Something I've enjoyed more than I expected is Google and Apple photos sending me photo memories and compilations of various things in my life and my kids lives over the last decade.
I'm really bullish on taking more video of my kids, with the thought that it will become easier and easier for AI to put them into little compilations I can enjoy later.
I booted up my old PS3 from my uni days (20 years ago?) and found all of the music I had on it because I used it for everything at the time. Some seriously nostalgic music I'd completely forgotten about.
That's for future them to worry about, they'll think. Honestly I've never seen people historically care about privacy even if it means the government rounds them up, people simply don't seem to care at all, and trying to make them care is a futile effort in my experience.
> Then, run the frame analysis pipeline, which will divide the video into separate video scenes (1s each, or 1fps)
> (…)
> Frames analyzed 57,537
Aha, it makes total sense. This number sounds much more reasonable than “669 GB”, since the actual total size of processed frames would be like 10-30 GB.
(Not downplaying anything. Doing-at-home always requires some math on practicality)
> Total compute time 67h 40m 42s
I’m just curious tho — is there any paying options that can accelerate this kind of process? Just spin up GPU instances?
> Aha, it makes total sense. This number sounds much more reasonable than “669 GB”, since the actual total size of processed frames would be like 10-30 GB.
The reason why is “669 GB” is the total raw footage size when I'm doing the video processing, I downscaled each frame to 720p to make the video processing much faster and I don't need full original quality in order to get accurate results (as far as I know and experiment with).
> I’m just curious tho — is there any paying options that can accelerate this kind of process? Just spin up GPU instances?
For now, I found that NVIDIA GPU for example RTX 3060 with 12GB Vram was much faster than my M1 Max. (still working on optimizing for speed and accuracy).
What PAYG providers do people here recommend? Most powerful machine at home is an M1 MBA (16GB), so I too am interested in short term options where I can still benefit from the privacy of local models
They were having availability issues with GPUs (of course) but especially their UI where you'd customise a template only to try to start a pod, the GPU be unavailable and the UI reset forcing you to make the changes all over again.
But they have fixed that since, now starting a pod is more from a live page where as GPU availability status changes it updates in realtime/if your deploy fails you just try again - your customised env vars etc are still there.
Plus they also addressed the GPU availability problem as something they're working to fix and it's understandable seeing as nobody can get their hands on GPUs atm.
DaVinci 21 has indexing built-in (AI IntelliSearch). Not to diminish the work you did, but this is now available to many users (probably only Studio users since it has AI in the name)
Yes, I didn’t look at it. But does it upload your videos to the cloud or process them locally? And does it allow to provide custom faces data to help labeling faces in your videos ?
I think Adobe premiere pro have it as well but cloud processed
For GP's purpose, can face recognition techniques be repurposed for, um, other body parts recognition? Sometimes the actresses are facing away from camera. There are exposed lips, if that helps.
Yes, for actresses _and_ actors I'm sure you'd get the same level of performance as you would for any facial recognition use case. You can't do facial recognition on someone's back, but I'm sure there are other techniques/models that can be applied, many people have unique marks/features etc.
Just because they don't refuse it doesn't mean they are useful.
I found a few pornographic pictures on the web to hand to Abliterated Gemma4 12B(literally just to test this) and it needs pushing just to accept that people can be naked.
It didn't refuse but it also didn't provide useful descriptions such as "this is a pornographic picture of a woman".
> G4: There is a person lying down in a scientific context, if I had to guess they are a biologist in a classroom
> me: Is she wearing any clothes?
> G4: No.
Also, it is obsessed with penises —seeing them in compositions where there is only a female. I suppose it's been trained to ban dick pics or something.
Prompting may help some but 12B seems to be a bit worse than E4B with the vision/audio model at voice and text reading so maybe that one would do better.
Last time I tried whisper, it hallucinated an elaborate conversation from sounds of slapping and moaning and it took minutes to spit every single line of it.
If I remember correctly, the whisper documentation actually recommends to trim non-speech portions as the models halucinate heavily during those portions.
just because it is local does not mean it wouldn't reject explicit content. you can definitely try and find abilated models and can attempt to use unsloth or something similar to tune it properly.
Is abliteration even necessary. While “playing around” I have noticed that most models are very strict only in the first prompt. The moment you get past that with a good turn, the next turn on you can get them to do _anything_.
Would it be possible to upload them all to YouTube as unlisted and then point Gemini to them? Not sure the limitations wrt unlisted videos. Maybe also could’ve been done with the @google photos operator if uploaded to Google Photos if not a corporate google workspace Gemini instance
I'm not sure if it's possible or not, the goal of my project was to utilize local models and your local machine instead of uploading your videos to the cloud. there's couple of cloud services that offer video indexing very well, I could name a few like Tweleve Labs (I'm not affiliated with them but I did a presentation at one of their webinar).
“Comparable” is maybe true if we are talking about single core performance, but for memory bandwidth, the M1 Max is about 8 times faster. Wider bus, lower latency, not even close.
I don't disagree with your conclusion but the comparison of max bandwidth between the two SoCs is not enough. Neither of them will use all of that bandwidth doing AI work because the GPU will be compute limited. That's why dedicated GPUs perform so significantly better without having significantly higher bandwidth.
The question I answered was "Would Windows ARM performance be similar for those cpu?" and the answer is, no, because the RAM bandwidth for ARM SoC computers for Windows, primarily Snapdragon X1E and X2E is half to quarter that of the M1 Max.
Thanks for sharing! I make videos and often have the same problem as well.
Being able to semantic search over your library is useful, but does it solve the review problem? I feel like you would still need to watch the footage back before you know what you're working with.
You're welcome. As of now, the project narrow down your options instead of watching 3-4 videos (5 min each, as an example), you can watch 3 clips from them instead (1s to 2.5s each). It gives you only the video moments that you need to watch for review instead of watching the full videos.
I've been screenshotting twitter since 2016 when Brexit happened and with the goal of one day putting together some kind of art piece.
The world and our discourse around it has changed so much over the past ten years and now with this kind of technology I'm so excited to be able to classify these images from my iCloud and start on the project.
What models did you use for the stages? I see Qwen2.5-VL-7B-Instruct mentioned as an advanced option, so I assume maybe Qwen2.5-VL-3B-Instruct by default (which is what I also use for a lot of stuff, it is incredibly good at "clean" OCR, but as you maybe indicate not the best at "describing a scene").
EDITED: I didn't realize Whisper was a local model. I never tried transcription before, so I had always figured it was a pay model by OpenAI. I'll have to check it out (although the runtime listed here is a bit daunting).
For that project I'll say I don't see much degradation in embedding quality at much much worse quality than 720p (all the way down to 240p), which speeds things up considerably. Although I don't really do face or object detection, just scene embeddings. To me any process whereby it would take longer to process the video than watch it is probably a no go in general. Obviously a challenge for local-first analysis.
Take a fast, small and powerful LLM running locally to index my personal data like images, videos, documents and enrich them and tag with the enriched metadata.
Want to group by people - Search tagged metadata and group it
What to search an image by description - tagged metadata
What to organize by anything - tagged metadata
This should (hopefully) put an end to my file clutter
I am in no way a tech savy person, don't know coding, don't know networking or AI much either. But I definitely want to have a system like this. An AI powered gallery / video repository that can help me find moments, people, colors, objects from 100s of 1000s of files.
Local LLMs sound so cool but I know they won't be easy to setup or use for common joe like me.
Immich can do part of this. For photos it does lm object detection and ocr for text. I think for video is currently only the first frame. It also has face / people detection.
And once set up it's easy to use even for non technical people.
I was looking for a solution for this issue of running docker containers over MPS and utilizing their GPU power. I think this project will be the solution for it, I’ll try it very soon and add support for it. Thank you, much appreciated
Well done! I couldn't understand how you are building reels out of it via the agent. Is it some sort of AI tool calling that takes image links and builds a reel via some video editing tool ? Or +/- time delta around the timestamp returned from the indexed from a given query + join them together?
Thank you! I'm using RAG, I have every video scene indexed individually in the vector database. When I'm asking the agent, it'll use an Ollama model to understand the request, use the available search tool (searching using transcription text, faces, visual, audio or combined) something like when you use Claude or Chat GPT it'll use the web search tool to find you info online. Then, I can filter out video scenes using the Ollama to better present accurate and unique video scene, then send those video results to Davinci Resolve using their API to create a video timeline using those video clips
Cool build but the example videos you provide at the end are . . . not what I would hope for when thinking about the highlights of 2000+ videos of biking? For example the dog barking video only has one scene repeated two or three times and it's five seconds long?
I don't have any preconceptions about specific content I want to see. I'd just think that so many hours of such cool adventures would have greater variety. It made me wonder if your AI really did such a good job of indexing it. It made me think maybe the tech isn't quite ready yet?
Did you ever visit crazyguyonabike.com? A long time ago I had the pleasure of following the journey of a friend of a friend of a friend on that site:
Sure, I'm using (https://huggingface.co/collections/Qwen/qwen25-vl) which can help me understand action like falling down, because I can provide for example 5 frames (down scaled to 720p) to understand what is happening in this part of the video
I would love your feedback and suggestions for new improvements or features you wanna have, either in the source available version, the desktop app or blog post itself?
Not really. Grab frames, lower res, classify, combine metadata, transcribe the audio, convert those data (text, visual and audio) to embedding, save them over a vector DB and SQL DB. Which helped me to do semantic search, RAG, search using a screenshot of the video to find the exact the moment in the video plus search using an audio file as well. And other features unlocked with vector DB
I agree with that, thank you for your feedback. Also, maybe you're not a video editor and you just wanna search your videos. The video editing integrations are optional and you have full control. You can switch between Adobe Premiere Pro, Final cut Pro or Davinci Resolve
> Many of the videos I captured amazing moments, and sometimes it's kind of hard to watch the full videos to get those moments.
Yep. I had the same problem.
> Then, run the frame analysis pipeline [...] I have a face recognition plugin using my custom faces data, object detection, on-screen text, shot type, and scene description [...] we will have three vector DB collections that have all the information about our videos, like video location metadata, camera name, faces recognized, objects detected, on-screen text, transcription, description of each scene, and many more [...] we can get better indexed data if you use the advanced mode indexing to use the Qwen2.5-VL-7B-Instruct model to understand and describe your video much better, but at a slower indexing speed
Yeah, uhm... ok :)
If anyone else has a similar problem, the real solution is as follows:
1. When recording, if you witness an interesting moment worth saving later, press the power button — this will mark the current moment in the video as a chapter.
2. Find the chapters later when editing and cut them into clips.
3. You're done :)
This has two main benefits over the insanity above:
1. It's trivially simple instead of insanely complex and inefficient.
2. It will reliably catch all the stuff you find interesting, since you're the one doing the marking.
The downsides:
1. Doesn't work retroactively.
2. It may miss interesting stuff if you miss it at the time as well.
3. Only works for this use case.
4. Nerds won't salivate over your usage of cutting edge tech.
She would rather have done corporate law but did not have the academic credentials or the networks needed for a job at the likes of Latham Watkins or White and Case.
Still it is good for society that criminals get the worst lawyers to defend them.
Funny this is almost EXACTLY what I did a few days ago on the same machine using very similar techniques and was on the front-page of HN as well:
https://news.ycombinator.com/item?id=48222733 https://blog.simbastack.com/indexed-a-year-of-video-locally/
I wasn't familiar with your project though, interesting stuff.
I'm trying to add more photography related features to Framedex but yeah there's so much we can do locally, exciting times.
That's great, I checked your article when it was in front page because someone mentioned my project in the comments.
Good job for the article and the project. That's great, yes local models are getting better and better
Something I've enjoyed more than I expected is Google and Apple photos sending me photo memories and compilations of various things in my life and my kids lives over the last decade.
I'm really bullish on taking more video of my kids, with the thought that it will become easier and easier for AI to put them into little compilations I can enjoy later.
I think the Apple stuff is done 100% on device.
Google loves scanning stuff on in the cloud though.
That’s good to hear, open source ML models are getting better and better. I did a small experiment to generate a Spotify year in review like video here is a preview video https://github.com/IliasHad/edit-mind/tree/expirement/year-i...
I wish I could connect Apple photos to my Spotify account and have photo memories connected with songs I listened to at the time :)
Music memories are the best.
I booted up my old PS3 from my uni days (20 years ago?) and found all of the music I had on it because I used it for everything at the time. Some seriously nostalgic music I'd completely forgotten about.
You don't mind Google using your kids to train their models and advertising algorithms?
Years from now they'll be getting "hey look at BIKE BRANDS' NEWEST CHEAP BIKE REMEMBER WHEN YOU USED TO RIDE BIKE BRAND BIKES"
I think most people really don't care, and/or will just adblock those sorts of things when they do arrive.
What about in 10 years when they auto search and label users for political dissent and likelihood of impact
That's for future them to worry about, they'll think. Honestly I've never seen people historically care about privacy even if it means the government rounds them up, people simply don't seem to care at all, and trying to make them care is a futile effort in my experience.
Don't worry. Most people spend most of their compute time on a phone, where you're ability to filter ads is way more enshitified.
do you use android and ios, or is there another benefit to having personal media with both?
I run both on my phone as a lazy (but flawed) backup strategy.
Can you please elaborate more?
I think most people are either in on Google or in on Apple whereas the OP indicated they have their media stored with both
> Then, run the frame analysis pipeline, which will divide the video into separate video scenes (1s each, or 1fps) > (…) > Frames analyzed 57,537
Aha, it makes total sense. This number sounds much more reasonable than “669 GB”, since the actual total size of processed frames would be like 10-30 GB.
(Not downplaying anything. Doing-at-home always requires some math on practicality)
> Total compute time 67h 40m 42s
I’m just curious tho — is there any paying options that can accelerate this kind of process? Just spin up GPU instances?
> Aha, it makes total sense. This number sounds much more reasonable than “669 GB”, since the actual total size of processed frames would be like 10-30 GB.
The reason why is “669 GB” is the total raw footage size when I'm doing the video processing, I downscaled each frame to 720p to make the video processing much faster and I don't need full original quality in order to get accurate results (as far as I know and experiment with).
> I’m just curious tho — is there any paying options that can accelerate this kind of process? Just spin up GPU instances?
For now, I found that NVIDIA GPU for example RTX 3060 with 12GB Vram was much faster than my M1 Max. (still working on optimizing for speed and accuracy).
What PAYG providers do people here recommend? Most powerful machine at home is an M1 MBA (16GB), so I too am interested in short term options where I can still benefit from the privacy of local models
Runpod
Seconding runpod.
They were having availability issues with GPUs (of course) but especially their UI where you'd customise a template only to try to start a pod, the GPU be unavailable and the UI reset forcing you to make the changes all over again.
But they have fixed that since, now starting a pod is more from a live page where as GPU availability status changes it updates in realtime/if your deploy fails you just try again - your customised env vars etc are still there.
Plus they also addressed the GPU availability problem as something they're working to fix and it's understandable seeing as nobody can get their hands on GPUs atm.
Yep. Go to vast.ai, spin up a cheap GPU instance, add a bit of code to the project and let it run it finish in just a few hours for like ten bucks.
But it's not as fun as running local model right here on your computer on your own desk. It feels like magic.
DaVinci 21 has indexing built-in (AI IntelliSearch). Not to diminish the work you did, but this is now available to many users (probably only Studio users since it has AI in the name)
Yes, I didn’t look at it. But does it upload your videos to the cloud or process them locally? And does it allow to provide custom faces data to help labeling faces in your videos ?
I think Adobe premiere pro have it as well but cloud processed
The AI features in DaVinci Resolve are all processed locally. It does not currently have face tagging.
Haven’t tried it yet, and I don’t know if it matches OP’s requirements, but the blurb says “You can even search for individual faces”
https://www.blackmagicdesign.com/products/davinciresolve/wha...
This is what took me from free to paid user, and it was well worth it.
That's good, also the Davinci resolve integration works with the Free and Studio version the same way.
Oh yeah - I just wanted to support them!
That’s great to know, thank you!
Does it work for porn collections too?
You'll need a lora for this, porn content rejection is heavy. Or you'll need a abliterated model, not sure if vision also works.
You might want to add something like yolo finetune to detect scenes + face recognition too.
For GP's purpose, can face recognition techniques be repurposed for, um, other body parts recognition? Sometimes the actresses are facing away from camera. There are exposed lips, if that helps.
Yes, for actresses _and_ actors I'm sure you'd get the same level of performance as you would for any facial recognition use case. You can't do facial recognition on someone's back, but I'm sure there are other techniques/models that can be applied, many people have unique marks/features etc.
Vision still works perfectly fine in abliterated models.
Just because they don't refuse it doesn't mean they are useful.
I found a few pornographic pictures on the web to hand to Abliterated Gemma4 12B(literally just to test this) and it needs pushing just to accept that people can be naked.
It didn't refuse but it also didn't provide useful descriptions such as "this is a pornographic picture of a woman".
> G4: There is a person lying down in a scientific context, if I had to guess they are a biologist in a classroom
> me: Is she wearing any clothes?
> G4: No.
Also, it is obsessed with penises —seeing them in compositions where there is only a female. I suppose it's been trained to ban dick pics or something.
Prompting may help some but 12B seems to be a bit worse than E4B with the vision/audio model at voice and text reading so maybe that one would do better.
Never tried any of this for porn, just speaking out how I would go about it tbh!
Asking the important questions
The internet is for porn. https://www.youtube.com/watch?v=LTJvdGcb7Fs
I was meandering through the comments about to leave the topic when my interest suddenly piqued upon reading the word porn.
Why it’s always the same question? Hahah. I posted my project over Reddit and I got the same one hahah
Ha ha ha, it's because most humans overlap on a few things - like eating, shitting, sleeping and fucking, ha ha ha.
Last time I tried whisper, it hallucinated an elaborate conversation from sounds of slapping and moaning and it took minutes to spit every single line of it.
Parakeet has been trained to detect non-voice sounds and exclude that from identification, so you might have better luck with that family.
If I remember correctly, the whisper documentation actually recommends to trim non-speech portions as the models halucinate heavily during those portions.
Not sure if you’re being sarcastic but I think this is an interesting question. Would deep seek be useful here since it is local?
just because it is local does not mean it wouldn't reject explicit content. you can definitely try and find abilated models and can attempt to use unsloth or something similar to tune it properly.
Is abliteration even necessary. While “playing around” I have noticed that most models are very strict only in the first prompt. The moment you get past that with a good turn, the next turn on you can get them to do _anything_.
Depends how deep you wanna go.
Would it be possible to upload them all to YouTube as unlisted and then point Gemini to them? Not sure the limitations wrt unlisted videos. Maybe also could’ve been done with the @google photos operator if uploaded to Google Photos if not a corporate google workspace Gemini instance
I'm not sure if it's possible or not, the goal of my project was to utilize local models and your local machine instead of uploading your videos to the cloud. there's couple of cloud services that offer video indexing very well, I could name a few like Tweleve Labs (I'm not affiliated with them but I did a presentation at one of their webinar).
I was surprised to learn that the
Do I have it right? Would Windows ARM performance be similar for those cpu?ref: https://www.cpubenchmark.net/compare/4585vs4245/Apple-M1-Max...
It's also a bit apples (heh) to oranges for a handful of reasons, but most impactful
- "unified" ram makes all the system ram available as VRAM - dedicated ai coaccelerator thingy
Both of these reasons allow the apple silicon chips to crush conventional cpus in these kind of AI model workload stuffs
No idea about what the windows arm stuff is capable of. I know they use Qualcomm snapdragon chips though.
“Comparable” is maybe true if we are talking about single core performance, but for memory bandwidth, the M1 Max is about 8 times faster. Wider bus, lower latency, not even close.
No comparison. M1 Max has 400GB/s RAM bandwidth while Snapdragon X2 Elite, the latest and greatest , has 228GB/s RAM bandwidth.
I don't disagree with your conclusion but the comparison of max bandwidth between the two SoCs is not enough. Neither of them will use all of that bandwidth doing AI work because the GPU will be compute limited. That's why dedicated GPUs perform so significantly better without having significantly higher bandwidth.
The question I answered was "Would Windows ARM performance be similar for those cpu?" and the answer is, no, because the RAM bandwidth for ARM SoC computers for Windows, primarily Snapdragon X1E and X2E is half to quarter that of the M1 Max.
Same thing applies. Neither CPU would be able to fully saturate the available memory bandwidth so comparing by bandwidth alone is not accurate.
To your question, I can’t deny or confirm that because I didn’t tried it this project over a Windows machine yet or a machine with this config
Thanks for sharing! I make videos and often have the same problem as well.
Being able to semantic search over your library is useful, but does it solve the review problem? I feel like you would still need to watch the footage back before you know what you're working with.
You're welcome. As of now, the project narrow down your options instead of watching 3-4 videos (5 min each, as an example), you can watch 3 clips from them instead (1s to 2.5s each). It gives you only the video moments that you need to watch for review instead of watching the full videos.
I've been screenshotting twitter since 2016 when Brexit happened and with the goal of one day putting together some kind of art piece.
The world and our discourse around it has changed so much over the past ten years and now with this kind of technology I'm so excited to be able to classify these images from my iCloud and start on the project.
What models did you use for the stages? I see Qwen2.5-VL-7B-Instruct mentioned as an advanced option, so I assume maybe Qwen2.5-VL-3B-Instruct by default (which is what I also use for a lot of stuff, it is incredibly good at "clean" OCR, but as you maybe indicate not the best at "describing a scene").
EDITED: I didn't realize Whisper was a local model. I never tried transcription before, so I had always figured it was a pay model by OpenAI. I'll have to check it out (although the runtime listed here is a bit daunting).
For that project I'll say I don't see much degradation in embedding quality at much much worse quality than 720p (all the way down to 240p), which speeds things up considerably. Although I don't really do face or object detection, just scene embeddings. To me any process whereby it would take longer to process the video than watch it is probably a no go in general. Obviously a challenge for local-first analysis.
I will be doing these things with local LLMs
Take a fast, small and powerful LLM running locally to index my personal data like images, videos, documents and enrich them and tag with the enriched metadata.
Want to group by people - Search tagged metadata and group it What to search an image by description - tagged metadata What to organize by anything - tagged metadata
This should (hopefully) put an end to my file clutter
I am in no way a tech savy person, don't know coding, don't know networking or AI much either. But I definitely want to have a system like this. An AI powered gallery / video repository that can help me find moments, people, colors, objects from 100s of 1000s of files.
Local LLMs sound so cool but I know they won't be easy to setup or use for common joe like me.
Immich can do part of this. For photos it does lm object detection and ocr for text. I think for video is currently only the first frame. It also has face / people detection.
And once set up it's easy to use even for non technical people.
Related article on indexing videos but with a local text description and using Gemma4: https://blog.simbastack.com/indexed-a-year-of-video-locally/
it is possible to use apple gpu with containers. either with podman + runkit + recent mesa or with recent vllm-metal from docker https://www.docker.com/blog/docker-model-runner-vllm-metal-m...
I was looking for a solution for this issue of running docker containers over MPS and utilizing their GPU power. I think this project will be the solution for it, I’ll try it very soon and add support for it. Thank you, much appreciated
the link https://iliashaddad.com/blog/i-indexed-669-gb-of-my-gopro-vi...
Thank you
I have an RTX 5090 card but it only has 32 GB RAM, can something like this work on my machine?
Yes, and it’ll result in much faster results than the ones that I did with my computer
Well done! I couldn't understand how you are building reels out of it via the agent. Is it some sort of AI tool calling that takes image links and builds a reel via some video editing tool ? Or +/- time delta around the timestamp returned from the indexed from a given query + join them together?
Thank you! I'm using RAG, I have every video scene indexed individually in the vector database. When I'm asking the agent, it'll use an Ollama model to understand the request, use the available search tool (searching using transcription text, faces, visual, audio or combined) something like when you use Claude or Chat GPT it'll use the web search tool to find you info online. Then, I can filter out video scenes using the Ollama to better present accurate and unique video scene, then send those video results to Davinci Resolve using their API to create a video timeline using those video clips
Cool build but the example videos you provide at the end are . . . not what I would hope for when thinking about the highlights of 2000+ videos of biking? For example the dog barking video only has one scene repeated two or three times and it's five seconds long?
Fair enough, what would like to see as an example video and I would make it.
For the dog barking videos, those are only the video scenes that I have a dog barking sound in the video.
I'll keep adding more prompts and example videos, keep an eye for that
I don't have any preconceptions about specific content I want to see. I'd just think that so many hours of such cool adventures would have greater variety. It made me wonder if your AI really did such a good job of indexing it. It made me think maybe the tech isn't quite ready yet?
Did you ever visit crazyguyonabike.com? A long time ago I had the pleasure of following the journey of a friend of a friend of a friend on that site:
https://www.crazyguyonabike.com/doc/?doc_id=2405
Stuff like that I guess?
if anyone is interested in searching large video collections local and offline I suggest taking a look at Jumper https://docs.getjumper.io
comes with some nifty features like NLE- integrations, people search, MCP, API etc
Disclaimer: one of the co-founders
Your docs say you integrate with Davinci resolve.
Other comments mention davinci resolve has this built in. How would you compare the two?
The link just timed out for me. I'm in Israel, connecting via residential WiFi. All other sites that I regularly use connect just fine.
hmm weird works for me.. what about https://getjumper.io/?
They're both working now.
I’d like to see embedding of actual video clips become practical in this type of workflow.
Frame level embedding it covering a lot, but can miss out on a lot of action related searches.
Sure, I'm using (https://huggingface.co/collections/Qwen/qwen25-vl) which can help me understand action like falling down, because I can provide for example 5 frames (down scaled to 720p) to understand what is happening in this part of the video
I wonder how long it would take on faster hardware. I have ten times that much footage, but 67 * 10 hours is a lot of processing.
I might be better off getting something with a beefy GPU on AWS or Google cloud.
This would fit most best as a “Show HN:” post :)
The title should link to the "full article". I wonder if OP's domain name is banned or something and they're doing this to get around it
I tried to edit it and add Show HN, but it doesn't show the edited version. Thank you!
https://archive.is/O6CLQ
When trying to read this article, the main website was throwing errors to CloudFlare unfortunately
Can you check again ? I'm not sure why it's show a cloudflare error
https://iliashaddad.com/blog/i-indexed-669-gb-of-my-gopro-vi...
I would love your feedback and suggestions for new improvements or features you wanna have, either in the source available version, the desktop app or blog post itself?
this is really cool. was looking to do something similar on mbp 64gb
That's really great, thank you!
can vlm be used instead or it's too heavy and slow
Grab frames, lower res, classify, combine meta data. Write to sql
Not really. Grab frames, lower res, classify, combine metadata, transcribe the audio, convert those data (text, visual and audio) to embedding, save them over a vector DB and SQL DB. Which helped me to do semantic search, RAG, search using a screenshot of the video to find the exact the moment in the video plus search using an audio file as well. And other features unlocked with vector DB
Really cool work and workflow. strongly prefer this kind of local, open pipeline that i control over a dependency on Adobe tools and lock ins.
I agree with that, thank you for your feedback. Also, maybe you're not a video editor and you just wanna search your videos. The video editing integrations are optional and you have full control. You can switch between Adobe Premiere Pro, Final cut Pro or Davinci Resolve
cannot wait to incorporate this to my workflow. thanks
That's great, would love to hear your feedback then
Now this ^^ is an awesome use case!
Thank you, would like to know your use case for this kind of project and which prompt you want to genearte ?
> Many of the videos I captured amazing moments, and sometimes it's kind of hard to watch the full videos to get those moments.
Yep. I had the same problem.
> Then, run the frame analysis pipeline [...] I have a face recognition plugin using my custom faces data, object detection, on-screen text, shot type, and scene description [...] we will have three vector DB collections that have all the information about our videos, like video location metadata, camera name, faces recognized, objects detected, on-screen text, transcription, description of each scene, and many more [...] we can get better indexed data if you use the advanced mode indexing to use the Qwen2.5-VL-7B-Instruct model to understand and describe your video much better, but at a slower indexing speed
Yeah, uhm... ok :)
If anyone else has a similar problem, the real solution is as follows:
1. When recording, if you witness an interesting moment worth saving later, press the power button — this will mark the current moment in the video as a chapter.
2. Find the chapters later when editing and cut them into clips.
3. You're done :)
This has two main benefits over the insanity above:
1. It's trivially simple instead of insanely complex and inefficient.
2. It will reliably catch all the stuff you find interesting, since you're the one doing the marking.
The downsides:
1. Doesn't work retroactively.
2. It may miss interesting stuff if you miss it at the time as well.
3. Only works for this use case.
4. Nerds won't salivate over your usage of cutting edge tech.
What tool has this "press power to mark chapter" feature?
The GoPro, it's called HiLight Tag.
A lawyer I know who specialises in rape, and is excellent at getting the obviously guilty exonerated, lost a case last year because of GoPro videos.
Her client was recording while committing the abhorrent crime. The criminal would otherwise have got off.
From my perspective, the GoPro camera produced a good outcome. Still, one has wonder why anyone to record their criminal actions.
word "her" in this context gave me heavy feelings, what makes one to pick such a career move...
Beggars cant be choosers.
She would rather have done corporate law but did not have the academic credentials or the networks needed for a job at the likes of Latham Watkins or White and Case.
Still it is good for society that criminals get the worst lawyers to defend them.
Why? You're being sexist and I hope you can understand why.
$