Have you tried running `rumpel codex foo123` in one of your repositories, asking it to commit something, then `rumpel merge foo123` to get the changes back to your local checkout? Use a different terminal for the merge command, or detach from the codex session with `ctrl-a d`. You can also look at the commit first with `rumpel review foo123`, or get a shell inside the agent environment via `rumpel enter foo123`.
I recently got the tool I use to orchestrate agents in (remote/secure) devcontainers open-sourced at work to solve this properly: https://github.com/nvidia/rumpelpod
As others here have pointed out, it's exceedingly unlikely that a blocklist like proposed in the issue would ever be complete. You shouldn't allow agents direct yolo-access to your machine if it has sensitive data.
Codex works particularly well as a remote agent harness because of its client-server architecture: The server component runs in the container, which might be remote, while the client runs locally. So, in contrast to e.g. the claude cli where the frontend also runs remotely, there's no lag when you write/edit prompts.
Sweden has lots of potential for long-term energy storage as hydro power, which makes wind power viable. Northern Germany is mostly flat and there's not even close to enough storage capacity (on the order of ~weeks) to make a wind powered grid economically competitive.
Not sure if you're serious, but this was not viable in the 2010s, or even today in Germany at all because of Germany's high latitude: No matter how efficient solar panels become, they will always be more economical to operate closer to the equator. Anyway, the Chinese factories for the most energy intensive parts of solar panel production mostly run on coal power.
Solar panel production is extremely energy intensive. Germany has one of the highest energy costs in the world. So there was no way for Germany to maintain a competitive solar panel industry.
Right, I think the argument should be that transitioning from a synchronous to asynchronous programming model can improve the performance of a previously CPU/Memory-bound system so that it saturates the IO interface.
I read this argument ("async is for I/O-bound applications") often, but it makes no sense to me. If your app is I/O bound, how does reducing the work the (already idling!) CPU has to spend on context switching improve the performance of the system?
How many systems are there that can't just spawn a thread for each task they have to work on concurrently? This has to be a system that is A) CPU or memory bound (since async doesn't make disk or network IO faster) and B) must work on ~tens of thousands of tasks concurrently, i.e. can't just queue up tasks and work on only a small number concurrently. The only meaningful example I can come up with are load balancers, embedded software and perhaps something like browsers. But e.g. an application server implementing a REST API that needs to talk to a database anyway to answer each request doesn't really qualify, since the database connection and the work the database itself does are likely much more resource intensive than the overhead of a thread.
I believe these ideas are much more mature and better explored for code gen, but similar techniques are useful also in the frontend of compilers, in the type checker. There's a blog post [1] by Niko Matsakis where he writes about adding equalities to Datalog so that Rust's trait solver can be encoded in Datalog. Instead of desugaring equality into a special binary predicate to normal Datalog as Niko suggests, it can be also be implemented by keeping track of equality with union-find and then propagating equality through relations, eliminating now-duplicate rows recursively. The resulting system generalizes both Datalog and e-graphs, since the functionality axiom ("if f(x) = y and f(x) = z, then y = z") is a Datalog rule with equality if you phrase it in terms of the graph of functions.
Systems implementing this are egglog [2] (related to egg mentioned in the article) and (self-plug, I'm the author) eqlog [3]. I've written about implementing Hindley-Milner type systems here: [4]. But I suspect that Datalog-based static analysis tools like CodeQL would also benefit from equalities/e-graphs.
Have you tried running `rumpel codex foo123` in one of your repositories, asking it to commit something, then `rumpel merge foo123` to get the changes back to your local checkout? Use a different terminal for the merge command, or detach from the codex session with `ctrl-a d`. You can also look at the commit first with `rumpel review foo123`, or get a shell inside the agent environment via `rumpel enter foo123`.