43 karma · joined September 9, 2021
do you think something like a /speed config can be introduced to adjust agent working speed and let people adjust?
there's your answer. You're likely a heavy user. It's just that in this particular case Auto is shuffling models & reasoning efforts just right so you stay afloat.
There are a lot caveats when people say they're running out credits in hours: lots of people runnings clawS (multiple) 24/7; lots of people using Agent Teams or similar; lots of people just max reasoning for everything; etc.
If you get your work done in the 20 plan, thank the credit Gods and sleep easy. Price hikes are probably coming, or just reasons to draw more credits out from you in one form or another (e.g. new OAI device draws from you 20 sub to use)
tangential: I've seen Mitchel tweet that people in SF have ran up to him showing him how they fully riced their Ghostty setup. How many people here have done this and how easy/manageable is it? e.g. just forking the repo and implementing whatever Warp feature I like?
This is like that but instead of the server/client sending messages it's you.
I know nothing...
better memory management: I have memories that get overlooked or forgotten (even though I can see them in the archive), then when I try to remind chatGPT, it creates a new memory; also updating a memory often just creates a new one. I can kind of tell that Chat is trying hard to reference past memories, so I try to not have too many, and make each memory contain only precise information.
Some way to branch off of a conversation (and come back to the original master, when I'm done; happens often when I'm learning, that I want to go off and explore a side-topic that I need to understand)
Those who know are annoyed but not enough that it will cause change; I don't think most believe it will get worse either.
Unfortunately the default will be people going on the App Store, getting the first app that has 'VPN' in the title, download, and forget. Completely failing to address a systemic issue.
As it says in the article, the device seems to be more robust, and ready for the market soon. After having used ML to tune the decoding model on many participants contributing EMG data.