AI

AI Catflap Prey Detector

Hacker News

AI Catflap Prey Detector

I built a small side project for my mom: the Catflap Prey Detector My mom loves her cat, Zelie, even if she’s a fearless little hunter. Birds, mice, the occasional surprise catch… all proudly delivered right through the catflap and into the house. The problem? My mom is terrified of mice. “Since you work with AI, can’t you make something to stop her from bringing me presents?” Usually, she calls me about her printer or her phone, but this time I couldn’t resist the challenge. After a bit of hardware tinkering, a dash of AI, and a few late-night experiments, it actually works! The setup uses a Raspberry Pi equipped with a camera and an RFID reader, running Python code with YOLO object detection models. It identifies when Zelie is carrying prey, triggers the door lock automatically, and pushes alerts in real time. Now my mom is happy (and mouse-free), I got to play with edge AI on a fun side project, and Zelie is mildly offended by the new security measures. If you want to see it in action or are curious about how it works, I’ve shared the project code, hardware setup, and instructions on GitHub so that you can build your own! Check it out here: https://github.com/fl2o/catflap-prey-detector and Happy building.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
76%76% 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 NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
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: occasional, calls · Missing: plus, platform, intuitive
48%48% 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
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: arr · Missing: mrr, revenue, profit
24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · 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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