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Bubble sort is pretty simple in most programming languages ... what about on a Turing Machine? I used all three of Claude 4.6, GLM 5, and GPT 5.2 to get a result, so this exercise was not quite trivial, at least at this time. The resulting machine, bubble_sort_unary.yaml, will take this input:

111011011111110101111101111

and give this output:

101101110111101111101111111

I.e., it's sorting the array [3,2,7,1,5,4]. The machine has 31 states and requires 1424 steps before it comes to a halt. It also introduces two extra symbols onto the tape, 'A' and 'B'. (You could argue that 0 is also an extra symbol because turinmachine.io uses blank, ' ', as well).

When I started writing the code the LLM (Claude) balked at using unary numbers and so we implemented bubble_sort.yaml which uses the tape symbols '1', '2', '3', '4', '5', '6', '7'. This machine has fewer states, 25, and requires only 63 steps to perform the sort. So it's easier to watch it work, though it's not as generalized as the other TM.

Some comments about how the 31 states of bubbles_sort_unary.yaml operate:

| Group | Count | Purpose | |---|---|---| | `seek_delim_{clean,dirty}` | 2 | Pass entry: scan right to the next `0` delimiter between adjacent numbers. | | `cmpR_`, `cmpL_`, `cmpL_ret_`, `cmpL_fwd_` | 8 | Comparison: alternately mark units in the right (`B`) and left (`A`) numbers to compare their sizes. | | `chk_excess_`, `scan_excess_`, `mark_all_X_` | 6 | Excess check: right number exhausted — see if unmarked `1`s remain on the left (meaning L > R, swap needed). | | `swap_` | 7 | Swap: bubble each `X`-marked excess unit rightward across the `0` delimiter. | | `restore_*` | 6 | Restore: convert `A`, `B`, `X` marks back to `1`s, then advance to the next pair. | | `rewind` / `done` | 2 | Rewind to start after a dirty pass, or halt. |

(The above is in the README.md if it doesn't render on HN.)

I'm curious if anyone can suggest refinements or further ideas. And please send pull requests if you're so inclined. My development path: I started by writing a pretty simple INITIAL_IDEAS.md, which got updated somewhat, then the LLM created a SPECIFICATION.md. For the bubble_sort_unary.yaml TM I had to get the LLMs to build a SPEC_UNARY.md because too much context was confusing them. I made 21 commits throughout the project and worked for about 6 hours (I was able to multi-task, so it wasn't 6 hours of hard effort). I spent about $14 on tokens via Zed and asked some questions via t3.chat ($8/month plan).

A final question: What open source license is good for these types of mini-projects? I took the path of least resistance and used MIT, but I observe that turingmachine.io uses BSD 3-Clause. I've heard of "MIT with Commons Clause;" what's the landscape surrounding these kind of license questions nowadays?

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For nearly a decade, I've wanted a SQL formatter that followed Simon Holywell's style guide (https://sqlstyle.guide). If you're unfamiliar with it, one of its key (and most controversial) features is “river alignment” of keywords. I couldn’t find an existing formatter that implemented it, so I made Holywell.

Try it in the browser: https://holywell.sh Repo: https://github.com/vinsidious/holywell

The site has a bunch of scrollable examples so you can quickly see what the formatted SQL looks like.

Dialect support is pretty basic right now (I’m mostly a Postgres user), but I’d love requests / failing examples for other dialects. Also, PRs are very welcome.

Disclaimer: not endorsed by Simon Holywell. I tried to be faithful to the guide (and where the guide is ambiguous, I had to interpret). Also: I’m not claiming this style is “best” — just that it’s the one I’ve wanted for a long time.

Please share your thoughts and let me know where it falls short!

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Hey HN, I built issy, a lightweight issue tracker that stores issues as markdown files in a `.issues/` directory in your repo.

The idea is simple: issues should live with your code. They show up in diffs, travel with branches, and don't require accounts, databases, or external services. They're just markdown files with YAML frontmatter.

It's built for AI coding assistants. You can tell Cursor/Claude/Codex (or any AI agent) things like "create a bug for the login redirect issue, high priority" and it handles the rest — creating, searching, updating, and closing issues through natural language. There's a Cursor skill you can install with `npx skills add miketromba/issy`.

It also comes with a CLI for filtering/searching and a local web UI at localhost:1554 for when you want a visual overview.

Key points: - Zero infrastructure — no DB, no accounts, no SaaS - Issues are git-native — visible in PRs, blame-able, branch-able - Fuzzy search with typo tolerance - Monorepo-aware (walks up the directory tree to find .issues/) - Works offline

Blog Post: https://mike.gg/issy

GitHub: https://github.com/miketromba/issy

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Do you live by the quote “What I cannot create, I do not understand” by Richard Feynman?

I got inspired by it and started building a small LLM in order to understand how it works and after learning that it's a deep neural network I started building a neural network in order to understand that.

So I wondered what others think of this approach.

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Article URL: https://techcrunch.com/2026/02/12/for-1-million-you-can-pay-bryan-johnson-or-bryanai-to-teach-you-how-to-live-longer/

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Article URL: https://fair-weather.query-farm.services

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