there seems to be very big misunderstanding about what the "ultra" is, so let me explain it basing on the codex source code:
it's similar to Claude code ultracode.
there is no ultra effort level implemented on the backend. it's just alias in the codex to max effort setting and single line addition to prompt to use subagents proactively. that's all
as far as we know pro models work differently. for once those are backend implementations and they probably run multiple parallel reasonings for any chunk and use some judgement model to pick best version as persistent one. but that's what I believe is most popular guess, because this is openai secret sauce.
there is still no way to use pro models from codex, or at leat so far there is no trace of it anywhere.
open weights model is like... Winamp for example.
it's free, you can download it and use it however you like, you could also do some binary patching or dll injections to alter it functionality but it's not enough to develop next version.
the same is with ai models, weights are the binary final artifacts. for development and improvements you need to have training data, pipelines, RL harnesses, etc.
also of you believe Chinese companies will be releasing weights indefinitely, you are not understanding motivations.
Chinese companies spend significant amounts of money to train a model so why they are releasing it for free? they basically provide researchers starting point for developing tooling and optimizations for serving the model in return. and also get some PR.
They also do not have to pay for inference of those models that much, as they probably serve them with loss anyways to gain market.
they are gov sponsored so money are not issue there, so they try to speedrun their way to what US companies have. And guess what happens when they reach it. they will stop releasing weights and increase pricing or will use them for gov purposes.
now, that should teach him to sell those on black marked instead
I'm mostly joking here, but Microsoft is one of few companies that handle cyber security in a way that really incentive people to not report them.
it's either by downplaying impact and not paying or paying very little or doing other researcher hostile activities.
especially that someone here mentioned some time ago that black market pays about 3x for the same class of vulnerability, so you need fairly high moral standards to go direct way
I'm assuming that right now all valves as computer controlled so why we cannot have best of both worlds?
cannot we dynamically switch between Otto and Atkinson cycles by just changing valve profiles?
computer could use Otto cycle in case more power is needed in rare situations
and you know that AI wrote all of it with minimal human supervision.
side note: last few days I noticed that vscode stopped leaking memory all over the place. when left idle it was taking all the ram I had + 20gb of swap space
and recently I noticed that I have half of the ram free.
I use insiders build btw, so stable might still not have those improvements
There is one more feature that zram can do: multiple compression levels. I use simple bash script to first use fast compression and after 1h recompress it using much stronger compression.
unfortunately you cannot chain it with any additional layer or offload to disk later on, because recompression breaks idle tracking by setting timestamp to 0 (so it's 1970 again)
counterargument: I always hated writing docs and therefore most of thing that I done at my day job didn't had any and it made using it more difficult for others.
I was also burnt many times where some software docs said one thing and after many hours of debugging I found out that code does something different.
LLMs are so good at creating decent descriptions and keeping them up to date that I believe docs are the number one thing to use them for.
yes, you can tell human didn't write them, so what?
if they are correct I see no issue at all.
but isn't it what we wanted?
we complained so much that LLM uses deprecated or outdated apis instead of current version because they relied so much on what they remembered
I used minimax M2 (context it's very unreliable) for installation and it didn't work and my document folder is missing, help
how do you even debug this? imagine you some path or behaviour is changed in new os release and model thinks it knows better?
if anything goes wrong who is responsible?
Previously they didn't officially quote how much limits were included in pro subscription, but you could determine it by upgrading from plus that reached weekly limits - after upgrade you ended up with 8% used limits, so we can assume they reduced limits by half just for pro users.
inference costs nothing in comparison to training (you have so many requests in parallel at their scale), for inference they should be profitable even when you drain whole weekly quota every week
but of course they have to pay for training too.
this looks like short sighted money grab (do they need it?), that trade short term profit for trust and customer base (again) as people will cancel their unusable subscriptions.
changing model family when you have instructions tuned for for one of them is tricky and takes long time so people will stick to one of them for some time, but with API pricing you quickly start looking for alternatives and openai gpt-5 family is also fine for coding when you spend some time tuning it.
another pain is switching your agent software, moving from CC to codex is more painful than just picking different model in things like OC, this is plausible argument why they are doing this.
>why do the results need to be decrypted by trustees after the election?
they probably design this system to be used for government elections, how they can convince anyone to use it when they do not use it for their own elections?
I gave it a spin with instructions that worked great with gpt-5-codex (5.1 regressed a lot so I do not even compare to it).
Code quality was fine for my very limited tests but I was disappointed with instruction following.
I tried few tricks but I wasn't able to convince it to first present plan before starting implementation.
I have instructions describing that it should first do exploration (where it tried to discover what I want) then plan implementation and then code, but it always jumps directly to code.
this is bug issue for me especially because gemini-cli lacks plan mode like Claude code.
for codex those instructions make plan mode redundant.
what are tested and fairly lightweight alternatives for Loki?
elastic stack is so heavy it's out of question for smaller clusters, loki integration with grafana is nice to have but separate capable dashboard would be also fine
https://github.com/openai/codex/blob/98d28aab54ed86714901b66...
https://github.com/openai/codex/blob/98d28aab54ed86714901b66...
https://github.com/openai/codex/blob/98d28aab54ed86714901b66...