🚀 Project Ideas
Generating project ideas…
Summary
- Transforms dense AI-generated Lean proofs into step‑by‑step, human‑readable narratives with visualizations and interactive checkpoints.
- Extracts the underlying intuition, key lemmas, and “why it works” explanations that mathematicians need to build further insights.
- Core value: turns opaque machine proofs into teachable objects, preserving the learning process that AI alone destroys.
Details
| Key |
Value |
| Target Audience |
Research mathematicians, grad students, and proof assistants users who receive AI‑produced proofs and need to understand them. |
| Core Feature |
Upload a Lean/AI proof → get a guided walkthrough with natural‑language commentary, clickable sub‑goals, dependency graphs, and optional “try‑your‑own‑variant” exercises. |
| Tech Stack |
Lean 4 backend, Python/FastAPI API, React + D3.js for visualization, optional LLM (open‑source) for language generation. |
| Difficulty |
Medium |
| Monetization |
Hobby (open‑source core; premium hosted instance or team licenses for advanced features). |
Notes
- HN commenters repeatedly lamented that AI proofs are “unreadable” and lose the intuition needed for future work (meken, mrbungie, SpicyLemonZest). ProofDigest directly gives them the “digest” Tao describes.
- By providing a shared, explorable artifact, it encourages collaboration rather than secrecy, addressing the fear that labs will scoop before humans can extract value.
- Could be integrated into arXiv or Lean community repositories, giving mathematicians a way to contribute value beyond raw proof verification.
Summary
- Allows researchers to cryptographically commit to a proof idea or sketch (via a hash) and receive a tamper‑proof timestamp without revealing the actual content.
- Prevents AI labs from “scooping” based on rumors while still establishing priority.
- Core value: gives mathematicians a low‑cost, privacy‑preserving way to stake a claim on an open problem before publishing full details.
Details
| Key |
Value |
| Target Audience |
Mathematicians working on sensitive or high‑profile problems who fear premature disclosure to AI labs or competitors. |
| Core Feature |
Submit a hash (e.g., SHA‑256 of a LaTeX file or Lean snippet) → receive a signed timestamp on a public blockchain or trusted timestamp authority; later reveal the full document to prove prior knowledge. |
| Tech Stack |
Web frontend (React), backend (Node.js), integration with Ethereum L2 or a trusted timestamping service (e.g., OpenTimestamps); optional zero‑knowledge proof circuits for privacy‑preserving verification. |
| Difficulty |
Medium |
| Monetization |
Hobby (free tier for individuals; paid API for institutions needing bulk submissions or higher assurance). |
Notes
- Commenters like alternator and ltbarcly3 warned that “the rumor of someone working on a problem can trigger a massive amount of AI-powered effort to flatten it.” PriorClaim lets them prove they were first without leaking the idea.
- Provides a neutral, verifiable record that could be used in disputes over priority, addressing the anxiety about losing credit to AI labs.
- Encourages early sharing of hashes, fostering a culture of openness while protecting the actual work until ready for publication.
Summary
- Scans recent math literature, preprints, and discussion forums to detect underexplored gaps, then suggests concrete, promising open problems that are likely to require human creativity (e.g., conjectures linking disparate fields).
- Core value: helps mathematicians find high‑impact, AI‑resistant directions, counteracting the fear that AI will simply solve everything and leave no interesting work.
Details
| Key |
Value |
| Target Audience |
Researchers seeking new project ideas, grant writers, and advisors looking to guide students toward fruitful problems. |
| Core Feature |
Input a topic or upload a bibliography → receive a ranked list of candidate open problems with brief motivation, related work, and difficulty estimate. |
| Tech Stack |
Python pipeline using Semantic Scholar API, arXiv OAI‑PMH, custom embedding model (SBERT) to detect topic clusters; LLM (open‑source) to formulate problem statements; simple Flask/Django web UI. |
| Difficulty |
Medium |
| Monetization |
Hobby (free web tool); optional premium features like personalized alerts or collaboration workspace for labs. |
Notes
- Many HN users (e.g., thymine_dimer, alternator) argued that the real scarcity is “the identification of a promising problem,” not solving it. ProblemGenie automates that scouting.
- By surfacing problems that AI is less likely to crack (e.g., those needing new definitions or cross‑field insight), it gives mathematicians a competitive edge and restores agency.
- Could be integrated into math departments’ internal idea‑management systems, providing a steady pipeline of worthy challenges.
Summary
- A private, end‑to‑end encrypted workspace (similar to a self‑hosted GitLab) where teams can share code, proofs, notes, and data with cryptographic access controls, audit logs, and watermarking to deter leaks.
- Core value: enables open collaboration without the risk that AI labs will scrape or use the shared material to scoop results.
Details
| Key |
Value |
| Target Audience |
Research groups, polymath‑style collaborations, and individual mathematicians who need to work jointly but fear premature exposure. |
| Core Feature |
Create encrypted repositories; invite collaborators via public‑key verification; all pushes/pulls are E2E encrypted; optional leak‑detection (e.g., honeytokens) and read‑only audit trails. |
| Tech Stack |
Backend: Go or Rust with libsodium for encryption; Frontend: React/Vue; Storage: S3‑compatible bucket with client‑side encryption; optional integration with Keybase or Matrix for identity. |
| Difficulty |
High (due to UX and key‑management challenges). |
| Monetization |
Hobby (self‑hosted open‑source); paid hosted offering with admin UI, support, and compliance features for universities or institutes. |
Notes
- Twotwotwo and others described how AI labs “exploit mathematics' reputation” and could “scoop” if they see promising work. CollabShield removes that vector by making the work cryptographically inaccessible to outsiders.
- Provides a trusted environment for large‑scale collaborations (like Polymath projects) where sharing early ideas is essential but risky without protection.
- Auditable logs also help settle disputes over contribution, reinforcing fair credit—a recurring concern in the thread.
Summary
- A lightweight blockchain‑style ledger where mathematicians can register contributions (proofs, definitions, conjectures, problem formulations) with a timestamp, hash, and optional metadata; later works can link to prior entries to show provenance.
- Core value: creates a transparent, immutable record of who did what, reducing disputes over priority and making it harder for AI labs to claim undue credit without acknowledgment.
Details
| Key |
Value |
| Target Audience |
Mathematicians, journals, and funding agencies that need reliable attribution and provenance tracking. |
| Core Feature |
Submit a contribution (e.g., a PDF hash + short description) → receive a permanent ledger entry with a cryptographic ID; future papers can cite this ID to show building upon prior work. |
| Tech Stack |
Lightweight permissioned blockchain (e.g., Hyperledger Fabric) or a simple append‑only log backed by IPFS + Merkle tree; web interface for submission and exploration; optional integration with ORCID. |
| Difficulty |
Medium |
| Monetization |
Hobby (free public ledger); premium tiers for institutions wanting private channels, higher throughput, or branded instances. |
Notes
- Commenters like gpm and others stressed that mathematicians’ value lies not just in proving but in creating definitions and new problem frames; AttributionChain captures those contributions alongside proofs.
- By giving a citable, timestamp‑stamped ID, it addresses the fear that AI labs will “take” work without credit, enabling proper acknowledgment in papers and grants.
- Encourages a culture of linking new work to prior ideas, fostering the cumulative growth that the community values.
