Vb

Vbare – a simple alternative to Protobuf for schema evolution

Hacker News

Vbare – a simple alternative to Protobuf for schema evolution

Hey HN – we’re sharing a tiny extension to BARE called VBARE that we’ve been using extensively to make schema evolution much cleaner & simpler. After our Protobuf schemas became a cluttered after numerous major changes, we evaluated many alternatives for better schema evolution - Cap'n Proto, FlatBuffers, Borsh, etc. - but none seemed that they’d make a noticable improvement. VBARE takes a different approach: it's a tiny extension to BARE that adds version headers and explicit migration functions between schema versions. This allows you to handle complex changes (like splitting a "name" field into "firstName"/"lastName") that are impossible with other tools, and your application logic stays clean. It's been powering all of Rivet's protocols in production, with TypeScript and Rust implementations available.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: tiny, using · Missing: mac, agents, macos
73%73% 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.
Hacker NewsStrong engagement from HN community · Strong signals: lua, io · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
39%39% 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
28%28% predicted probability of success on Indie Hackers, 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 · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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