hedgehog

From what I've seen the more complicated the deployment and the more tedious the investigation the more useful the automation is. The agent shouldn't be interpreting the alert much beyond gathering symptoms and identifiers for drilling down into all the underlying data and code. The security side of this is very tricky because the speed that makes the tooling useful also makes it hard to supervise.

I'm very happy to let the robot handle any incidental React or other extraneous tools that end up adjacent to the core work I'm doing. It avoids getting blocked without interrupting teammates or spending time and cognitive capacity on stuff that has no lasting value in my work.

Is it really that complicated to set up? A basic version is make everything work from a CLI in terminal coding agent of choice (probably already mostly there), hook it up to alerts, and let it run in a corner?

That was almost certainly my problem. If there was a warning about this I could have missed it.

Weirdly I know for sure there was an article behind that first link earlier today but now it's showing no body, just the update boxes. The second doc link is quite clear though.

Interesting could be a bug on my end. I'll look and see if it's a problem with how I set up my sandboxes, or something related to the precedence when both are available in the environment. With all the back and forth on their messaging at the time I took it as a cue to remove the Claude Code dependency from my workflow and start using other providers' models more.

The wording is ambiguous but empirically when I last ran claude -p as part of a job it drew from my $200 API credit.

Not true any more, read the note at the top of that page. They give you API credits but it's limited.

What's the rough approach? Do you support mixed fixed/variable, like could an editor show mono for code and variable for prose?

Either of those with the 2.5L hybrid drivetrain is about as boring of a car as you can get (in a good way).

If you're conservative and buying something as critical as a daily use car it makes a lot of sense to get second or later model year of the second or later generation since a major redesign. Preferably a model that has been sold in high volumes and used heavily as a taxi or something. Then at least you will know the most likely problems before you buy.

I didn't touch JIT (complexity and footprint way beyond what I can use or manage myself in any case) but the AOT path is pretty much what you'd get if you wrote naive C by hand. The emitted code is readable, in some cases you probably do better because more efficient algorithms are easier to code or reuse, but with the current refcounting that will have some overhead. I do want to try some ideas like supporting an @soa decorator for a List of DataClass type that keeps the visible interface but changes the memory layout. If someone wanted to sponsor tokens I could probably pure robot through a stage of fleshing out enough to port a few representative libraries and tune against the results.

My understanding is that almost all deals are built on floating rate debt, with some hedging. I don't work in PE though, maybe someone here knows the market in more detail.

As I understand it the situation is much worse than that, the existing debt is floating rate so in some situations can put previously sustainable companies into the red. This of course also affects their ability to refinance so it can spiral from there unless they take drastic measures (which still might not be enough).

I did some experiments translating a typed subset of Python to C for small system use, certain features like lists of primitive types, generators and comprehensions, frozendict, simple classes, generic functions, etc, map with little or no cost but are so much cleaner than the equivalent code in plain C. You also get to write test harnesses using all the tools available to full Python. The main issue is unsurprisingly memory management, Obj-C / Swift style automatic reference counting works but has drawbacks, and it gets much more complicated if you want to mix the static and CPython code.

The static + AOT approach has a lot of promise now that type annotations are widely adopted, predictable behavior and good performance on code that is mostly idiomatic Python seem like a good alternative in many situations to the weight of Rust.