Project ideas from Hacker News discussions.

The LLMentalist Effect (2023)

πŸ“ Discussion Summary (Click to expand)

Four Prevalent Themes in the HN Discussion on LLMs and Intelligence

1. Human vs. AI Contribution Debate

The discussion frequently centers on whether humans or AI deserve credit for achievements like solving Navier-Stokes, with emphasis on human steering and priming of models.

"Did an AI do that on its own though? I heard it was human mathematicians using a sophisticated machine as a tool." - rusk

"All of the latest big proofs were driven by professional human mathematicians steering and priming the models, yes." - meowface

"Nothing is solved in isolation but credit usually goes to wherever the new work in the paper comes from instead of the whole mountain of previous mathematics or existing tools used." - zamadatix

2. Intelligence and Consciousness of LLMs

Significant debate exists about whether LLMs possess genuine intelligence or merely simulate it, with differing views on the relationship between intelligence and consciousness.

"ai is not conscious. you can solve NS without thinking. the psychic con aspect is anthropomorphising the model." - lukewarm707

"FFS. Intelligence has nearly nothing to do with consciousness. You have the causation backwards. Consciousness arises because of intelligence in many subsystems below it." - pixl97

"I think LLMs are intelligent. What don’t I understand about them that would make me change my mind? Keeping in mind that for me 'intelligence' is the ability to solve complicated, intellectually demanding problems, and to understand novel concepts." - daishi55

3. Psychic/Con Artist Analogy

The original post's comparison between LLMs and psychics/con artists is repeatedly referenced and debated, focusing on how both exploit human tendencies to believe what we want to believe.

"Psychics and other types of con artists are intelligent and have minds. LLMs behave like Psychics and Con Artists. That's the whole point of this article" - krupan

"Humans tend to believe what they want to believe. They gain confidence when a confident person (or LLM) they work with exudes confidence. Where they might have hesitated or got stuck in analysis paralysis alone, the aid of a con (short for confidence) artist will get them moving." - krupan

"Delegating your decision-making, ranking, assessment, strategising, analysis, or any other form of reasoning to a chatbot becomes the functional equivalent to phoning a psychic for advice." - natbennett

4. Skepticism vs. Belief in AI Capabilities

Tension exists between those advocating for healthy skepticism about AI claims and those emphasizing AI's proven utility for intellectual work, with warnings about both excessive skepticism and credulity.

"People are missing the point. We should all be much more skeptical, much more careful about how we evaluate the claims made by the people selling these products because we as humans are super vulnerable to the types of scams the author of this article describes." - krupan

"Instead of desperately clinging to excuses and rationalizations, why don’t you just get used to the fact that these tools are insanely useful for demanding intellectual work, and that is an opinion held by many of the smartest people alive?" - daishi55

"Skepticism is a fine balance. If you're not skeptical you invite idiocy to your bed to sleep with you. If you're too skeptical you put yourself in a box of ignorance that puts you at a disadvantage." - pixl97


πŸš€ Project Ideas

LLM Experiment Logger & Reproducibility Suite

Summary

  • Logs prompts, model versions, parameters, hardware usage, energy estimates, and human interventions during LLM-assisted intellectual work (e.g., theorem proving), providing an auditable trail.
  • Core value proposition: Enables reproducible, transparent AI-assisted research, addressing the "psychic con" concern by showing exactly what the model did and what humans contributed.

Details

Key Value
Target Audience Researchers, mathematicians, AI enthusiasts who use LLMs for complex problem solving
Core Feature Immutable log of prompt/response pairs, system metadata, and optional human annotations, exportable as reproducible notebook
Tech Stack Python backend, SQLite or PostgreSQL, Electron or web frontend (React), optional OpenAI API middleware
Difficulty Medium
Monetization Revenue-ready: Subscription tier for teams ($10/user/mo) with cloud storage and sharing; free tier for individuals

Notes

  • HN commenters expressed frustration about opaque usage: "we don't even know all the details of how the tool was used, we haven't been allowed to use the exact tool they used ourselves" (krupan).
  • Provides practical utility for reproducible science and could spark discussion on best practices for LLM-assisted research.

ProofCheck: LLM-to-Proof Assistant Verifier

Summary

  • Integrates LLMs with formal proof assistants (Lean, Coq, Isabelle) to automatically translate and check AI-generated mathematical proofs for correctness.
  • Core value proposition: Reduces hallucination and increases trust in AI-generated proofs by providing machine-verifiable certification.

Details

Key Value
Target Audience Mathematicians, formal methods engineers, educators
Core Feature Takes LLM output (proof sketch), attempts to formalize in target proof assistant, runs verification, returns pass/fail with error highlights
Tech Stack Lean 4 library, Rust or Python bindings, LLM API wrapper, WebAssembly for running Lean in browser
Difficulty High
Monetization Revenue-ready: Per-verification API call pricing ($0.01 per proof) or enterprise license

Notes

  • Commenters noted the need for verification: "It isn't a con when it can find and exploit zero-days." but also desire for Lean-verified proofs (bonoboTP).
  • Could enable trustworthy AI-assisted mathematics and stimulate discussion on AI's role in formal proof.

Human-AI Collaboration Credit Tracker

Summary

  • Visual dashboard that records interactions in a shared workspace (e.g., chat with LLM, code edits, document changes) and quantifies contributions of human vs AI using diff-based attribution.
  • Core value proposition: Clarifies credit allocation, countering the perception that LLMs are taking undue credit (the "psychic con" effect).

Details

Key Value
Target Audience Teams using LLMs for collaborative writing, coding, research
Core Feature Timeline view with color-coded commits (human vs AI), contribution percentages, exportable report
Tech Stack Node.js backend, React frontend, uses Operational Transformation or CRDT for sync, integrates with VS Code or via web editor
Difficulty Medium
Monetization Revenue-ready: SaaS subscription ($15/user/mo) with free tier for open-source projects

Notes

  • HN users criticized the lack of credit clarity: "Most people I've seen talk about this online are unaware of the mathematicians steering the models" (TomGarden); "credit usually goes to wherever the new work in the paper comes from".
  • Useful for academic collaborations and could spark discussion on authorship norms with AI.

Open Prompt & Model Card Exchange (OPMX)

Summary

  • Community-driven repository where users can share prompts, model cards, datasets snippets (with licensing), and results for notable LLM achievements, enabling reproducibility and scrutiny.
  • Core value proposition: Combats proprietary secrecy by providing transparent, reusable artifacts that let others replicate or challenge claims.

Details

Key Value
Target Audience AI researchers, skeptics, journalists, educators
Core Feature Searchable database of prompt sets, associated model versions, hardware estimates, and outcome metrics, with versioning and discussion
Tech Stack Django or Flask backend, PostgreSQL, React frontend, optionally IPFS for storage
Difficulty Low
Monetization Hobby (open source, maybe donations via Open Collective)

Notes

  • Commenters demanded transparency: "we won't show you what training data we used, what prompts we used, what the harness was..." (krupan); "people want to turn this into a debate about priority and credit".
  • Could become a hub for reproducibility challenges and foster discussion on AI claim validation.

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