Two_hands

14 ms·

1.
EyesOff Spots Shoulder Surfers Using a Privacy-First Approach(eyesoff.app)
2 points by Two_hands 3mo ago|0 comments
2.
Overcoming OSS Contribution Anxiety(ym2132.github.io)
1 points by Two_hands 5mo ago|0 comments
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Show HN: EyesOff – On-device AI that catches shoulder surfers(eyesoff.app)
1 points by Two_hands 6mo ago|0 comments
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by Two_hands 6mo ago
I tried to build something: https://github.com/YM2132/PR_guard which aims to help in these cases. It's not perfect but with stronger AI detection tools (Pangram) it could be improved although the issue of cost the
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Why Some Models Quantize Better Than Others(ym2132.github.io)
2 points by Two_hands 7mo ago|0 comments
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by Two_hands 9mo ago
Agreed, I have seen some speedups with ONNX if I'm being honest but the process especially on MacOS is a bit messy. I'll try out Executorch and see how it compares, cheers for the recommendation
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by Two_hands 9mo ago
To be honest it's a shame the whole thing is closed up, I guess it's to be expected from Apple, but I reckon CoreML would be benefit a lot from at least exposing the internals/allowing users to define new ops. Also, the ANE o
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by Two_hands 9mo ago
Wow I didn't know that. The worst part of it is as you say we all accept it and no one talks about it. Is there any recommended reading you'd suggest to look into this more and the impacts of it?
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by Two_hands 9mo ago
Yeah I can see why they let it be that way, but the fact it is pretty undefined is what bugged me. I suppose it depends on what your goals are - efficiency vs reproducibility. Also I did run a test of FP16 vs FP32 for a large matmul on the
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by Two_hands 9mo ago
cheers for the tip, I'll give it a go
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by Two_hands 9mo ago
Thank you for reading. Also generally I think CoreML isn't the best. The best solution for ORT would probably be to introduce a pure MPS provider ( https://github.com/microsoft/onnxruntime/issues/21271 ),
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ONNX Runtime and CoreML May Silently Convert Your Model to FP16(ym2132.github.io)
98 points by Two_hands 9mo ago|17 comments
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by Two_hands 9mo ago
Working on a blog post about default behaviour in ONNX Runtime when using the CoreML execution provider. Basically the default args lead to your model being ran in FP16 not FP32. You can find more details at my site soon: https:/&#x2F
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Show HN: PR Guard – A GitHub Action to ensure authors understand their PRs(github.com)
2 points by Two_hands 10mo ago|1 comments
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by Two_hands 10mo ago
interesting, $10 per hour is pretty reasonable. ha, thats probably why I noticed the EyesOff accuracy drops so much at longer ranges, I suppose two models would do better but atm battery drain is a big issue. I'm not sure if it's
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by Two_hands 10mo ago
wow, this is great! You can't DM but my email is in my blog post, in the footnotes. Do you remember the cost of Mech Turk? It was something I wanted to use for EyesOff but never could get around the cost aspect. I need some time to pro
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by Two_hands 10mo ago
Thats a cool idea, thanks for sharing! It's cool to see other uses for a model I built for a completely different task. Is there any research/papers on this type of autism diagnosis tools for babies? To your last point, yes I agre
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by Two_hands 10mo ago
Yeah I did see something like this, may have been huawei. Not sure if they use a model or sensor based approaches though
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by Two_hands 10mo ago
This has been done! It’s the paper I first looked at for this task: https://github.com/rehg-lab/eye-contact-cnn They create this CNN for exactly this task, autism diagnosis in children. I suppose this model would work
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by Two_hands 10mo ago
Right now that's pretty much what I do. I use YuNet to get faces, crop them out and run detection. It's probably a factor of a not enough data/poor model choice.
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by Two_hands 10mo ago
Thanks glad you enjoyed it.
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by Two_hands 10mo ago
Yeah tbh I do recommend using this alongside a privacy screen for best protection. Privacy screens also suffer from the fact that they won’t block someone directly behind you from seeing the screen, so both methods have issues. Any tips on
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by Two_hands 10mo ago
Sorry, I haven't gotten around to gathering examples yet. I ran the models on some example videos which is where the accuracy stats come. Perhaps I have been jaded by the Mac webcam, I agree on most old webcams it wont be great but on
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by Two_hands 10mo ago
This models supports the EyesOff application which will prevent information extraction by either having a popup, switching to another app, or a notification(you can define the behaviour in a few different ways). Privacy screens are still us
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by Two_hands 10mo ago
Others are already trying to do this https://arxiv.org/abs/2504.06237 . I haven't seen anyone take the approach I tried though, as most uses cases focus on tracking the main user rather than others around.
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EyesOff: How I built a screen contact detection model(ym2132.github.io)
35 points by Two_hands 10mo ago|25 comments
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The Beauty of Building(ym2132.github.io)
2 points by Two_hands 11mo ago|0 comments
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by Two_hands 1y ago
I'm working on EyesOff[1] - v2.0.0 should be out soon. It's a simple (currently macOS) application which aims to target shoulder surfing by using a locally running neural network to detect those looking at your screen. [1] https:
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by Two_hands 1y ago
I think the best thing I realised was to post what and when I want, with little expectations regarding the number of readers. This way I produce the best content and any reader who does stumble across the posts will get the best read. I sup
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by Two_hands 1y ago
EyesOff a privacy focused macOS app (other platform support coming soon) - https://www.eyesoff.app Built using python the app alerts you to when someone is looking at your screen, using locally running deep learning models + you

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