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Project ideas from Hacker News discussions.

I don't want to read what you didn't write

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

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🚀 Project Ideas

LLM Context Link

Summary

  • Enables creators to generate shareable links that launch a pre‑configured LLM chat with their chosen context (documents, code, prompts) so receivers can interactively query the information instead of reading static AI‑generated text.
  • Core value: turns one‑way AI output into a reusable, on‑demand knowledge session, saving tokens for readers and preserving the ability to explore nuances.

Details

Key Value
Target Audience Technical writers, engineers, educators, anyone who shares LLM‑generated explanations or summaries.
Core Feature Create a link that encodes model selection, system prompt, and attached files; opening the link starts a chat UI (hosted or embedded) with that context pre‑loaded.
Tech Stack Frontend React/Vite, backend Node.js (or Python FastAPI) for session management, WebSocket for real‑time chat, optional integration with OpenAI/Anthropic APIs or local LLM via llama.cpp; storage S3 or similar for context files.
Difficulty Medium
Monetization Revenue-ready: $9/mo per creator + token usage for hosted LLM.

Notes

  • Quote: beloch: “we need a system to pass 'hyperlinks' to LLM's primed with context, ready to be interactively queried”.
  • Potential for discussion: enables a new way of sharing knowledge, reduces token waste, invites conversation about privacy and model licensing.

Prompt First

Summary

  • Lets authors share the original prompt (and optional source material) instead of the LLM’s generated text, with a one‑click “Run Prompt” button that executes the prompt using the reader’s preferred LLM.
  • Core value: shifts the burden of generation to the reader, ensuring they receive exactly the information they ask for and can verify or regenerate it on demand.

Details

Key Value
Target Audience Developers, researchers, bloggers who currently copy‑paste LLM output.
Core Feature Markdown‑like block that stores prompt text and attached files; clicking runs the prompt via a selected LLM API (OpenAI, Anthropic, local) and displays result.
Tech Stack Frontend Svelte or React, backend optional API‑key vault (encrypted), can be a browser extension or standalone site; uses LLM APIs directly.
Difficulty Low‑Medium
Monetization Hobby (open source) or Revenue‑ready: premium prompt‑library hosting ($5/mo).

Notes

  • Quote: selcuka: “Please don't send me AI‑generated text. Send me your prompt instead.”
  • Potential for discussion: encourages transparency, reduces AI slop, lets readers customize depth.

LLM Output Verifier

Summary

  • Scans supplied text for likely LLM‑generated patterns, provides a hallucination risk score, and offers one‑click fact‑checking against trusted sources or a user‑provided corpus.
  • Core value: helps readers detect AI slop and verify factual claims without leaving the page, increasing trust in shared content.

Details

Key Value
Target Audience Engineers, journalists, anyone who reads technical documentation or news online.
Core Feature Highlights suspicious sentences, shows confidence, and provides a “Check facts” button that queries a retrieval‑augmented LLM or search API to verify claims.
Tech Stack Browser extension (Manifest V3) using TensorFlow.js or a small classifier model for detection; backend microservice (Python/FastAPI) that calls embedding search (e.g., FAISS over Wikipedia) and an LLM for verification; optional offline mode.
Difficulty Medium
Monetization Revenue‑ready: freemium extension with paid API for higher‑verification quota ($4/mo).

Notes

  • Quote: clickety_clack: “facts aren’t checked… You get a big cheesy dramatic arc but the core story isn’t factually correct.”
  • Potential for discussion: addresses misinformation from LLMs, encourages critical reading, could be integrated into PR review tools.

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