LLM code optimizations
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Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 30/100
- Issue type
- Refactor
- Clarity
- Needs clarification
- Activity status
- Quiet
- Tech stack
- r
- Domain
- performance
Research direction
The issue names no files, tests, or specific scanning entry points. Start by locating the main scanning functions, then use profvis and debrief's pv_print_debrief() workflow to identify optimization targets; done would require demonstrated performance and memory improvements, but the target and success criteria are not specified.
Written by the indexing model from the issue text.
Description
Debrief is a recent package which is (broadly) aimed to provide LLM's profiling information in a clear manner. They have a case study describing major performance benefits they observed by setting an LLM loose on a problem. I tried something similar in one of my packages and am looking to get some nice performance benefits (including decreased memory usage not captured by touchstone).
My prompt was more minimal than theirs: "I want you to use the profvis and debrief package to iteratively make improvements to the main, scanning functions. The workflow: profile with profvis, analyze with pv_print_debrief(), optimize, and repeat."
I think we should do the same thing here.
- Dominant language
- R
- Stars
- 161
- Forks
- 69
- Avg merge
- 1d 10h
- Merged PRs (30d)
- 47
Getting set up
- No Dockerfile or Docker Compose file
- Has a pull request template
- Read the contributing guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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