100% open-source voice assistant – as a HAL9000

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It started in 2018 with my first 3D printer and some openscad designs - just something that looks like a HAL9000 (and had a controlled red LED for the visuals) - it ended up being a full software-project...that hasn't ended yet. But: it is at a (quality-)point at which I am OK with putting it up on stage. My project goals: A digital voice-assistant that is... - actually usable (response times, error rates, ...) - performs everything on-device ("look ma, no cloud") - is entirely Free/Libre Open-Source-Software - is as-easy-as-possible to build I consider all four goals to be accomplished even at this ~90% stage of the project. If you just want to get some quick impressions, check out the "Demos" (video-)page in the github wiki (prominently linked in the README in the github repo). If you want to spend some more time on this, it's probably easiest to watch my presentation at Hackaday Europe from two months ago: https://www.youtube.com/watch?v=oqJCU8FRMQg&t=14761s

Visit product View on Hacker NewsJun 15, 2024

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

82%82% 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

72%72% 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, 000, io · Missing: https docs, excited, just released

70%70% 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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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

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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Replace your voicemail with a smart voice assistant

Indie Hackers1$500/moai

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