TUI Bluetooth Connector for Mac

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Hi, since sometimes my AirPods didn't autoconnect to my Macbook and I didn't want my hands to leave the keyboard. I looked for alternative solutions to quickly connect devices. Found [blueutil] for MAC, but was not happy with writing long commands, so I build a pretty minimal inline TUI application on a beautiful saturday to: - display paired devices - pair/unpair devices - connect/disconnect devices - search devices Ive used [textual], a very nice framework to create TUIs with python. You can find the gh-repo here: https://github.com/Zaloog/blueutil-tui Direct use with uv via: `uvx --from blueutil-tui btui` [blueutil]: https://github.com/toy/blueutil [textual]: https://textual.textualize.io

Visit product View on Hacker NewsJan 29, 2025

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3points

Did not reach leaderboard

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

73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% 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

34%34% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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