[underlines]

I think what many people miss, is how long context LLMs get better (needle in a needle stack) and how Context is very important. With github copilot or with continue for VS Code the main issue lies in how they decide which context to give: Ideally it's a graph of all function calls and instantiations of classes so whenever your cursor is in a particular spot you could hop through the graph and get all important code pieces as context.

Currently continue uses vector search of chunks which is just a crutch. I am not sure what copilot or Aider does, but the right context is key.

Another way to improve a coding assistant is to move away from simple paradigms to agent based Workflow that can deploy sub agents and break down tasks. All in the back autonomously while you code and then it surfaces suggestions or changes. This will get possible with increasing inference speeds (groq) and better agent frameworks.

Most devs at our company say LLMs are useless for coding and github copilot is a glorified autocomolete costing 20USD. I think the tech and ecosystem will improve a lot over time and they underestimate LLM abilities due to their bias.

[relistan]

To your point: check out Supermaven. I have no affiliation. But it has way more context than Copilot and is way better at suggestions that make sense in the codebase.