Also developers often want more ram, and if youre on the mac side, the M series ram works as video ram for loading and running models, so there’s a good chance you can already run something better than is typical of others, and apple is focusing on this by adding more NPUs and increasing memory bandwidth. They arent good at training, but can do inference.
The vast majority of users ain’t running anything but 27b max, more likely 14b, and that shit just ain’t nearly as good as older saas models much less dominant like opus. Maybe for small shit but complex talks just ain’t fitting on home hardware.
Completely agree, I forgot to mention that part. I am testing a few models ranging from 18b to 26b on my 7900xt. It is far from “make this complete system”, but it can handle some smaller tasks. I think that will be the end goal anyway since cloud models fail a lot at maintainability, security, and other higher levels of thought that goes into coding. They can make a convincing prototype but I wouldn’t hook it up to production.
Local models are already functioning well as a force multiplier. It can help explain logic, do minor refactoring, debugging etc. but with a bit of latency. I do think this is where we’re headed since the frontier models required for generating a full prototype can’t make production quality code and it is prohibitively expensive to do so. As far as I’ve heard, they’re generally running spending ten times as much as they earn per token.
Tried to refactor a spaghetti code state machine and thought, well, AI should handle this well. All the logic is there, just separate it into small functions to clean up the large one.
None was able to, alone because of the context window already
To be fair though, I tried Mistral online and it also stumbled around. ChatGPT was a complete clusterfuck - haven’t tried Claude.
To be even fairer… it’s a really large state machine, which was written on site during a fever and in stress - so… To defend myself a bit as well, how it even came to that ;-)
But seems, I’ll need to go through this myself
Actually thought, that this would be a perfect example for using AI…
Yeah LLM’s can help with many tasks but then there are times they just spout nonsense, or syntactically correct nonsense, the model size and context window just changes when they hit their limit.
Sometimes you have to call it quits, and try another way.
Already is, take a look at devstral, qwen3.6, deepseek coder. All can be run on a hugh end GPU and if you’re a developer you likely have one.
Why would a developer likely have a high end GPU? Writing code doesn’t use a GPU.
There is a significant overlap between developers and gamers.
Most developers use their work provided machines, which aren’t gaming machines with giant GPUs because again, GPUs don’t help development at all.
Also developers often want more ram, and if youre on the mac side, the M series ram works as video ram for loading and running models, so there’s a good chance you can already run something better than is typical of others, and apple is focusing on this by adding more NPUs and increasing memory bandwidth. They arent good at training, but can do inference.
The vast majority of users ain’t running anything but 27b max, more likely 14b, and that shit just ain’t nearly as good as older saas models much less dominant like opus. Maybe for small shit but complex talks just ain’t fitting on home hardware.
Completely agree, I forgot to mention that part. I am testing a few models ranging from 18b to 26b on my 7900xt. It is far from “make this complete system”, but it can handle some smaller tasks. I think that will be the end goal anyway since cloud models fail a lot at maintainability, security, and other higher levels of thought that goes into coding. They can make a convincing prototype but I wouldn’t hook it up to production.
Local models are already functioning well as a force multiplier. It can help explain logic, do minor refactoring, debugging etc. but with a bit of latency. I do think this is where we’re headed since the frontier models required for generating a full prototype can’t make production quality code and it is prohibitively expensive to do so. As far as I’ve heard, they’re generally running spending ten times as much as they earn per token.
Sadly, that’s true
Tried to refactor a spaghetti code state machine and thought, well, AI should handle this well. All the logic is there, just separate it into small functions to clean up the large one.
None was able to, alone because of the context window already
To be fair though, I tried Mistral online and it also stumbled around. ChatGPT was a complete clusterfuck - haven’t tried Claude.
To be even fairer… it’s a really large state machine, which was written on site during a fever and in stress - so… To defend myself a bit as well, how it even came to that ;-)
But seems, I’ll need to go through this myself
Actually thought, that this would be a perfect example for using AI…
Yeah LLM’s can help with many tasks but then there are times they just spout nonsense, or syntactically correct nonsense, the model size and context window just changes when they hit their limit.
Sometimes you have to call it quits, and try another way.