JustHTML had been written with AI assistance at all. The README caught my eye due to some attractive characteristics:

• It's pure Python. I like libraries that are pure Python (no C extensions or similar) because it makes them easy to use in less conventional Python environments, including Pyodide.
• "Passes all 9,200+ tests in the official html5lib-tests suite (used by browser vendors)" - this instantly caught my attention! HTML5 is a big, complicated but meticulously written specification.
• 100% test coverage. That's not something you see every day.
• CSS selector queries as a feature. I built a Python library for this many years ago and I'm always interested in seeing new implementations of that pattern.
• html5lib has been inconsistently maintained over the last few years, leaving me interested in potential alternatives.
• It's only 3,000 lines of implementation code (and another ~11,000 of tests.)

I was out and about without a laptop so I decided to put JustHTML through its paces on my phone. I prompted Claude Code for web on my phone and had it build this Pyodide-powered HTML tool for trying it out:

This was enough for me to convince myself that the core functionality worked as advertised. It's a neat piece of code!

Turns out it was almost all built by LLMs

At this point I went looking for some more background information on the library and found Emil's blog entry about it: How I wrote JustHTML using coding agents:

Writing a full HTML5 parser is not a short one-shot problem. I have been working on this project for a couple of months on off-hours.

Tooling: I used plain VS Code with Github Copilot in Agent mode. I enabled automatic approval of all commands, and then added a blacklist of commands that I always wanted to approve manually. I wrote an agent instruction that told it to keep working, and don't stop to ask questions. Worked well!

Emil used several different models - an advantage of working in VS Code Agent mode rather than a provider-locked coding agent like Claude Code or Codex CLI. Claude Sonnet 3.7, Gemini 3 Pro and Claude Opus all get a mention.

Vibe engineering, not vibe coding

What's most interesting about Emil's 17 step account covering those several months of work is how much software engineering was involved, independent of typing out the actual code.

I wrote about vibe engineering a while ago as an alternative to vibe coding.

Vibe coding is when you have an LLM knock out code without any semblance of code review - great for prototypes and toy projects, definitely not an approach to use for serious libraries or production code.

I proposed "vibe engineering" as the grown up version of vibe coding, where expert programmers use coding agents in a professional and responsible way to produce high quality, reliable results.

You should absolutely read Emil's account in full. A few highlights:

1. He hooked in the 9,200 test html5lib-tests conformance suite almost from the start. There's no better way to construct a new HTML5 parser than using the test suite that the browsers themselves use.
2. He picked the core API design himself - a TagHandler base class with handle_start() etc. methods - and told the model to implement that.
3. He added a comparative benchmark to track performance compared to existing libraries like html5lib, then experimented with a Rust optimization based on those initial numbers.
4. He threw the original code away and started from scratch as a rough port of Servo's excellent html5ever Rust library.
5. He built a custom profiler and new benchmark and let Gemini 3 Pro loose on it, finally achieving micro-optimizations to beat the existing Pure Python libraries.
6. He used coverage to identify and remove unnecessary code.
7. He had his agent build a custom fuzzer to generate vast numbers of invalid HTML documents and harden the parser against them.

This represents a lot of sophisticated development practices, tapping into Emil's deep experience as a software engineer. As described, this feels to me more like a lead architect role than a hands-on coder.

It perfectly fits what I was thinking about when I described vibe engineering.

Setting the coding agent up with the html5lib-tests suite is also a great example of designing an agentic loop.

"The agent did the typing"

Emil concluded his article like this:

JustHTML is about 3,000 lines of Python with 8,500+ tests passing. I couldn't have written it this quickly without the agent.

But "quickly" doesn't mean "without thinking." I spent a lot of time reviewing code, making design decisions, and steering the agent in the right direction. The agent did the typing; I did the thinking.

That's probably the right division of labor.

I couldn't agree more. Coding agents replace the part of my job that involves typing the code into a computer. I find what's left to be a much more valuable use of my time.

Tags: html, python, ai, generative-ai, llms, ai-assisted-programming, vibe-coding, coding-agents

2025-12-14 13:06:19

Copywriters reveal how AI has decimated their industry

It's a tough read. Freelance copywriting does not look like a great place to be right now.

AI is really dehumanizing, and I am still working through issues of self-worth as a result of this experience. When you go from knowing you are valuable and valued, with all the hope in the world of a full career and the ability to provide other people with jobs... To being relegated to someone who edits AI drafts of copy at a steep discount because “most of the work is already done” ...

The big question for me is if a new AI-infested economy creates new jobs that are a great fit for people affected by this. I would hope that clear written communication skills are made even more valuable, but the people interviewed here don't appear to be finding that to be the case.

Tags: copywriting, careers, ai, ai-ethics

2025-12-13 22:01:31

If the part of programming you enjoy most is the physical act of writing code, then agents will feel beside the point. You’re already where you want to be, even just with some Copilot or Cursor-style intelligent code auto completion, which makes you faster while still leaving you fully in the driver’s seat about the code that gets written.

But if the part you care about is the decision-making around the code, agents feel like they clear space. They take care of the mechanical expression and leave you with judgment, tradeoffs, and intent. Because truly, for someone at my experience level, that is my core value offering anyway. When I spend time actually typing code these days with my own fingers, it feels like a waste of my time.

— Obie Fernandez, What happens when the coding becomes the least interesting part of the work

Tags: careers, ai-assisted-programming, generative-ai, ai, llms

2025-12-13 11:47:43

How to use a skill (progressive disclosure):

1. After deciding to use a skill, open its SKILL.md. Read only enough to follow the workflow.
2. If SKILL.md points to extra folders such as references/, load only the specific files needed for the request; don't bulk-load everything.
3. If scripts/ exist, prefer running or patching them instead of retyping large code blocks.
4. If assets/ or templates exist, reuse them instead of recreating from scratch.

Description as trigger: The YAML description in SKILL.md is the primary trigger signal; rely on it to decide applicability. If unsure, ask a brief clarification before proceeding.

— OpenAI Codex CLI, core/src/skills/render.rs, full prompt

Tags: skills, openai, ai, llms, codex-cli, prompt-engineering, rust, generative-ai

2025-12-13 07:29:51

One of the things that most excited me about Anthropic's new Skills mechanism back in October is how easy it looked for other platforms to implement. A skill is just a folder with a Markdown file and some optional extra resources and scripts, so any LLM tool with the ability to navigate and read from a filesystem should be capable of using them. It turns out OpenAI are doing exactly that, with skills support quietly showing up in both their Codex CLI tool and now also in ChatGPT itself.

Skills in ChatGPT

I learned about this from Elias Judin this morning. It turns out the Code Interpreter feature of ChatGPT now has a new /home/oai/skills folder which you can access simply by prompting:

Create a zip file of /home/oai/skills

I tried that myself and got back this zip file. Here's a UI for exploring its content (more about that tool).

So far they cover spreadsheets, docx and PDFs. Interestingly their chosen approach for PDFs and documents is to convert them to rendered per-page PNGs and then pass those through their vision-enabled GPT models, presumably to maintain information from layout and graphics that would be lost if they just ran text extraction.

Elias shared copies in a GitHub repo. They look very similar to Anthropic's implementation of the same kind of idea, currently published in their anthropics/skills repository.

I tried it out by prompting:

Create a PDF with a summary of the rimu tree situation right now and what it means for kakapo breeding season

Sure enough, GPT-5.2 Thinking started with:

Reading skill.md for PDF creation guidelines

Then:

Searching rimu mast and Kākāpō 2025 breeding status

It took just over eleven minutes to produce this PDF, which was long enough that I had Claude Code for web build me a custom PDF viewing tool while I waited.

Here's ChatGPT's PDF in that tool.

(I am very excited about Kākāpō breeding season this year.)

The reason it took so long is that it was fastidious about looking at and tweaking its own work. I appreciated that at one point it tried rendering the PDF and noticed that the macrons in kākāpō were not supported by the chosen font, so it switched to something else:

Skills in Codex CLI

Meanwhile, two weeks ago OpenAI's open source Codex CLI tool landed a PR titled feat: experimental support for skills.md. The most recent docs for that are in docs/skills.md.

The documentation suggests that any folder in ~/.codex/skills will be treated as a skill.

I dug around and found the code that generates the prompt that drives the skill system in codex-rs/core/src/skills/render.rs - here's a Gist with a more readable version of that prompt.

I used Claude Opus 4.5's skill authoring skill to create this skill for creating Datasette plugins, then installed it into my Codex CLI skills folder like this:

git clone https://github.com/datasette/skill \
  ~/.codex/skills/datasette-plugin

You have to run Codex with the --enable skills option. I ran this:

cd /tmp
mkdir datasette-cowsay
cd datasette-cowsay
codex --enable skills -m gpt-5.2

Then prompted:

list skills

And Codex replied:

- datasette-plugins — Writing Datasette plugins using Python + pluggy (file: /Users/simon/.codex/skills/datasette-plugin/SKILL.md)
- Discovery — How to find/identify available skills (no SKILL.md path provided in the list)

Then I said:

Write a Datasette plugin in this folder adding a /-/cowsay?text=hello page that displays a pre with cowsay from PyPI saying that text

It worked perfectly! Here's the plugin code it wrote and here's a copy of the full Codex CLI transcript, generated with my terminal-to-html tool.

You can try that out yourself if you have uvx installed like this:

uvx --with https://github.com/simonw/datasette-cowsay/archive/refs/heads/main.zip \
  datasette

Then visit:

http://127.0.0.1:8001/-/cowsay?text=This+is+pretty+fun

Skills are a keeper

When I first wrote about skills in October I said Claude Skills are awesome, maybe a bigger deal than MCP. The fact that it's just turned December and OpenAI have already leaned into them in a big way reinforces to me that I called that one correctly.

Skills are based on a very light specification, if you could even call it that, but I still think it would be good for these to be formally documented somewhere. This could be a good initiative for the new Agentic AI Foundation (previously) to take on.

Tags: pdf, ai, kakapo, openai, prompt-engineering, generative-ai, chatgpt, llms, ai-assisted-programming, anthropic, coding-agents, gpt-5, codex-cli, skills

2025-12-13 04:20:14

LLM 0.28

• New OpenAI models: gpt-5.1, gpt-5.1-chat-latest, gpt-5.2 and gpt-5.2-chat-latest. #1300, #1317
• When fetching URLs as fragments using llm -f URL, the request now includes a custom user-agent header: llm/VERSION (https://llm.datasette.io/). #1309
• Fixed a bug where fragments were not correctly registered with their source when using llm chat. Thanks, Giuseppe Rota. #1316
• Fixed some file descriptor leak warnings. Thanks, Eric Bloch. #1313
• Type annotations for the OpenAI Chat, AsyncChat and Completion execute() methods. Thanks, Arjan Mossel. #1315
• The project now uses uv and dependency groups for development. See the updated contributing documentation. #1318

That last bullet point about uv relates to the dependency groups pattern I wrote about in a recent TIL. I'm currently working through applying it to my other projects - the net result is that running the test suite is as simple as doing:

git clone https://github.com/simonw/llm
cd llm
uv run pytest

The new dev dependency group defined in pyproject.toml is automatically installed by uv run in a new virtual environment which means everything needed to run pytest is available without needing to add any extra commands.

Tags: projects, python, ai, annotated-release-notes, generative-ai, llms, llm, uv