Neon is basically this same idea: postgres on a copy-on-write file system.
These aren't really "branches" though, they're hard forks. You can't merge them back after making changes. Dolt is still the only SQL database with branch and merge.
It was https://threekit.com. It was a while ago now but we had to use MySQL for our primary copy that users used (e.g. prod), and only when they were working on branches did we use dolt. I think the second complication was that Dolt was not stable enough to use in heavy load scenarios as well.
I can delete this comment if you do not want to discuss this publicly.
I remember that engineering decision. You guys were pretty early customers for your throughput and durability requirements (we hadn't even added standby replication yet when you started your integration). We've come a long way in the years since then.
Regardless of whether this particular project goes anywhere, it's at least very interesting that Yegge has discovered a way to make multi-agent setups work better. Giving them discrete personas ("you are a senior database engineer with 30 years of experience") and narrower scopes makes them much more effective. This was surprising to me but makes a lot of sense in retrospect.
The part that always struck me as weird about this stuff is that all of these "agents" with their "personas" are the same baseline LLMs with the same training ultimately, just told to basically pretend they're different. How far can that really get you?
I'm not actually a database engineer with 30 years of experience. If somebody demanded that I pretend to be one, I guess I'd give it a shot, but I would expect any actual employer would be able to tell that I don't have the level of knowledge and experience that you'd expect from somebody like that.
If the base LLM actually has the knowledge of all of these specialties, why can't it just apply them all at once, instead of needing to be told to I guess pretend to be only one of them.
Agreed, would really like to understand what this (setting the LLM up to assume a role to improve performance) is doing under the cover and why it works.
Why aren't the labs training models to pick a mantra appropriate to the task and do this themselves? "Huh, a database question. I am going to pretend I'm a database expert with lots of experience. OK, here we go!"
We build DoltDB, which is a version-controlled SQL database. Recently we've been working with customers doing exactly this, giving an AI agent access to their database. You give the agent its own branch / clone of the prod DB to work on, then merge their changes back to main after review if everything looks good. This requires running Dolt / Doltgres as your database server instead of MySQL / Postgres, of course. But it's free and open source, give it a shot.
My n-1 firm was a household name in cybersecurity. We had a _lot_ of contractors but almost no visa-based positions. I worked with two of the few who were, and they became citizens to stay with the company permanently.
Yeah this person is just making things up, the majority are not H1Bs. Also, there's this popular idea (usually on places like reddit) that H1Bs at big tech are paid "slave labor wages" because "They can't leave". This is just not true, they get the same salary and stock ranges as everyone else.
Source: was H1B (worked at a couple of big N, but not Amazon) until I got my green card (and then citizenship).
These aren't really "branches" though, they're hard forks. You can't merge them back after making changes. Dolt is still the only SQL database with branch and merge.
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