Scaling LLMs to Larger Codebases • Hacker News Board

Re: Scaling LLMs to Larger Codebases

#121

Post by coldtea » Tue, Dec 23, 2025, 7:38 PM UTC

the_sleaze_ wrote: post #79

coldtea wrote: post #76

Earlier quoted context omitted.

> Instead of considering that something should be written encapsulated into an object to maintain state, it will instead write 5 functions, passing the state as parameters between each function. Sounds very functional, testable, and clean. Sign me up.

I know this is tongue in cheek, but writing functional code in an object oriented language, or even worse just taking a giant procedural trail of tears and spreading it across a few files like a roomba through a pile of dog doo is ... well.. a code smell at best. I have a user prompt saved called clean code to make a pass through the changes and remove unused, DRY and refactor - literally the high points of uncle bob…

Not so much tongue in cheek, but a little on the light side, sure.

I'd argue writing functional code in C++ (which is multi-paradigm anyway), or Java, or Typescript is fine!

Re: Scaling LLMs to Larger Codebases

#122

Post by je42 » Tue, Dec 23, 2025, 8:27 PM UTC

jarjoura wrote: post #59

mstank wrote: post #16

As the models have progressively improved (able to handle more complex code bases, longer files, etc) I’ve started using this simple framework on repeat which seems to work pretty well at one shorting complex fixes or new features. [Research] ask the agent to explain current functionality as a way to load the right files into context. [Plan] ask the agent to brainstorm the best practices way to implement a new featur…

As of Dec 2025, Sonnet/Opus and GPTCodex are both trained and most good agent tools (ie. opencode, claude-code, codex) have prompts to fire off subagents during an exploration (use the word explore) and you should be able to Research without needing the extra steps of writing plans and resetting context. I'd save that expense unless you need some huge multi-step verifiable plan implemented. The biggest gotcha I found…

If claude ignores your claude.md you can force it to read via settings to cat it every session start for example.

Re: Scaling LLMs to Larger Codebases

#123

Post by avereveard » Thu, Dec 25, 2025, 4:56 PM UTC

saxenaabhi wrote: post #104

avereveard wrote: post #58

Earlier quoted context omitted.

Mostly gemini 3 pro when I ask to investigate a bug and provide fixing options (i do this mostly so i can see when the model loaded the right context for large tasks) gemini immediately starts fixing things and I just cant trust it Codex and claude give a nice report and if I see they're not considering this or that I can tell em.

fyi that happened to me with codex. but, why is it a big issue? if it does something bad, just reset the worktree and try again with a different model/agent? They are dirt cheap at 20/m and I have 4 subscription(claude, codex, cursor, zed).

Same I have multiple subscription and layer them. I use haiku to plan and send queue of task to codex and gemini whose command line can be scripted

The issue to me is that I have no idea of what the code looks like and have to have a reliable first layer model that can summarize current codebase state so I can decide whether the next mutation moves the project forward or reduces technical debt. I can delegate much more that way, while gemini "do first" approach tend to result in many dead ends that I have to unravel.