GoSync – Local-First Sync Engine for Go and WASM

Hacker News

Hi HN, I built an open-source sync engine that brings "Local-First" capabilities to Go web applications. The Problem: Most offline-first solutions (Firebase, PouchDB) force you into the JS ecosystem. If you are writing a Go backend, you often have to re-implement sync logic or settle for simple REST APIs that break when the user goes offline. The Solution: GoSync runs the exact same Go code in the browser (via WebAssembly) and on the server. - Client: Go compiled to WASM, persisting to IndexedDB (via a small JS bridge). - Server: Go with SQLite or Postgres, persisting to disk. - Protocol: Merkle Trees to detect diffs, and WebSockets to sync them. If the server dies or the user goes offline, the client keeps working. When connectivity returns, it heals the state automatically. I'd love feedback on the Merkle Tree implementation and the WASM/JS bridge architecture.

Visit product View on Hacker NewsDec 29, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, code, apis · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

67%67% 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

33%33% 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

29%29% 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

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.

Incorrect prediction on native model

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