So often it'll cut off just as it's about to finish.
> Codex, translate the entire PostgreSQL codebase to idiomatic Rust, make no mistakes.
The day after that, Zig broke compatibility, fix that.
According to their web interface, I'm also on 23% of my weekly usage. That feels crazy, as I do a lot more than that on the 20 USD Cursor plan, and never got even a warning.
I thought OpenCode was doing something wrong, but you had the same issue with Kimi Code.
There is a small link below the tiles for these plans saying something "looking to use it for code? Use Kimi code" and upon clicking you'll get a basically identically looking subscription page with different sub prices and more quota
Non coding plans:
* Plus (19 "base agent" -- wahtever that might be)
* Pro (39 x2 )
* Max (99 x6)
* Ultra (199 x12)
Coding plans:
* Starter (19 - "base coding")
* Explorer (49 - x4)
* Expert (99 x10)
* Master (219 x25)
I stumbled on that when i tried Kimi K2.5 back when that was released.
It's also worth mentioning that the $20 get you a 0.25 priority for API usage - because the others get x4 priority. So get ready for a _slow_ experience
The plan I got is called "Moderato", which somehow isn't any of those 8... I'll try to read more about it.
Moderato: 19 USD or 15 USD annually
Allegretto: 39 USD or 31 USD annually
Allegro: 99 USD or 79 USD annually
Vivace: 199 USD or 159 USD annually
However, they're saying that the plans might get split up again:
> New Membership Plans Coming Soon
> Kimi and Kimi Code benefits will be separated. Existing subscribers are unaffected.
> You can still buy the current plan before launch.
Same as with the Tweet:
> Going forward, we'll also split membership into two more focused plans: Kimi Membership for Kimi Web, App, and Work; and Kimi Code Membership for coding workflows. This will help us match compute more precisely and keep the experience stable.
Problem is I don't know whether to pull the trigger on the annual Vivace now or wait for after the split - whether it's gonna be a cash grab afterwards on the account of the demand, or whether I might save some money if I buy it after the split.
That said, yes, it is pretty common for me to wait 10, 20 and sometimes even 30 minutes without steering the model or looking at what its doing. I usually write a pretty detailed prompt at the start that describes the issue, the usecases, the testing to do and the definition of success; then I sometimes ask it to draft a plan and give it a read, but after that I just press enter, let it run and come back when it's done.
In the end I do a manual review, both of the code and the functionality, but most of the time I get exactly what I wanted.
If my AI prompt takes more than 5 minutes to execute, I'm almost certain to stop it and break up the task or be more specific about what files it should look at or what subtask it should tackle first. The people who let it run for hours are absolutely not getting anything worth keeping in the end.
This was definitely truer with older models but isn't necessarily the case now.
They frequently do other things apart from navel-gazing that take a lot of time but get good results, like spinning up subagents to solve some hairy task in a loop.
Stopping/distrusting long-running AIs is a habit I've had to unlearn myself.
I frequently get good results from a 30+ minute Fable session, when I've asked it to do something complex (e.g. run the QA tester in a loop and eliminate all crashes, one commit per crash fixed)
The main failure point for me with long running Fable sessions now is just that it might hit a safety guardrail and downgrade to Opus midway.
I've found 6 minutes or so the sweet spot for upper bound with 5.6 Sol.
And it sounds like the OPs query above requires scanning throughout a large portion of the codebase, which will inherently consume a large number of input tokens. No locality to it.
But if I were to look in on them they’d be giving feedback during that time, but they wouldn’t need steering.
This varies based on the maturity of my projects but most of my sessions live in VMs with very specific egress rules and permissions.
It works how _i_ want, which is really refreshing.
Today we needed to migrate to a cheaper CI/CD provider and do a better robotic for a particular task. GPT-5.6 Sol.