Latchor – Tang-compatible NBDE server written in Elixir

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Hi! I've been learning Elixir for a while and wanted to build something more challenging than the usual tutorial projects. Tang, the network-bound disk encryption (NBDE) server used with Clevis, caught my eye because the protocol is small, well-defined, and practical — but I couldn't find an implementation in Elixir. So I decided to write one. It sticks closely to the Tang protocol, but I replaced the file‑based key storage with a SQLite‑backed design to make key rotation and cleanup safer. For the ECDH key exchange, I opted to call the same jose CLI Tang uses. The repo includes a development log describing the design decisions, trade‑offs, and a few lessons learned from trying (and failing) to get an LLM to write code. I believe this compatible implementation could also be useful as a reference for implementations in other programming languages. FYI, I'm not a native English speaker, so please forgive any awkward phrasing.

Visit product View on Hacker NewsSep 20, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · 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 HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

28%28% 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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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