IdeaWell

Project ideas from Hacker News discussions.

Using go fix to modernize Go code

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

Three prevailing themes in the discussion

Theme Key points Representative quotes
LLMs tend to output mediocre, outdated Go code Models often reproduce old idioms, ignore newer language features, and can produce unsafe concurrency patterns. “In December 2024… such tools tended—unsurprisingly—to produce Go code in a style similar to the mass of Go code used during training… even when there were newer, better ways to express the same idea.” – homarp
“The code that LLMs output is pretty average… it’s what I would expect a mid‑level engineer to produce.” – cedws
“They’re particularly bad about concurrent go code… it routinely slips past review because it seems simple and simple is correct, right?” – Groxx
Go’s stability and tooling make it attractive for LLM‑assisted development Backward‑compatibility, robust standard library, and integrated tooling (testing, linting, compilation) give developers confidence to use Go even when LLMs are imperfect. “Even though all the code compiles because go is backwards compatible it all looks so much different.” – robviren
“Go’s tooling is so good that I still end up writing it in Go.” – jjice
“The Go team has built such trust with backwards compatibility… other ecosystems… everything seems to be @Deprecated or @Experimental.” – iamcalledrob
Difficulty correcting LLMs’ knowledge base and the risk of perpetuating bad advice Once a model learns outdated or incorrect patterns, they persist across generations; fixing requires community effort and careful training data curation. “Once the bad advice is in the model it’s never going away… it’s unlikely that model trainers will submit their RC models to various communities to make sure it isn’t lying about those specific topics.” – munk‑a
“I definitely see that with C++ code… Not so easy to ‘fix’, though.” – BiraIgnacio

These themes capture the main concerns and observations about LLM‑generated Go code, the language’s strengths, and the challenges of keeping AI models up‑to‑date.

🚀 Project Ideas

Generating project ideas…

Go Modernizer

Summary

  • A static‑analysis and automated refactoring tool that updates existing Go codebases to use the latest language idioms, concurrency patterns, and standard‑library features.
  • Provides a fast, repeatable way to keep LLM‑generated or legacy code modern without manual rewrites.

Details

Key Value
Target Audience Go developers, CI/CD teams, open‑source maintainers
Core Feature Automatic migration of deprecated patterns, insertion of rangeint, goroutine best practices, and error‑handling improvements
Tech Stack Go, AST manipulation libraries (go/ast, golang.org/x/tools), Docker for CI integration
Difficulty Medium
Monetization Revenue‑ready: subscription (free tier + paid plans for enterprise CI integration)

Notes

  • HN commenters complain that LLMs produce “middling” Go code that sticks to old idioms. This tool directly addresses that pain by modernizing code automatically.
  • Useful for teams that want to keep codebases up‑to‑date while still leveraging LLMs for new features.

LLM Code Quality Auditor

Summary

  • A service that ingests LLM‑generated Go code, runs static analysis, race detection, and unit‑test coverage checks, then returns a concise quality report with actionable fixes.
  • Helps developers spot subtle concurrency bugs and missing error handling that LLMs often miss.

Details

Key Value
Target Audience Developers using LLMs for code generation, code reviewers, CI pipelines
Core Feature Automated linting, race detector, error‑handling audit, and suggested refactor snippets
Tech Stack Go, static analysis tools (golangci-lint, race detector), Docker, REST API
Difficulty Medium
Monetization Revenue‑ready: pay‑per‑scan or subscription for CI integration

Notes

  • Users like “Groxx” note that LLMs produce code that looks correct but contains subtle data races. This auditor surfaces those issues before merge.
  • Encourages discussion on best practices for LLM‑generated code and can be integrated into pull‑request workflows.

Go Scaffold & LLM

Summary

  • An interactive web IDE that lets developers define high‑level specs (e.g., “create a concurrent worker pool”), generates a skeleton with modern idioms, and then uses an LLM to fill in implementation details under strict constraints.
  • Combines the strengths of scaffolding with LLM creativity while enforcing up‑to‑date Go patterns.

Details

Key Value
Target Audience Go developers, rapid prototyping teams, educators
Core Feature Spec‑to‑skeleton generator, LLM prompt templates with idiom constraints, real‑time code review
Tech Stack React, Go backend, OpenAI API, Docker for sandboxed execution
Difficulty High
Monetization Revenue‑ready: freemium with paid LLM credits and enterprise API access

Notes

  • Addresses the frustration that LLMs often produce “middling” code by giving developers a solid, idiomatic foundation to build upon.
  • Sparks practical utility discussions on how to best combine human scaffolding with AI assistance.

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