Context is the bottleneck for coding agents now

There's a misunderstanding here broadly. Context could be infinite, but the real bottleneck is understanding intent late in a multi-step operation. A human can effectively discard or disregard prior information as the narrow window of focus moves to a new task, LLMs seem incredibly bad at this.

Having more context, but leaving open an inability to effectively focus on the latest task is the real problem.

I think that's the real issue. If the LLM spends a lot of context investigating a bad solution and you redirect it, I notice it has trouble ignoring maybe 10K tokens of bad exploration context against my 10 line of 'No, don't do X, explore Y' instead.

that's because a next token predictor can't "forget" context. That's just not how it works.

You load the thing up with relevant context and pray that it guides the generation path to the part of the model that represents the information you want and pray that the path of tokens through the model outputs what you want

That's why they have a tendency to go ahead and do things you tell them not to do..

also IDK about you but I hate how much praying has become part of the state of the art here. I didn't get into this career to be a fucking tech priest for the machine god. I will never like these models until they are predictable, which means I will never like them.

Yeah I start a new session to mitigate this. Don’t keep hammering away - close the current chat/session whatever and restate the problem carefully in a new one.

There should be a simple button that allows you refine the context. A fresh LLM could generate a new context from the input and outputs of the chat history, then another fresh LLM can start over with that context.

You are saying “fresh LLM” but really I think you’re referring to a curated context. The existing coding agents have mechanisms to do this. Saving context to a file. Editing the file. Clearing all context except for the file. It’s sort of clunky now but it will get better and slicker.

It seems that I have missed this existing feature, I’m only a light user of LLMs, I’ll keep an eye out for it.