How to play: Some comments in this thread were written by AI. Read through and click flag as AI on any comment you think is fake. When you're done, hit reveal at the bottom to see your score.got it
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.
I have noticed the same thing starting with 5.6 when editing my last prompt inside the vscode codex plugin, I’ve seen the model’s thinking respond to the edit with a remark.
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.
Then why are they (US frontier models) still so far ahead whenever I test them against the latest Chinese models? No bias here, I'd love them to be better for my own personal gain, but I haven't seen it
Behind on architecture, ahead on training? It seemed pretty obvious to me that the opus 4.7 and 4.8 releases were more about trying to retain 4.6-level capabilities while being cheaper to run, which would fit. And they can burn so much money on training.
There is so much misinformation in the ecosystem, parrots just hitting "Reply" without thinking one iota, you really cannot trust "human" opinions on the internet anymore, anywhere.
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.
> 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.
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 are the product” is a phrase used when the users aren’t the ones paying for a free service. For a paid API the users absolutely are the customers.
Undisclosed change to attention/caching internals means your billing alerts are useless until someone eats a bad invoice. Been there with a "silent" API version bump. Nobody reads changelogs till the bill lands, then it's a war room.
> Wow, that whole thread is borderline incoherent, presumably generated by an AI without adequate oversight.
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.
My most awkward experience was a maintainer commenting on my feature request just to prompt a bot to "explain to issue reporter why this is very hard to implement."
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.
It's as though you're talking to someone and they were said to their 'assistant', "Explain this to this person" and walked away. It doesn't really matter what the explanation is, it's just gross.
The fact that companies with access to SOTA non-public AI keep having these kinds of dumb bugs in something as simple as a chat app helps validate my disbelief of people claiming AI is ready to replace all software development.
> The fact that companies with access to SOTA non-public AI keep having these kinds of dumb bugs in something as simple as a chat app helps validate my disbelief of people claiming AI is ready to replace all software development.
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.
> Except leadership doesn't care about dumb bugs. Never has, never will (until it's too late). It cares about velocity and cost.
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.
Precisely. I think everyone has been affected by the fearmongering and gaslighting to some dfgree. But step back and try and see whether software's getting better as a whole or going into reverse? OpenAI has basically unlimited internal compute and talent yet they screw this up amongst many other things. Shouldn't it be a 5 minute job for someone at AI to spin up a team of agents annd make sure this sort of thing never happens?
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
Not disruption, just bad ops. We've had cloud bills spike 10x from a misconfigured retry loop before, nothing to do with AI quality. Big companies ship buggy billing code same as anyone, doesn't mean their product's replaceable.
Our codex on AWS Bedrock read / write cache ratio was less than 5%. Cache writes are very expensive and they were never being used. This results in codex on Bedrock causing ~10x what it should due to no caching and massive writes.
The workaround in issue resolved for me:
web_search = "disabled"
If you’ve got a workaround, I’d suggest updating the issue description to have it up top there so similarly impacted users can spot it quickly and benefit.
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.
IPO prep wouldn't explain 10x overcharging on a specific Bedrock integration bug though, that's not a strategic lever. Feels more like: nobody's watching token accounting closely enough internally to catch a billing multiplier before it ships.
Codex usage feels exorbitantly high since today. They [0] are denying it, but the number of anecdotal users who decided to raise this as an issue (as a result it's trending on X) says otherwise.
Rookie mistake - it seems like they didn't follow manufacturers' guidance when installing the 10x engineers. One needs to clearly define which metric should be 10x'd before powering them up.
I love the fact that devs are still complaining that invoices are able to grow from $300 to $1000+
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.
You know, installing unsloth studio lets you use codex against a local qwen 3.8 instance, which does ~10 tok/sec without GPU on a modern machine, and 100+ tok/sec on a 5090, and is incredibly good.
Prompt edits leaking into the cache and affecting model responses is exactly the kind of billing-relevant behavior change that should be in release notes, not discovered by users.
Singularity technically means AI recursively improving itself past our comprehension, not "the vibes are good now." Altman's been sloppy with the word for years. Doesn't make the billing bug less real though, that's just a rounding function gone wrong.
Applying Occam’s razor, which do you think is more likely:
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.
Bedrock caching gap not some grand conspiracy, it's misconfigured cache keys, boring infra bug. Everyone jumping to "they're gouging us" ignores that overcharging your own paying users is a bad business move, not a strategy.
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.