This thread is extremely negative - if you can't see the value in this, I don't know what to tell you.
It's somewhat strange to regularly read HN threads confidently asserting that the cost of software is trending towards zero and software engineering as a profession is dead, but also that an AI dev tool that basically hooks onto Git/Claude Code/terminal session history is worth multiples of $60+ million dollars.
I definitely see the potential of AI-native version control, it will take a bit more to convince me this is a similar step-level improvement though.
I have never seen any thread that unanimously asserts this. Even if they do, having HN/reddit asserting something as evidence is wrong way to look at things.
> if you can't see the value in this, I don't know what to tell you
You are correct, that isn't the moat. Writing the software is the easy part
This is not their offering, this is a tool to raise interest.
github for agent code is dropbox final_final2.zip
And it was sold to Microsoft at $7B.
I’m sure there’d be some value to extract from the agent produced code in this thing, but I doubt it’s anywhere near as much.
Look at Xamarin, almost everything that they had is now gone in modern .NET.
"pfft! I could set all this up myself with a NAS xyz".
I do see value in this, but like you I think it’s too trivial to implement to capture the value unless they can get some kind of lead on a model that can consume these artifacts more effectively. It feels like something Anthropic will have in Claude Code in a month.
In my experience LLMs tend to touch everything all of the time and don't naturally think about simplification, centralization and separation of concerns. They don't care about structure, they're all over the place. One needs to breathe on their shoulders to produce anything organized.
Maybe there's a way to give them more autonomy by writing the whole program in pseudo-code with just function signatures and let them flesh it out. I haven't tried that yet but it may be interesting.
My mental model is that LLMs are obedient but lazy. The laziness shows in the output matching the letter of the prompt but with as high "code entropy" as possible.
What I mean by "code entropy" is, for example, copy-paste-tweak (high entropy) is always easier (on the short term) for LLMs (and humans) to output than defining a function to hold concepts common across the pastes with the "tweak" represented by function arguments.
LLMs will produce high entropy output unless constrained to produce lower entropy ("better") code.
Until/unless LLMs are trained to actually apply craft learned by experienced humans, we must be explicit in our prompts.
For example, I get good results from say Claude Sonnnet when my instruction include:
- Statements of specific file, class, function names to use.
- Explicit design patterns to apply. ("loop over the outer product of lists of choices for each category")
- Implementation hints ("use itertools.product() to iterate over the combinations")
- And, "ask questions if you are uncertain" helps trigger an iteration to quickly clarify something instead of fixing the resulting code.
This specificity makes prompting a lot more work but it pays off. I only go this far when I care about the resulting code. And, I still often "retouch" as you also describe.
OTOH, when I'm vibing I'll just give end goals and let the slop flow.
The Dropbox take was wrong because they didn't understand the market for the product. This time the people commenting are the target audience. You even get the secondary way this product will lose even if turns out to be a good idea, existing git forges won't want to lose users and so will standardize and support attaching metadata to commits.
Nah. People post about k8s on here all the time, but that doesn't mean I'm the target audience. Just because _someone_ on HN has a bad take doesn't mean they're the person who needs this. Nor does it mean they even understand it.
How is LangChain doing? How about OpenAI's Swarm or their Agent SDK or whatever they called it? AWS' agent-orchestrator? The crap ton of Agent Frameworks that came out 8-12 months ago? Anyone using any of these things today? Some poor souls built stuff on it, and the smart ones moved away, and some are stuck figuring out how to do complex sub-agent orchestration and handoffs when all you need apparently is a bunch of markdown files.
> The reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man. - George Bernard Shaw
The dropbox-weekend take wasn't made by the intended target for the product.
This is.
If it were also their last, I would be inclined to agree.
But yes, I would totally love to invest in startups with people's pension funds. It seems like the perfect scam where the only losers are the public that allows such actions.
But more to the point, I was talking about how you apparently think VCs just make up super high valuations of the companies they invest in to justify those investments? Are you not aware that VC is a competitive market?
I LOVE THIS FOUNDER - I am a 10 out of 10 - YES!!!
Take my (investors) money
It's because of everybody there.
Currently no one is on Entire - the investor are betting they will be.
Everything else about the featureset was copy pasted from Slack. No one cares about that part.
I still remember the reaction when Dropbox was created: "It's just file sharing; I can build my own with FTP. What value could it possibly create".
I also seen examples of it before. I've got opencode running right now and it has a share session feature. That whole idea is just a spinoff on the concept of the same parent that led to this one.
We forget that human consumption doesn't increase with manufacturing complexity (it can be correlated, but not cause and effect). At the end of day, it's about human connection, which is dependent on emotion, usefulness, and availability.
I use AI a ton, but there are just way too many grifters right now, and their favorite refrain is to dismiss any amount of negativity with "oh you're just mad/scared/jealous/etc. it replaces you".
But people who actually build things don't talk like that, grifters do. You ask them what they've built before and after the current LLM takeoff and it's crickets or slop. Like the Inglourious Basterds fingers meme.
There's no way that someone complaining about coding agents not being there yet, can't simultaneously be someone who'd look forward to a day they could just will things into existence because it's not actually about what AI might build for them: it's about "line will go up and I've attached myself to the line like a barnacle, so I must proselytize everyone into joining me in pushing the line ever higher up"
These people have no understanding of what's happening, but they invent one completely divorced from any reality other than the reality them and their ilk have projected into thin air via clout.
It looks like mental illness and hoarding Mac Minis and it's distasteful to people who know better, especially since their nonsense is so overwhelmingly loud and noisy and starts to drown out any actual signal.
You could perhaps start by telling what value you see in this? And what this company does that someone can't easily do themselves while committing to GH?
Runs git checkpoint every time an agent makes changes?
E.g., if you’ve ever wondered why code was written in a particular way X instead of Y then you’ll have the context to understand whether X is still relevant or if Y can be adopted.
E.g., easier to prompt AI to write the next commit when it knows all the context behind the current/previous commit’s development process.
That's how a trillion dollar company also does it, turns out.
I find the framing of the problem to be very accurate, which is very encouraging. People saying "I can roll my own in a weekend" might be right, but they don't have $60M in the bank, which makes all the difference.
My take is this product is getting released right now because they need the data to build on. The raw data is the thing, then they can crunch numbers and build some analysis to produce dynamic context, possibly using shared patterns across repos.
Despite what HN thinks, $60M doesn't just fall in your lap without a clear plan. The moat is the trust people will have to upload their data, not the code that runs it. I expect to see some interesting things from this in the coming months.
I have a lot of concurrent agents working on things at the same time, so I'm not always sure why a piece of code is the way it is months later.
- It's nice to see conversation context alongside the change itself. - I wasn't able to see Claude Code utilise past commit context in understanding code. - It's a tad unclear (and possible unreliable) in what is called 'checkpointing'. - It mucked up my commit messages by replacing the first line with a sort of AI request title or similar.
Sadly, because of the last point (we use semantic release and git-cz) I've had to uninstall it.
It's not 1:1 with checkpoints, but I find such things to be useful.
I still have kinks to work out in mine but it's already usable for building software. Once I get to v1 I think it will provide enough value to be useful for me in particular. I don't have enough data to speak about months on yet, but if I think the experiment is a success then I will do a Show HN or something.
The gist is you can clone a repo or start a project from scratch, each engineering agent gets a worktree, you work with the manager agent and it dispatches and manages other agents. there are playbooks which agents contextually turn into specific tasks, each of which is tracked much like CI/CD. You can see all the tool calls, and all of the communication between both agents and humans.
The application model is ticket-based. Everything revolves around the all-holy ticket. It's like a prompt, but it becomes a foundation for tying together every bit of work related to the process of developing the feature. So you can see the progress of the ticket through the organization kanban style, or watch from a dashboard, or look at specific tickets.
There are multiple review steps where human review and intervention are required. Agents are able to escalate to humans whenever they think they need to. There is a permission system, where agents have to seek permissions from other agents or humans in a chain of command in order to do certain tasks. Everything is audited and memoized, allowing for extreme accountability and process refinement stages.
Additionally, every agent "belongs" to either another agent or a human, so there is always a human somewhere in the chain of command who is responsible and accountable for the actions of his agent team. This team includes the manager agent, engineering agents, test agents, QA agents, etc, each loaded with different context, motivations and tools to keep them on track and attempt to minimize the common failure modes I experience while working closely with these tools all day.
This sounds a lot like that line from Microsoft's AI CEO "not understanding the negativity towards AI". And Satya instructing us to not use the term "slop" any more. Yes we don't see value in taking a git primitive like "commit" and renaming it to "checkpoint". I wonder whether the branches going to be renamed to something like "parallel history" :)
I’m happy to believe maybe they’ll make something useful with 60M (quite a lot for a seed round though), but Maybe not get all lyrical about what they have now.
It's almost a meme: whenever a commercial product is criticized on HN, a prominent thread is started with a classic tone-policing "why are you guys so negative".
(Well, we explained why: their moat is trivial to replicate.)
The fact that you aren't haven't offered a single counterargument to any other posters' points and have to resort to pearl-clutching is pretty good proof that you can't actually respond to any points and are just emotionally lashing out.
We can articulate it but why should we bother when it’s so obvious.
We are at an inflection point where discussion about this, even on HN, is useless until the people in the conversation are on a similar level again. Until then we have a very large gap in a bimodal distribution, and it’s fruitless to talk to the other population.
You could have someone collect and analyze a bunch of them, to look for patterns and try to improve your shared .md files, but that's about it