I'm testing a theory that large-scale (LoC) generated projects in Rust tend to have fewer functional bugs compared to e.g. Go or Java because Rust as a language is a little stricter.
I've not yet formed a full opinion or conclusion, but in general I'm starting to prefer Rust.
Re: generalizing mocks, it sounds interesting but after getting full-fidelity clones of so many multi-billion dollar SaaS offerings, I really like it and am hooked. It pays nice dividends for developing using agentic coders at high scale. In a few more model releases having your own exhaustive DTU could become trivial.
> The Go to Rust rewrite is interesting - was that driven by performance or more about the ecosystem/tooling for this kind of work?
I'm testing a theory that large-scale (LoC) generated projects in Rust tend to have fewer functional bugs compared to e.g. Go or Java because Rust as a language is a little stricter.
I've not yet formed a full opinion or conclusion, but in general I'm starting to prefer Rust.
Re: generalizing mocks, it sounds interesting but after getting full-fidelity clones of so many multi-billion dollar SaaS offerings, I really like it and am hooked. It pays nice dividends for developing using agentic coders at high scale. In a few more model releases having your own exhaustive DTU could become trivial.
I did have an initial key insight which led to a repeatable strategy to ensure a high level of fidelity between DTU vs. the official canonical SaaS services:
Use the top popular publicly available reference SDK client libraries as compatibility targets, with the goal always being 100% compatibility.
You've also zeroed in on how challenging this was: I started this back in August 2025 (as one of many projects, at any time we're each juggling 3-8 projects) with only Sonnet 3.5. Much of the work was still very unglamorous, but feasible. Especially Slack, in some ways Slack was more challenging to get right than all of G-Suite (!).
Now I'm part way through reimplementing the entire DTU in Rust (v1 was in Go) and with gpt-5.2 for planning and gpt-5.3-codex for execution it's significantly less human effort.
IMO the most novel part to this story is Navan's Attractor and corresponding NLSpec. Feed in a good Definition-of-Done and it'll bounce around between nodes until it gets it right. There are already several working implementations in less than 24 hours since it was released, one of which is even open source [0].
This is great feedback, appreciate you taking the time to post it. I will set some agents loose on optimization / purification passes over CXDB and see which of these gaps they are able to discover and address.
We only chose to open source this over the past few days so it hasn't received the full potential of technical optimization and correction. Human expertise can currently beat the models in general, though the gap seems to be shrinking with each new provider release.
I'm one of the StrongDM trio behind this tenet. The core claim is simple: it's easy to spend $1k/day on tokens, but hard (even with three people) to do it in a way that stays reliably productive.
By default it runs in docker, and also includes an extra sophisticated macOS-native --darwin mode which goes beyond the capabilities and guarantees of the likes of sandbox-exe, bubblewrap, and in some ways docker. Leash provides visibility into and control over every command and network request attempted by the coder agent. Would appreciate any feedback, and will try to get in touch with the author (Gordon).
Now I'll definitely look into automatically supporting pass-through auth for at least gh cli in Leash - always looking for what folks will find useful.
> Regulatory capture is an economic theory that regulatory agencies may come to be dominated by the interests they regulate and not by the public interest. The result is that the agency instead acts in ways that benefit the interests it is supposed to be regulating.
I'm not sure if I am reading your comment correctly.
Regulatory capture doesn't sound like a desirable thing, and wouldn't do the desired regulating. What did/do you believe the definition is?
I've not yet formed a full opinion or conclusion, but in general I'm starting to prefer Rust.
Re: generalizing mocks, it sounds interesting but after getting full-fidelity clones of so many multi-billion dollar SaaS offerings, I really like it and am hooked. It pays nice dividends for developing using agentic coders at high scale. In a few more model releases having your own exhaustive DTU could become trivial.