IdeaWell

Project ideas from Hacker News discussions.

Let's be honest, Generative AI isn't going all that well

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

Here are the 5 most prevalent themes from the discussion:

1. GenAI is a Force Multiplier for Individual Developers Many users report significant personal productivity gains, often citing specific examples of coding, design, or migration tasks that are completed in a fraction of the traditional time.

"I myself am saving a small fortune on design and photography and getting better results while doing it." — mattmaroon "I’m saving months of efforts using AI tools to fix old... codebases." — serf

2. Code Quality vs. Maintenance Trade-offs A major debate centers on whether AI-generated code is an asset (rapid execution) or a liability (technical debt). Some argue that code is inherently a maintenance burden, while others contend that unreviewed AI code introduces unacceptable risk.

"Code is a liability. What the code does for you is an asset." — merlincorey (quoting c2.com) "Code that solves problems and makes you money is by definition an asset." — _vertigo

3. Professional Skepticism and the "Vibe Coding" Problem Experienced engineers express concern that over-reliance on AI by juniors or non-developers leads to "slop" and fragile systems. They argue that AI tools lack the context and judgment required for complex software architecture.

"Your '10%' supervisory contribution takes just as long as doing 100%." — Dylan16807 "LLMs do the jobs of developers without telling semi-technical arrogant MBA holders 'no, you’re dumb'..." — UncleMeat

4. The Subjectivity of Productivity and Study Skepticism Users frequently cite personal anecdotes to counter studies suggesting AI slows developers down, highlighting the tension between self-reported "vibe" metrics and formal observational data.

"It’s wild that somehow with regards to AI conversations lately someone can say 'I saved 3 months doing X' and someone can willfully and thoughtfully reply 'No you didn’t , you're wrong.' without hesitation." — serf "I think any measurement of development velocity is shaky, especially when measured by the person doing the development." — Dylan16807

5. The Disparity Between Hype and Utility There is a consensus that while the technology is useful, the marketing promises of AGI and total automation are overblown. Users distinguish between the actual utility of LLMs as tools and the inflated valuations or claims of the industry.

"It’s becoming clear the tech is ultimately just a tool, not a precursor to AGI." — emp17344 "The whole discourse around LLMs is so utterly exhausting... If I try and use it the 'right' way and it still gets extremely basic things wrong, then my expectations are too high." — drewbug01

🚀 Project Ideas

AI-Generated Code Review and Refactoring Assistant

Summary

  • A tool that uses LLMs to review AI-generated code, flagging potential bugs, security issues, and architectural anti-patterns before they enter the codebase.
  • Provides a "refactoring report" with confidence scores and specific remediation steps, bridging the gap between "vibe coding" speed and production-ready quality.

Details

Key Value
Target Audience Developers using AI coding assistants (Cursor, Copilot, Claude Code) who need validation, especially solo founders and junior engineers.
Core Feature Static analysis + LLM-based review for AI-generated code, focusing on hallucinated APIs, memory leaks, and security vulnerabilities.
Tech Stack Python/Go (backend), LLM APIs (OpenAI/Claude), AST parsing (Tree-sitter), GitHub/GitLab integrations.
Difficulty Medium
Monetization Revenue-ready: Freemium SaaS (free for small projects, subscription for enterprise features and high-volume review).

Notes

  • Directly addresses HN concerns about "code as a liability" and the danger of unreviewed AI code (e.g., merlincorey, jvanderbot).
  • Provides a necessary safety net for "vibe coding" mentioned by rerdavies and others, making AI coding safer for professional use.
  • High utility for teams where junior devs might over-rely on AI without understanding the output.

"Code Debt" Visualization and ROI Calculator

Summary

  • A tool that analyzes a codebase to quantify "code as a liability" by visualizing maintenance costs, complexity hotspots, and technical debt trends over time.
  • It calculates the potential savings of refactoring or rewriting specific modules, helping managers justify rewriting legacy code (like the example in mattmaroon's comment).

Details

Key Value
Target Audience CTOs, Engineering Managers, and Technical Founders managing legacy systems.
Core Feature Parses repository history and complexity metrics to generate a financial model of code maintenance costs and projected ROI of refactoring.
Tech Stack Python (backend), React (frontend), Git analysis libraries, basic ML for trend prediction.
Difficulty Low
Monetization Hobby: Open-source core with paid enterprise consulting for analysis.

Notes

  • Solves the debate about code being a "liability" vs. "asset" by putting hard numbers on maintenance costs (referencing Ygg2's comment on tech debt).
  • Validates the decision to rewrite old codebases by quantifying the pain points expressed by wouldbecouldbe and others.
  • Useful for discussions on Hacker News regarding software economics and project planning.

Legacy Codebase Context Injector for LLMs

Summary

  • A CLI tool that dynamically injects context from a legacy codebase into LLM prompts to prevent the "hallucination" of APIs and architectural patterns during refactors.
  • Unlike generic IDE plugins, this specifically targets the problem of rewriting old code (e.g., pre-iPhone era codebases mentioned by mattmaroon) where the model lacks historical context.

Details

Key Value
Target Audience Senior developers and architects refactoring large, undocumented legacy systems.
Core Feature Automatically maps dependencies, data flows, and custom utility functions in a repo to provide relevant context to the LLM before code generation.
Tech Stack Python, AST parsing, vector databases (for semantic search of code context), CLI wrapper.
Difficulty Medium
Monetization Revenue-ready: Subscription model for teams; enterprise license for on-premise deployment.

Notes

  • Directly addresses the "spaghetti vibe code" fear expressed by Bridged7756 and the need for competent oversight.
  • Enables the productivity gains described by phito and solumunus by providing the LLM with the necessary grounding to avoid errors.
  • Appeals to the HN user base interested in tools that augment developer workflow rather than replace it.

AI Codebase "Circuit Breaker"

Summary

  • A monitoring tool that runs in the CI/CD pipeline to detect when AI-generated code introduces "liabilities" like unmaintainable complexity or security risks.
  • It acts as a gatekeeper, preventing the "cowboy coder" scenario described by fzeroracer by enforcing specific architectural constraints before merging.

Details

Key Value
Target Audience DevOps engineers and security-conscious teams using AI coding tools.
Core Feature Custom rules engine that flags code violating established patterns, exceeding complexity thresholds, or introducing known vulnerabilities often hallucinated by LLMs.
Tech Stack GitHub Actions/GitLab CI, Docker, Static Analysis tools (Semgrep, SonarQube), LLM integration for pattern matching.
Difficulty Low
Monetization Hobby: Open-source plugin.

Notes

  • Addresses the "liability" concerns raised by hshdhdhj4444 regarding selling codebases and the risks of unmaintainable software.
  • Provides a concrete solution for the "long-term maintainability" issues discussed by array_key_first.
  • Fits the Hacker News ethos of rigorous engineering standards and skepticism toward "vibe coding" taking over production systems.

LLM-Assisted Technical Debt Auditing Service

Summary

  • A service where users upload a codebase, and an LLM performs a deep audit to identify "code liabilities"—legacy code that is costly to maintain or high-risk to modify.
  • It generates a prioritized backlog of refactoring tasks, estimating the labor hours saved by using AI for the cleanup, addressing the skepticism about time savings.

Details

Key Value
Target Audience Organizations with aging codebases looking to modernize without a full rewrite.
Core Feature Automated code analysis to identify dead code, anti-patterns, and high-maintenance modules, outputting a business-case report.
Tech Stack Python, LLM analysis (Claude/GPT), data visualization libraries (D3.js), PDF generation.
Difficulty Medium
Monetization Revenue-ready: One-time audit fee or subscription for continuous monitoring.

Notes

  • Provides empirical data to counter the skepticism expressed by Dylan16807 regarding self-estimated productivity gains.
  • Validates the "code is a liability" argument with data, supporting the discussions between simonsmithies and _heimdall.
  • Offers a tangible business utility that aligns with the pragmatic concerns of engineering management.

Read Later