Stack-chan – Open-source companion robot easy to assemble and customize

Hacker News

Introducing Stack-chan, an open-source hardware robot designed with Fusion360 and Kicad that is easy to assemble and customize. With just 6 screws, you can easily put together the robot and add your own features using JavaScript with the Moddable SDK. Stack-chan is completely open-source and the design files, firmware and software are all available on GitHub repository ( https://github.com/meganetaaan/stack-chan ) . Give it a try and customize it to your needs. We'd love to see what you create and hear your feedback on this open-source project.

Visit product View on Hacker NewsJan 17, 2023

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76points

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

85%85% 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

62%62% 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

60%60% 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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% 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

12%12% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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