41–50 of 334 posts
gpt-6-astra is a bitch, it constantly scope creeps itself with "yet another thing" to give it that darn polished lick. the results are eventually a little bit better but at what cost? let's do the math. gpt-5.6-sol: 1x base gpt-6-astra 2.5x base in subscription then gpt-6-astra tends to spawn subagents a lot, often with all kinds of models such as gpt-5.6, 5.3-codex etc., which is neat. it's a good coordinator but ev…
I’ve observed exactly these patterns with Opus and Fable as well - for example, forgetting that they can edit files and instead use python scripts as a patching tool…
Sounds like you don't have enough experience with coding agents. Deterministic scripts must always be preferred instead of LLM tool calls. In fact, you should instruct your agents to write code to execute instead of letting them call tools.
LLMs sometimes like to execute one-off Python scripts to make edits to files rather than just calling the edit tool directly. Both are tool calls so saying that you should have it write code instead of doing tool calls makes no sense because writing code is a tool call for it...
It’s been 2 days and it made no real progress on the actual app. It created docs, scripts, workflows, and it’s doing a bunch of reviewing on every PR.
I told it that I just need an MVP.
I’m pretty sure an average senior engineer would have finished that task much quicker, and guaranteed with more readable, higher-quality code. Meanwhile, I think I’ve easily crossed 100k tokens so far on nothing.
Funny world we’re living in that this is “SOTA” and “AGI”.
I’m genuinely curious what these OAI and A/ engineers are working on that they praise these models so much. I did not see any improvement since Opus 4.5.
Also, I’m really unimpressed by any “one shot” demo that’s out there in the wild. It means nothing for serious software engineering.
In SWE I've found gpt-6-astra (high) inconsistent and oddly focused on overtly taking responsibility for mistakes it made rather than prioritizing concrete steps to rectify problems. Such steps once elicited are often either incomplete or beyond the scope.
> And potentially as a byproduct of enabling all of this, you can now slop your way to a one-shot 3D game over the weekend which looks impressive. I think we've finally reached a weird point where AI has effectively reduced the amount of competition that real game developers have to endure. Nothing unravels faster than a game project being built with AI. You can achieve impressive results in a day, but you can't get…
Leaves me to wonder whether the OpenAI glazers just never played games in their lives, or are just really superficial tech bros. Most likely, both.
These machines are doing some crazy things to get to the result. That said, I can't help but feel like this is the compilers argument all over again. Are the methods used to get to the result good? No. Is the code that it generates good? No. Does it achieve the goal. Yes. Is it likely to get better with time. Also yes. In my use cases, jobs that would have taken weeks to months are being done in minutes to hours. Inv…
What is the "compilers argument"?
Compilers also had bugs, so we still had to debug the assembly to understand how to fix the problem. Nowadays, almost nobody has to resort to those steps, except of course compiler developers. But that is just a testament to the quality of compilers.
Comparing LLMs to compilers is a take I often see, but I am not sure the comparison quite holds. The problem is that LLMs are inherently non-deterministic, so we always get a different output on the same prompt.
Maybe if LLMs are powerful enough it won't matter. I doubt it but we will see.
My own observations are that I used to target turn lengths of 10-15 minutes and these new models (since 5.6) extended that a bit to ~25 minutes, as they tend to do more tests and reviews. Targeting hours-long turns makes as much sense, as putting on cruise control and going to sleep.
I’ve observed exactly these patterns with Opus and Fable as well - for example, forgetting that they can edit files and instead use python scripts as a patching tool…
Why would you use a constrained edit tool when you are also allowed to use the complete power of python?
Or something, I don't remember...
I’ve observed exactly these patterns with Opus and Fable as well - for example, forgetting that they can edit files and instead use python scripts as a patching tool…
It 'knows' (from simply training) with an extremely high degree of certainty when its prompt is written by an LLM/itself - and thus will change what it writes.