AI native in the sense that it papers over the pain points.

New JIRA admin? AI will set it up to do what you want (after all, Atlassian has a great training set as they can see which Cloud installs work well)

Need to set up a workflow? Bam, AI to do that.

Need to onboard a user or manage permissions? Again, have a chatbot to do it (as a time-to-time Jira standin Admin, changing permissions always needs doing in 2+ places and devolves into a "Can you see this yet?" round of questions)

Agree.

Plus there's the monitoring of the host that is always overlooked in articles. I've ended up chucking Monit on there to monitor disk usage et al, and also used it for monitoring compose too and restarting containers.

And then there's Healthchecks.io, and external uptime monitoring... the list goes on. Properly monitoring systems, even single server systems, is not simple.

Thanks for the links, going to have a read and see if I can apply any to my work.

Because spotting holes in specs has never been one of my strengths. And working without technical colleagues much of the time, it's a boon to be able to "rubber-duck" my ideas with something that is at least more intelligent than plastic.

Grabbing multipliers from thin air, the coding bit may only be 2x faster with a poorer-quality outcome, but working out what's needed is a good 5x faster.

And yes, I'm using the same adversarial AI MO as @wood_spirit, combined with Matt Pocock's excellent /grill-me and /grill-with-docs skills [1] and Plannotator [2] to review the plans.

1. https://github.com/mattpocock/skills

2. https://github.com/backnotprop/plannotator

Comments URL: https://news.ycombinator.com/item?id=47990495

Points: 1

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=47964692

Points: 12

# Comments: 0

So that's what it is. Reading its README I thought it was another harness like Pi [1], but with built-in memory so it remembers what it learns, and gets more capable the longer it runs.

Like Letta [2], Dirac [3][4] and the other "more experimental harnesses that look interesting but I haven't had time to try out".

1. https://pi.dev/

2. https://www.letta.com/

3. https://dirac.run/

4. https://news.ycombinator.com/item?id=47920787

Edit: found it.

> We may use your Content to operate, maintain, improve, and develop the Services, to comply with legal obligations, to enforce our policies, and to ensure security. You may opt out of allowing your Content to be used for model improvement and research purposes by contacting us at membership@moonshot.ai. We will honor your choice in accordance with applicable law.

Section 3 of https://www.kimi.com/user/agreement/modelUse?version=v2

I'm working with Stripe subscriptions at the moment for a charity taking donations via their website. The subtle differences between subscriptions done through Stripe checkout and subscriptions set up yourself using Stripe elements are by turn infuriating and frustrating.

The documentation is geared towards people using checkout. Stripe's own AI help could find us a bit of information which going through the documentation didn't give us, and it even struggled to find the reference in the docs for it.

One product, two different ways to use it, and slightly diverging feature sets between the two. Argh!

Nothing has helped, the Google forums are tumbleweed and there's no one to reach out to for what could be an algorithm change or something gone wrong. I'm a paying Workspace customer and it's made me think I need a backup plan in case I'm ever suspended. Reports like this don't encourage.

> Just remember to disable CC’s auto-updater if that’s what you’re using.

Why?

> std::slop is a persistent, SQLite-driven C++ CLI agent. It remembers your work through per-session ledgers, providing long-term recall, structured state management. std::slop features built-in Git integration. It's goal is to be an agent for which the context and its use fully transparent and configurable.