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TanStack DB – Reactive DB with Differential Dataflow for TanStack Query

Hacker News

TanStack DB – Reactive DB with Differential Dataflow for TanStack Query

Hi HN, Kyle, Sam and the TanStack team here. We’ve been working on TanStack DB, an embedded, reactive client database for TanStack Query, and are proud to announce today that with the 0.1 release that it's now in BETA! TanStack DB plugs into your existing TanStack Query useQuery calls and uses Differential Dataflow to incrementally recompute only what changed, so updates stay sub-millisecond even with 100k rows. You get live queries, optimistic updates with automatic rollback, and streaming joins — all in the client! TanStack DB works with REST, GraphQL, WebSockets, and shines with sync engines like ElectricSQL or Firebase, letting you load large, normalized collections once and stream real-time changes into the client without manual bookkeeping. It sits on top of queryClient so you can adopt it incrementally, one route at a time. - Intro post: https://tanstack.com/blog/tanstack-db-0.1-the-embedded-clien... - Local-first sync via Electric: https://electric-sql.com/blog/2025/07/29/local-first-sync-wi... - Web starter with TanStack Start: https://github.com/electric-sql/electric/tree/main/examples/... - Mobile starter with Expo: https://github.com/electric-sql/electric/tree/main/examples/... - Project website and docs: https://tanstack.com/db - GitHub repo: https://github.com/tanstack/db Try it out and let us know what you think!

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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
39%39% 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
38%38% 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
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Acquire.comPre-revenue stage for this audience · Strong signals: active · 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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