Here are the docs:
https://developers.openai.com/api/docs/guides/prompt-caching...
The thread has little explanation as to what weird thing they’re doing to Codex that is making the default work poorly, and it kind of seems like it’s getting confused about whether it wants to set the caching mode or the breakpoint or both.
In any case, I find the behavior change interesting. It sounds to be like 5.5 and below may have been using a conventional attention scheme where a cached KV sequence can be easily used to restore a prefix of itself, but perhaps 5.6 is using linear attention or LSTM or another recurrent scheme where you cannot rewind the model state by just truncating it.
Slightly bummed out about it because in the past you could try different situations during a planning session and it wouldn’t pollute the cache but now it does. I’m not sure if forking the conversation has the same problem.
Same with local LLMs, I'd love to use them for my day-to-day software engineering, and I'm not exactly GPU poor, then people with 12GB VRAM try to convince me their local setup is perfectly fine running latest Qwen and it does real engineering but whenever I try, they're a far cry from what Codex+GPT 5.x would do.
Only way to be sure is creating your own private benchmarks and use those, and the difference in quality becomes very apparent, very quickly, for your specific use cases.
I feel like this is the kind of substantial change to your product that you would need to tell your customers about. It would be simply disrespectful to your customers to not disclose this upfront.
Users is the product.
What sucks is that every issue tracker for these agent harnesses are the same, and this shit hides real issues!
For example, Codex started encrypting messages from a agent to the sub-agents when you use Sol + Ultra, which is terrible for debugging for obvious reasons. This GitHub issue exists for this: https://github.com/openai/codex/issues/28058
Fine, the opening issue isn't concise exactly, but it's mostly clear what's going on. After a few messages, someone who uses LLMs without reviewing their output starts participating in the discussion, pastes huge walls of texts completely missing the point and overall just bloating the conversation so now whenever a maintainer actually want to address it, they have to wade through 20+ messages of just pure shit and bloat, to even understand what's going on.
Kind of wish some projects started having forums specifically for people who pay for forum access, or some other gate to get rid of these LLM lowlifers who cannot compose a simple message to explain what's in their head, and instead have to ruin perfectly fine conversations/discussions with their verbal poop.
I mean i use AI too but was taken back when an agent popped up dictating what i should do and so on....felt weird
It felt like they were trying to avoid me. They could have simply addressed me and given me the explanation they gave to the bot: it would have been simpler for him and more polite. I did in fact reply without waiting for the bot.
Except leadership doesn't care about dumb bugs. Never has, never will (until it's too late). It cares about velocity and cost.
They'd totally replace all software development with worse AI software development in a heartbeat.
This is the very moment at which I started my own business. I prefer to work for myself with some quality standards than to be in a rush in front of a prompt (not that I do not use AI at all, I do, but not for generating code most of the time).
I knew the future, at that time was basically: pressure for speed, taking ownership of course, even if they rush you. Wild-guess, probably with an AI, to add on top more trch debt. Make everything unmaintainable in the long term.
So this was the perfect moment to show that things can be done in another way and quality can be kept higher than the competition bc what I am seeing lately is people throwing things in a rush. Better twopieces of well-crafted software than 10 pieces of unmantainable junk.
At a high level, most uses of AI I've seen seem to be people building other AI tools, orchestrators, managers, agent managers etc. But these are all means to ends. I mean I guess it's nice to play aroud with harnesses and command agents to do this and that, but where are the tangible outputs?
I just see so many people boasting of their token burn and the complexity of their agentic setup, yet they rarely show the actual outputs
The companies you see struggling are ripe for disruption.
The workaround in issue resolved for me: web_search = "disabled"
How can anyone use a platform where this is even an issue? Just because AWS is a failure in this regard, doesn't mean there aren't alternatives with fixed prices or others with easily settable limits.
My conspiratorial mind thinks they're doing this deliberately and using the resets to mask things so people can't tell their limits are reduced. The $200 / month plan covers about 2 days of usage for me right now.
"Random" accidents that always go against you, too biased to be random.
But don't notice that too much, you might start to see patterns here and there that you're not allowed to, might get you banned from places, etc.
1. OpenAI intentionally adds random overcharges. 2. OpenAI deprioritizes fixing actual bugs that cause occasional overcharges because doing so won’t affect their bottom line.