This is sick. Nice work
Indexing a year of video locally on a 2021 MacBook with Gemma4-31B (50GB swap)
131–140 of 151 posts
Re: Indexing a year of video locally on a 2021 MacBook with Gemma4-31B (50GB swap)
#132did you know that this existed and is pretty good and doesn't hog 50GB of swap? https://github.com/iliashad/edit-mind
Thanks for that link -- deserves more attention.
Re: Indexing a year of video locally on a 2021 MacBook with Gemma4-31B (50GB swap)
#133Earlier quoted context omitted.
> 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
Fans shouldn't be running at max speed if the model fits in RAM with room to spare for context. Usually fans max out when the model doesn't fit and the CPU is chugging to make up the difference (or the user didn't tune LLM settings)
I didn't realize GPUs operate with zero heat output.
Re: Indexing a year of video locally on a 2021 MacBook with Gemma4-31B (50GB swap)
#134Earlier quoted context omitted.
I've been getting this weekly from colleagues. It's very much an epidemic right now! And the port number is indeed almost always a random number between 8000 and 8100.
> I've been getting this weekly from colleagues. It's very much an epidemic right now! And the port number is indeed almost always a random number between 8000 and 8100. Really? A bit hard to believe, unless you have many dumb colleagues.
Not sure about dumb, but "not giving a shit" for sure. I routinely see tickets with markdown links to files on their local filesystem, drives me insane how someone can pull shit like this with a straight face.
Re: Indexing a year of video locally on a 2021 MacBook with Gemma4-31B (50GB swap)
#135[flagged]
Re: Indexing a year of video locally on a 2021 MacBook with Gemma4-31B (50GB swap)
#136Interesting. I've been doing similar stuff with my archive on a weak Celeron laptop with 4GB RAM using vanilla ML tech that I'm learning by prompting LLMs (heh). Extract all info from media as sidecar files and all, exploring low power approaches.
I can sell this as a service to people who can't even run an LLM, or don't want to cook their hardware.
Waitlist open:
"Catalog, search, preview, and generate production-ready prompts & scripts from your entire archive — on your existing hardware. Then render in the cloud."
Re: Indexing a year of video locally on a 2021 MacBook with Gemma4-31B (50GB swap)
#137Earlier quoted context omitted.
I had exactly the same impression, and I recall seeing this style other times recently. First time I thought it was just bad writing skills, now I'm thinking it's AI generated.
I'm the author, yes it is AI-assisted. You can make AI-generated content without it being slop. Slop, to me at least, is content that's wrong, padded, or generic. I see the cadence / short-sentence issues but if there's something else beyond those, I'd actually want to know what made it feel bad. I would've put off documenting what I did over the weekend but instead, I did document everything, spent quite some time (…
> The first half is a constant flood of footage from the iPhone, the DJI Pocket, the drone, the Nikon Z8, and lately the Ray-Ban Metas too. There's always something being recorded. Every photographer or videographer I know is sitting on the same problem: an archive that grows faster than they can edit it. The second half is why mine never gets touched.
This is your second paragraph but reads awkwardly. You mention two halves in the previous paragraph, so I kind of try to map those two halves to the halves in this paragrpah. But I don't understand what the second half is in this second paragraph.
> Three months ago the lodge's social channels went dark. Not for lack of content; the lodge has years of raw footage across multiple SSDs. The bottleneck was editing time, and my time disappeared. Claude Code with Opus 4.5 (and then 4.6) hit the point in February where you could leave agents running for hours and come back to merged PRs. KaribuKit was going live with its first paying property in the same window. I stopped sleeping properly, started running three or four agents in parallel in the background, and the months when I would have cut reels turned into months when I shipped software instead.
I don't fully understand this paragraph either. Your time disappeared? Into what? Was it the lack of sleep? I don't know what KaribuKit is.
> I asked it out loud: how does the agent know what's in each clip?
Did you? Really?
> Four bugs, four lessons
I've noticed that AI tends to rathole into random things when summarizing a piece of work, so I'm skeptical that these were actually the most four interesting bugs you could have shared.
I would recommend you just remove this section or take the time to actually think about some learnings you had from this project. Syntax errors or missed CLI params are mildly interesting but what makes these four bugs interesting to your readers?
> The actual take
The same criticism here applies. Are these your real takes, or did Claude make these up too?
Some obvious tells to me of things that AI likes to write that humans rarely ever say:
> Both real, both consuming attention.
> Four constraints set the shape:
There's way more than just this (the writing style of nearly the entire post screams Opus 4.7), but that's just what jumped out at me when I started reading your post.
I don't mind you used AI to write this but in the future when you write using AI, take the time to read the entirety of the article and consider the goals of what you want to write and if the AI achieved that. Take out what doesn't belong and make sure that what you have left says things in your voice.
Re: Indexing a year of video locally on a 2021 MacBook with Gemma4-31B (50GB swap)
#138Earlier quoted context omitted.
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?
You need to ask macOS people for their prefill speed as well, there are two numbers you care about here, and current MacBooks have generally terrible numbers when it comes to prefill performance. Surely it'll get better with time, but if you already have a desktop, I'd go the "beefy GPU" route first.
> current MacBooks have generally terrible numbers when it comes to prefill performance
Previous MacBooks. Prefill speed on M4 Pro and M5 Pro are hugely different.
Re: Indexing a year of video locally on a 2021 MacBook with Gemma4-31B (50GB swap)
#139Earlier quoted context omitted.
Cheers, I'll give it a try. How are those models at returning structured results? When I was writing the prompts for the analysis step and testing with older Claude models, it would have trouble structuring the XML consistently. Sonnet 4.6 handles it really well.
Use function calling/tool use, not XML output. The models are all trained for that now. Ie, instead of telling it to generate Name 19 whatever give it a function details(name: string, age: int, address: string) That is actually a JSON schema, and the models do great at it. Here's the claude docs, but they are all similar: https://platform.claude.com/docs/en/agents-and-tools/tool-us...
Hey, just want to thank you for this suggestion. Spent this morning swapping to open router and changing all my prompts to use tools instead of XML. Not only is Gemma and Gemini much cheaper, the output tokens from the tool call are much less too. Cost to analyse one 20 minute video with 10 snapshots went from $0.21 to $0.009, and I'm even sending full HD snapshots instead of the 960x540 ones I was sending before (to save costs). The results so far are pretty good. It looks like the larger images are giving the model more context, so in some cases making the cheaper models results better than the expensive models. I'm going to run this over a few hundred videos today and see how it goes in bulk!
Re: Indexing a year of video locally on a 2021 MacBook with Gemma4-31B (50GB swap)
#140Earlier quoted context omitted.
I'm running unsloth/Qwen3.6-35B-A3B-UD-Q8_K_XL on an M3 Max, 64GB at ~57 t/s with llama-server
Prefill speed and 27B number?
Prefill is around ~600 t/s.
I don't remember what the 27B was, I tried a 27B with different quantization at some point for that one, but I settled on the 31B.