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A private, PQ-secure, infinitely scalable blockchain[fully open-source]

Hacker News

A private, PQ-secure, infinitely scalable blockchain[fully open-source]

For the past few months, I’ve been working on a new Layer 1 blockchain designed to solve what I see as the fundamental trilemma in the space: achieving strong privacy, infinite scalability, and post-quantum security simultaneously. The result is NERV: a private-by-default, horizontally scalable blockchain that replaces Merkle trees with 512-byte neural state embeddings, proven inside Halo2 circuits and attested in hardware enclaves. Key technical innovations: Neural State Embeddings Instead of Merkle trees, the entire state of a shard is compressed into a 512-byte vector using a transformer encoder. Transfers are homomorphic updates in this space—no decompression needed. This reduces inclusion proofs by ~900× compared to zkEVMs. Blind Validation & TEE-Bound Privacy Transactions are routed through a 5-hop onion mixer running inside remotely attested enclaves (SGX/SEV/TrustZone). No addresses, amounts, or metadata are ever visible on-chain. Dynamic Neural Sharding Shards split and merge autonomously based on an LSTM load predictor, updated via federated learning. This allows >1M sustained TPS with no theoretical ceiling. AI-Native Consensus Validators predict the next embedding hash using a distilled transformer. Disagreements are resolved via Monte Carlo simulations in TEEs, achieving sub-second finality. Useful-Work Economy Nodes earn tokens by contributing gradients to improve the network’s own encoder via differentially private federated learning—replacing PoW/PoS with useful computation. Why this might be interesting to HN: The system is fully open-source (MIT/Apache 2.0), with all code, circuits, and tests already public. No pre-mine, no VC allocation, no foundation treasury. Fair launch is scheduled for October 2027. Built with post-quantum primitives from genesis (Dilithium-3, ML-KEM, SPHINCS+). Implements formal privacy guarantees (ProVerif models included). The core ZK circuit (LatentLedger) is ~7.9M constraints—far smaller than equivalent zkEVM proofs. Current status: ~85-90% of the code is complete, including core circuits, TEE attestation pipelines, sharding simulations, and federated learning incentives. Testnet is targeted for Q4 2026. I’m sharing this here not to promote a token, but to invite technical feedback, cryptographic review, and collaboration from the community to help make that crucial leap from being a strong prototype to being a fully-realized, live, ever-evolving private money . If you’re into ZK, TEEs, federated learning, or post-quantum crypto, I’d love your thoughts. Links: Whitepaper: https://github.com/nerv-bit/nerv/blob/main/NERV_Whitepaper_v... GitHub repo: https://github.com/nerv-bit/nerv Code & tests: https://github.com/nerv-bit/nerv/tree/main/NERVCodeV2 Happy to answer any technical questions or discuss design trade-offs.

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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