IdeaWell

Project ideas from Hacker News discussions.

A sufficiently detailed spec is code

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

4 Prevailing Themes

# Theme Supporting Quote
1 A detailed specification behaves like code – it can define behavior with the same precision as an implementation. “A sufficiently detailed spec is code.” – quotemstr
2 AI agents need explicit specifications; under‑specification leaves ambiguous choices to the model. “Exactly. The real speed up from AI will come when we can under specify a system and the AI uses its intelligence to make good choices on the parts we left out.” – systemsweird
3 Effective LLM‑assisted development separates design, data structures, tests, and then lets the agent execute a plan. “Define the data structures… Then at this point you involve the agent and tell it to plan how to complete the changes without barely having to specify anything in the prompt.” – trane_project
4 Most software is a permutation of existing ideas; truly novel designs are rare and hard for LLMs to generate without exhaustive guidance. “Most of the economically valuable software written is pretty unique, or at least is one of few competitors in a new and growing niche.” – nostrademons

🚀 Project Ideas

Specification-to-Executable Compiler

Summary

  • Turns formal system specifications written in a high‑level DSL into type‑safe code with automatically generated tests.
  • Bridges the spec‑code gap, reducing manual translation errors.

Details

Key Value
Target Audience Software engineers, research teams, small startups building reliable systems
Core Feature Specification parsing → code generation (Rust/Go) + test suite
Tech Stack Rust compiler toolchain, LLVM, PostgreSQL (spec storage), Docker
Difficulty High
Monetization Revenue-ready: SaaS subscription per spec version

Notes

  • HN commenters repeatedly ask for “a compiler for my spec” – this delivers it.
  • Potential utility: immediate reduction of spec‑implementation mismatches and faster onboarding for new engineers.

Agentic Planning Orchestrator

Summary

  • Generates multi‑step execution plans for AI agents, complete with validation checkpoints.
  • Persists plans in Git and integrates with LLM APIs for iterative refinement.

Details

Key Value
Target Audience Dev teams using AI‑assisted coding, product managers, solo hackers
Core Feature Plan creation, checkpointing, auto‑retry on failure
Tech Stack Python backend, PostgreSQL, React front‑end, OpenAI + Claude APIs
Difficulty Medium
Monetization Revenue-ready: Tiered pricing based on plan‑minute usage

Notes

  • Addresses the “under‑specification” pain point highlighted in the discussion.
  • Users can finally “plan before they vibe‑code,” improving reliability and reducing rework.

PromptDSL

Summary

  • A domain‑specific language for writing unambiguous natural‑language specifications that compile to optimized LLM prompts.
  • Encodes invariants, constraints, and desired behavior in a concise syntax.

Details

Key Value
Target Audience Developers who want deterministic LLM output, technical writers, researchers
Core Feature DSL → prompt templates + constraint validation
Tech Stack TypeScript front‑end, Rust backend, JSON Schema validator
Difficulty Low‑Medium
Monetization Revenue-ready: Pay‑per‑prompt generation or monthly subscription

Notes

  • Directly tackles the “spec should be code” argument by providing a true formalism for prompts.
  • HN community will appreciate a language that makes AI‑driven development precise and repeatable.

SpecDiff Review Service

Summary

  • Diffs specification documents against codebases to flag missing or mismatched requirements.
  • Integrates with CI pipelines to auto‑generate compliance reports.

Details

Key Value
Target Audience Engineering leads, CI/CD maintainers, large development organizations
Core Feature Spec‑code diff engine, requirement coverage metrics
Tech Stack Go microservice, GitHub Actions, SQLite, REST API
Difficulty Medium
Monetization Revenue-ready: CI add‑on subscription per repository

Notes

  • Solves the “vague requirements lead to bugs” issue discussed by many commenters.
  • Provides immediate utility by catching spec drift before it becomes costly.

Contract Enforcement Platform for LLMs

Summary

  • Generates formal contracts (pre‑ and post‑conditions) from natural‑language specs and enforces them via automated test harnesses.
  • Integrates with CI to block non‑compliant LLM output.

Details

Key Value
Target Audience Teams building production‑grade services with LLMs, reliability engineers
Core Feature Contract synthesis → test generation → CI gate
Tech Stack Rust contract DSL, Python test generator, Docker sandbox, GitHub Actions
Difficulty High
Monetization Revenue-ready: Enterprise license per project

Notes

  • Directly addresses the reliability concerns raised in the discussion about LLM‑generated code.
  • HN users emphasizing “bugs from unspecified behavior” will see immediate value in contractual safety nets.

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