Re

Redux Remote – A high level networking library for realtime apps

Hacker News

Redux Remote – A high level networking library for realtime apps

I am a fan of serverless. I love how it voids the burden of building a server, which can be half of the work in a client-server architecture. However, serverless has a few blocking issues when I try to build an action-based multiplayer online games: - The latency is unacceptably high. A typical round trip time is user noticable, because database accesses are slow. If a serverless function cold starts, the latency will be even higher. Google search shows me an interesting blog measurnig the exact latency numbers ( https://serialized.net/2021/03/serverless_gaming_limits/ ), which aligns with my impression. - The game can still use a server. Many in game actions required atomic updates to shared state, and many clients can request such updates simultaneously (e.g. increments to team scores on a scoreboard). At minimum, the clients need a serverless function to sequentialize simultaneous updates, such that they do not update based on stale state. However, one database access is already slow, let alone multiple, sequentialized ones. One solution is to add an in-memory cache (such as Redis) for shared state and update that instead. However, an in-memory cache is expensive and unnecessarily complex. The lowest tier of ElasticCache on AWS is $10/month. For comparison, the lowest tier of EC2 server is $5/month, which is sufficient for small games like what I am building. Why use an in-memory cache when I can use memory? Hence, let me introduce the library.

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, apps, user · Missing: mac, agents, macos
74%74% 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
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, month, google · Missing: mobile apps, ios, personal
36%36% 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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