Coding tools are less stable as the code grows for several reasons.
Some recent techniques claim to be solving this problem but none reached a release yet.
Working with what we have now, this is a recipe for disaster.
Agents often lies about the outputs. The shorter the context space they have to manage while the bigger the data already in context makes it prone to lie and deceive.
It works ok for small changes on top of human code. That's what we know works now.
The rest is more yet to be reached
I'm writing my side project as if I can afford only a 70B model in 2028, even if I have VC-subsidised unlimited GLM-5 now. I'm trimming away most of the generated code and generating more tests.
Would prefer if 2028 models are concise and generates perfect refactors.
I have semi unlimited budget forAI tools. And doing it purely with Claude code or similar has been in effective.
The architecture is too simple or unmaintainable rubbish.
One can say that I don't need to care about that, but after few weeks of work, every small chance needs to read everything which quickly gets expensive to do anything.
Using human code and doing similar tasks is incredibly cheaper and more effective.
I run few simultaneous green field projects and I can evaluate basically any tool I want. And I will say that quality wise, all ones I tried are disappointing.
Comments
Coding tools are less stable as the code grows for several reasons.
Some recent techniques claim to be solving this problem but none reached a release yet.
Working with what we have now, this is a recipe for disaster. Agents often lies about the outputs. The shorter the context space they have to manage while the bigger the data already in context makes it prone to lie and deceive.
It works ok for small changes on top of human code. That's what we know works now. The rest is more yet to be reached
I'm writing my side project as if I can afford only a 70B model in 2028, even if I have VC-subsidised unlimited GLM-5 now. I'm trimming away most of the generated code and generating more tests.
Would prefer if 2028 models are concise and generates perfect refactors.
I have semi unlimited budget forAI tools. And doing it purely with Claude code or similar has been in effective.
The architecture is too simple or unmaintainable rubbish. One can say that I don't need to care about that, but after few weeks of work, every small chance needs to read everything which quickly gets expensive to do anything.
Using human code and doing similar tasks is incredibly cheaper and more effective.
I run few simultaneous green field projects and I can evaluate basically any tool I want. And I will say that quality wise, all ones I tried are disappointing.