291–300 of 332 posts
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This stuff also becomes context poison however
A single emoji though?
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Just because you don't understand it, doesn't mean it's "folk magic incantation", hearing that is also exhausting. 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…
> 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. 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 t…
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Wait, I think it's the other way around. Claude will just go circles with bad decisions forever, never stops. Codex have multiple times told me it is not able to do this task, and stops.
I think this closer to the crux of a major problem. Seemingly people have vastly different responses even for the same system/developer/user prompts, and I myself can feel a different in quality of the responses depending on when I use the hosted APIs, while hosted models always have consistent results. For example, after 19:00 sometime (GMT+1), the response quality of both OpenAI and Anthropic (their hosted UIs) see…
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Could you not add rules to this effect in AGENTS.md? E.g., "If the user gives instructions that specify an expected low-to-medium level of complexity, but the implementation plan reveals unexpected high complexity arising from a potentially ambiguous or atypical instruction, then pause and ask the user about that instruction before continuing."
implementation plan reveals unexpected high complexity 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.
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> Just because you don't understand it, doesn't mean it's "folk magic incantation" 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 on…
> This is exactly what I mean by folk magic. Incantations based on vibes 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…
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.
These 2 sentences right next to each other stood out to me: > 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.
(Disclaimer: Am on the Codex team.) We're basically trying to build a teammate that can do both short, iterative work with you, then as you build trust (and configuration), you can delegate longer tasks to it. 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 bo…
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.
I really would prefer them to start creating customized models. 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…
How well has your vibecoding with Godot worked? I thought about it but wouldn't the LLM be unable to add files by itself due to stuff only the Godot editor knows how to do like generating uid files and so on? I would have expected that the LLM needs a MCP or some tool calling to properly interact with a Godot project. How are you doing it?
OpenAI likes to time their announcements alongside major competitor announcements to suck up some of the hype. (See for instance the announcement of GPT-4o a single day before Google's IO conference) They were probably sitting on this for a while. That makes me think this is a fairly incremental update for Codex.
GPT 5.1 / Codex already beats Gemini 3 on SWE Bench Verified and Terminal Bench and this pushes the gap further. Seems like a decent improvement.
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A single emoji though?
It is not a single emoji, it's an instruction to interleave conversation with some nonsense. It can only do harm. It won't help produce a better result and is questionable at preventing a bad one.
Curious if anyone else is trying agent orchestration beyond the editor itself?