I feel that for some time now, the biggest constraint when working with models is not their intelligence, but their speed. It does not matter how smart the model is, it will make mistakes, because the instructions are ambiguous and new facts are found during implementation. The biggest problem I've had working with software developers has always been the lag between seeing the results and steering towards the right direction, not the skills of the developer (with many exceptions of course).
Regardless, working on the wrong things is time wasted. And again, I'm procrastinating here while waiting for Fable to run a benchmark on a few solutions to a problem I have. We can guess what would work, but we only know after the benchmark. A faster model, with fewer capabilities, would've been a much better choice this time... well, "git gud" they said... and live and learn! Faster model = less time for procrastination.
PS. AI models don't live and learn; the discussion about AGI is pretty pointless imo. It's a tool. Does it matter if it is AGI or not if it does what you want it to do? Does the IQ of your colleague matter if he's good at what he's supposed to do? Or bad? Well... I guess it does matter, as many people are up in arms about whether Astro is AGI or not. Personally, I think we're past the point for that debate. These are amazing tools.
I think speed is going to be important for a second reason - ever since I've started using LLM's a lot in my work I enjoy it much less. The main reason is that I ask the LLM something and have to wait because I know it's going to prompt me at random every few minutes. A lot of the day has become staring aimlessly at the screen. The prompts are too random and frequent for me to do something else in the interim. It's pretty soul destroying and this can't be how 'work' looks going forward or people are going to burn out.
This is why I switched to codex --yolo running in a container/vm. Now it does what it needs to do without bugging me and I can do other stuff. When it pings me I know there is something to review.
Yes I know they can escape containers, but that's not what I'm tasking them with.
You can easily setup codex rules to auto approve local stuff but gate external effects like push/jira write/curl - works better for me than full yolo mode - depends if it's a solo project or working with a team.
> It does not matter how smart the model is, it will make mistakes
It does matter, otherwise why are we all using GPT 5.6 rather than GPT 3.5? Because it's way smarter, makes less mistakes and therefore finishes tasks faster.
The smarter the model is, the faster it can complete what you actually wanted.
> Regardless, working on the wrong things is time wasted
Agreed. And "smart" for me, would mean understanding what is the right thing to work on vs the wrong thing, so a smart model would waste less time, thinking like this.
AI models do not live and learn - it's worse. They actually get DUMMER if you don't start with a clean slate. This is important. One has to curate the context carefully.
I use codex daily and a moderately complex plan + a few files is more than enough context to make it blatantly ignore prompts like "stop and ask before adding new layers or data structures"
I'm sorry but no - output quality matters a lot more for me.
Just yesterday I tried to use Google antigravity to do a side project I've had on the back burner for 10 years now. Gemini flash is insanely fast - at first I was amazed at how quickly I was getting responses, and it seemed to hold it's own in technical discussion, although sycophancy is next level. But then when I actually let it do the coding part it was just drivel. I wouldn't even bother improving that code - like cleaning up after a lazy unskilled coworker - throw everything away and start over because the foundation is just leading in bad direction.
I spun up Astra on the same problem and although it was sluggish in comparison, and much more pedantic about irrelevant details - the feedback/pushback was actually meaningful. The implementation PoC also took tweaking but we got on the same page really fast.
Gemini Flash 3.8 was just producing garbage ultra fast, Astra could actually be steered into a direction I want and it provides valuable/insightful feedback.
I don't have infinite reading capacity/mental stamina - I would rather the model take it's time and let me see something high quality rather than get bombarded with garbage. If it can be faster that's great - but I'll always default to smarter model. The only exception is stupid trivial tasks like log analysis and similar.
Regardless, working on the wrong things is time wasted. And again, I'm procrastinating here while waiting for Fable to run a benchmark on a few solutions to a problem I have. We can guess what would work, but we only know after the benchmark. A faster model, with fewer capabilities, would've been a much better choice this time... well, "git gud" they said... and live and learn! Faster model = less time for procrastination.
PS. AI models don't live and learn; the discussion about AGI is pretty pointless imo. It's a tool. Does it matter if it is AGI or not if it does what you want it to do? Does the IQ of your colleague matter if he's good at what he's supposed to do? Or bad? Well... I guess it does matter, as many people are up in arms about whether Astro is AGI or not. Personally, I think we're past the point for that debate. These are amazing tools.