IdeaWell

Project ideas from Hacker News discussions.

Writing Rust code that's fast by asking agents to make the code faster

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

Three prevalent themes in the discussion

  1. LLMs can improve code when a clear, measurable goal and feedback loop are provided
  2. “If it can be measured, then LLMs can optimize it.” – hombre_fatal
  3. “They can use tools like perf and do some analysis on the output… as long as you’re sort of babysitting the process, you can make pretty rapid improvement to naive code.” – loeg

  4. “Give it a benchmark feedback loop then it might get there eventually…” – metalspot

  5. LLMs often stall, over‑fit to the benchmark, or suggest superficial changes that lack deeper architectural insight

  6. “They can also spin round and round making the numbers worse because they don’t actually know what to do.” – Capricorn2481
  7. “The point of this post is that this is explicitly not the case… learns from iterations where changes caused a regression after a revert.” – minimaxir
  8. “Unless its an easy memory/parallel/algorithmic win, its not worth it.” – vatsachak

  9. “They won’t suggest writing a tool to make their own life easier…” – ashkankiani

  10. Human guidance, proper measurement harnesses, and documentation (e.g., ADRs) are essential for useful LLM‑driven optimization

  11. “You need a place were you can document invariants, why you have them + the rejected ideas and acceptable risks.” – hombre_fatal
  12. “I find that really just need a sensible framework within which the optimization can take place. Essentially just providing the measurement harness…” – lordnacho
  13. “When I run into things like this, it becomes a one‑liner in my instructions/harness… I instruct agents to proactively build/improve diagnostic tooling…” – hombre_fatal

These themes capture the consensus that LLMs excel at measurable, iterative tuning but still rely on human expertise to set goals, avoid pitfalls, and steer toward meaningful improvements.

🚀 Project Ideas

PerfLoop

Summary

  • An LLM‑driven performance optimization framework that provides a plug‑and‑play measurement harness (perf, valgrind, CPU counters) and a closed‑loop feedback system for proposing, testing, and ranking code changes.
  • Core value: turns “measure‑then‑optimize” into an automated, safe process that avoids benchmark overfitting and encourages LLMs to suggest useful diagnostic tooling.

Details

Key Value
Target Audience Systems engineers, performance‑focused developers, and teams using LLMs for code improvement
Core Feature Measurement harness + LLM idea generator + statistical evaluator + auto‑generated diagnostics suggestions
Tech Stack Python (orchestration), Rust/Cgo for low‑level probes, Prometheus/Grafana for metrics, LLM API (OpenAI/OSS), Docker for sandbox
Difficulty Medium
Monetization Hobby

Notes

  • HN users stressed the need for a “benchmark feedback loop” and warned against LLMs optimizing the harness itself (pushpendraw, minimaxir). PerfLoop’s built‑in guardrails and varying input shapes directly address that.
  • By automatically proposing diagnostic tooling when helpful (as hombre_fatal suggested), it satisfies the desire for LLMs to “make their own life easier” while staying grounded in real performance gains.

BenchGuard

Summary

  • A CI‑integrated service that runs LLMs against a user‑provided benchmark harness, validates each candidate change with statistical tests (T‑test, effect size), and prevents memorization by varying input shapes and workloads.
  • Core value: gives teams confidence that LLM‑suggested optimizations truly improve real‑world performance, not just the benchmark.

Details

Key Value
Target Audience DevOps teams, performance labs, open‑source projects seeking automated performance tuning
Core Feature Automated benchmark loop, statistical significance checking, input‑shape fuzzing, impact‑vs‑confidence ranking
Tech Stack Go (service), SQLite/Postgres for results, LLM API, GitHub Actions/GitLab CI integration, Jupyter‑lite for report visualization
Difficulty Medium
Monetization Revenue-ready: SaaS tiered pricing (free for OSS, paid for private repos)

Notes

  • Commenters highlighted the problem of LLMs “spinning round and round making the numbers worse” (Capricorn2481, loeg) and the need for “measurement that can falsify hypotheses” (hombre_fatal). BenchGuard’s statistical guardrails directly provide that falsification capability.
  • The service would generate discussion on HN about reliable LLM‑driven optimization and could be showcased as a case study in performance engineering blogs.

ADR-Assist

Summary

  • An LLM‑augmented Architecture Decision Record (ADR) tool that captures invariants, trade‑offs, rejected ideas, and acceptable risks, making it easy for LLMs to reason about performance vs. correctness and to suggest useful tooling improvements.
  • Core value: turns informal, lost design rationale into searchable, version‑controlled documentation that both humans and agents can consult.

Details

Key Value
Target Audience Software architects, tech leads, and teams using LLMs for code evolution
Core Feature Structured ADR templates, LLM‑assisted drafting, conflict detection, change‑impact linking, CLI + VS Code extension
Tech Stack TypeScript (VS Code extension), Node.js (backend), Markdown/YOML storage, LLM API for suggestion generation
Difficulty Low
Monetization Hobby

Notes

  • hombre_fatal noted that “ADRs are an important of a software project, especially with LLMs” to document invariants and rejected ideas, enabling agents to make better trade‑off decisions. ADR-Assist implements exactly that.
  • By providing a clear place for LLMs to see why certain optimizations were rejected (e.g., sacrificing data model), it reduces the risk of LLMs proposing harmful “micro‑optimizations that never improved performance in the large” (devin). This would resonate strongly with HN’s performance‑conscious audience.

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