Lh

Lhdns – A Ledger-Based, Privacy-Preserving Decentralized Naming System

Hacker News

Lhdns – A Ledger-Based, Privacy-Preserving Decentralized Naming System

LHDNS (Ledger-based Hashed Decentralized Naming System) is a next-generation alternative to DNS — designed to provide censorship resistance, privacy, and cryptographic accountability for name resolution. It replaces central trust roots with an ephemeral, ledger-backed system that uses rotating hash-tokens, decentralized gossip propagation, and short-lived identifiers instead of static records. Every query is unlinkable, every service is verifiable, and no single entity controls the namespace. Key Features: - Ledger-based resolution with cryptographic proofs - Ephemeral state (no permanent name mappings) - End-to-end privacy via onion routing and cover traffic - Sybil resistance through staking + adaptive PoW - Compatibility layer with existing DNS (via gateways) Whitepaper, annexes, and technical modules: → https://github.com/twincodesworld/LHDNS Goal: to make global name resolution private, verifiable, and censorship-resistant — for everyone. Feedback and collaboration welcome.

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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: single, code · Missing: mac, agents, macos
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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