llimllib's notes

Redis array type - short story of a long development

2026-05-04T14:37:12.222Z

For high quality system programming tasks you have to still be fully involved, but I ventured to a level of complexity that I would have otherwise skipped. AI provided the safety net for two things: certain massive tasks that are very tiring (like the 32 bit support that was added and tested later), and at the same time the virtual work force required to make sure there are no obvious bugs in complicated algorithms. To write the initial huge specification was the key to the successive work, as it was the key to review each single line of sparsearray.c and t_array.c and modifying everything was not a good fit.

Salvatore Sanfilippo

Salvatore gives an example of how he used AI in the development of a large major feature.

Using AI does not have to mean vibe coding - we can choose to use it to make better software

Contributor Poker and Zig's AI Ban

2026-04-30T12:38:01.827Z

Contributing to an open source project is an iterated game and the majority of the value that a contributor can bring to a project lies in the later iterations. In other words, you initially invest some energy (i.e. place a bet) to onboard a new contributor, and you hope that later on that relationship starts paying you back as the contributor becomes more trusted and prolific.

The reason I call it “contributor poker” is because, just like people say about the actual card game, “you play the person, not the cards”. In contributor poker, you bet on the contributor, not on the contents of their first PR.

Having an explicit understanding of this dynamic has netted the Zig project a huge amount of value over time. Building a compiler toolchain from the ground up is a huge scope that would have been impossible to cover without significant help from contributors.

Loris Cro

I am building a cloud

2026-04-23T11:14:19.193Z

I do not like the cloud today.

I want to. Computers are great, whether it is a BSD installed directly on a PC or a Linux VM. I can enjoy Windows, BeOS, Novell NetWare, I even installed OS/2 Warp back in the day and had a great time with it. Linux is particularly powerful today and a source of endless potential. And for all the pages of products, the cloud is just Linux VMs. Better, they are API driven Linux VMs. I should be in heaven.

But every cloud product I try is wrong. Some are better than others, but I am constantly constrained by the choices cloud vendors make in ways that make it hard to get computers to do the things I want them to do.

David Crawshaw

Clubs, really? That's what you call those?

2026-04-16T15:00:54.775Z

https://approximateknowledge.net/inkhaven/2026/04/14/playing-cards.html

A fun history of how we ended up with the suits common in playing card decks.

I tried to find some word to describe which playing card decks they're common in, but I failed because I have no idea how widespread the playing cards we have in America are

Use Apple’s App Store at your own risk

2026-04-16T14:54:51.317Z

The App Store, in other words, is rotten. And whatever Apple’s app-vetting procedure is, it’s not working. Perhaps that reflects the magnitude of the job. At last count there were approximately two million iOS apps on the store, which across its 18-year history equates very roughly to 9,000 per month. Factor in the acceleration over time, not to mention all the other apps that were vetted once but have since been removed because the developers stopping updating them, and that’s a lot of vetting, even for a company with major resources.

But is that an excuse? Not really. If running an app store is too much trouble, close it down. If comprehensive vetting is impractical, stop pretending the App Store is completely safe. (And definitely stop scaremongering about sideloading.) If you can’t make the App Store a truly reliable resource for good, safe, legitimate software, then give iPhone users the freedom to install from other places. Or just stop pretending the App Store monopoly is about anything other than revenue.

David Price

amen

Retrofitting JIT compilers into C interpreters

2026-04-15T12:41:08.38Z

https://tratt.net/laurie/blog/2026/retrofitting_jit_compilers_into_c_interpreters.html

Very cool work by Laurence Tratt, Edd Barrett, Lukas Diekmann, and Pavel Durov to create a system, yk, which accepts an interpreter for a language (lua or python for example) and emits a meta-tracing JIT compiler for it.

the blog post explains what a _meta-tracing JIT_ is much better than I could here

The upshot of this is that you can, with a reasonable amount of work, automate the construction of JIT compiler from an interpreter that doesn't natively support it.

Put another way, you can build a Pypy-like JIT interpreter from the regular C python interpreter, automatically.

The article does a great job of walking you through what all this means and how it does it. Very cool work!

No one can force me to have a secure website!!!

2026-04-14T00:23:56.928Z

In which the always-worth-watching author tom7 creates the least-secure HTTPS server they can manage

Previously: BoVeX

Earth

2026-04-07T14:33:21.659Z

The shame I feel at my own country's behavior makes me wish I were out there with the astronauts

via Robert Simmon

Eight years of wanting, three months of building with AI

2026-04-06T12:59:41.948Z

https://lalitm.com/post/building-syntaqlite-ai/

The author shares their experience of building syntaqlite (github) an impressive set of SQLite tools.

Through most of January, I iterated, acting as semi-technical manager and delegating almost all the design and all the implementation to Claude. Functionally, I ended up in a reasonable place: a parser in C extracted from SQLite sources using a bunch of Python scripts, a formatter built on top, support for both the SQLite language and the PerfettoSQL extensions, all exposed in a web playground.

But when I reviewed the codebase in detail in late January, the downside was obvious: the codebase was complete spaghetti. I didn’t understand large parts of the Python source extraction pipeline, functions were scattered in random files without a clear shape, and a few files had grown to several thousand lines. It was extremely fragile; it solved the immediate problem but it was never going to cope with my larger vision, never mind integrating it into the Perfetto tools. The saving grace was that it had proved the approach was viable and generated more than 500 tests, many of which I felt I could reuse.

So they threw away everything they'd gotten so far and started fresh.

For complicated technical projects, you have to keep close control over the output of the LLM or it will churn out a mess.

More importantly, I completely changed my role in the project. I took ownership of all decisions and used it more as “autocomplete on steroids” inside a much tighter process: opinionated design upfront, reviewing every change thoroughly, fixing problems eagerly as I spotted them, and investing in scaffolding (like linting, validation, and non-trivial testing) to check AI output automatically.

Worth reading in full. Excellent technical report on building something complex with LLM tools.

I really admire the author's dedication to their journal, and clear-eyed summary of the process.

Why do we do Astrophysics?

2026-04-06T12:59:38.794Z

Nothing we have has the track record that the traditional journals do for long-term dissemination and preservation of knowledge; we don’t know what computing hardware platforms we will have in 15 years, let alone 200, but I can pretty-much guarantee that—if human civilization is still in existence—we will still be able to read Vera Rubin’s papers about the dark matter

David Hogg

On the benefits: Astrophysics-by-LLM is almost as good as human astrophysics in terms of feeding humanity’s love of the subject; it delivers benefits to physics (provided that it is asked to do so); it can be used for military development; it can contribute to remote sensing; it creates new knowledge (provided that the projects it creates are indeed novel). That is, astrophysics-by-LLM delivers many of the benefits that we have identified and associated with the practice of astrophysics.

So is everything fine? No everything is not fine... the practice of astrophysics cannot be learned from reading about astrophysics, and that astrophysics is not about obtaining the answers, but rather the work we do, ourselves, to find those answers. When we offload that work to LLMs, we are no longer doing astrophysics, we are no longer becoming astrophysicists, and, eventually, we no longer are astrophysicists. The let-them-cook policy, in the end, leads to the death of astrophysics, the end of astrophysics at universities, and the end of astrophysics education. Astrophysics would no longer be by humans, and then it would no longer be for humans.

Worth reading in full. The most thoughtful decronstruction of the value of LLMs in a profession I've read yet.

apfel

2026-04-06T12:59:38.794Z

https://apfel.franzai.com/#install
https://github.com/Arthur-Ficial/apfel

A CLI tool wrapping Apple Intelligence. Has a small context window (4096 tokens) but runs completely on-device and is fast.

There's some neat demo ideas in the demos folder

The Cult of Vibe-coding is Insane

2026-04-06T12:59:38.793Z

I’ll start a conversation by saying “Let’s audit this codebase for unreachable code,” or “This function makes my eyes bleed,” and we’ll have a conversation about it until something actionable comes up. Then I explain what I think should be done and we’ll keep discussing it until I stop having more thoughts to give and the machine stops saying stupid things which need correcting. Then I tell it to make a plan and hit build. This is my life. The AI is very bad at spontaneously noticing, “I’ve got a lot of spaghetti code here, I should clean it up.” But if you tell it this has spaghetti code and give it some guidance (or sometimes even without guidance) it can do a good job of cleaning up the mess.

You don’t have to have poor quality software just because you’re using AI for coding.

Bram Cohen

I don't think the headline accurately reflects the more measured take in the article, but I work with LLMs similarly to how it sounds like he does.

Lisette

2026-04-06T12:59:38.791Z

https://lisette.run/
https://github.com/ivov/lisette

A little language inspired by Rust that compiles to Go

very neat idea!

Contourist

2026-04-06T12:59:38.786Z

https://elliej.dev/contourist/
https://github.com/null-jones/contourist

A very neat site that lets you select an area, download terrain data for it, and generates a Joy Division plot for it.

Here's the White Mountains:

via bsky

A tail-call interpreter in (nightly) Rust

2026-04-06T12:59:38.786Z

https://www.mattkeeter.com/blog/2026-04-05-tailcall/

Matt Keeter continues his quest to implement performant UXN CPUs.

Well-explained and thorough article about using the become operator in nightly rust (TIL) to create a tail-call interpreter, and compare it against his assembly backends on a few architectures.

He also points to this Massey Meta Machine writeup, where he learned about tail-call interpreters. See Beating the compiler for previous discussion of tail-call interpreters in discussion of a previous post by the same author.