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ApexBot – An (speech-based) interactive web-based bot

Hacker News

ApexBot – An (speech-based) interactive web-based bot

Hello fellow combinators, First of all I've been reading Ycombinator for a while (especially show HN), but have never really posted anything. The ideas that people post on show HN always intrigue me. So I thought I'd share something that I have been working on for the past weeks. My idea was to write a webapp for my smart mirror, so I have built an (speech-based) interactive web-based bot (which currently only works on Chrome ;( ). The current (alpha) version consists of following features: Speech recognition: which allows you to interact with the bot by using voice commands. Text-to-speech: so that the bot can give feedback. Face detection: with this the bot knows you're in front of it (a camera is ofcourse a must). Github repository link: https://github.com/Kamuffel/ApexBot A working demo can be found at: https://kamuffel.github.io/ApexBot/ Try for example the commands: - tell me a quote; - sleep; - wake up; - be quiet; - you may speak. I would like to hear some feedback, tips or some cool ideas that may can be added. Thanks! Kamuffel

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Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos
68%68% 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
63%63% 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, io · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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