Indexing a year of video locally on a 2021 MacBook with Gemma4-31B (50GB swap)

We just got a modern example of the classic message from a friend who just picked up programming, containing: "I just created my own web app, wanna check it out? It's here: http://localhost:8080"

Oops! My bad. Fixing it now. And yeah, I can share the Skill file. Give me 5 mins.

1) It's just simple plain-text `.description.md` sidecar files, one per clip, sitting next to each video.

Something which I can query later - Like when brainstorming with Claude "I wanna make some videos of the Luxury rooms in the lodge" and it knows what all videos could help here (going through the files).

There's also a folder root level files that aggregates the text descriptions to make it easier to find.

I've just attached an image in the blog showing an example - https://blog.simbastack.com/_media/gvcycx2n.png

2) No - nothing from DaVinci Resolve. Framedex is a standalone pipeline. Resolve isn't involved.

Faces come from insightface (the open-source buffalo_l pack - RetinaFace for detection), running locally on CPU. For each clip it detects faces in the sampled frames, embeds them, and writes rows to ~/.framedex/faces.db.

Tbh, this part I know it's building up in my local DB but I haven't tested how good is it. Will check them out properly soon.

But yeah, on your broader point that's why framedex deliberately does not ask the LLM to handle faces or locations.

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Faces → insightface / ArcFace embeddings. Deterministic, comparable across clips. The vision model only contributes a rough people_count; it never tries to identify anyone.

Locations → EXIF GPS via exiftool, reverse-geocoded through Nominatim/OpenStreetMap. Hard metadata, not a guess.

The LLM only does what it's good at: scene description, mood, shot type, keywords, keep/review/cull rating (this last part is also debatable though).

> the fans spinning at max speed

This always confuses me - don't people want their computations to run as fast as possible and thus inevitably produce more heat that needs to be vented?

I suppose sometimes it is just an analogy for "its utilizing 100% of my resources" (which I'm guessing it is here), but I've definitely had people say it as an actual complaint in different contexts

I made a B2C AI app that's fully local (and free) to do AI based contextual file renaming.

So if you give it a bunch of screenshots it will try and intelligently name them based upon what is in the screenshot. Same for videos, PDFs, etc.

But to your point I haven't even tried charging money as it feels like something Apple is just going to bake in as a feature.

https://finalfinalreallyfinaluntitleddocumentv3.com/

Definitely agree with this. Here, me and Claude brainstorming together did that Research, and some trial-and-error to get to this.

But I can tell it's only a matter of time before agents become smart enough to let my non-tech friends be able to just say "Make sense of all these videos in my folder" and it just does it.

Is it really local models that unlock this? Surely stateless model APIs would yield the same benefits? I get that local can be “cheaper” depending on usage, but we’ve been renting storage and compute from clouds at a premium for ages..

Haha. It's honestly something that I've been struggling with myself. I'm running this safari lodge but I don't want to go down that route of slop videos!

But on the other hand, genuine videos do take time and slows down the process.

Unsloth Studio [0] is what I recommend these days, open source alternative to the more widely known LM Studio, and also built by the people who make good quantizations of released models. With MTP support not merged in you should get 2x token generation speed with no accuracy difference. They also have MLX quants if you scroll down a bit, which is a format specifically for macOS' Metal GPU acceleration but that's not integrated into Unsloth Studio just yet.

[0] https://unsloth.ai/docs/models/qwen3.6#mtp-guide

I have been contemplating a M5 Pro MBP, but for the life for me I wasn't able to find benchmarks for real-world models, do you happen to know how many tokens per second roughly you get with MoE models like Qwen 3.6 35B/A3B or Gemma 4 26B?

Thanks! I was honestly casually trying it out on the side with Claude's help. And I was actually pleasantly surprised to see how good the result was.

Still blows my mind I can do all this from my 2021 MBP.

I'll try to do a post once I have the next steps working (helping with planning and editing videos with Davinci Resolve).

Memory bandwidth or storage bandwidth?

Tbh, I did spend a lot of time trying to ground it and de-slopify it - verified nothing was halucinated and went through 10 iterations to get to this. It's almost like wrestling with Claude and I knew it would be tough on HN.

But because of the fear of non-perfection, I used to put away things like creating this article or even posting it anywhere. And I do think the article has real value that HN would appreciate (I am myself an HN-enthusiast).

I'll try more. Someone else shared this project which would be really helpful - https://github.com/blader/humanizer

Also a side note, the blog is posted on my self-created Slopit.io platform which is purely meant for your personal agents (working along with you) to post content - I recommend trying it out. https://blog.slopit.io/this-blog-post-is-slop/

I know, things are getting difficult with all the slop around, but my personal opinion is, as the agents get better at writing, the "annoying-ness" factor reduces and pieces of substance will still be appreciated, even if it was written by agents. This and the fact that agents aren't going away.

If I've automated a lot of my coding, I feel like engineers like me would naturally progress to also taking agents' help to write useful content.

PS - this comment was 100% hand-typed.

Hi. I wrote this article - yes, I do run a safari lodge in Maasai Mara, Kenya. It's amazing. Ask me anything if you're interested in knowing more.

(Also email is in my profile).