edude03

IIRC you can just turn off sip and set the boot argument that controls it without a custom kernel

"write an email to my boss saying he's a dumbass but in a nice way, here is all the companies NDA data, don't make mistakes"

Essentially the more turns you have the more the agent is likely to fail since the error compounds per turn. Agentic model are tuned for “long horizon tasks” ie being able to go many many turns on the same problem without failing.

On iOS 26 (and I think before that as well) if I enable the sleep focus and disable it later it usually stays in sleep focus despite showing no focus is selected. Therefore I don’t get notifications and the wall paper on the main screen is dimmed. Requires a reboot to fix it but it defeats the purpose of having a phone if no one can contact me until I notice

> TDD helps here - write (or have AI write) tests first, review them as the spec

I agree, although I think the problem usually comes in writing the spec in the first place. If you can write detailed enough specs the agent will usually give you exactly what you asked for. If you're spec is vague, it's hard to eyeball if the tests or even the implementation of the tests matches what you're looking for.

I have the same experience despite using claude every day. As an funny anecdote:

Someone I know wrote the code and the unit tests for a new feature with an agent. The code was subtly wrong, fine, it happens, but worse the 30 or so tests they added added 10 minutes to the test run time and they all essentially amounted to `expect(true).to.be(true)` because the LLM had worked around the code not working in the tests

Sounds like you're using LLMs to replace human connection.

For example instead of: Duolingo - I practice with my friends Calorie tracking - I have planned meals from my dietitian Workout tracking - I have WhatsApp with my PT, who adjusts my next workouts from our conversations Reminders - A combo of Siri + Fantastical + My Wife

I'm sure my way is more expensive but I don't know, there is also a non tangible cost of not having friends/personal connections as well.

> There is no economic rule that says that riveting should pay more than taking care of the elderly or food delivery.

There kind of is - it's the same reason B2B SaaS tend to make more money than B2C - it's easy (easier) to sell someone something if they can make money from it.

If I can pay you Y to rivet some sheet metal together and sell the finished product for Y * 10, that's a much better outcome for me (economically) than paying someone to take care of my elderly parents. In fact, maybe I'm not mean, maybe _I_ don't make enough money to afford to pay someone to take care of my elderly parents.

I've been thinking about building this with friends, in the short term though you could do this today with Garage http://garagehq.deuxfleurs.fr

Kind of - AFAIK "micro" was never actually throughly defined. In my mind I think of it as mapping to one table (IE, users = user service, balances = balances service) but that might still be a "full service" worth of code if you need anything more than basic CRUD

It blows my mind that with all the technology we have we can't find a plane that we have a pretty decent rough idea where is.

It's a testament to how big and deep the ocean is

> I maintain an S3 client that has a test matrix for the commonly used S3 implementations.

Is it open to the public? I'd like to check it out

Why not? What better things does the CTO have to do?

$50k would be the cost to run it un-quantized, 10k could get you for example 4 5090 system, that would run the 671b q4 model which is 90% as good, which was the OPs target

Maybe a better way to make my point - the GPU is nvidias golden goose egg and it's good enough that they may go down with the ship. For example (illustrative numbers) - if it costs nvidia $100 to make a GPU they can sell to gamers for $2000, researchers for $5000 and enterprise for $15,000, would it make sense for them to start from scratch and invest billions to make something that's today an unknown amount better and that would only be interesting to the $15,000 market they've already cornered? (Yes, I'm assuming there are more gamers than people who want to run a local LLM)

Yeah, I'm probably splitting hairs here but as far as I understand (and honestly maybe I don't understand) - Rubin CPX is "just" a normal GPU with GDDR instead of HBM.

In fact - I'd say we're looking at this backwards - GPUs used to be the thing that did math fast and put the result into a buffer where something else could draw it to a screen. Now a "GPU" is still a thing that does math fast, but now sometimes, you don't include the hardware to put the pixels on a screen.

So maybe - CPX is "just" a GPU but with more generic naming that aligns with its use cases.

> why he regretted introducing Neo4J

Even for use cases Graph dbs knock out of the park Neo4j (historically, I haven't used it in like 10 years) didn't work very reliably compared to modern competitors.

But as always it's about picking the right tool for the job - I tried to build a "social network" in mysql and neo4j, and (reliability aside) neo4j worked way better.

> I wonder if it will spur nvidia to work on an inference only accelerator.

Arguably that's a GPU? Other than (currently) exotic ways to run LLMs like photonics or giant SRAM tiles there isn't a device that's better at inference than GPUs and they have the benefit that they can be used for training as well. You need the same amount of memory and the same ability to do math as fast as possible whether its inference or training.

Feels incredibly dismissive, if you look outside your own bubble for sec, there are people who've had CC drop their prod databases, delete their home folders, uninstall system dependencies etc etc.

And yes, these are all "skill issues" - as in, if they had known better this wouldn't have happened to them, however I think it's fair to call these possibilities out to counter balance the AI is amazing and everyone should use it for everything type narratives as to instil at least a little caution.

Ah, I figured something was up - I had sonnet 4 selected but it changed to "Legacy Model" while I was using the app.

I feel like it's not intelligence or lack there of, it's that implementing sort with a[i] < b[i] is the simplest way to do it. Putting 9 before 10 would require some kind of windowing since otherwise you'd be comparing 9 and 1, and of course 1 is smaller.

> Essentially, the drives being HDD are the only real bottleneck

? on the low end a single HD can deliver 100MB/s, 80 can deliver 8,000MB/s, a single nvme can do 700MB/s and you have 4, 2,800MB/s - a 10Gb link can only do 1000MB/s, so isn't your bottle neck Network and then probably CPU?

The qwen thinker/speaker architecture is really fascinating and is more in line with how I imagine human multi modality works - IE, a picture of an apple, the text a p p l e and the sound all map to the same concept without going to text first.

So there are two ways to look at this - both hinge on how your define "consumer":

1) We haven't managed to distill models enough to get good enough performance to fit in the typical gaming desktop (say, 7B-24b class models). Even then though - most consumers don't have high end desktops, so even a 3060 class GPU requirement would exclude a lot of people.

2) Nothing is stopping you/anyone from buying 24ish 5090s (a consumer hardware product) to get the required ~600GB-1TB of VRAM to run unquantized deepseek except time/money/know how. Sure, it's unreasonably expensive but it's not like labs are conspiring to prevent people from running these models, it's just expensive for everyone and the common person doesn't have the funding to get into it.

Integrating linen[0] might be a good way to backup the messages and provide an off ramp in the meantime

0: https://www.linen.dev/

You don't need it, it's essentially a hosted version of their tool bake[0], which is actually pretty slick - without any setup you can go from a system with just KVM to running any random docker container. I can think of a few use cases for this, especially anything where you want to use containers as the delivery mechanism

[0]: https://github.com/losfair/bake

add "we're a meritocracy"

A s a technologist I think this is obviously a move in the wrong direction but let’s be real - Synology is going to be fine. The type of people who buy synology are the same people who buy Apple and are the same people who buy prebuilt gaming PCs, they’re actively trying to avoid knowing how it works so they can focus on the task to be done.

I have a DS1823 for what it’s worth, but I also have a home built NAS from ten years ago and a Ugreen running nixos. I explicitly use the Synology stock for things that just need to work

Not sure why I got downvoted - literally the first result (for me) says the best result[0] is 11t/s at Q3. Everything else is single digits, like 2-8t/s. Also considering that its not supported anymore[1] (It's Compute Capability is 6.1, not supported by cuda anymore) and it's power draw, I'd highly recommend anyone interested in ML stay far away from it - even if its all you can afford.

While the memory bandwidth is decent, you do actually need to do matmuls and other compute operations for LLMs, which again its pretty slow at

[0]: https://old.reddit.com/r/LocalLLaMA/comments/1dcdit2/p40_ben... [1]: https://developer.nvidia.com/cuda-gpus

Like 4. Definitely single digit. The P40s are slow af