Pixel Machha gives event photographers and social media a professional platform — FTP camera uploads, AI face recognition, branded guest albums, Social Media specific editing and exporting, and live slideshows. All in one place.
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Maker
📌
📸⚡ Ever seen a conference photo appear on social media while the speaker is still on stage?
That was exactly the problem that led to Pixel Machha.
I volunteer as a photographer at tech conferences. Taking photos was the easy part.
Getting them online? 😅 Not so much.
The workflow looked something like this:
📸 Photographer clicks photo
🏃 Runs to the social media team
💾 Hands over the memory card (or transfers photos via phone)
✂️ Social media team crops images for different platforms
🏷️ Adds logos/watermarks
🚀 Finally publishes the post
By then, 20–30 minutes had already passed.
And during all of this, the photographer was missing moments happening on stage because they were busy acting as a human USB cable. 🔌😂
There was another challenge too.
After the event, attendees wanted access to photos, but sharing thousands of images through generic cloud folders wasn't ideal. Privacy was a concern, and finding your own photos felt like searching for a needle in a haystack.
So I built Pixel Machha.
⚡ Pixel Machha connects directly to professional cameras using built-in FTP capabilities.
That means:
✅ Photos upload automatically from the camera to the cloud
✅ Social media teams receive images instantly
✅ Attendees can view photos in real time through live albums
✅ Automatic overlays, branding, and watermarking
✅ Face recognition can help guests see only their own photos
✅ No memory card shuffling. No WhatsApp compression. No photographer cardio sessions. 😄
🎯 Who is this for?
- Event photographers
- Event management companies
- Conference organizers
- Photography agencies
- Corporate events
- Weddings & parties
- Anyone who wants to share event photos instantly
💰 Pricing
Pixel Machha is currently free with generous usage limits.
If you're running larger events or have higher-volume requirements, reach out and I'd be happy to help tailor a solution for your needs.
Would love your feedback, questions, feature requests, and even your wildest event-photography pain points. 🚀
Thanks for checking out Pixel Machha!
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@sumanth_bettadapura It's a wonderful product. This is very useful for me as i can get all the photos i am in easily while the event happens especially with the selfie feature .
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Maker
@_yathee_ glad I built something that can be of your use
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How does the face recognition handle group shots with people who haven't opted in, and is the AI matching done on your servers or locally on the photographer's machine?
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Maker
@fatmaodef39623 yes, it handles group photos very well, it works on the people who hasn’t opted in as well, I mean- if you are in a photo you would get that photo, the face recognition happens on server side , with the help of AWS Rekognition Service
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Maker
@fatmaodef39623 I handles the group shots very well, I mean for people who hasn't opted in wont be able to find their photo but the others who has opted in they can find their photo even if they are in group photo,. The AI matching happens over server side, dont want to put pressure over the photographer's machine and also its something not possible to have computation over cameras. cameras dont have that ability.
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the FTP upload straight to guest albums is a genuinely nice detail, most tools in this space make you export from lightroom or your camera app first which kills the "live" part of live events. at a big wedding with a few hundred guests, does the face recognition hold up with people in and out of shadow/backlight, or does accuracy drop enough that photographers still have to manually sort some of it
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Maker
@omri_ben_shoham1 thank you for your positive vibes, the face recognition has worked almost all the times I have tested , I would say it’s almost 99% accurate…
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99% is solid if that holds in low light too, wedding dance floors and dim receptions are usually where face matching falls apart. is that number from indoor venue testing or mostly daytime outdoor stuff
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Maker
@omri_ben_shoham1 Want to be honest here, I said 99% with the experience I had from using the platform, for face recognition I am using AWS service called Rekognition and Its super accurate, I have not built the face recognition from the ground up, I'm just using AWS Service for that, but that's a very solid service - I dont think I could have done better job myself compared to their service.
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honestly that's the smart call, no point reinventing face recognition when Rekognition already handles it well. curious about the cost side though - are you paying AWS per API call as usage scales up, or did you lock in a flat rate? seems like that could get pricey fast with photo-heavy events like weddings.
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Maker
@omri_ben_shoham1 yup, the price is a banger, for a 2 day event the bill shot up to 223$ , and another reason for that was wrong architecture design, now I’m working on either building a face recognition system of my own or trying to bring down AWS cost down
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$223 for 2 days is rough, especially if it's mostly architecture overhead and not actual usage. before you build your own face recognition from scratch, worth checking if it's a data transfer / instance sizing issue rather than the recognition calls themselves - that's usually where AWS bills balloon on short events
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Maker
@omri_ben_shoham1 what actually happened is a missed bug, on each selfie upload I was scanning through all the photos via Rekognition Face search feature , What I had in mind was keeping the vector data with myself and comparing the vector data of the selfie against the pre parsed photos, since that didn't happen as rekognition was not providing the vector data the bill shot up, hence Im looking at implementing face recognition system of my own, that too in the sense I would be using Open Source available solution than building everything from scratch.
but thanks a lot for the concern and the interaction you are doing <3
This is an excellent solution to a big problem in any conferences… I have used it in recent React Nexus... It was amazing. All the best with it. 👍
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Maker
@tapasadhikary Thanks a lot for your kind words and glad to hear that it came in handy for you
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makes sense re: the vector data approach, that's a much cleaner design than re-scanning the whole gallery per selfie. open source face rec (insightface or similar) should get you most of the way there without the per-call AWS bill, just budget extra time for GPU inference if you're running it yourself instead of a managed API
@sumanth_bettadapura It's a wonderful product. This is very useful for me as i can get all the photos i am in easily while the event happens especially with the selfie feature .
How does the face recognition handle group shots with people who haven't opted in, and is the AI matching done on your servers or locally on the photographer's machine?
@fatmaodef39623 I handles the group shots very well, I mean for people who hasn't opted in wont be able to find their photo but the others who has opted in they can find their photo even if they are in group photo,. The AI matching happens over server side, dont want to put pressure over the photographer's machine and also its something not possible to have computation over cameras. cameras dont have that ability.
the FTP upload straight to guest albums is a genuinely nice detail, most tools in this space make you export from lightroom or your camera app first which kills the "live" part of live events. at a big wedding with a few hundred guests, does the face recognition hold up with people in and out of shadow/backlight, or does accuracy drop enough that photographers still have to manually sort some of it
99% is solid if that holds in low light too, wedding dance floors and dim receptions are usually where face matching falls apart. is that number from indoor venue testing or mostly daytime outdoor stuff
@omri_ben_shoham1 Want to be honest here, I said 99% with the experience I had from using the platform, for face recognition I am using AWS service called Rekognition and Its super accurate, I have not built the face recognition from the ground up, I'm just using AWS Service for that, but that's a very solid service - I dont think I could have done better job myself compared to their service.
honestly that's the smart call, no point reinventing face recognition when Rekognition already handles it well. curious about the cost side though - are you paying AWS per API call as usage scales up, or did you lock in a flat rate? seems like that could get pricey fast with photo-heavy events like weddings.
$223 for 2 days is rough, especially if it's mostly architecture overhead and not actual usage. before you build your own face recognition from scratch, worth checking if it's a data transfer / instance sizing issue rather than the recognition calls themselves - that's usually where AWS bills balloon on short events
@omri_ben_shoham1 what actually happened is a missed bug, on each selfie upload I was scanning through all the photos via Rekognition Face search feature , What I had in mind was keeping the vector data with myself and comparing the vector data of the selfie against the pre parsed photos, since that didn't happen as rekognition was not providing the vector data the bill shot up, hence Im looking at implementing face recognition system of my own, that too in the sense I would be using Open Source available solution than building everything from scratch.
but thanks a lot for the concern and the interaction you are doing <3
daily.dev
This is an excellent solution to a big problem in any conferences… I have used it in recent React Nexus... It was amazing. All the best with it. 👍
@tapasadhikary Thanks a lot for your kind words and glad to hear that it came in handy for you
makes sense re: the vector data approach, that's a much cleaner design than re-scanning the whole gallery per selfie. open source face rec (insightface or similar) should get you most of the way there without the per-call AWS bill, just budget extra time for GPU inference if you're running it yourself instead of a managed API
Blaze
Congrats on the launch!
@akashhamirwasia Thank you Akash !
@akashhamirwasia Thanks a lot Akash !
Macaly
instant guest delivery is such a pain solved 📸 congrats on the launch!!