Local models are capable of assisting coding in even enormous, complex pieces of software. That’s where having the right harness really shines - it provides the model with the tools it needs to focus in on just the bits of code it’s currently working on. Sort of like how a regular human programmer can do a much better job working on a large project with Visual Assist or equivalent in their IDE. I’ve also installed some tools like Context7 that provides the LLM with API documentation (no more hallucinated APIs) or Firecrawl (it can just go search whatever it needs like a regular person). It’s not so great at large-scale architecture questions, but that’s not what 90% of coding usually involves.
If you want a step up from what can run on just commodity desktops, some of the bigger models are also open-weight and a company or studio with a programming department could run one of those on their own hardware in the server room. GLM 5.3 is right up there next to Claude in the benchmarks, for example, and doesn’t take a data center to run.
Or you could go halfsies on the “big tech” thing and have an API subscription for a big model that your coding harness uses for doing the large-scale architecture planning and then hands off to small local models for implementing the plans the big model wrote. That’s a very common pattern too.
The key point is that there’s a lot of options here, it’s not “pay Anthropic or nothing.”
Local models are capable of assisting coding in even enormous, complex pieces of software. That’s where having the right harness really shines - it provides the model with the tools it needs to focus in on just the bits of code it’s currently working on. Sort of like how a regular human programmer can do a much better job working on a large project with Visual Assist or equivalent in their IDE. I’ve also installed some tools like Context7 that provides the LLM with API documentation (no more hallucinated APIs) or Firecrawl (it can just go search whatever it needs like a regular person). It’s not so great at large-scale architecture questions, but that’s not what 90% of coding usually involves.
If you want a step up from what can run on just commodity desktops, some of the bigger models are also open-weight and a company or studio with a programming department could run one of those on their own hardware in the server room. GLM 5.3 is right up there next to Claude in the benchmarks, for example, and doesn’t take a data center to run.
Or you could go halfsies on the “big tech” thing and have an API subscription for a big model that your coding harness uses for doing the large-scale architecture planning and then hands off to small local models for implementing the plans the big model wrote. That’s a very common pattern too.
The key point is that there’s a lot of options here, it’s not “pay Anthropic or nothing.”