DriftDB – an open source WebSocket backend for real-time apps

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Hey HN! I’ve written a bunch of WebSocket servers over the years to do simple things like state synchronization, WebRTC signaling, and notifying a client when a backend job was run. I realized that if I had a simple way to create a private, temporary, mini-redis that the client could talk to directly, it would save a lot of time. So we created DriftDB. In addition to the open source server that you can run yourself, we also provide https://jamsocket.live where you can use an instance we host on Cloudflare’s edge (~13ms round trip latency from my home in NY). You may have seen my blog post a couple months back, “You might not need a CRDT”[1]. Some of those ideas (especially the emphasis on state machine synchronization) are implemented in DriftDB. Here’s an IRL talk I gave on DriftDB last week at Browsertech SF[2] and a 4-minute tutorial of building a cross-client synchronized slider component in React[3] [1] https://news.ycombinator.com/item?id=33865672 [2] https://www.youtube.com/watch?v=wPRv3MImcqM [3] https://www.youtube.com/watch?v=ktb6HUZlyJs

Visit product View on Hacker NewsFeb 3, 2023

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

89%89% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, apps, new · Missing: agents, macos, agent

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

35%35% 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

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BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

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