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Franz – A desktop client for Apache Kafka

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Franz – A desktop client for Apache Kafka

This is the source code to Franz, a native desktop client for Apache Kafka I've been working on for the past ~three years or so. Around 2021, I started using Kafka at a startup and I didn't like any of the clients that were available at the time, so I decided to build my own in my spare time[1]. I wanted a solid native experience on the Mac that wasn't web based and didn't require any Docker containers to run. A few months ago, I decided to make the code source-available[2] in part because I like the idea of my users being able to see the code they're running and in part because I want to showcase that one can build serious applications with Racket. The app also has a somewhat unusual architecture: the "backend" (the "core" folder) is written in Racket, the Mac "frontend" (the "FranzCocoa" folder) is Swift + Cocoa/SwiftUI[3, 4], and the two sides communicate over pipes. The Windows and Linux versions (the "FranzCross" folder) reuse the Racket core and implement the frontend using a wrapper around Racket's cross-platform GUI toolkit[5, 6]. My goal is to eventually port the frontend for the latter two platforms to use their native toolkits directly as well (see [3] for some more details re. Windows). Happy to answer any questions! [1]: https://defn.io/2022/11/20/ann-franz/ [2]: https://defn.io/2023/08/10/ann-franz-source-available/ [3]: https://defn.io/2023/03/19/racketfest-talk-2023/ [4]: https://defn.io/2022/08/21/swiftui-plus-racket-screencast/ [5]: https://defn.io/2021/11/07/racketcon-talk-2021/ [6]: https://arxiv.org/abs/2308.16024v1

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45points
5comments
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, user, dock · Missing: agents, macos, agent
86%86% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, platform, users · Missing: intuitive, reviews, host
38%38% 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
17%17% 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.

Correct prediction on native model

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