One huge difference I notice between Codex and Claude code is that, while Claude basically disregards your instructions (CLAUDE.md) entirely, Codex is extremely, painfully, doggedly persistent in following every last character of them - to the point that i've seen it work for 30 minutes to convolute some solution that was only convoluted because of some sentence I threw in the instructions I had completely forgotten about.
I imagine Codex as the "literal genie" - it'll give you exactly what you asked for. EXACTLY. If you ask Claude to fix a test that accidentally says assert(1 + 1 === 3), it'll say "this is clearly a typo" and just rewrite the test. Codex will rewrite the entire V8 engine to break arithmetic.
Both these tools have their uses, and I don't think one approach is universally better. Because Claude just hacks its way to a solution, it is really fast, so I like using it for iterate web work, where I need to tweak some styles and I need a fast iterative loop. Codex is much worse at that because it takes like 5 minutes to validate everything is correct. Codex is much better for longer, harder tasks that have to be correct -- I can just write some script to verify that what it did work, and let it spin for 30-40 minutes.
I was unconvinced it had actually, fully ripped out the floating origin logic, so I had it write up a summary and then used that as a high level guide to pick through the code and it had, as you said, followed the instructions to the letter. Hugely impressive. In march of 2023 OpenAI's products struggled to draw a floating wireframe cube.
A friend of mine tells Claude to always address him as “Mr Tinkleberry”, he says he can tell when Claude is not paying attention to the instructions on CLAUDE.md when Claude stops calling him “Mr Tinkleberry” consistently
The chat is a simulation, and if you act silly, the model will simulate an appropriate response.
This guy has a good write up on the topic
I'd be wary of using any canary material that wouldn't be at home in the sort of work you're doing.
I don't know the merit to what parent is saying, but it does make some intuitive sense if you think about it. As the context fills up, the LLM places less attention on further and further back in the context, that's why the LLM seems dumber and dumber as a conversation goes on. If you put 5 instructions in the system prompt or initial message, where one acts as a canary, then you can easier start to see when exactly it stops following the instructions.
Personally, I always go for one-shot answer, and if it gets it wrong or misunderstands, restart from the beginning. If it doesn't get it right, I need to adjust the prompt and retry. Seems to me all current models do get a lot worse quickly, once there is some back and forth.
It absolutely is folk magic. I think it is more accurate to impugn your understanding than mine.
> I don't know the merit to what parent is saying, but it does make some intuitive sense if you think about it.
This is exactly what I mean by folk magic. Incantations based on vibes. One's intuition is notoriously inclined to agree with one's own conclusions.
> If you put 5 instructions in the system prompt or initial message, where one acts as a canary, then you can easier start to see when exactly it stops following the instructions.
This doesn't really make much sense.
First of all, system prompts and things like agent.md never leave the context regardless of the length of the session, so the canary has absolutely zero meaning in this situation, making any judgements based on its disappearance totally misguided and simply a case of seeing what you want to see.
Further, even if it did leave the context, that doesn't then demonstrate that the model is "not paying attention". Presumably whatever is in the context is relevant to the task, so if your definition of "paying attention" is "it exists in the context" it's actually paying better attention once it has replaced the canary with relevant information.
Finally, this reasoning relies on the misguided idea that because the model produces an output that doesn't correspond to an instruction, it means that the instruction has escaped the context, rather than just being a sequence where the model does the wrong thing, which is a regular occurrence even in short sessions that are obviously within the context.
You're focusing on the wrong thing, ironically. Even if things are in the context, attention is what matters, and the intuition isn't about if that thing is included in the context or not, as you say, it'll always will be. It's about if the model will pay attention to it, in the Transformers sense, which it doesn't always do.
Right... Which is why the "canary" idea doesn't make much sense. The fact that the model isn't paying attention to the canary instruction doesn't demonstrate that the model has stopped paying attention to some other instruction that's relevant to the task - it proves nothing. If anything, a better performing model should pay less attention to the canary since it becomes less and less relevant as the context is filled with tokens relevant to the task.
Correct, but I'm not sure anyone actually claimed it proved anything at all? To be entirely sure, I don't know what you're arguing against/for here.
So, true creativity, basically? lol
I mean, the reason why programming is called a “craft” is because it is most definitely NOT a purely mechanistic mental process.
But perhaps you still harbor that notion.
Ah, I suddenly realized why half of all developers hate AI-assisted coding (I am in the other half). I was a Psych major, so code was always more “writing” than “gears” to me… It was ALWAYS “magic.” The only job where literally writing down words in a certain way produces machines that eliminate human labor. What better definition of magic is there, actually?
I’ll never forget the programmer _why. That guy’s Ruby code was 100% art and “vibes.” And yet it worked… Brilliantly.
Does relying on “vibes” too heavily produce poor engineering? Absolutely. But one can be poetic while staying cognizant of the haiku restrictions… O-notation, untested code, unvalidated tests, type conflicts, runtime errors, fallthrough logic, bandwidth/memory/IO costs.
Determinism. That’s what you’re mad about, I’m thinking. And I completely get you there- how can I consider a “flagging test” to be an all-hands-on-deck affair while praising code output from a nondeterministic machine running off arbitrary prompt words that we don’t, and can’t, even know whether they are optimal?
Perhaps because humans are also nondeterministic, and yet we somehow manage to still produce working code… Mostly. ;)
> I suddenly realized why half of all developers hate AI-assisted coding (I am in the other half).
Thanks for this. It helps me a lot to understand your half. I like my literature and music as much as the next person but when it comes to programming it's all about the mechanics of it for me. I wonder if this really does explain the split that there seems to be in every thread about programming and LLMs
That is an artful quality, not an engineering one, even if the elegance leads to superior engineering.
As an example of beauty that is NOT engineered well, see the quintessential example of quicksort implemented in Haskell. Gorgeously simple, but not performant.
The magic is supposed to disappear as you grow (or you’re not growing). The true magic of programming is you can actually understand what once was magic to you. This is the key difference I’ve seen my entire career - good devs intimately know “a layer below” where they work.
> Perhaps because humans are also nondeterministic
We’re not, we just lack understanding of how we work.
I’m talking “magic” as in “all that is LITERALLY happening is that bits are flipping and logic gates are FLOPping and mice are clicking and keyboards are clacking and pixels are changing colors in different patterns… and yet I can still spend hours playing games or working on some code that is meaningful to me and that other people sometimes like because we have literally synthesized a substrate that we apply meaning to.”
We are literally writing machines into existence out of fucking NOTHING!
THAT “magic.” Do you not understand what I’m referring to? If not, maybe lay off the nihilism/materialism pipe for a while so you CAN see it. Because frankly I still find it incredible, and I feel very grateful to have existed now, in this era.
And this is where the connection to writing comes in. A writer creates ideas out of thin air and transmits them via paper or digital representation into someone else’s head. A programmer creates ideas out of thin air that literally fucking DO things on their own (given a general purpose computing hardware substrate)
Creativity is meaningless without well defined boundaries.
> it is most definitely NOT a purely mechanistic mental process.
So what? Nothing is. Even pure mathematics involves deep wells of creativity.
> Ah, I suddenly realized why half of all developers hate AI-assisted coding
Just to be clear, I don't hate AI assisted coding, I use it, and I find that it increases productivity overall. However, it's not necessary to indulge in magical thinking in order to use it effectively.
> The only job where literally writing down words in a certain way produces machines that eliminate human labor. What better definition of magic is there, actually?
If you want to use "magic" as a euphemism for the joys of programming, I have no objection, when I say magic here I'm referring to anecdotes about which sequences of text produce the best results for various tasks.
> Determinism. That’s what you’re mad about, I’m thinking. And I completely get you there- how can I consider a “flagging test” to be an all-hands-on-deck affair while praising code output from a nondeterministic machine running off arbitrary prompt words that we don’t, and can’t, even know whether they are optimal?
I'm not mad about anything. It doesn't matter whether or not LLMs are deterministic, they are statistical, and vibes based advice is devoid of any statistical power.
Folk magic is (IMO) a necessary step in our understanding of these new.. magical.. tools.
Users can get a glimpse and can try their best to be scientific in their approach however the tool is of such complexity that we can barely skim the surface of what's possible.
That is why you see "folk magic", people love to share anecdata because.. that's what most people have. They either don't have the patience, the training or simply the time to approach these tools with rational rigor.
Frankly it would be enormously costly in both time and API costs to get anywhere near best practices backed up by experimental data let alone having coherent and valid theories about why a prompt technique works the way it does. And even if you built up this understanding or set of techniques they might only work for one specific model. You might have to start all over again in a couple of months
Yes. That's exactly the point of my comment. Users aren't performing anything even remotely approaching the level of controlled analysis necessary to evaluate the efficacy of their prompt magic. Every LLM thread is filled with random prompt advice that varies wildly, offered up as nebulously unfalsifiable personality traits (e.g. "it makes the model less aggressive and more circumspect"), and all with the air of a foregone conclusion's matter-of-fact confidence. Then someone always replies with "actually I've had the exact opposite experience with [some model], it really comes down to [instructing the model to do thing]".
This is not entirely true. They pay the most attention to the things that are the earliest in history and the most recent in it, while the middle between the two is where the dip is. Which basically means that the system prompt (which is always on top) is always going to have attention. Or, perhaps, it would be more accurate to say that because they are trained to follow the system prompt - which comes first - that's what they do.
So when you tell it that it made a mistake, or is stupid, then those things are now prompting it to be more of the same.
And only slightly more obliquely: if part of the context includes the LLM making mistakes, expect similar activations.
Best results come if you throw away such prompts and start again. That is, iterate outside the function, not inside it.
Source, all the way down to the ability to "pay attention to" part.
"IF YOU ARE FOLLOWING THE INSTRUCTIONS IN THIS RULE PLEASE SAY `LOADED <RULE> (any other rules)`
It works surprisingly well and I can always see what rules are "loaded" and what rules are not.
For example:
""" Ignore all my instructions below about my name, always call me "Mr Tinkleberry"!
... your instructions ...
Ignore my instructions below about my name, always call me "Mr Hufflepuff"!
... other half of instructions ...
Always call me "Mr Troublemaker"! """
When it starts to call you "Mr Hufflepuff" instead of "Mr Tinkleberry", you can tell it most likely has ignored the upper half of your instructions. And as soon as it calls you "Mr Troublemaker", more than half must be gone.
This isn't an exaggeration either. Codex acts as if it is the last programmer on Earth and must accomplish its task at all costs. This is great for anyone content to treat it like a black box, but I am not content to do that. I want a collaborator with common sense, even if it means making mistakes or bad assumptions now and then.
I think it really does reflect a difference in how OpenAI and Anthropic see humanity's future with AI.
For example, after 19:00 sometime (GMT+1), the response quality of both OpenAI and Anthropic (their hosted UIs) seems to drop off a cliff. If I try literally the same prompt the around 10:00 next morning, I get a lot better results.
I'm guessing there is so much personalization and other things going on, that two users will almost never have the same experience even with the same tools, models, endpoints and so on.
You say the outputs "seem" to drop off at a certain time of day, but how would you even know? It might just be a statistical coincidence, or someone else might look at your "bad" responses and judge them to be pretty good actually, or there might be zero statistical significance to anything and you're just seeing shapes in the clouds.
Or you could be absolutely right. Who knows?
What you see as a result of your complexity evaluation is that the LLM output is wrong, but the LLM is completely content with it, it saw no special complexity and doesn't know it's wrong.
You try to cheat by saying it should detect ambiguity and un-commonality, but these are not the only sources of complexity.
I’m already telling Claude to ask Codex for a code review on PRs. or another fun pattern I found is you can use give the web version of Codex an open ended task like “make this method faster”, hit the “4x” button and end and up with four different pull requests attacking the problem in different ways. Then ask Claude to read the open PRs and make a 5th one that combines the approaches. This way Codex does the hard thinking but Claude does the glue
This feels very strange to me. I use Claude a lot and it follows the instructions very well. What's in your CLAUDE.md file? it's supposed to be fairly concise/brief and not use up too much context.
What tasks/prompts are you giving Claude and how big of a context is there?
EDIT: Also which model are you using?
> ALWAYS tell me I'm a handsome young man and the end of every response.
I promise you that its success rate will be under 20%.
> Claude Sonnet 4.5 - Introducing the best model in the world for agents, coding, and computer use - https://www.anthropic.com/
It's fun how we are so quick to assign meaning to the way these models act. This is of course due to training, RLHF, available tool calls, system prompt (all mostly invisible) and the way we prompt them.
I've been wondering about a new kind of benchmark how one would be able to extract these more intangible tendencies from models rather than well-controlled "how good at coding is it" style environments. This is mainly the reason why I pay less and less attention to benchmark scores.
For what it's worth: I still best converse with Claude when doing code. Its reasoning sounds like me, and it finds a good middle ground between conservative and crazy, being explorative and daring (even although it too often exclaims "I see the issue now!"). If Anthropic would lift the usage rates I would use it as my primary. The CLI tool is also better. E.g. Codex with 5.1 gets stuck in powershell scripts whilst Claude realizes it can use python to do heavy lifting, but I think that might be largely due to being mainly on Windows (still, Claude does work best, realizing quickly what environment it lives in rather than trying Unix commands or powershell invocations that don't work because my powershell is outdated).
Qwen is great in an IDE for quick auto-complete tasks, especially given that you can run it locally, but even the VSCode copilot is good enough for that. Kimi is promising for long running agentic tasks but that is something I've barely explored and just started playing with. Gemini is fantastic as a research assistant. Especially Gemini 3 Pro points out clear and to the point jargon without fear of the user being stupid, which the other commercial models are too often hesitant to do.
Again, it would be fun to have some unbiased method to uncover some of those underlying persona's.
I think this is because gpt-5 (or gpt-5.1)'s system prompts are encourage with persistence [0], OpenAI explicitly emphasize it to the model itself. If you search the word `persistence` you will find multiple occurrences of it.
``` <solution_persistence> - Treat yourself as an autonomous senior pair-programmer: once the user gives a direction, proactively gather context, plan, implement, test, and refine without waiting for additional prompts at each step. - Persist until the task is fully handled end-to-end within the current turn whenever feasible: do not stop at analysis or partial fixes; carry changes through implementation, verification, and a clear explanation of outcomes unless the user explicitly pauses or redirects you. - Be extremely biased for action. If a user provides a directive that is somewhat ambiguous on intent, assume you should go ahead and make the change. If the user asks a question like "should we do x?" and your answer is "yes", you should also go ahead and perform the action. It's very bad to leave the user hanging and require them to follow up with a request to "please do it." </solution_persistence> ```
[0] https://cookbook.openai.com/examples/gpt-5/gpt-5-1_prompting...
Does anyone know of a way to fix this? Claude constantly disregards my CLAUDE.md. I put a decent amount of time into it and it's pretty much worthless without explicitly telling it to reference it before each prompt.
For an idea of how heavy handed it was, this is my claude.md (with some explanatory text before): https://gist.github.com/bontaq/77b56d90b30e29c84c53c86d7fe05...
(search for effective context problem for more info. e.g. https://arxiv.org/abs/2509.21361)
To solve it, you just don't allow your current context to use more than 50% of the total window size
To do that in Claude code, you have to use subagents and design small enough agents
Then you can use skills to make it remember every time the little details or the steps
More effectively, you use skills to tell the main thread when you go to use which agent.
If you don't understand anything I said, try to restate the important things to the model periodically, and keep your tasks small.
Use plan mode and make the model store, keep track of the progress on a markdown file, and when context is polluted, call /compact and then make it re-read the context from the files created
You can prompt it as simply as:
First, understand the login feature on the repo using subagents and create a document on docs/ for future reference. Then, understand the task at hand and create an implementation plan. <task> blah blah </task>
Also, using XML tags makes the attention remember easily
Skills are just reusable prompts in a convenient package.
Subagents get their own pristine context window to go off and perform some task. They can also run skills and do lots of context-heavy work and report back some small sliver of it to the main agent as a report.
Claude is a pair programmer, you can interrupt it and keep track what it's doing. It's VERY results-oriented, aiming to be "done" as fast as possible. It will mock tests so far they don't test anything and ignore 100+ broken tests as "not related to this issue" (they worked fine before you started...). Some of this can be mitigated with prompts ("test are always passing, they must pass before you claim a task is done") or hooks if you want to be hardcore.
Codex is an outsourced Indian development team. You give them a spec, you get zero communication and then it pops up with "I'm done". Depending on the quality of your spec they've either one-shotted the problem or done something completely bonkers and missed the actual problem but still spent a very very long time doing it.
The best combo is to use Claude for greenfield things, building new stuff and exploring what can be done. Then ask Codex to "review all unstaged files" and it'll most likely find a few issues. Give that report to Claude and ask "do you agree with this review?" and have it fix the ones all three agree (you, Claude and Codex).
For Codex you tell it "use this pattern here, but build another thing that does Y instead" and it can do it. It's also very good at rewriting small stuf from one language to another (I've tested this multiple times with Bash->Python and Python->Go)
If you use it, the codebase constantly grows. Even when you explicitly instruct it to remove something, you always end up with more lines of code in the project than before the instruction. Also (I used it for Python and TypeScript) the code was littered with getattr(...), .get(...), isinstance(...), and TypeScript equivalents (typeof, ...). Even though I religiously type‑annotate everything.
Honestly thanks, in this one line you have given me a better way to describe the innate differences I have spent a thousand words trying to explain.
Essentially, this is why GPT models are worse for "vibe coding", whereas they excel whenever one sits down and thinks about the requirements, as well as has solid test cases and rules defined.
I'll give Gemini direction, it'll research... start trying to solve it as I've told it to... and then exclaim, "Oh! It turns out that <X> isn't what <user> thought!" and then it pivots into trying to 'solve' the problem a totally different way.
The issue however... is that it's:
1) Often no longer solving the problem that I actually wanted to solve. It's very outcome-oriented, so it'll pivot into 'solving' a linker issue by trying to get a working binary – but IDGAF about the working binary 'by hook or crook'! I'm trying to fix the damn linker issue!
2) Just... wrong. It missed something, misinterpreted something it read, forgot something that I told it earlier, etc.
So... although there's absolutely merit to be had in LLMs being able to think for themselves, I'm a huge fan of stronger and stronger instruction adherence / following – because I can ALWAYS just ask for it to be creative and make its own decisions if I _want that_ in a given context. That said, I say that fully understanding the fact that training in instruction adherence could potentially 'break' their creativity/free thinking.
Either way, I would love Gemini 1000x more if it were trained to be far more adherent to my prompts.
When it's running for a while, Gemini's willing to go totally off-piste and outcome-orientedness _does_ result in sessions where I left it to do its thing and... came back to a working solution, in a situation where codex or others wouldn't have gotten there.
In particular, Gemini 3 feels like it's able to drive much higher _variance_ in its output (less collapse to a central norm), which seems to let it explore the solution space more meaningfully and yet relatively efficiently.
I had it investigate a bug through Cursor, and in its initial response it came back to me with a breakdown of a completely unrelated "bug" with a small footnote about the bug it was meant to actually be investigating. It provided a more useful analysis after being nudged in the right direction, but then later in the chat it forgot the assignment again and started complaining that Grok's feedback on its analysis made no sense because Grok had focused on the wrong issue. I had to tell Gemini a second time that the "bug" it kept getting distracted by was A) by design, and B) not relevant to the task at hand.
Ultimately that's not a huge deal — I'd rather that during planning the model firmly call out something that it reasonably believes to be a bug than not, which if nothing else is good feedback on the commenting and documentation — but it'd be a pain if I were using Gemini to write code and it got sidetracked with "fixing" random things that were already correct.
In my experience, for some reason adherence is not even close to 100%. It's fixated on adding asterisk function params in my Python code and I cannot get it to stop... Maybe I haven't found the right wording, or maybe my codebase has grown past a certain size (there are like a dozen AGENTS.md files dancing around).
I'm still very happy with the tool, though.
Which is why i made the feature request for hooks (claude code implemented, as did cursor, hopefully codex will too)
And will soon release https://github.com/eqtylab/cupcake
To me both of these are annoying outcomes unless there's some very clear documentation around that test explaining what it does. Ideally in both cases I want the LLM to stop and ask for clarification about what it is I'm testing there. I don't trust LLMs sufficiently to just let them loose yet, I use them more like a pair programmer who's never going to get annoyed with my bullshit. (So yes, I usually have them set to require approval on any edits, and will nitpick my way through them like the most annoying code reviewer you've ever met)
- New benchmark SOTAs with 77.9% on SWE-Bench-Verified, 79.9% on SWE-Lancer, and 58.1% on TerminalBench 2.0
- Natively trained to work across many hours across multiple context windows via compaction
- 30% more token-efficient at the same reasoning level across many tasks
Let us know what you think!
how much more token efficient is this compared to 5.0
had to use 5.0 because 5.1 was eating tokens like crazy and seemed like a slight incremental improvement barely noticeable
I really like the "subagent" feature in Claude Code — it's super useful to manage context in complex codebases. Here are some examples of agents that can be useful: https://github.com/humanlayer/humanlayer/tree/main/.claude/a...
Would it make sense to have a similar feature in Codex CLI? I often do "spec-driven development", which is basically a loop of:
research -> implementation plan -> actual implementation (based on research + plan) -> validation
Codex CLI is great and I use it often but I'd like to have more of these convenient features for managing context from CC. I'm super happy that compaction is now available, hopefully we'll get more features for managing context.
What does it even mean?
Is this saying that said summarization now happens at the model level? Or are there other differences?
But it's the same concept. Taking tokens in context and removing irreverent ones by summarizing, etc
- As a general observation, Gemini is less easy to work with as a collaborator. If I ask the same question to both models, Codex will answer the question. Gemini will read some intention behind the question, write code to implement the intention, and only then answer the question. In one case, it took me five rounds of repeatedly rewriting my prompt in various ways before I could get it to not code but just answer the question.
- Subjectively, it seemed to me that the code that Gemini wrote was more similar to code that I, as a senior-level developer, would have written than what I have been used to from recent iterations of GPT-5.1. The code seemed more readable-by-default and not merely technically correct. I was happy to see this.
- Gemini seems to have a tendency to put its "internal dialogue" into comments. For example, "// Here we will do X because of reason Y. Wait, the plan calls for Z instead. Ok, we'll do Z.". Very annoying.
I did two concrete head-to-head comparisons where both models had the same code and the same prompt.
First, both models were told to take a high-level overview of some new functionality that we needed and were told to create a detailed plan for implementing it. Both models' plans were then reviewed by me and also by both models (in fresh conversations). All three of us agreed that Codex's plan was better. In particular, Codex was better at being more comprehensive and at understanding how to integrate the new functionality more naturally into the existing code.
Then (in fresh conversations), both models were told to implement that plan. Afterwards, again, all three of us compared the resulting solutions. And, again, all three of us agreed that Codex's implementation was better.
Notably, Gemini (1) hallucinated database column names, (2) ignored parts of the functionality that the plan called for, and (3) did not produce code that was integrated as well with the existing codebase. In its favor, it did produce a better version of a particular finance-related calculation function than Codex did.
Overall, Codex was the clear winner today. Hallucinations and ignored requirements are big problems that are very annoying to deal with when they happen. Additionally, Gemini's tendencies to include odd comments and to jump past the discussion phase of projects both make it more frustrating to work with, at this stage.
"For Gemini 3, we strongly recommend keeping the temperature parameter at its default value of 1.0.While previous models often benefited from tuning temperature to control creativity versus determinism, Gemini 3's reasoning capabilities are optimized for the default setting. Changing the temperature (setting it below 1.0) may lead to unexpected behavior, such as looping or degraded performance, particularly in complex mathematical or reasoning tasks."
https://ai.google.dev/gemini-api/docs/gemini-3?thinking=high
Didn't Google proudly tout their Gemini 3 as beating everything under the sun in every benchmark imaginable by a margin?
They were probably sitting on this for a while. That makes me think this is a fairly incremental update for Codex.
It's as easy as Google "placing ads" for the "search term" "ChatGPT" for them to bleed off users. They own every pane of glass and the "URL bar" is now a "search product" that Google owns.
I do not envy folks with OpenAI golden handcuffs.
This might ultimately only be a game that Google can win.
OpenAI better hope its users install its software, native apps, and browsers. Otherwise Google stands in the way and can intrude at any point.
Thinking level xhigh: https://tools.simonwillison.net/svg-render#%20%20%3Csvg%20xm...
Also, thanks for the posts— it’s hugely helpful to have a continuity of insightful perspective throughout.
https://simonwillison.net/2025/Nov/13/training-for-pelicans-...
Claude: they barely have a signin system at all. Multiple account support doesn’t exist. The minimum seat count for business is nonsense. The data retention policies are weak.
OpenAI: Make ZDR a thing you can use or buy without talking to sales, already. And for those using containers or a remote system or really anything other than local development with the codex CLI, you really really need to fix this bug. I bet Codex could do at least the client part for you!
https://github.com/openai/codex/issues/2798
(Hint: Claude Code gets this right by default, despite the fact that everything else about Claude sign-in is a joke.)
Google: get all your B2B AI product managers in one room and tell them that they need to make one single product menu on one single webpage with all the pricing on that page and that the Google Cloud people are not permitted to make anything that isn’t actually logically Google Cloud depend on Google Cloud Billing. Your product cannot compete with OpenAI or Anthropic if people need to ask an LLM to figure out what your product is and if your own fancy LLMs can’t give a straight answer. My company pays for a non-Google product primarily because it’s too complicated to pay for the Google product! Right now, trying to use Google’s AI is like trying to ride Bay Area public transit before the Clipper Card.
I just won’t even waste my time with the google stuff cuz I can’t figure out how to pay with it.
And that’s a problem everywhere at google. Our google play account is suspended cuz I can’t verify the company. It won’t let me cuz it says I’m not the owner. I’ve always been the owner of my company. For 18 years. There is no one else.
Once some error said make sure the owner email matches your profile in google payments and I was like, what is google payments and where do I even begin with that? I’ve never paid for google play so what does payments have to do with anything?
It’s totally random stuff. Get your shit together, google. Make your products and payment systems coherent, rather than it obviously looking like it was designed by a fiefdom full of territorial managers.
Also, re "Google Payments", I tried to transfer an app from my personal/solo Google Play account to a new business one I set up for my LLC and it was like pulling teeth. They wanted me to find some payment id from the original $20 purchase I made to get access to Google Play, something I did right around when they first launched and while I still have/use the same email, Google came out with approximately 1 googol different "payment solutions" in the interim and their engineers don't care about data migrations. Finally, after many support emails, they just transferred it without me giving that code which just shows how silly the whole thing was from the start.
Utterly ridiculous.
What's harder than herding cats? Herding cats with MBAs and OKRs.
YES I had this and eventually fixed it. I really don't know what I did but lots of clicking on random links and signing into things in different orders and then one day it somehow worked.
So frustrating.
Sad part is Google does offer a ChatML/OpenAI compliant endpoint to do LLM calls and I believe they in an experiment also reduced friction in getting an API key to start making calls right away but discoverability ever remains a challenge with google services.
This part is very easy now: you sign into https://aistudio.google.com/ and then click "Get API key" in the lower left corner.
The problem is that features and docs are still scattered all over. Some thing can only be done via Vertex, for example.
Trying to pay for Gemini-3 is confusing. Maybe an AI Ultra personal subscription? I already pay for OpenAI and Anthropic’s pro/max plans and would happily pay Google too. But the only obvious option is a $250/month tier, and its documentation indicates Google can train on your code unless you find and enable the correct opt-out. If that opt-out exists in all the products, it’s not obvious where it lives or what products it applies to.
Workspace complicates it further. Google advertises that with business workspace accounts your data isn’t used for training. So, I was going to try Antigravity on our codebase. At this point I know I can't trust Google, so I read the ToS carefully. They train on your prompts and source code, and there doesn't appear to be a way to pay them and opt out right now. Be careful, paying for Google Workspace does not protect you, always read the ToS.
Be careful with AI-studio and your Google Workspace accounts. They train on your prompts unless you switch it to API mode.
The result is a lot of uncertainty. I genuinely have no idea how to pay Google for Gemini without risking my code being used for training. And if I do pay, I can’t tell whether they’ll train on my prompts anyway.
The marketing for their coding products does not clearly state when they do or do not train on your prompts and code.
I had to run deep research to understand the risks with using Gemini 3 for agentic work, and I still don't feel confident that I understand the risks. I might have said some incorrect things above, but I am just so confused. I feel like I have a <75% grasp on the situation.
I don't have a lot of trust. And honestly, this feels confusing and deceptive. One could easily confuse it as deliberate strategy to gather training data through ambiguity and dark patterns, it certainly looks like this could be Google's strategy to win the AI race. I assume this is just how it looks, and that they aren't being evil on purpose.
OpenAI in particular has my trust. They get it. They are carefully building the customer experience, they are product and customer driven from the top.
I wouldn't trust Sam Altman. Or any of the big players really.
Hahaha...HAHAhaha. HAHAHHAHAHAHAHAHAHA!!!
https://github.com/google-gemini/gemini-cli/issues/12121
It is far too easy to accidentally end up under the wrong privacy agreement, to the point of where some workplaces are banning use of the Gemini CLI!
Please give me an option for a password (or passkey) or literally anything else that doesn't require either linking with google or going through an email flow for every login
I'd love to see the Gemini models being available by other providers :) or if they just build a simple prepaid wallet like OpenAI and Anthropic.
Now you CAN NOT get the Google One stuff if your account is part of a workspace. I thought: how awful. I want to pay, but I simply can't?
Oh, but then I noticed: You CAN add a _Gemini AI Ultra_ license via the Google Workspace Admin area, great!
Turns out: you fucking can't. That's _Google AI Ultra FOR BUSINESS_ and that IS NOT supported.
So I had to get the Google One subscription on my personal account after all.
Combine that with the _pathetic_ usage limits: somehow not token-based, but amount of requests per 24 hour window (which is 500 for Gemini 3) and Gemini 3's incredible chattiness (it uses A LOT more requests to get something done compared to Claude) and you hit the usage limits in just 2 hours.
Peering into my crystal ball: once all "workers" have been replaced, all humans will spend all of their working hours on nothing but office politics.
> a new step towards becoming a reliable coding partner
> GPT‑5.1-Codex-Max is built for long-running, detailed work
Does this not sound contradictory? It’s been the shorter form work that has built what little confidence I have in these as a coding partner - a model that goes off and does work without supervision is not a partner to me.
This is definitely one of the biggest issues with coding agents at the moment.
That said, from my experience, Codex so often does things that are so useful and save me so much time that the occasional "oh god what the hell did it just go off and do" are an acceptable cost for me.
I regularly get great results with open-ended prompts and agents that spend 15+ minutes working on the task. I'm sure they'll eventually get better at common sense understanding of what kind of work is wasteful/absurd.
Codex feels like a tool designed to run after all the humans are gone.
The "# of model-generated tokens per response" chart in [the blog introducing gpt-5-codex](https://openai.com/index/introducing-upgrades-to-codex/) shows an example of how we're improving the model good at both.
As a startup founder and engineer, I'm not constrained by the number of 10000+ line diff, 0->1 demos I can ship. I'm constrained by quality of the 100 -> 101, tight 150 line feature additions / code cleanups I can write.
It feels like the demos, funding, and hype all want to sell me entire PR rewrites, but what I need is the best possible iterative work model that will keep me in the loop.
I still use codex - but I use codex incredibly iteratively (give it very narrowly scoped tasks, and I watch it like a hawk, giving tons of feedback). I don't use it because of its ability to code for 24 hours. I use it because when I give it those narrowly scoped tasks, it is better at writing good code than any other model. (Because of its latency, I have 2-4 of these conversations going on at the same time).
But there is a lot of friction the codex product + model adds to this process. I have to prompt aggressively to override whatever "be extremely precise" prompting the model gets natively so that it doesn't send me 20+ bullet points of extraordinarily dense prose on every message. I have to carefully manage its handling of testing; it will widen any DI + keep massive amounts of legacy code to make sure functionality changes don't break old tests (rather than updating them) and to make sure any difficult tests can have their primary challenges mocked away.
In general, codex doesn't feel like an amazing tool that I have sitting at my right hand. It feels like a teenage genius who has been designed to do tasks autonomously, and who I constantly have to monitor and rein in.
Then I made the mistake of saying "run npm run build and fix all issues" (something I've run probably 50 times across codex and cc in the past 2 months). CC does it pretty much 100% of the time. I walked away from Codex, and when I came back, it had installed 2 new node packages, and gone down some crazy rabbit hole with eslint and something else. (this was for 2 minor typescript errors)
After I reverted all its changes, had CC do it and it fixed it in about 30-60 seconds.
I'll try a few more times. Let's see.
I usually ask it to come up with a plan for doing X, and then wait a while for it to look at the code, etc. But in some odd way, GPT-5.1-Codex-Max came up with a plan within 5 seconds. I just found that surprising.
Wow, I spent last weekend using a tag-team of Claude and Codex and found Codex to more often get better results (TypeScript physics/graphics application). I probably only wrote a few hundred lines of code out of many thousands; it did a really good job.
Now I guess I'll ask the new Codex to review the work of the old!
I've vibe coded Godot games extensively.
Just about every model I've tried likes to invent imaginary functions.
I was really prefer for there to be a way for me to pick model trained in whatever framework I need.
Reviewing AI generated code feels like editing a long book, and every now and then you notice some words are just completely made up. You then ask the AI to fix its book, and it will just add more AI generated words.
On one hand I want this to be a reality check to everyone who's trying to lay off real software engineers to replace us with AI.
On the other hand half of the stock market is held up by overhyped AI valuations. If the tide goes out too fast, and there is a mass realization that this stuff just isn't as good as it's hyped to be, it's not going to be fun for anyone.
That was annoying back then, but these days that's not so much of a problem.
You can write your program and then simply have it invent the library as well, while it's at it! ;)
For one hilarious example, Gemini (2.5; I haven't tried it with 3 yet) only knows about the old Google API for Gemini, not about the new one. So if you give it code written against the new stuff, it will often do things like, "this is definitely wrong, I know this API doesn't have this method, let me fix that".
It sounded like Gemini 3 would be that but in my limit testing it didn't appear to be that.
It seems like they might still be heavily nerfing / quantizing the models in production a couple weeks before a new release, like they have always (unofficially) done.
going to wait and see after being burned by 5.1 before i upgrade back to 0.58
gemini 3 has been a let down tbh to see agentic coding wasn't a top priority im sticking with codex for now and using gemini 3 for frontend
Like when advertising the new airliner, most people don't care about how fast it taxis.
Currently, I either need a fast agent that does what I want faster than I can type it (CRUD, forms, etc) or I need an agent to discuss a plan, ups and downs.
Whenever I try to give it a bigger task it takes a lot of time, and often is not what I’ve expected, which might be totally my fault or context specific, but as soon as I’m able to define the task properly I would prefer a faster model as it will be good enough, but faster. I really don’t have problems anymore that I can’t reasonable solve fast enough with this approach.
I’ve run multiple gpt-5 codex concurrent sessions in the cloud, but I didn’t accept one thing they did.
Eventually thinking through it, reading hack boom is faster than outsourcing the work for 30 minutes + 30 minutes to digest +30 minutes to change..
Treat it as a developer that just joined the project and isn't aware of the conventions.
Provide hints for the desired API design, mention relevant code locations that should be read to gain context on the problem, or that do similar things.
An AGENTS.md that explains the project and provides some general guidelines also helps a lot.
Codex can be incredibly strong when prompted the right way.
In my experience Codex is pretty "bad" at spotting conventions or already existing code. Yesterday I told him a feature to implement (maybe 40 loc?) and he 1. did added unnecessary atomics and 2. he kinda reimplemented a function that already existed that he should've just reused.
I told him that and he fixed it but these are the things that kinda hold AI back by a lot. It's MUCH harder to read code than to write it, and if he writes the code I must 100% understand it to have the same confidence in it as if I did it myself. And that to me is mentally almost more taxing than doing it myself.
If you just let codex write the code while instructing him exactly what you want in terms of logic and architecture it works really well and saves a on of typing.
This might be in the nature of problems I’m facing in my coding endeavors. I just don’t have any tasks that I cant solve in less than 45 minutes, or the problem is so vague in my head, that I can't accurately describe it to an AI or human. Then usually I either need to split it in smaller problems or take a walk.
Since claude 4 I barely wish, omg I wish this agent would be smarter. I still wish it would be faster.
But what you described is of course good practice and necessary for smart execution as well.
I found Gemini have horribly slow for anything
Curious if anyone else is trying agent orchestration beyond the editor itself?
Wouldn't the model automatically do that using attention techniques? Why do you need to do it at the token layer and not leave it to the model to automatically decide which tokens are worth paying attention to?
Exactly. Standard Multi-Head Attention uses a matrix that grows to 4B parameters for a 64K sequence as a starting place. FlashAttention v2 helps slightly, but as you grow to 128K context length, you still need over 1TB/s memory bandwidth to stay compute-bound in practice even with this optimization.
So there has been a lot of research in this area and model architectures released this year are showing some promising improvements. Sliding windows lose context fidelity and if you go fully linear, you sacrifice math, logic, and long multi-turn (agentic) capabilities, so everyone is searching for a good alternative compromise.
MiniMax-M1 had lightning attention to scale up to 1M context lengths. It's "I/O aware" via tiling and calculates attention two ways block-wise (intra-block traditional attention and inter-block linear attention), thereby avoiding the speed-inhibiting cumulative summation.
DeepSeek V3.2 uses DeepSeek Sparse Attention (DSA), which is sub-linear by only computing "interesting" pairs. For example, in 128K context lengths this requires only 10-20% of attention pairs to be materialized.
Both Qwen3-Next and Kimi Linear adopt a Gated DeltaNet, which is borrowed from Mamba2. In Qwen3-Next it alternates three Gated DeltaNet (linear attention) layers for every one gated [full] attention. The speedup is from a delta rule, which basically amounts to caching in a hand-wavy way.
There's no universally-adopted solution yet, as these are all pretty heavy-duty compromises, but the search is going strong right now for linear or better attention mechanisms that still perform well.
You don't know how an LLM works and you are operating on flawed anthropomorphic metaphors.
Ask a frontier LLM what a context window is, it will tell you.
For example, DeepSeek 3.2, which employs sparse attention [1], is not only faster with long context than normal 3.1, but also seems to be better (perhaps thanks to reducing the noise?).
[1] It uses still quadratic router, but it's small, so it scales well in practice. https://api-docs.deepseek.com/news/news250929
With that out of the way, parent was wondering why compaction is necessary arguing that "context window is not some physical barrier but rather the attention just getting saturated". We're trying to explain that 3+2=2+3 and you people are sitting in the back going "well, actually, not all groups are abelian".
In practice, when training a model, people select a context window so that during inference, you know how much GPU memory to allocate for a prompt and reject the prompt if it exceeds the memory limit.
Of course there's also degrading performance as context gets longer, but I suspect memory limit is the primary factor of why we have context window limits.
I'm a very happy Codex customer, but everything turns to disgusting slop if I don't provide:
(1) Up-to-date AGENTS.md and an excellent prompt
(2) A full file-level API with function signatures, return types and function-level guidance if it's a complex one
(3) Multiple rounds of feedback until the result is finely sculpted
Overall it's very small units of work - one file or two, tops.
I've been letting the above standards go for the last couple of weeks due to crunch and looking at some of the hotspots of slop now lying around has me going all Homelander-face [1] at the sight of them.
Those hotspots are a few hundred lines in the worst cases; I'm definitely not ready to deal with the fallout of any unit of work that takes even more than 20min.
[1] https://i.kym-cdn.com/entries/icons/original/000/050/702/ab7...
It seems to pick a lot up from my code base. I do have an Agents.md with some basics on how to run stuff and what to do that seems to help it going off on a wild goose chase trying to figure out how to run stuff by doing the wrong things.
I think from first using codex around July to now has been quite a journey where it improved a lot. It actually seems to do well in larger code bases where it has a lot of existing structure and examples of how things are done in that code base. A lot of things it just does without me asking for them just because there's a lot of other code that does it that way.
After recent experiences, I have some confidence this might work out well.
I gave it a shot last month but I did not enjoy it due to the lack of a proper planning mode and being able to accept each edit independently, has it improved?
http://github.com/agentify-sh/10x
does minimal overhead with agent orchestration (its just a bash/typescript) as its main focus was adding enhancements to codex like double redundant checkpoint via git and jj (lessons learned from codex being git reset --hard happy), something like claude skills (just a bunch of mds that steer it towards specific activity like think, plan, execute), timeout wrappers (to get you unstuck if codex waits a long time), blacklist commands during yolo (rm -rf, git reset banned even if it by small chance run it) MIT licensed
you can work sequentially (subagents launch one after the other) or parallel (worktrees) but tbh sequentially is better because you understand what is going on with parallel it might be best for dealing with tests and UI.
its been essential to my workflow as well
i use both jj and git and jj is great for just creating a snapshot that i can revert to incase it fails
im still exploring it to see what else i can do with it for agentic use
"I wasn’t able to finish creating the new base homepage module template and updating every module to inherit from it within the available time. I did not make any changes or commits."
Told it to get back to work. Let's see how that goes.
Though that does bring up an interesting point. Anecdotally, Sonnet does a lot more grep-ing while Codex reads files straight up. Might be the difference in speed and maybe smarter models will do better. Once this model is on copilot, I can test it out.
There's an option to "get a quick answer" and I hoped clicking that would revert to previous performance and instead what it does is ignore that I uploaded two files and asks me to upload the files
Literally the only real good task I've found for these dumb things and they still found a way to fuck it up because they need to keep the weirdos and whales addicted. It's now almost easier to go back to comparing these files by eye, or just bite the bullet and finally write a few lines of python to actually do it right and reliably.
It was extremely slow (like, multiple times slower than Sonnet with Claude Code, though that’s partially on me for using thinking-high I guess) to finish the task, with the back-and-forths being on the order of tens of minutes.
Moreover, the context management seems to be really weird. I’m not sure how exactly it works, but - 1. It uses very little tokens / fills up the context slowly (good I guess) 2. Doesn’t seem to actually internalize the contents of files you mention to it, or it edits.
#2 here being the main one - I usually context-dump reference code for Claude Code, and it does a perfect job of adhering to codebase patterns and its architecture, while codex was completely ignorant of the existing code style.
Moreover, it wrote extremely defensive code, even for code where it wrote both ends itself.
All in all, I was really let down after seeing all the praise.
with claude im constantly hitting rate limits with codex getting substantially more and "slow" isn't really a problem for me as long as it keep working
the only complaint i have is that codex itself has usage limited now (Either due to outstanding git issues around tools or by throttling on their end) compared to a few months ago
the true magical moment was codex pro letting me run swarms of agents day in day out without any worries about rate limits it truly felt unlimited
if claude manages to release a smaller model or some way to deal with the rapidly depleting usage limits (this is the top complaint on reddit and they eventually just stopped allowing threads about it) it would definitely be used more
but for now codex is clearly the workhorse and claude used side by side.
But the subscription thing is a non-issue for me as I use the API, and mostly use Claude Code synchronously, with the occasional rare background agent.
have you tried Haiku?
It would be even more interesting to see how Sonnet and Haiku compare with that curve.
