alexhans

It was already very good and has been used/battle tested by many us for a long time.

Some tools used to be 0.x for ages and, in this case, the 1.0 signals they're happy enough and allows them to promote things in a better way.

This (edit the durable part) is I guess the natural evolution of playing around building temporal like things for a need that many have.

I've not virtualized things in that way for ages. I think I was using VirtualBox years ago and com0com for serial communication.

What do you use for VMs these days?

> We have decided to organize these like departments similar to the way you might hire out humans. I'm not 100% sure if that's the best approach, but I will say it's been easier for people to understand because they're more mentally easily able to traverse the bot org chart if it somewhat reflects a traditional business org chart

I think this way of thinking is going to be very important to drive adoption of AI systems because the human analogies benefit from the pre-existing domain knowledge and expectations of people.

I like using the exam analogy for evals as a qualifier for work for your "AI hires" so you can trust them to work on a specific domain.

I'd be quite curious to see what your approach to evals/testing/tracing and agent/system mutation is.

Since I install them with symlinks in the tools "global" locations I get access to them across projects.

Think ~/.codex/skills/<symlink-to-myskill-a/

Same for ~/.Claude or any other tool that supports skills.

When I say evals I mean the evals you write that verify that your use cases are upheld. Think of it like a regression test for different behaviours/user stories.

The idea would be that if you already know what you want from an autonomous system, you don't need to verify manually every time and instead just run these tests to see if there's any regression of any kind. Generally I recommend structure output and evals that are just a plain assertion, if possible. Cheaper, faster, deterministic assertions.

Does that make more sense?

I could but I'd probably miss the angle that is helpful to you. Do you want to instead share what your pain points are, what are you trying to delegate to AI and what you aren't? Based on that I can expand my answer better with a thing or two you can try.

I'm a broken record but with:

- evals

- limiting AIs to tool calling, bounded planning, interpreting/producing natural language.

- bounding non determinism

- investing in small tools/security (If something shouldn't happen, then it shouldn't not be possible, RBAC style).

They can be good enough for a massive amount of contexts.

Every Linux user or FOSS enthusiast knows the acronym FUD: Fear, Uncertainty and Doubt, which were a set of techniques commonly used to disparage efforts of open source communities. Linux was evil and anticapitalist and we needed to use "CorporateTool" and ban/restrict Linux.

The same companies later would be running their entire infrastructures on it and on open source.

With AI, open weights and local models, we will see the same claims, even if the named fears change.

The end users and humanity are better served by collaboration and openness than by creating oligarchies.

Got it. Different audience. Cool stuff. I'll take a better look as soon as I can.

I've helped people get into programming face to face and also in a site I liked called exercism which also had a multi language track unit test passing style which I really value and it was purely command line, and I can't stress enough how important the command line is for me for people who want to dabble. Nowadays it's easier to get people into the command line because of Claude/codex.

I only have browsed your site from a phone and looks interesting but I wanted to ask if you had particular insights around getting people to approach learning, design through tests, breaking down problems, without having someone to guide them. Have you had a chance to observe people using your tool and adjust or it's been mostly dog fooding something you would've loved to have.