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Willow – Open-source privacy-focused voice assistant hardware

Hacker News

Willow – Open-source privacy-focused voice assistant hardware

As the Home Assistant project says, it's the year of voice! I love Home Assistant and I've always thought the ESP BOX[0] hardware is cool. I finally got around to starting a project to use the ESP BOX hardware with Home Assistant and other platforms. Why? - It's actually "Alexa/Echo competitive". Wake word detection, voice activity detection, echo cancellation, automatic gain control, and high quality audio for $50 means with Willow and the support of Home Assistant there are no compromises on looks, quality, accuracy, speed, and cost. - It's cheap. With a touch LCD display, dual microphones, speaker, enclosure, buttons, etc it can be bought today for $50 all-in. - It's ready to go. Take it out of the box, flash with Willow, put it somewhere. - It's not creepy. Voice is either sent to a self-hosted inference server or commands are recognized locally on the ESP BOX. - It doesn't hassle or try to sell you. If I hear "Did you know?" one more time from Alexa I think I'm going to lose it. - It's open source. - It's capable. This is the first "release" of Willow and I don't think we've even begun scratching the surface of what the hardware and software components are capable of. - It can integrate with anything. Simple on the wire format - speech output text is sent via HTTP POST to whatever URI you configure. Send it anywhere, and do anything! - It still does cool maker stuff. With 16 GPIOs exposed on the back of the enclosure there are all kinds of interesting possibilities. This is the first (and VERY early) release but we're really interested to hear what HN thinks! [0] - https://github.com/espressif/esp-box

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: activity, open · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, 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.
TrustMRRLess likely to generate early MRR · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · 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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