Em

Empirical Eye – Data Collection and Streaming Device for ML+AI

Hacker News

Empirical Eye – Data Collection and Streaming Device for ML+AI

Hey guys, Our company has just released a sensor + computer module called the Empirical Eye. It's basically a stereo vision system + on-board embedded CPU+GPU + WiFi (plus some mounting straps). It's meant to be easily attached to physical machines, to collect and stream data, and enable novel machine learning applications (and one day: control those machines!). The goal with this device is to bring ML/AI to the physical world. We've made our own "visual quality control" software with the device. We're doing a device giveaway to developers for feedback and troubleshooting, which will help us refine it. We'd love to send some out to the HN crowd (quantities limited to about 75), and get your feedback and see what you build! We want to make life easier for running ML experiments and developing new applications. The primary motivation has been the field of robotics, but we see it as a flexible module that can be attached to any number of devices or machines, so feel free to be creative :) We're looking to do another production run in a month or so, with a cleaner industrial design that'll look a bit more professional. Upon delivery you'll get the device's software API. It's a Linux OS not that different from a Raspberry Pi OS, and we have basic data collection + streaming scripts written in Python for your convenience. If you're interested, definitely check out the site and sign up for a free device. Let us know what you want to develop, and ideally a quick one-liner describing your background experience. www.empiricalautomation.com Happy to answer any questions, cheers!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% 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: mac, computer, new · Missing: agents, macos, agent
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: just released, ide, io · Missing: https docs, excited, exist
49%49% 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 · Strong signals: plus · Missing: platform, intuitive, reviews
34%34% 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
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.

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