I because of this, the next task I gave it on the larger side, I ran its work through Codex which identified 7 glaring unfinished parts of the task.
The trend was starting the part of the task but then leaving a "skeleton" of what I has requested without any of the actual working parts.
The way I would describe it is a kid cramming his 3 month project into a Sunday evening for Monday's due date.
I have no idea what link it made to ask that, what in its training data or prompts, but it's very much "not a useful result".
I don't remember seeing anything similar, but have only been using Claude on and off for 6 months or so.
This is something I see all the time with AI consumers and I am continuously baffled. If anything else (autocomplete, intellisense, etc.) produced this much garbage it would be immediately abandoned. Why is there such a high tolerance for the chat bot equivalent?
Cynicism aside - I do wonder what the future will hold given that current token burn rates aren't sustainable without VC cash. Anthropic even pushed us to use haiku for claude code for "many" tasks in our enterprise training, so I'm wondering if it's not a company need of sorts to reduce the burn?
In reality as they scale up, the models lose nuance and become noisier. The boosters do not want to admit this.
We need highly-specialised models/interfaces. Not one thing and trying to force-fit it.
> Not one thing and trying to force-fit it.
> The frustrating part is that it's not a workflow _or_ model issue, but a silently-introduced limitation of the subscription plan. They switched thinking to be variable by load, redacted the thinking so no one could notice, and then have been running it at ~1/10th the thinking depth nearly 24/7 for a month. That's with max effort on, adaptive thinking disabled, high max thinking tokens, etc etc.
So Boris' explanation isn't really an explanation.
While simultaneously drastically reducing the amount of work you can get done even at $200 a month. I've cancelled my subscription, it's not worth it anymore.