BSD licensed data acquisition package in Python suitable for labs

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Dear all, you can develop web apps, dashboards and networked data acquisition for your hardware with this tool. I made this for use in research labs, for students in universities, for hobbies like home automation & for commercial projects if the application domain is suitable. Its supposed to be as simple as possible to understand so that students from non-coding background (but studying engineering or sciences) can understand. Therefore, any feedback, contributions or comments are appreciated. I hope it is useful to you. The documentation is lacking, but I share it anyway so that feedback is taken into consideration for further development.

Visit product View on Hacker NewsJul 26, 2024

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

66%66% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

50%50% 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: apps, way · Missing: mobile apps, ios, personal

49%49% 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

15%15% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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