IdeaWell

Project ideas from Hacker News discussions.

The future of software development is software developers

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

1. Hard Part of Programming: Precise Computational Thinking

Many agree the core challenge is translating fuzzy human ideas into exact specs, unchanged by AI.
"simonw: The hard part has always been – and likely will continue to be for many years to come – knowing exactly what to ask for."
"nrhrjrjrjtntbt: Hardest part of programming is knowing wtf all the existing code does and why."

2. AI Boosts Productivity for Routine Tasks but Needs Oversight

Users report gains on boilerplate, conversions, and reviews, but stress human review for quality.
"thisoneisreal: I've already done some professional 'mini projects' that just would not have gotten done without an AI."
"jmogly: I would say it varies from 0x to a modest 2x... my job as a senior dev has gotten a lot easier."

3. Limitations on Novelty/Complexity; Skepticism of Job Displacement

AI hallucinates on new problems, security, or edge cases; productivity not 20x, humans essential.
"belter: You are mistaking a massive combinatorial search over seen patterns for genuine reasoning."
"hansmayer: at the end of the day, as an experienced engineer, I am not being more productive with it."
"davnicwil: I flatly cannot believe a claim... that one person is doing the work of 20."

🚀 Project Ideas

PreciseSpec AI

Summary

  • AI-powered tool that decomposes ambiguous human requirements into precise, testable computational specs using structured prompts and iterative clarification, solving the "hard part" of turning woolly human thinking into unambiguous logic (as per simonw's quote).
  • Core value: Reduces garbage-in-garbage-out by generating verifiable specs before code gen, enabling reliable LLM use for novel tasks.

Details

Key Value
Target Audience Developers using LLMs for complex/novel projects (e.g., germandiago, belter)
Core Feature Interactive spec builder with ambiguity detection, auto-generates test cases and pseudocode skeletons
Tech Stack Claude 3.5 Sonnet / GPT-4o API + LangChain for chaining + SQLite for spec storage
Difficulty Medium
Monetization Revenue-ready: Freemium ($10/mo pro for unlimited specs)

Notes

  • HN users like treespace8 (GIGO in Godot) and memoriuaysj (best practices queries) would love auto-clarifying vague ideas into testable plans: "Claude will not tell me if I'm following the wrong path."
  • High utility for agentic flows; sparks discussions on spec-driven dev like joefourier's tool-calling agents.

CodeWhy Explainer

Summary

  • Service that annotates legacy/existing codebases with "why" explanations, diffs AI-generated code against best practices, and traces decisions to docs/research, addressing nrhrjrjrjtntbt/doug_durham's pain: "Hardest part is knowing wtf all the existing code does and why" and elboru's "code describes what and how, but not why."
  • Core value: Builds trust in AI code by making it human-understandable, reduces maintenance nightmares from vibe coding.

Details

Key Value
Target Audience Teams reviewing AI-generated code (hansmayer, Verdex on PR messes)
Core Feature Upload code/repo → AI generates layered annotations (what/why/how), flags anti-patterns with fixes
Tech Stack DeepSeek Coder V2 + Tree-sitter for parsing + GitHub API integration + Vercel for hosting
Difficulty Medium
Monetization Revenue-ready: $20/user/mo SaaS

Notes

  • Quotes like doug_durham: "LLMs are better at reading code than writing it. Have it annotate some code" – this automates/extends that.
  • Practical for production; fosters HN debates on AI reliability vs. human oversight (e.g., manmal's "vibed software irresponsible").

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