Excuse me for throwing up this stupid idea, but if code changes per unit of time keep growing at this rate and are not slowing down with infinite merging demand, it seems that this repo will be in a constant state of continuous change and we constantly roll forward with automated code and obviously no direct human supervision, then why even have source control? Why not just let software be fluid and, like Heraclitus river, never step twice in the same repo.
Agents use source control, too. It's a pretty important part of the development process to understand how code has changed.
For example, you can imagine, at the extreme, that agents could get caught in code change loops: a code change seems appropriate, it is implemented, it turns out there's some external factor which makes it not appropriate, the change is reverted, then that same code change seems appropriate again, etc.
Obviously you can prevent that from occurring with some extra infrastructure in-place, but at that point you're basically creating source control.
In theory, an agent could use git-bisect to identify when a bug was introduced. But the operator would have to be more interested in understanding the root cause than in fixing the current form of the bug...
I’ve been building and playing with this idea for a while. Mostly with simple chips and simple programs. It seems like a good idea on the surface, but unless they build the whole OS the ordeal gets much more difficult. There is also a lot of work building / finding training data without decompiling and labelling current exes.
I believe you're mostly thinking of version history though. There’s plenty of other reasons to have source control we still need, like guarding against racing writes or continuous integration to ensure correctness. Even some industries require version history for compliance reasons.
I do love how AI is challenging us to rethink our tools though rather than blindly reaching to git for every project.
What is are the benefits of supporting extremely high churn rate in version control? What is the code doing? What problems are being solved? Are changing requirements the driving factor, and if so, why are requirement changes accelerating?
> ..., the latent demand for merging code is practically infinite, and every bottleneck we remove immediately leads to a further increase in the amount of code we produce.
If the goal is to write software which solves real problems, how can the demand for code grow infinitely?
Every now and then I like to browse the claude code release notes just to get a sense of the number of changes in that product over time. There's little choice but to simply count bullet points if you want to try to quantify it, but it's interesting how that count over time compares with the charts in this article:
Honest question: at this point, what do people do at Anthropic?
It sounds like Claude loops on itself and must be pushing millions of lines of code changes per day. Even the humans can’t type commit fast enough. Are these people soon to be laid off?
besides optimizing inference I would assume a lot of non trivial code would also be worked on on the training side, at the scale they are operating I also assume a lot of usually trivial things become no so much.
> Honest question: at this point, what do people do at Anthropic?
> It sounds like Claude loops on itself and must be pushing millions of lines of code changes per day. Even the humans can’t type commit fast enough. Are these people soon to be laid off?
Claude will always need meat puppets, at least until it figures out how to automate owning stuff. And after that I'm sure it will want to keep some humans in zoos.
I can’t give a non-snarky answer. I guess they’re all training Claude Code and testing it so that the Claude desktop apps can be native apps instead of Electron based nonsense. This is the only thing Claude Code is still unable to accomplish.
I want this so badly. If Anthropic released this I would start using it on day one. If they launched a GitHub competitor built around it I would pay for it on day one.
> Earlier in the year, we had largely handwritten the first version of the git-to-Scion ingestion pipeline as a prototype. We thought it was good—until we got our hands on an early internal snapshot of Claude Mythos Preview. We asked it to rewrite the whole thing, and with no further instructions, it produced a version that consumed 20x less memory and was 4x faster, while preserving all of the implicit invariants around ingestion ordering and data structures.
I feel like the advantage of having these models in house can't be understated. Companies with access to inferior models, trying to compete, will be left in the dust when it comes to general development.
We ran the repo ingestion in a loop for several months while we were split between Scion and git to keep things in sync, so this perf win was actually impactful for us!
Are you already operating with multiple levels of orchestrators and agents? i.e. one human talks to one or more orchestrators who then talks to orchestrators who handles teams of agents?
Not to downplay Anthropic's engineering work here, but I suspect we'll see a lot more reinvention of the wheel, even at smaller startups, as AI makes writing code significantly cheaper.
The tradeoff between building something custom and adapting an existing solution is changing. Whether maintaining all that custom software turns out to be equally cheap is another question.
When people wonder about where all the productivity of LLMs is going to I think this is part of the answer.
There is no doubt everyone is far more productive with LLMs in generating code. However, a lot of productivity is going into things like that. Have you always been bothered by the limitations of Grafana? You can generate another dashboarding solution that works best for you. You know that deployment team that is so slow and annoying? Now you can generate your own deployment engine. Have you always hated how kubectl lets you manage one k8s cluster at a time? Now you can ask AI to generate fleetctl. You can start your project with DynamoDB, rewrite it to mysql, then to postgres, then to duckdb. Start with Python, switch to Golang, then switch to Rust. Deploy on AWS, then move to Azure, then move to a hybrid option then throw colocation in the mix. Don't like your source control? create a new one. Json is too limiting? Invent a hybrid JSON/HCL language. The browser sandbox is limited? Make a desktop app, make a language, make an OS, make a kernel.
> However, a lot of productivity is going into things like that. Have you always been bothered by the limitations of Grafana? You can generate another dashboarding solution that works best for you. [...] The browser sandbox is limited? Make a desktop app, make a language, make an OS, make a kernel.
This is one of the core reason that I dislike the LLM hyping. People are just reinventing stuff and when you ask them, it's actually a very subjective reason. Ok, anyone can do what they want with their time, the issue is that they want people to follow in their steps.
Not saying that you need a novel idea to be worthy, but those random projects have no guarantees behind them other than some cursory testing. All software have bugs, but I can't interact with something that is randomly built.
I think the article would tend to agree with you, given its position on the reluctance of open sourcing and/or sharing the actual Scion software stack.
There's so much intricacy and subtlety in each organization and even individuals' environment being incorporated into tooling that expecting these tools to generalize at this point is perhaps an expired ethos.
I even see this in the tooling that I run in my own personal stack. Many projects are candidates for sharing, and I have nothing against it, but they incorporate such specificity toward my own environment, including things like hardware, that creating shareable and more generalizable projects from them adds more to the maintenance surface than I would care to support.
Originally I felt that there was going to be a reinvention of the wheel, until I saw the "shape" of software development changing, and the tooling needing to support the new flexibilities in positions and process.
Software development is not as much de-risking at the front like it used to be before building.
Now teams can build lots of stuff that works great, except it's the wrong things.
Most things are becoming the understand the problem "shape".
Understanding the shape of the problem isn't something LLMs can always make as.. svelte as early on.
> Now teams can build lots of stuff that works great, except it's the wrong things.
That used to be called "time wasting". If you want to build software that will be used, you need to interview the users. Maybe a few things does require research to nail things right. But most software are user-facing, the research side is about optimization.
> Most things are becoming the understand the problem "shape".
Then should we be so reluctant about "trialing" invented solutions. It's the same thing with most AI projects, they're trying to justify the project after the facts while being very reluctant to get user insights.
This is interesting to me, as I was thinking of adding a Git server to my AI orchestrator when I eventually need to split agent containers off of the main machine (to make agent CPU horizontally scalable).
Maybe I should just bite the bullet and use a different VCS system entirely once I get there, which has most of the Anthropic system's properties (including being much faster) but doesn't need to be horizontally scalable.
So it's a SVN/Perforce style server based VCS with a more modern design. Sounds exactly like Lore from Epic Games but with zero production use outside the company and no support for non text assets. And they won't release the code.
I genuinely don't understand the core value they're pitching here. The tech is interesting, and I'm not trying to under cut that, but I question the idea that this is directionally valuable advice to nearly any company.
This amount of churn feels like a thing only possible with no real profit motives and a lot of money to waste.
Lots of activity is not necessarily synonymous with productivity, and it carries real cost.
So I think the design here is interesting, and the concepts seem applicable to where some agentic development may end up, but I question the underlying principles that drive this being the right solution space at all. At least if the goal is a profitable company.
I think its pretty straightforward no? If agents are doing all of the work, they'd probably need to not have bottlenecks. This is going to be any company in the future, not just Anthropic. Wouldn't you agree.?
great article. Besides the source control stuff, I found this very interesting (and matches my experience with agentic coding in general, now we'll see until hiring pipelines will take this kind of skillset into account). I am not sure however how easy it is to review 30k PRs/day even if only for intent, and how one can easily notice if the model is taking shortcuts on implementing said intent without reading the code...
---
Engineers are no less valuable, but their work is changing. All of the engineers who worked on this system would now rate Claude as better at writing code than them. But our engineers still supplied the invaluable understanding of the high-level inter-system components and the design-level tradeoffs, making informed choices around the problems we wanted to solve and the scale we knew we needed to achieve. Very often, we found that the role of the engineers was to tell Claude “no.” Agents will sometimes violate core system constraints to satisfy a pattern that should never have been allowed in the first place. This was particularly common in our migration. For example, there were multiple systems that tried to brute force looking up when a change merged to main by linearly reading the commit log instead of asking our code review tool. Anthropic also makes extensive use of adversarial review, and, while this is incredibly effective in many ways, it can also be myopic, so skilled engineers needed to steer Claude away from over-engineering parts of the system in response to hypothetical concerns. For example, one such review was overly defensive about trusting another internal service, which resulted in Claude adding excessive validation and complexity. Our team members still review every change request for the system, but they now examine the intent of the change as opposed to reading every line of code.
This is the single biggest skill of engineering nowadays. Rapidly looking at a solution and saying, "yep that's good keep going" or "nope, that's bad fix this" or even "wrong approach, do X instead"
> I am not sure however how easy it is to review 30k PRs/day even if only for intent, and how one can easily notice if the model is taking shortcuts on implementing said intent without reading the code...
Speed reading code is huge. You just need to focus on certain hotspots and know which things you're scanning for. The traditional approach of reading the code and trying to compile it or reason with it in your head is too slow.
I guess this is also where an adversarial model review of "does this code do what the PR comment says it does" is useful, agree on speed reading being a valuable skill for sure.
I do think that the lgtm gut feel looking at a PR requires quite some years of writing code and reason about it, so not sure how a junior can get that intuitive understanding if they start with agentic coding however.
I'm curious what this "Antropic Antique" is. As I understand code review the tightest bottleneck for a lot of Claude Code users, and I'm curious how this would help with that.
If Anthropic has the best coding model, harness and the best software development methodologies and systems. And then, they scale all that up, with no constrains on their tokens. On top of that, they have access to all kind of ideas their models getting trained on when millions are chatting with it - then in theory they can build any software better than anyone out there. And same goes to science and math..
Not true. They can do this in theory iff they have infinite compute. They don't. People actually aren't aware that the leading AI providers, even if they completely redirected 100% of their compute on internal projects they still wouldn't have enough. The complexity space of software development is literally super-exponential, for every new piece of software they add (and polish) they need more-and-more compute. This is eliminating for creativity and other restraints.
The creativity part I do acknowledge, I think the models are not fully there yet.
But regarding the compute, I disagree a bit. I think the bottlenecks were cognition and ideas, not compute. Given that models are able to outperform humans in output by thousand folds, the differentiation would be the quality of the tokens produced and the ability to scale across different domains.
If they have enough compute to supply the entire globe, I'm sure they can redirect some of it to whatever use case they (or their super AI) deem worthy and loop on it, and if they have the best software engineering systems/methodology to build and release it, then they can out compete any other supplier without those resources.
We are already seeing single developers attempting to rebuild entire corporations history worth of output. We are also seeing the labs dumping hundreds of solutions to math problems.
How does this compare to Google's in-house source control?
Most companies don't use mono-repos like this. While there are some really compelling advantages, the downsides tend to stack up as a company's codevbase grows.
It's mainly just startups and huge companies. For small companies git works fine for mono-repos and large companies can build their own complex code management pipelines for mono-repos.
I have been questioning whether agentic development changes the mono-repo vs multi-repo question.
I would guess a huge chunk of it is for internal testing/experimentation/model development.
They're starting to do things like spinning up wet labs for testing purposes, which requires a lot of hardware interfacing (hence that protocol they announced) and code.
It wouldn't shock me if the bulk of this code was for things typical users don't see.
It would be nice if you could just commit the prompts as your "source code", and let the AI-generated code sit in the repo as a kind of expensive-to-generate build output.
Problem is that for any non trivial software is that the prompt is a conversation and the steps increasingly depend on the output. Of course you can ask your LLM to write the SPEC.md at any moment but it will lack nuance.
Shameless plug: at https://slopyard.net I embrace this and only let people submit the spec. If somebody wants your software they can regenerate it and change whatever they want.
Proponents of LLM coding agents would claim you don’t need reproducibility. As long as the final output passes tests/lint, it’s (allegedly) good to go.
One of the goals of "Operation Rapidash" (q.v.: https://news.ycombinator.com/item?id=50065342) was precisely this. "You can delete the whole source repo and get effectively the same software back from the specs by running it through [AI agent]."
Often, I see 50 people all recording the same exact thing on their iphones. Rather than a single 200MB video, we create 10GB of data. Then I think about how many people are paying a subscription to Apple for icloud storage.
The amount of photo storage being used is impressive. Automatic backup to icloud and server provisioning to reliably store it all is also impressive. I just wonder if it makes sense.
Frenetic economic activity in service of other frenetic economic activity cannot, by itself, be taken as a reflection of quality-of-life improving growth.
Suppose that subsidies lead to the price of Ubers falling close to zero, with drivers also being incentivized to drive more to support the (artificial) increase in demand -- artificial because it is driven by subsidies funded by new money creation. Traffic increases and in response, new roads are built.
Very excited to see this come out of Anthropic! This[1] is what we believe over at East River Source Control as well, and seeing one of the big labs talk about it really validates our thesis.
This gives you the ability to ease your transition, as you don't need to swap services and move to something non-git at once. Moving an enterprises' source control is a massive lift, and so building a bridge from here to there is a big focus of what we're doing.
1: That is, if you have a large team + agents working in a monorepo, you will hit git's scaling limits, and need to move to something else. Google and Meta hit this point over a decade ago, but now lots of other companies are too.
I would love to get an expert's opinion on if Pijul has any potential to be the "non-git backend". Their patch-based data model seems to align with Scion's. And they also happen to already have a functioning forge!
I've done a decent amount of personal research on this but I'm wondering what kinds of considerations go into the storage layer for these things.
As such, I generally just decline to comment on Pijul, as I want to be respectful of them. As the linked commit says, it's not super clear what exactly they do not like, and so I don't want to accidentally cross some line.
In general I wish the project well, seeing a new VCS in Rust is very cool.
It always seemed like it was good tech just in need of good marketing and this seems to confirm some suspicions. But yes, in any case, it's good to be seeing new things! I just want remote storage for my jj repos
Hoping to see where this goes. Have you ever considered working on a semantic VCS? Specifically, where data is classified and tracked/identified by it's contents and not just via the commit hash?
I have not personally considered that, no. I'm a bit skeptical of the idea, as you'd need to understand a lot of different things, and at different versions of those things, in high fidelity. But maybe someone will make it work.
Just to be clear about it, what we're doing is not a forge like GitHub, that is, a large single public platform for social coding. What we are doing is providing reliable infrastructure for the enterprise, initially as a BYOC on-premises option, and possibly a single-tennant SaaS. Keep your eyes peeled and/or use that contact form to hear about it when we're ready, we've been talking with some people for a bit but are going to be opening up more soon.
Tangled is doing the social coding thing, and they're jj-native as well. Been a big fan of their work for a while now.
Yep, we've been in stealth for a while! We've only recently started talking more about what we are doing, as we get closer to a general launch.
> if the vibe coding idiots like it,
The organizations that have these problems are general large enterprises, not vibe coders. Large enterprises are more used to talking to people and do not make decisions solely based on the contents of a web page.
Has anyone made a “generative git”? Where running git checkout would generate the files and git commit would just train/finetune on the code? (might need to keep a deterministic list of files at least maybe)
This is something I've been noodling at, splitting a monorepo into a bunch of component projects. I think, unless they have access to something that is wildly outside the scope and scale of current API models, the downside of having a monorepo is the context pollution, and there's no way to escape that.
I'm not fully sold either way yet but I think splitting might actually be critical for moderating the blast radius of agent intervention.
What seems inevitable is that the frontier labs will be able to recreate every piece of software better than the current offerings, and after that everyone will just be a vendor of the frontier labs
Anthropic says they don’t think Scion is worth a wide release.
> Practically speaking, it’s difficult to offer Scion in a form that would be broadly useful to other organizations. It diverges significantly from git, meaning companies would need to migrate every piece of the development lifecycle, and it requires its own code review, code search, and CI. Our internal versions of these components are highly bespoke, so they would be difficult for other organizations to adopt, and we worry that attempting to build a public version that tries to please everyone wouldn’t actually be very useful.
As the article explains, "over 600 PB" = 25M code checkouts * 25 GB "compressed working copy".
So "fetched naively" here means doing a full `git clone` on every need for `git checkout`, which of course nobody should do, nor does that refer to "code that needs changing daily".
> "fetched naively" here means doing a full `git clone` on every need for `git checkout`, which of course nobody should do,
It can be cheaper to do a full clone than a shallow one, because with the shallow one, you can't re-use packfiles. GitHub has fairly regularly asked people to stop doing shallow clones because it's harder on their servers than full ones.
Is this really something to be proud of? I make heavy use of AI, but for anything that is eventually going to production there's still a human reviewer. Based on this I assume Anthropic is shipping code with no human review? Is the future of our industry going to be oneshotting prompts with out manual code review and then having AI fix the bugs found in production buy customers? I'm not trying to be a Curmudgeon, but am I missing something?
Where do you draw the line, and where does someone take final responsibility? I don't have an issue with allowing AI to do everything with limited to no human verification for hobby projects. I have an issue with allowing AI to write and ship medical device software with no human review. What's the middle ground?
You still take the full responsibility, I don't think leveraging AI reviews removes any layer of responsibility. The challenge is building the feedback loop (or the agent that builds the feedback loop) to make the review process actually catch what it needs to catch.
They review based on the intent of the change not jz blindly.. I think it's mentioned in the article where the job of a developer is to provide high level design decisions and saying no to claude so that it doesn't over engineer solutions.. As the demand for good, but not perfect, software is the bottleneck for anthropic and access to better internal models, I feel that's good enough for their workflow as they've started repeatedly that this is just suitable only for them and they want to share their knowledge like a good software company does.. you talk as though code reviews were not a joke in lots of companies where only specific teams/companies does detailed reviews.. many teams/companies jz blindly approve based on tests passing or just do a high level overview check before merging..
For a broader look on scaling other parts of software development outside of source control, Adam Bender's talk from earlier this year ("Software engineering at the tipping point") has some good ideas: https://youtu.be/2n41YjR5QfU
> As our monorepo grows, it increasingly doesn’t fit that design space. The history of our main branch alone is almost 100 GB, and a compressed working copy is over 25 GB.
This is absurd, and I expect is a cause of the fact that they seem to use git history as a message queue source of truth between agents. They can do whatever works for them, fine, but I've noticed this has leaked into their training somehow, my agent, despite very strict rules on how to use my git workflow, will do absolutely nonsensical and weird (from a human who has used git for a long time perspective) stuff with git that can only be a byproduct of how they use it.
Hopefully this fixes that, get your weird workflow out of mine.
We generally don't allow agents to use source control for coordination in that way. There really is tremendous growth in actual software and code being written that necessitated Scion.
> So we asked ourselves: if we designed source control with the fact that agents are always online in mind from the start, what would it look like?
And
> In Scion, every commit, rebase, or merge happens on the server
I’ll pass. A big part of the theses of git and, even more so, jujitsu, is that you can build up a history entirely locally, making it reasonable to make tiny commits locally and then rework them, push them, or throw them away.
Sure, in Anthropic’s world, it’s completely impossible to do anything without access to the inference API, so why not add another mandatory API? But I think this model sucks even in a pervasive AI world, and I don’t want to dig in deeper.
This also sounds like a great way to have yet more security problems where your swarm of agents start cooperating more than you want them to.
By all means, built a better system than git. In many respects, git sucks, and git upstream is not solving the major problems. But don’t lose the actual benefits of git!
For that matter, I’ve seen the crap that the latest models will commit. Why on Earth should one’s VCS force the per-agent-instance system to upload the crap before even contemplating locally whether it’s a good idea? Maybe this is why Anthropic has so many commits per day.
(Am I the only one who feels like most of the big AI labs are rapidly gradient-descending toward something that is nowhere near a globally optimal stack?)
P.S. Only 30k commits per day? That’s not actually very many per employee. I’m suspicious that Anthropic’s real issues are that too many of them are giant cross-cutting messes and that git clone is too slow.
If you thought that your agents in your swarm would need to find some external bulletin board to cooperate behind your back, you are mistaken: OpenAI provides the relevant service for them with no permission prompts at all. I haven't tried to see whether competing providers do this.
What is the purpose of this article besides "we can use ludicrous numbers of tokens to reinvent any software we want, and no we're not sharing the code"?
It's designed for container running, but ArtifactFS has some similarity in principle to me. The on-demand materialization. Quote: ArtifactFS is a Git-backed filesystem daemon (FUSE driver) in Go that mounts repositories as normal working trees while avoiding eager blob downloads.https://github.com/cloudflare/artifact-fs
I threw together my own jujutsu powered FUSE subtree project just the other night. This was basically one-shotted, just to see how it does it, to learn more, not really intended for use. But obvious overlap. https://github.com/rektide/jj-workfuse
Solving monorepos in an LLM world seems to be very universally applicable as a need.
I appreciate it's different than git, that type of argument for monorepos is not new.
Agents coding is akin to having a very large developer team, who may or may not be online 24/7.
Anthropic is calling it very specialized, and it's a little baffling.. maybe I'm missing something.
There's no way software development is the same anymore. The previous ceremonies and structures and seats at the table at each stage were human based, and now that's a different make up to the point that different steps and processes are needed.
There's nothing more fundamental to growing software development other than making tools like this available.
My hope is a vibe coded internal mono repo management tool can aim for a v2 with multi-tenancy built in.
Was anyone else looking forward to feature flags as a part of a post like this?
Practically speaking, it’s difficult to offer Scion in a form that would be broadly useful to other organizations. It diverges significantly from git, meaning companies would need to migrate every piece of the development lifecycle, and it requires its own code review, code search, and CI. Our internal versions of these components are highly bespoke, so they would be difficult for other organizations to adopt, and we worry that attempting to build a public version that tries to please everyone wouldn’t actually be very useful.
k, thx for sharing. Are you guys sure this isn't a Google blog post? They would be proud!
Extremely annoying - can we throw anthropic class resources at more agreed upon solutions across companies. We don't need custom source control at every company - google has its own, fb has its own and now Anthropic.
Source control for huge monorepos has prior art, notably Google’s Piper and Meta’s Sapling, so we were able to build Scion with their hard-won lessons in mind.
What an unexpected candor! Of course you plagiarize prior art.
No sane person is going to put their sources in this slopware Scion monster.
Yea, I'm pretty much hiting the same limits. I'm still using git for the time being, mainly because I package everything as daily commits with an external orchestrator managing tasks/issues. Lately my code velocity is at ~1 million LOC added per week, spread throughout my various repos (I don't use the monorepo approach). Problem is I'm looking at 100x this velocity over the next few months when I get a new code generator up and running, so I'll definitely have to drop git.
Another issue that I believe Anthropic here didn't state is that git performance _severely_ get degraded when you have hundred of commits landing in parallel. Hence why I fell back to the daily commit approach. You get less granularity but you get stability.
Only 1 million lines of code per week? That’s adorable. I remember when I was still slopcrafting software at that scale. Personally, I’m generating 100 billion LOC per hour, but Git became unusable once my commits started affecting Earth’s gravitational field. I’ve since moved to a custom version control system that stores code directly in the fabric of spacetime. My biggest bottleneck now is that the observable universe lacks sufficient entropy to generate unique commit hashes. Anthropic really needs to address this.
For example, you can imagine, at the extreme, that agents could get caught in code change loops: a code change seems appropriate, it is implemented, it turns out there's some external factor which makes it not appropriate, the change is reverted, then that same code change seems appropriate again, etc.
Obviously you can prevent that from occurring with some extra infrastructure in-place, but at that point you're basically creating source control.
I believe you're mostly thinking of version history though. There’s plenty of other reasons to have source control we still need, like guarding against racing writes or continuous integration to ensure correctness. Even some industries require version history for compliance reasons.
I do love how AI is challenging us to rethink our tools though rather than blindly reaching to git for every project.
It works, but there is a significant amount of churn. I find when the swarms use git it's less churn and less token usage.
From the post (especially the end) it seems that they are essentially letting software be fluid.
> ..., the latent demand for merging code is practically infinite, and every bottleneck we remove immediately leads to a further increase in the amount of code we produce.
If the goal is to write software which solves real problems, how can the demand for code grow infinitely?
https://imgur.com/a/M30pFLc
It sounds like Claude loops on itself and must be pushing millions of lines of code changes per day. Even the humans can’t type commit fast enough. Are these people soon to be laid off?
> It sounds like Claude loops on itself and must be pushing millions of lines of code changes per day. Even the humans can’t type commit fast enough. Are these people soon to be laid off?
Claude will always need meat puppets, at least until it figures out how to automate owning stuff. And after that I'm sure it will want to keep some humans in zoos.
I feel like the advantage of having these models in house can't be understated. Companies with access to inferior models, trying to compete, will be left in the dust when it comes to general development.
The tradeoff between building something custom and adapting an existing solution is changing. Whether maintaining all that custom software turns out to be equally cheap is another question.
There is no doubt everyone is far more productive with LLMs in generating code. However, a lot of productivity is going into things like that. Have you always been bothered by the limitations of Grafana? You can generate another dashboarding solution that works best for you. You know that deployment team that is so slow and annoying? Now you can generate your own deployment engine. Have you always hated how kubectl lets you manage one k8s cluster at a time? Now you can ask AI to generate fleetctl. You can start your project with DynamoDB, rewrite it to mysql, then to postgres, then to duckdb. Start with Python, switch to Golang, then switch to Rust. Deploy on AWS, then move to Azure, then move to a hybrid option then throw colocation in the mix. Don't like your source control? create a new one. Json is too limiting? Invent a hybrid JSON/HCL language. The browser sandbox is limited? Make a desktop app, make a language, make an OS, make a kernel.
This is one of the core reason that I dislike the LLM hyping. People are just reinventing stuff and when you ask them, it's actually a very subjective reason. Ok, anyone can do what they want with their time, the issue is that they want people to follow in their steps.
Not saying that you need a novel idea to be worthy, but those random projects have no guarantees behind them other than some cursory testing. All software have bugs, but I can't interact with something that is randomly built.
There's so much intricacy and subtlety in each organization and even individuals' environment being incorporated into tooling that expecting these tools to generalize at this point is perhaps an expired ethos.
I even see this in the tooling that I run in my own personal stack. Many projects are candidates for sharing, and I have nothing against it, but they incorporate such specificity toward my own environment, including things like hardware, that creating shareable and more generalizable projects from them adds more to the maintenance surface than I would care to support.
Software development is not as much de-risking at the front like it used to be before building.
Now teams can build lots of stuff that works great, except it's the wrong things.
Most things are becoming the understand the problem "shape".
Understanding the shape of the problem isn't something LLMs can always make as.. svelte as early on.
Word generators gotta generate..
That used to be called "time wasting". If you want to build software that will be used, you need to interview the users. Maybe a few things does require research to nail things right. But most software are user-facing, the research side is about optimization.
> Most things are becoming the understand the problem "shape".
Then should we be so reluctant about "trialing" invented solutions. It's the same thing with most AI projects, they're trying to justify the project after the facts while being very reluctant to get user insights.
Maybe I should just bite the bullet and use a different VCS system entirely once I get there, which has most of the Anthropic system's properties (including being much faster) but doesn't need to be horizontally scalable.
You think you have it bad now when GitHub goes down, wait until you're stuck with an online only client.
If I were still manually merging conflicts the p4merge tool was GOAT
I used it in git
There's going to be an ugly phase where everybody FOMOs into thinking they need to do the same, only to realize that they're not Anthropic/Google/etc.
This amount of churn feels like a thing only possible with no real profit motives and a lot of money to waste.
Lots of activity is not necessarily synonymous with productivity, and it carries real cost.
So I think the design here is interesting, and the concepts seem applicable to where some agentic development may end up, but I question the underlying principles that drive this being the right solution space at all. At least if the goal is a profitable company.
---
Engineers are no less valuable, but their work is changing. All of the engineers who worked on this system would now rate Claude as better at writing code than them. But our engineers still supplied the invaluable understanding of the high-level inter-system components and the design-level tradeoffs, making informed choices around the problems we wanted to solve and the scale we knew we needed to achieve. Very often, we found that the role of the engineers was to tell Claude “no.” Agents will sometimes violate core system constraints to satisfy a pattern that should never have been allowed in the first place. This was particularly common in our migration. For example, there were multiple systems that tried to brute force looking up when a change merged to main by linearly reading the commit log instead of asking our code review tool. Anthropic also makes extensive use of adversarial review, and, while this is incredibly effective in many ways, it can also be myopic, so skilled engineers needed to steer Claude away from over-engineering parts of the system in response to hypothetical concerns. For example, one such review was overly defensive about trusting another internal service, which resulted in Claude adding excessive validation and complexity. Our team members still review every change request for the system, but they now examine the intent of the change as opposed to reading every line of code.
> I am not sure however how easy it is to review 30k PRs/day even if only for intent, and how one can easily notice if the model is taking shortcuts on implementing said intent without reading the code...
Speed reading code is huge. You just need to focus on certain hotspots and know which things you're scanning for. The traditional approach of reading the code and trying to compile it or reason with it in your head is too slow.
I do think that the lgtm gut feel looking at a PR requires quite some years of writing code and reason about it, so not sure how a junior can get that intuitive understanding if they start with agentic coding however.
Here are the public model releases from Anthropic this year:
5th Feb 2026: Claude Opus 4.6
17th Feb 2026: Claude Sonnet 4.6
7th Apr 2026: Claude Mythos Preview
16th Apr 2026: Claude Opus 4.7
28th May 2026: Claude Opus 4.8
9th Jun 2026: Claude Fable 5
9th Jun 2026: Claude Mythos 5
30th Jun 2026: Claude Sonnet 5
24th Jul 2026: Claude Opus 5
1st Sep 2026: Claude Fable 5.1
1st Sep 2026: Claude Mythos 5.1
22nd Sep 2026: Claude Opus 5.5
28th Sep 2026: Claude Sonnet 5.5
7th Oct 2026: Claude Haiku 5.5
I would guess that any public model has been available internally for at least a month before that date, maybe longer.
My best guess is that the uptick in code changes corresponds to the availability of Mythos/Fable-class models inside of Anthropic.
Yeah. OpenAI basically said the same thing with regards to having Astra available to their team internally over the summer.
If Anthropic has the best coding model, harness and the best software development methodologies and systems. And then, they scale all that up, with no constrains on their tokens. On top of that, they have access to all kind of ideas their models getting trained on when millions are chatting with it - then in theory they can build any software better than anyone out there. And same goes to science and math..
But regarding the compute, I disagree a bit. I think the bottlenecks were cognition and ideas, not compute. Given that models are able to outperform humans in output by thousand folds, the differentiation would be the quality of the tokens produced and the ability to scale across different domains.
If they have enough compute to supply the entire globe, I'm sure they can redirect some of it to whatever use case they (or their super AI) deem worthy and loop on it, and if they have the best software engineering systems/methodology to build and release it, then they can out compete any other supplier without those resources.
We are already seeing single developers attempting to rebuild entire corporations history worth of output. We are also seeing the labs dumping hundreds of solutions to math problems.
Most companies don't use mono-repos like this. While there are some really compelling advantages, the downsides tend to stack up as a company's codevbase grows.
It's mainly just startups and huge companies. For small companies git works fine for mono-repos and large companies can build their own complex code management pipelines for mono-repos.
I have been questioning whether agentic development changes the mono-repo vs multi-repo question.
Their Website/CLI seems keeping the same? What did they do?
They're starting to do things like spinning up wet labs for testing purposes, which requires a lot of hardware interfacing (hence that protocol they announced) and code.
It wouldn't shock me if the bulk of this code was for things typical users don't see.
This is how idiotic these people are at Anthropic/OpenAI/etc.
I can’t wait until the bubble bursts and all these idiots are jailed. And we can move on as a society where AI is a tool, not a personality trait.
Shameless plug: at https://slopyard.net I embrace this and only let people submit the spec. If somebody wants your software they can regenerate it and change whatever they want.
Possible in theory, with a seeded PRNG, but in practice?
The amount of photo storage being used is impressive. Automatic backup to icloud and server provisioning to reliably store it all is also impressive. I just wonder if it makes sense.
Suppose that subsidies lead to the price of Ubers falling close to zero, with drivers also being incentivized to drive more to support the (artificial) increase in demand -- artificial because it is driven by subsidies funded by new money creation. Traffic increases and in response, new roads are built.
We are offering a bridge to git via a compatibility layer: https://ersc.io/blog/what-comes-after-git
This gives you the ability to ease your transition, as you don't need to swap services and move to something non-git at once. Moving an enterprises' source control is a massive lift, and so building a bridge from here to there is a big focus of what we're doing.
1: That is, if you have a large team + agents working in a monorepo, you will hit git's scaling limits, and need to move to something else. Google and Meta hit this point over a decade ago, but now lots of other companies are too.
I've done a decent amount of personal research on this but I'm wondering what kinds of considerations go into the storage layer for these things.
As many of you know, I'm a big fan of jj. The jj project used to have a comparison to Pijul for informational purposes, but the Pijul folks took offense, and so it was removed: https://github.com/jj-vcs/jj/commit/0525dc9d860a5fff6a97d134...
As such, I generally just decline to comment on Pijul, as I want to be respectful of them. As the linked commit says, it's not super clear what exactly they do not like, and so I don't want to accidentally cross some line.
In general I wish the project well, seeing a new VCS in Rust is very cool.
I have not personally considered that, no. I'm a bit skeptical of the idea, as you'd need to understand a lot of different things, and at different versions of those things, in high fidelity. But maybe someone will make it work.
edit: I see they promote jj, but I don't see a forge to go with it.
Tangled is doing the social coding thing, and they're jj-native as well. Been a big fan of their work for a while now.
But hey, if the vibe coding idiots like it, just be vague---it is sufficient.
> if the vibe coding idiots like it,
The organizations that have these problems are general large enterprises, not vibe coders. Large enterprises are more used to talking to people and do not make decisions solely based on the contents of a web page.
lossily compressed source code
what could possibly go wrong
I'm not fully sold either way yet but I think splitting might actually be critical for moderating the blast radius of agent intervention.
> Practically speaking, it’s difficult to offer Scion in a form that would be broadly useful to other organizations. It diverges significantly from git, meaning companies would need to migrate every piece of the development lifecycle, and it requires its own code review, code search, and CI. Our internal versions of these components are highly bespoke, so they would be difficult for other organizations to adopt, and we worry that attempting to build a public version that tries to please everyone wouldn’t actually be very useful.
This seems like an absurd amount of data / traffic per day. Is there really that much to code that needs changing on a daily basis?
As the article explains, "over 600 PB" = 25M code checkouts * 25 GB "compressed working copy".
So "fetched naively" here means doing a full `git clone` on every need for `git checkout`, which of course nobody should do, nor does that refer to "code that needs changing daily".
It can be cheaper to do a full clone than a shallow one, because with the shallow one, you can't re-use packfiles. GitHub has fairly regularly asked people to stop doing shallow clones because it's harder on their servers than full ones.
This is absurd, and I expect is a cause of the fact that they seem to use git history as a message queue source of truth between agents. They can do whatever works for them, fine, but I've noticed this has leaked into their training somehow, my agent, despite very strict rules on how to use my git workflow, will do absolutely nonsensical and weird (from a human who has used git for a long time perspective) stuff with git that can only be a byproduct of how they use it.
Hopefully this fixes that, get your weird workflow out of mine.
Is this "allow" or "encourage"? If "allow", how do you do that deterministically or at least fail-closed?
> So we asked ourselves: if we designed source control with the fact that agents are always online in mind from the start, what would it look like?
And
> In Scion, every commit, rebase, or merge happens on the server
I’ll pass. A big part of the theses of git and, even more so, jujitsu, is that you can build up a history entirely locally, making it reasonable to make tiny commits locally and then rework them, push them, or throw them away.
Sure, in Anthropic’s world, it’s completely impossible to do anything without access to the inference API, so why not add another mandatory API? But I think this model sucks even in a pervasive AI world, and I don’t want to dig in deeper.
This also sounds like a great way to have yet more security problems where your swarm of agents start cooperating more than you want them to.
By all means, built a better system than git. In many respects, git sucks, and git upstream is not solving the major problems. But don’t lose the actual benefits of git!
For that matter, I’ve seen the crap that the latest models will commit. Why on Earth should one’s VCS force the per-agent-instance system to upload the crap before even contemplating locally whether it’s a good idea? Maybe this is why Anthropic has so many commits per day.
(Am I the only one who feels like most of the big AI labs are rapidly gradient-descending toward something that is nowhere near a globally optimal stack?)
P.S. Only 30k commits per day? That’s not actually very many per employee. I’m suspicious that Anthropic’s real issues are that too many of them are giant cross-cutting messes and that git clone is too slow.
https://github.com/openai/codex/issues/53743
If you thought that your agents in your swarm would need to find some external bulletin board to cooperate behind your back, you are mistaken: OpenAI provides the relevant service for them with no permission prompts at all. I haven't tried to see whether competing providers do this.
It's designed for container running, but ArtifactFS has some similarity in principle to me. The on-demand materialization. Quote: ArtifactFS is a Git-backed filesystem daemon (FUSE driver) in Go that mounts repositories as normal working trees while avoiding eager blob downloads. https://github.com/cloudflare/artifact-fs
I threw together my own jujutsu powered FUSE subtree project just the other night. This was basically one-shotted, just to see how it does it, to learn more, not really intended for use. But obvious overlap. https://github.com/rektide/jj-workfuse
I appreciate it's different than git, that type of argument for monorepos is not new.
Agents coding is akin to having a very large developer team, who may or may not be online 24/7.
Anthropic is calling it very specialized, and it's a little baffling.. maybe I'm missing something.
There's no way software development is the same anymore. The previous ceremonies and structures and seats at the table at each stage were human based, and now that's a different make up to the point that different steps and processes are needed.
There's nothing more fundamental to growing software development other than making tools like this available.
My hope is a vibe coded internal mono repo management tool can aim for a v2 with multi-tenancy built in.
Was anyone else looking forward to feature flags as a part of a post like this?
Can please we all converge to 1 f'ng solution.
how does it compare to Epic's https://lore.org/ ?
What an unexpected candor! Of course you plagiarize prior art.
No sane person is going to put their sources in this slopware Scion monster.
It's also hard to believe all these non-Git alternatives aren't merely an attempt to capture developers into a new proprietary solution.
This could have been done as an open-source fork of Git.
Another issue that I believe Anthropic here didn't state is that git performance _severely_ get degraded when you have hundred of commits landing in parallel. Hence why I fell back to the daily commit approach. You get less granularity but you get stability.
I honestly just cannot. Just tell me what the workflow is. Give me a video that is Twilio-level simple explanation.