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.
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?
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.
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:
> 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.
If Anthropic has the best coding model, harness and the best software development methodologies. 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..
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?
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.
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.
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.
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.
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.
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.
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
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.
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
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".
> 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.
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?
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..
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.
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?
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.
> 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.
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.
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.
Even with humans out of the loop you will still want features like branches, pull requests, tags/versioning, build reproducibility, etc.
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.
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 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.
I apologize for what may come across as a bit of a cop-out answer.
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.
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?
If only we had a jj-native forge to launch all these new backends.
edit: I see they promote jj, but I don't see a forge to go with it.
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.
You might already know this, but in case anyone doesn't, but ERSC's CTO is the creator of jj.
Maybe finish that web page before advertising. There is no real product description.
But hey, if the vibe coding idiots like it, just be vague---it is sufficient.
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.
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:
https://imgur.com/a/M30pFLc
> 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.
Does it really matter since It's one-time import pipeline.
Ripe for optimization against maintainability, of course, but yeah it would seem to matter if that generalizes.
This is a nice thoughtful article.
If Anthropic has the best coding model, harness and the best software development methodologies. 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..
Did they reinvent subversion?
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?
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.
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.
Prompt Claude for PR articles?
as the article says one of the main jobs of the humans is to say no to Claude regularly
https://news.ycombinator.com/item?id=50075832
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.
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.
That many commits in a day I wouldn’t call it engineering. That’s basically brute forcing.
> as of September, we merged over 30,000 per day.
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.
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.
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.
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 of September, we merged over 30,000 per day.
Their Website/CLI seems keeping the same? What did they do?
Exactly. Commit counts are no better than line counts as a software development metric. Where is the actual software?
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.
You can do that - it's called Spec Driven Development.
Prompts are not specs.
I don't see the term used in the article.
its not a Claude-specific term
Do we have reproducible LLMs?
Possible in theory, with a seeded PRNG, but in practice?
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]."
So... Anthropic reinvents SVN?
Feels more like perforce, but effectively, yes.
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.
Very little about modern technology "makes sense" in the way you mean. But it does currently make money.
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
> Fetched naively, that would be over 600 PB of git traffic per day.
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?
No, of course not.
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".
That 600 PB was mostly agents doing ”git clone” and downloading the entire history of their huge monorepo, as I understand it.
AI iterate quickly, with 100s of followups to fix results
No, that’s why they don’t do it that way.
As someone that has revived and started maintaining an internal code review tool, I'm very interested in reading more about Antique.
If any engineering org needed its own VCS, it would be anthropic.
why does it feel like they are creating something so its more difficult to use a competitor?
> 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.
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?
If AI can improve/speed up writing code, why shouldn't it improve/speed up code review?
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 can probably still find the leaked code online. It’s not exactly a bastion of software engineering. Plenty of folks have reviewed it deeply.
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..
What was the "quiet week"?
maybe they visited huggingface or something similar
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.
Seems similar to how version control works for large game and 3D assets. Neat.
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.
I though the basic idea was reasonable, until:
> 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.
Yes and they have no choice. And that matters.
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.
Can please we all converge to 1 f'ng solution.
how does it compare to Epic's https://lore.org/ ?
> Git offers tools for working with a slice of a repository, such as shallow clones, sparse checkouts, and promisor remotes.
I honestly just cannot. Just tell me what the workflow is. Give me a video that is Twilio-level simple explanation.
the quote does not seem to match your statement here