IdeaWell

Project ideas from Hacker News discussions.

LLMs corrupt your documents when you delegate

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

1. Compoundingdegradation (semantic ablation)

"AI‑washing any text will degrade it, compounding with each pass." — causal

2. Human‑in‑the‑loop quality control

"The act of writing isn't just the production of text, it is about wrangling a topic, rotating it in your mind and finding the perfect expression for a thought you have and that you want to convey to others." — atoav

3. Editing primitives matter

"An LLM is like an author of a book that immediately closes its eyes and wipes its memory after writing a chapter." — dangus

🚀 Project Ideas

IntentLockEditable Intent Tracker

Summary

  • Prevents degradation by capturing explicit “why” notes for each edit.
  • Core value: Preserves original author intent across multiple LLM passes.

Details

Key Value
Target Audience Developers, technical writers, knowledge workers
Core Feature Atomic intent‑store editing UI that forces LLM to output minimal diff + intent label; stores intent in a version‑controlled ledger
Tech Stack React frontend, PostgreSQL backend, Python (LangChain) for LLM orchestration, Git‑style storage
Difficulty Medium
Monetization Revenue-ready: subscription $15/mo per user

Notes

  • HN commenters repeatedly lament losing context when “each pass” erodes meaning – IntentLock makes every edit traceable back to the author’s stated reason.
  • Provides a practical tool for teams that need auditable edits without relying on manual diff reviews.

FactVault Lightweight Knowledge Repository

Summary

  • Solves memory loss in multi‑pass editing by storing facts separately from narrative.
  • Core value: Enables reliable synthesis without semantic ablation.

Details

Key Value
Target Audience Researchers, content managers, AI‑assisted writers
Core Feature Structured fact library with metadata; LLM queries facts and generates output referencing IDs; automatic provenance tracking
Tech Stack Next.js front‑end, SQLite for storage, Sentence‑Transformers embeddings, FastAPI backend
Difficulty Low
Monetization Revenue-ready: tiered SaaS (Free tier, $10/mo Professional)

Notes- Users express frustration that “LLMs dissolve the nuance” when summarizing or editing – FactVault lets them keep a pristine fact base that LLMs can reference without altering it.

  • Aligns with HN sentiment about wanting genuine human‑written voice and not being “tricked” into reading AI‑generated blogs.

AgentGuard Deterministic Editing Harness

Summary- Provides safe, reversible edits by requiring LLM to output explicit patch commands.

  • Core value: Eliminates round‑trip corruption while keeping LLM flexibility.

Details

Key Value
Target Audience Engineering teams, DevOps, documentation maintainers
Core Feature Command‑only edit interface with diff preview, automated test harness integration, audit log of touched files
Tech Stack Rust backend, WebAssembly front‑end, Cobra CLI, integrates with GitHub Actions for CI validation
Difficulty High
Monetization Revenue-ready: enterprise licensing $200 per seat annually

Notes

  • Discussions highlight the need for “very obvious what it touched” and robust harnesses – AgentGuard delivers that by forcing surgical diffs instead of full‑document regurgitation.
  • Offers a clear path to reduce the “degradation” worries discussed in the thread while still leveraging LLM power.

Read Later