Edit: I looked over some of the code.
It's not good. It's certainly not anywhere near SQLite's quality, performance, or codebase size. Many elements are the most basic thing that could possibly work, or else missing entirely. To name some examples:
- Absolutely no concurrency.
- The B-tree implementation has a line "// TODO: Free old overflow pages if any."
- When the pager adds a page to the free list, it does a linear search through the entire free list (which can get arbitrarily large) just to make sure the page isn't in the list already.
- "//! The current planner scope is intentionally small: - recognize single-table `WHERE` predicates that can use an index - choose between full table scan and index-driven lookup."
- The pager calls clone() on large buffers, which is needlessly inefficient, kind of a newbie Rust mistake.
However…
It does seem like a codebase that would basically work. At a large scale, it has the necessary components and the architecture isn't insane. I'm sure there are bugs, but I think the AI could iron out the bugs, given some more time spent working on testing. And at that point, I think it could be perfectly suitable as an embedded database for some application as long as you don't have complex needs.
In practice, there is little reason not to just reach for actual SQLite, which is much more sophisticated. But I can think of one possible reason: SQLite has been known to have memory safety vulnerabilities, whereas this codebase is written in Rust with no unsafe code. It might eat your data, but it won't corrupt memory.
That is impressive enough for now, I think.
I've lost every single shred of confidence I had in the comment's more optimistic claims the moment I read this.
If you read through SQLite's CVE history, you'll notice most of those are spurious at best.
Some more context here: https://sqlite.org/cves.html
> All historical vulnerabilities reported against SQLite require at least one of these preconditions:
> 1. ...
> 2. The attacker can submit a maliciously crafted database file to the application that the application will then open and query.
> Few real-world applications meet either of these preconditions, and hence few real-world applications are vulnerable, even if they use older and unpatched versions of SQLite.
This 2. precondition is literally one of the idiomatic usage of sqlite that they've suggested on their site: https://sqlite.org/appfileformat.html
Lucky that SQLite is also robust against random process death.
- if you're not passing SQLite's open test suite, you didn't build SQLite
- this is a "draw the rest of the owl" scenario; in order to transform this into something passing the suite, you'd need an expert in writing databases
These projects are misnamed. People didn't build counterstrike, a browser, a C compiler, or SQLite solely with coding agents. You can't use them for that purpose--like, you can't drop this in for maybe any use case of SQLite. They're simulacra (slopulacra?)--their true use is as a prop in a huge grift: tricking people (including, and most especially, the creators) into thinking this will be an economical way to build complex software products in the future.
I believe it's an ad. Everything about it is trying so hard to seem legit and it's the most pointless thing I have ever seen.
There are lot of embedded SQL libraries out there. I'm not particularly enamoured with some of the design choices SQLite made, for example the "flexible" approach they take to naming column types, so that isn't why I use it.
I use it for one reason: it is the most reliable SQL implementation I know of. I can safely assume if file corruption, or invariants I tried to keep aren't there, it isn't SQLite. By completely eliminating one branch of the failure tree, it saves me time.
That one reason is the one thing this implementation lacks - while keeping what I consider SQLite's warts.
It is true that the half-million lines of test code found in the public source tree are not the entirety of the SQLite test suite. There are other parts that are not open-source. But the part that is public is a big chunk of the total.
That isn't true, not by a long shot. Improvements happen because someone is inspired to do something differently.
How will that ever happen if we're obsessed with proving we can reimplement shit that's already great?
While I'm generally sympathetic to the idea that humans and LLM creativity is broadly similar (combining ideas absorbed elsewhere in new ways), when we ask for something that already exists it's basically just laundering open source code
In reality, LLMs can (currently) build worse versions of things that already exist: a worse database than SQL, a worse C compiler than GCC, a worse website than one done by a human. I'd really like to see some agent create a better version of something that already exists, or, at least, something relatively novel.
But it enables people who can't do these things at all to appear to be able to do these things and claim reputation and acclaim that they don't deserve for skills they don't have.
Parallelism over one code base is clearly not very useful.
I don't understand why going as fast as possible is the goal. We should be trying to be as correct as possible. The whole point is that these agents can run while we sleep. Convergence is non linear. You want every step to be in the right direction. Think of it more as a series of crystalline database transactions that must unroll in perfect order than a big pile of rocks that needs to be moved from a to b.
I wrote a rant about this a while back to try and encourage people to be more responsible: https://sibylline.dev/articles/2026-01-27-stop-orchestrating...
Agreed, a flat set of workers configured like this is probably not the best configuration.
Can you imagine what an all human team configured like this would produce?
That's the real unlock in my opinion. It's effectively an automated reverse engineering of how SQLite behaves, which is something agents are really good at.
I did a similar but smaller project a couple of weeks ago to build a Python library that could parse a SQLite SELECT query into an AST - same trick, I ran the SQLite C code as an oracle for how those ASTs should work: https://github.com/simonw/sqlite-ast
Question: you mention the OpenAI and Anthropic Pro plans, was the total cost of this project in the order of $40 ($20 for OpenAI and $20 for Anthropic)? What did you pay for Gemini?
Gemini is free, I don't even know if they have a paid plan?
* After a long vibe-coding session, I have to spend an inordinate amount of time cleaning up what Cursor generated. Any given page of code will be just fine on its own, but the overall design (unless I'm extremely specific in what I tell Cursor to do) will invariably be a mess of scattered control, grafted-on logic, and just overall poor design. This is despite me using Plan mode extensively, and instructing it to not create duplicate code, etc.
* I keep seeing metrics of 10s and 100s of thousands of LOC (sometimes even millions), without the authors ever recognizing that a gigantic LOC is probably indicative of terrible heisenbuggy code. I'd find it much more convincing if this post said it generated a 3K SQLite implementation, and not 19K.
Wondering if I'm just lagging in my prompting skills or what. To be clear, I'm very bullish on AI coding, but I do feel people are getting just a bit ahead of themselves in how they report success.
And for the most part I use either opus or sonnet, but for planning sometimes I switch to chatgpt since I think claude is too blunt and does not ask enough questions. I also have local setups with OLlama and have tried for personal projects some kimi models. The results are the same for all, but again claude models are slighly better.
What model? Cursor doesn't generate anything itself, and there's a huge difference between gpt5.3-codex and composer 1 for example.
cf. SV conventional wisdom: he who ships first wins the market
in fairness, there is real value in iteration speed. i'm not holding my breath on human comprehensible corporate code bases moving forward. a slew of critical foundational projects, mostly run by the big names, may still care about what used to be called "good engineering practices".
- the memory, thread safety, and build system of Rust
- the elegant syntax of OCaml and Haskell
- the expressive type system of Haskell and TypeScript
- the directness and simplicity of JavaScript
Think coding agents can help here?
How well does the resulting code perform? What are the trade-offs/limitations/benefits compared to SQLite? What problems does it solve?
Why did you use this process? this mixture of models? Why is this a good setup?
- The choice of two workers per model is purely pragmatic: I can't afford more. - I chose heterogeneous agents because it has not been done yet. There is no performance justification for this choice.
- Rust needs to mature a little more, stop changing so fast, and move further toward being old and boring. - Rust needs to demonstrate that it can be used to create general-purpose libraries that are callable from all other programming languages. - Rust needs to demonstrate that it can produce object code that works on obscure embedded devices, including devices that lack an operating system. - Rust needs to pick up the necessary tooling that enables one to do 100% branch coverage testing of the compiled binaries. Rust needs a mechanism to recover gracefully from OOM errors. - Rust needs to demonstrate that it can do the kinds of work that C does in SQLite without a significant speed penalty.
https://sqlite.org/whyc.html#why_isn_t_sqlite_coded_in_a_saf...
Which aims to match SQLite quality and provide new features (free encryption, multiple simultaneous writers, and bitflip resistance.)
If its sqlites suite then its great the models managed to get there, but one issue (without trying to be too pessimistic) is that the models had the test suite there to validate against. Sqlites devs famously spend more of their time making the tests than building the functionalities. If we can get AI that reliably defines the functionality of such programs by building the test suite over years of trial and error, then we'll have what people are saying
590x the application code
A small, highly experienced team steering Claude might be able to replicate the architecture and test suite reasonably quickly.
1-shotting something that looks this good means that with a few helping hands, small teams can likely accomplish decades of work in mere months.
Small teams of senior engineers can probably begin to replicate entire companies worth of product surface area.
It can even do that in a loss-less way, instead of burning a bunch of tokens to get a bad, barely working half-copy.
Don't get me wrong, I'm no AI hater, they are an impressive technology. But both AI-deniers and hypers need a reality check.
I provided a repo (mine) that already implemented double-double arithmetic, trigonometry, and logarithms/exponentials, with plenty of tests.
It produced something that looked this good. It had tests, it followed the style of the existing code base, etc. But it was full of shit and outright lies.
After I reviewed it to fix deficiencies, I don't think there was anything left of the original.
I had much more success the previous week using an AI to rubber duck the algorithms to implement trig.
I am incredibly sceptical that just adding more loops — and less critical thinking/review — to brute force through a solution, is a good idea.
Someone could have a swarm of agents build “wine for macos apps”.
And this is ultimately pointless, because it’s just a shitter SQLite. It’s nothing new. If you’re going to build something big like this, there needs to be a real business case
You could already slop out a replica of SQLite if you wanted. But you don’t, because of the effort it would take to test and maintain it.
I view SQLite as just an objective to attain and optimize for, but nothing more. I agree 100% that this is just a shittier SQLite.
lol
