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Doesn’t that just mean Flock makes more money from making replacements?

I'm sure they'd charge the municipalities and private entities for those replacements one way or another, which ultimately decreases the reliability and value proposition of their product.

the damage is showing that Flock, from an objective technology point of view, is really quite much more limited in terms of its efficacy than its sellers are leading the buyers to believe.

what good is their platform if it is easily defeated by a guy with a ladder and a hammer?


All paid for by taxpayers because a few extremists have appointed themselves kings

They're supposed to serve you, not the other way, and you're supposed to start chopping heads off when they abuse the power you gave them.

"Ah, see, criminals hate Flock cameras. We'll send you a replacement for free, but you should buy two more and point it at that one so you can catch the bastard next time." is how I imagine that goes.

It really is a prosthetic for minds that struggle to organize themselves.

Like a calendar

Flash is (was?) was better than Pro on these fronts.

Maybe they choose not to exude those traits by choice (when a leader slot opens up), and can use them when they are circumstantially relevant. I have gone full founder, and I’m not happy with who I was. It’s an IC’s life for me.

The only thing is, we’re all managers now. We’ve been given a fleet of robots to support a set of outcomes. We have to set expectations, monitor outputs, coach, intervene, step back, onboard new team members, train regularly, make sure they have the tools they need, etc and so on. Are those “soft skills” or just engineering? I’m curious if and how people are lacking in these areas when it’s just text.


Those transformations happen to mirror what happens to human intelligence when you take antipsychotics. Please know the risks before taking them. They are innumerable and generally irreversible.

My friend works at Shopify and they are 100% all in on AI coding. They let devs spend as much as they want on whatever tool they want. If someone ends up spending a lot of money, they ask them what is going well and please share with others. If you’re not spending they have a different talk with you.

As for me, we get Cursor seats at work, and at home I have a GPU, a cheap Chinese coding plan, and a dream.


> If someone ends up spending a lot of money, they ask them what is going well and please share with others. If you’re not spending they have a different talk with you.

Make a "systemctl start tokenspender.service" and share it with the team?


I get $200 a month, I do wish I could get $1000 and stop worrying about trying the latest AI tools.


> I have a GPU, a cheap Chinese coding plan, and a dream

Right in the feels


What results are you getting at home?


It feels like working with a professional. It just keeps churning until the work is done, and actually is pretty damn compact with token usage. Definitely lowest output tokens to value of the frontier models.


Which takes a $20k thunderbolt cluster of 2 512GB RAM Mac Studio Ultras to run at full quality…


Most benchmarks show very little improvement of "full quality" over a quantized lower-bit model. You can shrink the model to a fraction of its "full" size and get 92-95% same performance, with less VRAM use.


> You can shrink the model to a fraction of its "full" size and get 92-95% same performance, with less VRAM use.

Are there a lot of options how "how far" do you quantize? How much VRAM does it take to get the 92-95% you are speaking of?


> Are there a lot of options how "how far" do you quantize?

So many: https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overvie...

> How much VRAM does it take to get the 92-95% you are speaking of?

For inference, it's heavily dependent on the size of the weights (plus context). Quantizing an f32 or f16 model to q4/mxfp4 won't necessarily use 92-95% less VRAM, but it's pretty close for smaller contexts.


Thank you. Could you give a tl;dr on "the full model needs ____ this much VRAM and if you do _____ the most common quantization method it will run in ____ this much VRAM" rough estimate please?


It’s a trivial calculation to make (+/- 10%).

Number of params == “variables” in memory

VRAM footprint ~= number of params * size of a param

A 4B model at 8 bits will result in 4GB vram give or take, same as params. At 4 bits ~= 2GB and so on. Kimi is about 512GB at 4 bits.


Depending on what your usage requirements are, Mac Minis running UMA over RDMA is becoming a feasible option. At roughly 1/10 of the cost you're getting much much more than 1/10 the performance. (YMMV)

https://buildai.substack.com/i/181542049/the-mac-mini-moment


I did not expect this to be a limiting factor in the mac mini RDMA setup ! -

> Thermal throttling: Thunderbolt 5 cables get hot under sustained 15GB/s load. After 10 minutes, bandwidth drops to 12GB/s. After 20 minutes, 10GB/s. Your 5.36 tokens/sec becomes 4.1 tokens/sec. Active cooling on cables helps but you’re fighting physics.

Thermal throttling of network cables is a new thing to me…


I admire patience of anyone who runs dense models on unified memory. Personally, I would rather feed an entire programming book or code directory to a sparse model and get an answer in 30 seconds and then use cloud in rare cases it's not enough.


Luckily we're having a record cold winter and your setup can double as a personal space heater.


And that's at unusable speeds - it takes about triple that amount to run it decently fast at int4.

Now as the other replies say, you should very likely run a quantized version anyway.


"Full quality" being a relative assessment, here. You're still deeply compute constrained, that machine would crawl at longer contexts.


[flagged]


70B dense models are way behind SOTA. Even the aforementioned Kimi 2.5 has fewer active parameters than that, and then quantized at int4. We're at a point where some near-frontier models may run out of the box on Mac Mini-grade hardware, with perhaps no real need to even upgrade to the Mac Studio.


>may

I'm completely over these hypotheticals and 'testing grade'.

I know Nvidia VRAM works, not some marketing about 'integrated ram'. Heck look at /r/locallama/ There is a reason its entirely Nvidia.


> Heck look at /r/locallama/ There is a reason its entirely Nvidia.

That's simply not true. NVidia may be relatively popular, but people use all sorts of hardware there. Just a random couple of recent self-reported hardware from comments:

- https://www.reddit.com/r/LocalLLaMA/comments/1qw15gl/comment...

- https://www.reddit.com/r/LocalLLaMA/comments/1qw0ogw/analysi...

- https://www.reddit.com/r/LocalLLaMA/comments/1qvwi21/need_he...

- https://www.reddit.com/r/LocalLLaMA/comments/1qvvf8y/demysti...


I specifically mentioned "hypotheticals and 'testing grade'."

Then you sent over links describing such.

In real world use, Nvidia is probably over 90%.


r/locallamma/ is not entirely Nvidia.

You have a point that at scale everybody except maybe Google is using Nvidia. But r/locallama is not your evidence of that, unless you apply your priors, filter out all the hardware that don't fit your so called "hypotheticals and 'testing grade'" criteria, and engage in circular logic.

PS: In fact locallamma does not even cover your "real world use". Most mentions of Nvidia are people who have older GPUs eg. 3090s lying around, or are looking at the Chinese VRAM mods to allow them run larger models. Nobody is discussing how to run a cluster of H200s there.


Mmmm, not really. I have both a4x 3090 box and a Mac m1 with 64 gb. I find that the Mac performs about the same as a 2x 3090. That’s nothing stellar, but you can run 70b models at decent quants with moderate context windows. Definitely useful for a lot of stuff.


>quants

>moderate context windows

Really had to modify the problem to make it seem equal? Not that quants are that bad, but the context windows thing is the difference between useful and not useful.


Equal to the 2x3090? Yeah it’s about equal in every way, context windows included.

As for useful at that scale?

I use mine for coding a fair bit, and I don’t find it a detractor overall. It enforces proper API discipline, modularity, and hierarchal abstraction. Perhaps the field of application makes that more important though. (Writing firmware and hardware drivers).

It also brings the advantage of focusing exclusively on the problems that are presented in the limited context, and not wandering off on side quests that it makes up.

I find it works well up to about 1KLOC at a time.

I wouldn’t imply they were equal to commercial models, but I would definitely say that local models are very useful tools.

They are also stable, which is not something I can say for SOTA models. You cal learn how to get the best results from a model and the ground doesn’t move underneath you just when you’re on a roll.


Are you an NVIDIA fanboy?

This is a _remarkably_ aggressive comment!


Not at all. I don't even know why someone would be incentivized by promoting Nvidia outside of holding large amounts of stock. Although, I did stick my neck out suggesting we buy A6000s after the Apple M series didn't work. To 0 people's surprise, the 2xA6000s did work.


Which while expensive is dirt cheap compared to a comparable NVidia or AMD system.


It's still very expensive compared to using the hosted models which are currently massively subsidised. Have to wonder what the fair market price for these hosted models will be after the free money dries up.


I wonder if the "distributed AI computing" touted by some of the new crypto projects [0] works and is relatively cheaper.

0. https://www.daifi.ai/


Inference is profitable. Maybe we hit a limit and we don't need as many expensive training runs in the future.


Inference APIs are probably profitable, but I doubt the $20-$100 monthly plans are.


I wouldn’t be so sure. Most users aren’t going to use up their quota every week.


For sure Claude Code isn’t profitable


Neither was Uber and … and …


Businesses will desire me for my insomnia once Anthropics starts charging congestion pricing.


that is coming for sure to replace the "500" errors


What speed are you getting at that level of hardware though?


I always thought that in the case of a rouge AI breakout that we could just cut the power or network. This makes both impossible. The sick genius of SkyNet was having the most defensible infrastructure when it became clear that whoever controls the biggest robot army can take out enemy data centers and control the world. Now I hope that shooting down LEO satellites is cheap and DIY-able.

I think it’s all farce and technically unsound, but I also think that grok-5-elononly is a helluva drug. It’s really got him ready to rally investors behind “spreading the light of consciousness to the universe”. Oh to see the chat logs of their (Elon and his machine girlfriend)’s machinations.


I suppose one of the ADR’s read something like “…who cares about bitflips, man. Isn’t AI all about probability?”

Knowing the insane level of hardening that goes into putting microcontrollers into space, how to the expect to use some 3nm process chip to stand a chance?


A trend at the moment is to just hope for the best in cubesats and other small satellites in LEO. If you’re below the radiation belt it’s apparently tenable. I worked somewhere designing satellite hardware for LEO and we simply opted to use consumer ARM hardware with a special OS with core level redundancy / consensus to manage bit flips. Obviously some problems will present for AI there… but there are arguably bigger problems with AI data centres like the fact that they offer almost no benefit with respects to the costs of putting and maintaining stuff in space!


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