Simon Willison's Weblog: donald-knuth

Quoting Donald Knuth

2026-03-03T23:59:04+00:00

Shock! Shock! I learned yesterday that an open problem I'd been working on for several weeks had just been solved by Claude Opus 4.6 - Anthropic's hybrid reasoning model that had been released three weeks earlier! It seems that I'll have to revise my opinions about "generative AI" one of these days. What a joy it is to learn not only that my conjecture has a nice solution but also to celebrate this dramatic advance in automatic deduction and creative problem solving.

— Donald Knuth, Claude's Cycles

Tags: ai, generative-ai, llms, anthropic, claude, llm-reasoning, november-2025-inflection, donald-knuth

[red-knot] type inference/checking test framework

2024-10-16T20:43:55+00:00

[red-knot] type inference/checking test framework

Ruff maintainer Carl Meyer recently landed an interesting new design for a testing framework. It's based on Markdown, and could be described as a form of "literate testing" - the testing equivalent of Donald Knuth's literate programming.

A markdown test file is a suite of tests, each test can contain one or more Python files, with optionally specified path/name. The test writes all files to an in-memory file system, runs red-knot, and matches the resulting diagnostics against Type: and Error: assertions embedded in the Python source as comments.

Test suites are Markdown documents with embedded fenced blocks that look like this:

```py
reveal_type(1.0) # revealed: float
```

Tests can optionally include a path= specifier, which can provide neater messages when reporting test failures:

```py path=branches_unify_to_non_union_type.py
def could_raise_returns_str() -> str:
    return 'foo'
...
```

A larger example test suite can be browsed in the red_knot_python_semantic/resources/mdtest directory.

This document on control flow for exception handlers (from this PR) is the best example I've found of detailed prose documentation to accompany the tests.

The system is implemented in Rust, but it's easy to imagine an alternative version of this idea written in Python as a pytest plugin. This feels like an evolution of the old Python doctest idea, except that tests are embedded directly in Markdown rather than being embedded in Python code docstrings.

... and it looks like such plugins exist already. Here are two that I've found so far:

pytest-markdown-docs by Elias Freider and Modal Labs.
sphinx.ext.doctest is a core Sphinx extension for running test snippets in documentation.
pytest-doctestplus from the Scientific Python community, first released in 2011.

I tried pytest-markdown-docs by creating a doc.md file like this:

# Hello test doc

```py
assert 1 + 2 == 3
```

But this fails:

```py
assert 1 + 2 == 4
```

And then running it with uvx like this:

uvx --with pytest-markdown-docs pytest --markdown-docs

I got one pass and one fail:

_______ docstring for /private/tmp/doc.md __________
Error in code block:
```
10   assert 1 + 2 == 4
11   
```
Traceback (most recent call last):
  File "/private/tmp/tt/doc.md", line 10, in <module>
    assert 1 + 2 == 4
AssertionError

============= short test summary info ==============
FAILED doc.md::/private/tmp/doc.md
=========== 1 failed, 1 passed in 0.02s ============

I also just learned that the venerable Python doctest standard library module has the ability to run tests in documentation files too, with doctest.testfile("example.txt"): "The file content is treated as if it were a single giant docstring; the file doesn’t need to contain a Python program!"

Via Charlie Marsh

Tags: python, testing, markdown, rust, pytest, ruff, uv, astral, donald-knuth

Quoting Donald Knuth

2023-05-20T16:51:39+00:00

I find it fascinating that novelists galore have written for decades about scenarios that might occur after a "singularity" in which superintelligent machines exist. But as far as I know, not a single novelist has realized that such a singularity would almost surely be preceded by a world in which machines are 0.01% intelligent (say), and in which millions of real people would be able to interact with them freely at essentially no cost.

I myself shall certainly continue to leave such research to others, and to devote my time to developing concepts that are authentic and trustworthy. And I hope you do the same.

— Donald Knuth

Tags: ai, generative-ai, llms, donald-knuth

Donald E. Knuth: Musings and more

2004-11-11T17:55:25+00:00

Donald E. Knuth: Musings and more

Downloadable videos of Knuth lectures.

Via Knuth: Recent News

Tags: donald-knuth