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Context is the bottleneck for coding agents now

(runnercode.com)
196 points zmccormick7 | 1 comments | 26 Sep 25 15:06 UTC | HN request time: 0s | source
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aliljet ◴[26 Sep 25 15:27 UTC] No.45387614[source]▶
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.

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1. sdesol ◴[26 Sep 25 17:32 UTC] No.45388965[source]▶
> 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.

This is how I designed my LLM chat app (https://github.com/gitsense/chat). I think agents have their place, but I really think if you want to solve complex problems without needlessly burning tokens, you will need a human in the loop to curate the context. I will get to it, but I believe in the same way that we developed different flows for working with Git, we will have different 'Chat Flows' for working with LLMs.

I have an interactive demo at https://chat.gitsense.com which shows how you can narrow the focus of the context for the LLM. Click "Start GitSense Chat Demos" then "Context Engineering & Management" to go through the 30 second demo.