Open-source, self-hostable Deno KV binary

Hacker News

Hey HN community, Andy from Deno here. When we built Deno KV, a zero-config, ACID/multiple consistency supported data store, that when used with Deno Deploy becomes a globally distributed serverless database, there were growing concerns of potential vendor lock-in. These are important concerns, as Deno is intentionally built on web standards. This aligns with our philosophy: knowing Deno means knowing the web, which empowers you to reach over a billion internet users. We're excited to share the open source, standalone Deno KV binary (MIT licensed), that you can self-host with any VPS of your choosing. This should make using Deno KV more flexible with whatever architecture you prefer. We'd love to hear your feedback. Would you self-host Deno KV? What would you improve about the experience of using Deno KV? Any other part of your stack that gives you headaches? Thanks!

Visit product View on Hacker NewsNov 15, 2023

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

91%91% 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.

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

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

75%75% 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 · Strong signals: host, users · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% 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

19%19% 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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