Kweb 1.0.0-RC.1, a lightweight Kotlin web framework for back end devs

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GitHub: https://github.com/kwebio/kweb-core User Manual: https://docs.kweb.io/book Modern websites consist of at least two tightly coupled components, one runs in the browser, the other on the server. These are often written in different programming languages and must communicate with each other over an HTTP(S) connection. Kweb's goal is to eliminate this server/browser separation so that your webapp's architecture is determined by the problem you're solving, rather than the limitations of today's tools. Kweb is a self-contained Kotlin library that can be added easily to new or existing projects. When Kweb receives a HTTP request it responds with the initial HTML page, and some JavaScript that connects back to the web server via a WebSocket. The page then waits and listens for instructions from the server, while notifying the server of relevant browser events. Features: * Allows the problem to determine your architecture, not the server/browser divide * End-to-end Kotlin * Keep the web page in sync with your back-end data in realtime, Kweb does all the plumbing for you * Server-side HTML rendering with rehydration * Efficient instruction preloading to avoid unnecessary server communication * Very lightweight, Kweb is less than 5,000 lines of code

Visit product View on Hacker NewsOct 17, 2022

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

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

75%75% 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: para · Missing: supports, reddit linkedin, podcasting

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% 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

13%13% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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