Or

OrderlyID – typed, time-sortable, 160-bit IDs with checksums

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OrderlyID – typed, time-sortable, 160-bit IDs with checksums

I've been working on OrderlyID, a new identifier format for distributed systems. It's like UUID/ULID/TypeID, but with a few twists: - Typed: every ID has a human-readable prefix (order_xxx, user_xxx). - K-sortable: lexicographic order ≈ creation time. - Structured fields: 160-bit body includes time, tenant, shard, sequence, random. - Checksums: optional 4-char integrity check to catch copy/paste errors. - Privacy flag: can bucket timestamps for public-facing IDs. Format: <prefix>_<payload>[-<checksum>] Example: order_00myngy59c0003000dfk59mg3e36j3rr-9xgg There's a draft spec with conformance tests: https://github.com/kpiljoong/orderlyid/blob/main/spec/0001-s... Go reference implementation and CLI: https://github.com/kpiljoong/orderlyid Compared to TypeID, OrderlyID adds larger bit size (160 vs 128), tenant/shard/sequence fields, optional checksum, and a privacy bucket flag. Status: Draft v0.1 — stable enough for experimentation. Feedback and contributions very welcome. Repo: https://github.com/kpiljoong/orderlyid

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
63%63% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
34%34% 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
25%25% 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
14%14% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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