When evaluating the complete bun install improvements, it came out speed-wise to about the same as the existing git usage (due to networking being the big bottleneck time-wise despite more cases being slightly faster with ziggit over multiple benchmarks). Except, it's done in 100% zig and those internal improvements pile up as projects consist of more git dependencies. All in all, it seems like a sensible upstream contribution.
Edit And then I go their repository and read commits like this https://github.com/hdresearch/ziggit/commit/31adc1da1693e402... which confirms it wasn't even looked over by a human.
Then there's stuff like this: https://github.com/hdresearch/ziggit/blob/master/src/cmd_bra...
It's just one giant function. Sometimes big functions are necessary. This one is clearly AI generated and not very readable for a human. This is just from a quick glance.
That was so the commit authors don't all appear like blank accounts on GitHub
It's very easy to be faster than git's CLI if you don't have to do any of the things that git's CLI does!
If git were a rapidly evolving project then I'd think this'd be a stronger issue.
With git being more of an established protocol that projects can piggy-back off of from GitHub to jj, filling a library in a new language seems like something that contributes
Sure, if you have a complete test suite for a library or CLI tool, it is possible to prompt Claude Opus 4.6 such that it creates a 100% passing, "more performant", drop-in replacement. However, if the original package is in its training data, it's very likely to plagiarize the original source.
Also, who actually wants to use or maintain a large project that no one understands and that doesn't have a documented history of thoughtful architectural decisions and the context behind them? No matter how tightly you structure AI work, probabilistic LLM logorrhea cannot reliably adopt or make high-level decisions/principles, apply them, or update them as new data arrives. If you think otherwise, you're believing an illusion - truly.
A large software project's source code and documentation are the empirical ground-truth encoding of a ton of decisions made by many individuals and teams -- decisions that need to be remembered, understood, and reconsidered in light of new information. AI has no ability to consider these types of decisions and their accompanying context, whether they are past, present, or future -- and is not really able to coherently communicate them in a way that can be trusted to be accurate.
That's why I can't and won't trust fully AI-written software beyond small one-off-type tools until AI gains two fundamentally new capabilities:
(1) logical reasoning that can weigh tradeoffs and make accountable decisions in terms of ground-truth principles accurately applied to present circumstances, and
(2) ability to update those ground-truth principles coherently and accurately based on new, experiential information -- this is real "learning"
And this is a huge "if". Having 100% test coverage does not mean you've accounted for every possible edge or corner case. Additionally, there's no guarantee that every bugfix implemented adequate test coverage to ensure the bug doesn't get reintroduced. Finally, there are plenty of poorly written tests out there that increase the test coverage without actually testing anything.
This is why any sort of big rewrite carries some level of risk. Tests certainly help mitigate this risk, but you can never be 100% sure that your big rewrite didn't introduce new problems. This is why code reviews are important, especially if the code was AI generated.
People seem not to realize that AI companies can not only plagiarize someone's original source code, but any source code that people connected to it are feeding and uploading to it. The shell game is taking Tom's code (with a few changes) and feeding it to Bill (based on prompts given). Both Tom and Bill are paying fees to the AI company, yet don't realize their code (along with many others) can be spit back at them.
You, the humans, are doing a lot of the work, and many don't realize it. Because Tom is not realizing someone has or is working on something similar. The AI company is connecting Tom and Bill together, without either of them realizing it. If they type in the right prompt, the search then feeds back that info. It's not the only thing going on or only way things work, but it is part of it, that is often not publicly acknowledged.
In the Tom and Bill shell game example given, where they are being used for their code and to correct code that is sold to other customers, it's not a "now" thing either. Meaning Tom, Bill, and the other customers don't have to be exchanging code in real time, when that code is being uploaded, saved, and trained on by AI companies. Tom could have worked on some code a month ago, that was slurped up from Susan. Tom fixed many of the errors of Susan's code, which is now fed to Bill, when he inputs the correct prompts. Bill thinks the AI is the "genius", but is unknowingly benefiting from Bill's and Susan's work, review, and corrections. Potentially more devastating to Bill, is what he may mistakenly think was private or secret to only him, is fed to other customers for profit.
AI and their companies are also connecting people, in that indirect black box way, where those people may not realize they are connected, being fed, and are correcting each others code. Yeah, some may not care where the code comes from or how, but that they can use it for their personal purposes. Sure, that's not the only part of the story and LLMs are doing some interesting and amazing things, but there is another part of that story that is not being more widely acknowledged. In a similar way in which has angered so many artists and authors, where they feel aggrieved and taken advantage of; relative to many art, song, and book lawsuits.
This was the "validation" used for determining how much progress was made at a given point in time. Re training data concerns, this was done and shipped to be open source (under GPLv2) so there's no abuse of open source work here imo
Re the tradeoffs you highlight - these are absolutely true and fair. I don't expect or want anyone to just use ziggit because it's new. The places where there performance gains (ie internally with `bun install` or as a better WASM binary alternative) are places that I do have interest or use in myself
_However_, if I could interest you in one thing. ziggit when compiled into a release build on my arm-based Mac, showed 4-10x faster performance than git's CLI for the core workflows I use in my git development
But after the massive one-off rewrite, what are the chances that (a) humans will want to do any personal effort on reading it, documenting it, understanding it, etc., or that (b) future work by either agents or humans is going to be consistently high-quality?
Beyond a certain level of complexity, "high-quality work" is not just about where a codebase is right now, it's where it's going and how much its maintainers can be trusted to keep it moving in the right direction - a trust that only people with a name, reputation, observable values/commitments, and track record can earn.
But that approach would create significant instability. You can't write tests that will cover every possible edge case. That's why good thinking & coding, not good tests, is the foundation of good software.
> The git CLI test suite consists of 21,329 individual assertions for various git subcommands (that way we can be certain ziggit does suffice as a drop-in replacement for git).
<snip>
> While we only got through part of the overall test suite, that's still the equivalent of a month's worth of straight developer work (again, without sleep or eating factored in).
The way it gets organized is there are "scripts" which encompass different commands (status, diff, commit, etc) however each of these scripts themselves contain several hundred distinct assertions covering flags and arguments.
The test suite was my way of validating I not only had a feature implemented but also "valid" by git's standards
It doesn't matter if the parent model is GPT GOD mode mythos opus 100x Ultra. What matters is the performance of the quantized model.
I find that to be a much more remarkable claim. Git doesn't have a C library, and even if it did, In which world is literally shelling out faster than a C library call? I suppose libgit2 could be implemented poorly.
If we follow their link[1] we get some clarity. It's an markdown (ai prompt?) file which just states it. Apparently they've "microbenchmarked" creating new git repositories. I really wonder if creating new git repositories is really on their hot path, but whatever.
Where does that claim in that random markdown file come from then? Well apparently, 3 years ago when somebody was "restructuring docs"[2] and just made it up.
I guess there really is a class of "engineers" that the AI can replace.
[1]: https://github.com/oven-sh/bun/blob/3ed4186bc8db8357c670307f... [2]: https://github.com/oven-sh/bun/commit/011e157cac7698050370e2...
Your comment does not seem to be in good faith, implying that they've made up the performance difference. There's a comment with a benchmark here: https://github.com/oven-sh/bun/blob/4760d78b325b62ee62d6e47b...
referencing the commit where they removed the ability to link with libgit2 because it was slower.
Having built a service on top of libgit2, I can say that there are plenty of tricky aspects to using the library and I'm not at all surprised that bun found that they had to shell out to the CLI - most people who start building on libgit2 end up doing so.
I don't know what the bun team actually did or have details - but it seems completely plausible to me that they found the CLI faster for creating repositories.
I believe I have accurately represented what the article says. Had the article provided the comment you have just linked, I would have commented on that as well. I did not intend to imply that they manufactured the performance difference, merely that they don't know what they are talking about. The thought I have in my head is that they are incompetent, not that they are malicious.
I wholeheartedly agree that libgit2 is full of footguns, that's why it matters that it's not actually "git's own C library" but a separate project. I also agree that you usually end up shelling out to git, exactly because of those problems libgit2 has. If those problems aren't speed though, and I don't think they are, the blog post would have to cover how this reimplementation of libgit2 avoids those problems.
I'm not here to litigate if bun would be faster with libgit2. I am however here to make the argument that the blogpost does not make a convincing argument for why libgit2 isn't good enough.
As for what happened with Bun and libgit2, my best guess honestly is smth to do with zig-c interops but don't doubt there are optimizations everywhere to be done
The micro-benchmarks are for the internal git operations that bun rn delegates to CLI calls. Overall, network time (ie round trip to GitHub and back) is what balances the performance when evaluating `bun install` but there are still places where ziggit has better visible wins like on arm-based Macs https://github.com/hdresearch/ziggit/blob/master/BENCHMARKS....
I also noticed that their version of the "blame src/main.zig" command doesn't actually work (it shows all lines as not being committed). Sure, it's easy to optimize an algorithm if you just don't do the work. Git does indeed take longer, but at least it actually gives you a blame of the file.
_However_, for the use cases that most developers or agents are looking for, ziggit should have enough features covered. Happy to fix issues or bugs if that's not the case
What use cases are those? How did you determine that these are the use cases most developers/agents are looking for?
For me, git has a ton of features that I rarely use. But when I need them, I really need them. Any replacement that doesn't cover these edge cases is fundamentally incomplete and insufficient, even if it works fine 99% of the time.
And even mass-produced products benefit from thoughtful design by a human.
Why didn't the blog post explain anything about why the rewrite is faster? Or about Zig, or C, programming, at all?
99% of the time (such as in this article), it doesn't. What do you mean 'cloneBare + findCommit + checkout: ~10x win'? Does that mean running those commands back to back result in a 10x win over the original? Does that mean that there's a specific function that calls these 3 operations, and that's the improvement of the overall function? What's the baseline we're talking about, and is it relevant at all?
Those questions are partially answered on the much better benchmark page[1], but for some reason they're using the CLI instead of the gitlib for comparisons.
[1] https://github.com/hdresearch/ziggit/blob/5d3deb361f03d4aefe...
Under the hood, bun's calling these operations when doing a `bun install` and these are the places where integrating 100% gives the most boost. When more and more git deps are included in a project, these gains pile up.
However, the results appear more at 1x parity when accounting for network times (ie round trip to GitHub)
Cool article though!
They really stretch the limits of an honest title there.
despite it’s paucity of features, the changes i landed in it from my design notes actually have been so smooth in terms of comparative ux/ llm behavior that its my daily driver since ive stood it up.
Previously, since early december, ive had to run a patch script on every update of claude code to make it stop undermining me. I didnt need a hilarious code leak to find the problematic strings in the minified js ;)
I regard punkin-pi as a first stab at translating ideas ive had over the past 6 months for reliable llm harnesses. I hit some walls in terms of mono pi architecture for doing much more improvement with mono pi.
so Im working on the next gen of agent harnesses! stay tuned!
The post is also not about the speed increase, it's about how proud this team is of their agent orchestration scheme.
As I understand it, there is really no speed difference at all between Zig and C, just some cognitive overhead associated with doing things "right" in C. It's all machine code at bottom.
So why is this rewrite faster? Why did the authors choose Zig? How has the logic or memory management changed?
The authors give us absolutely no insight whatsoever into the the Zig code. No indication that they know anything about Zig, or systems programming, at all. I wish this was an exaggeration.
And really. With all this agentic power at your fingertips, why wouldn't they just contribute these improvements to git itself? I can think of at least one reason, that they don't want their changes to be rejected as unhelpful or low-quality.
Big if true, etc
https://news.ycombinator.com/item?id=47618895 to discuss the git implementation
yawn immediate skip
https://vers.sh/hdr_legacy/images/manager_vs_manager_of_mana...
