tuvix

Yeah I checked on DDR4 prices too recently and it made me pretty sad. Glad I upgraded to 64 GB in 2023 though. Another homelab dream deferred

The incentives driving state lotteries are not nearly as strong as those driving private companies like Draft Kings but yeah not great either.

Used to work at 7-Eleven and we would have people come in and out for hours on end pouring money into scratch tickets.

State lotteries are just a regressive tax targeting the poor.

I’ve owned a few clones of theirs and they aren’t bad at all. Build quality sucks though sometimes.

Didn’t think about that, not sure if the market for an Octatrack clone is big enough for them to go after. Hope they do it though

Well I think it’s technically pretty similar to 5G, but it doesn’t use existing 5G towers. It’s this company in Denver that sets up receivers on top of certain buildings and distributes using a line of sight connection. What’s weird is that we plug our modem into the phone jack in the wall

Not my area of expertise though so I could be leaving out info

Edit: Starry is the company, I believe they were just purchased by Verizon

Just got this at my apartment and it’s dirt cheap. Not the fastest internet on the market but more than enough

Wonderful, soon an old Octatrack will be as much as a car

For sure, I just know it’s tempting given the power of large transformers to throw things at an existing model.

For instance, OCR is something that can be done locally with no access to a GPU but people (including me) still often use cloud hosted multi-modal large language models for it.

Are there any LLMs being widely used for audio classification? I know VLMs are being used a lot in image stuff.

It always seems kind of silly to me to throw everything at an LLM. I know they’re huge and can automatically handle a huge number of tasks but something in me finds it wasteful when we could be creating easily trainable, cheap to run bespoke models for a lot of stuff

Solving millennium problems doesn’t pay the bills.

Their best models might be quite good when directed at extremely difficult and very focused problems, but most business use cases are nothing like that.

Edit:

Not trying to imply these new frontier models aren’t also better at other things, just that there’s really no reason for most people to use them when cheaper alternatives exist that get the job done just as well.

Why would someone hold the opposite opinion?

It’s much more likely that our current approach to large language models for general use will eventually show diminishing improvements (even if you think it hasn’t yet), than the opposite situation where valuable improvements can be made forever.

The threat of distillation and efficiency gains from competitors mean that providing value at the top end of the market is an existential necessity for these labs. I don’t think they can do that forever and, in my opinion, for the vast majority of use cases we’ve already reached very little improvement for new models when compared with available offerings.

I don’t think you’ve fully considered what can happen when concentration of wealth continues past a certain point.

If you can learn Elektron gear it’s like a music creation superpower though. I tried with a Digitakt and couldn’t get into the flow

From what I remember reading it was pretty limited and only applied to context menu actions.

I was pretty disappointed when I saw that, but they’ll probably expand it

Haha yeah I think I’m mostly speaking from nostalgia. I do remember wrestling with knobs in Massive or whatever free VSTi back in the day was pretty terrible.

I can’t deny I love the look, though.

Reminds me of VST GUIs, looks great! But, like other commenters have noted, I think a number of these controls might be desktop only, which is totally fine. There’s too much UI stuff centered around mobile anyway these days

Are there significant differences re: sandboxing between Lua and monty? Just curious if there are any holes that a Lua sandbox has that monty might account for

I also often need EQ in the reverse situation, when I need less bass through my nice studio monitors when watching TV. Otherwise I’ll be watching Star Trek with dialogue at a normal volume and BOOM, the loudest photon torpedo you’ve ever heard.

In the context of lab hardware there’s a ton of proprietary software and barriers to automating things. I think this is less about how to communicate, and more guaranteeing that “yes, this piece of hardware can interface with an agent” and is meant to do so. Kind of like using MCP vs letting your agent make raw HTTP requests

Agreed that DuckDB as a server feels far from their initial mission, but how does async IO divert from it?

I think the fact that these kinds of companies were portrayed as such in fiction kind of prevents them from adopting the cool aesthetic. Like they need something to separate them from being the exact image of an evil futuristic corporation that lives in our heads. Or else we might actually recognize what they are.

So this is just a collection of citations to places where misaligned or illegal things happened in the real world?

Isn’t this affected heavily by adoption of a model? I feel like this might as well be a proxy for how popular a model is.

In any case it’s an interesting concept for a benchmark.

DuckDB is an in-process (now I guess less so with Quack) OLAP database for analytical workloads. PostgreSQL or MariaDB are OLTP row-oriented databases that are great for application/transaction-focused workloads but are less great when you want to query across a giant amount of data.

I don’t think I really agree with the author’s approach here, but I will say LLMs have been a huge help to me as I’ve been reviewing linear algebra and diving into signal processing. Anything in a textbook that I don’t fully grasp or am confused about, I just take a snapshot or copy paste then ask a model to derive it or explain it in different terms.

It reduces friction a ton, but at the end of the day I’m not skipping anything.

I’m not talking about the actions we take or how we might perform at certain tasks, I’m talking about how our brains actually work. My point is that we have no idea how I’m able to imagine an apple and see it in my mind’s eye. It’s basically biological magic to us at this point.

There are processes at work there that we don’t even have the language to describe.

All arguments like this boil down to semantics at a certain point, but yes large language models can “intuit” because they can generalize between examples. The issue then becomes how you pack new examples into context.

Humans can “intuit” based on a much larger, if not unlimited, context. Also I just want to say that human cognition is something so insanely complex and deep that we will not understand it at all in my lifetime. To attribute all, or really any, aspects of human cognition to a machine at this point is silly to me.

I would imagine the extension system they built would be much more difficult to manage. They could have opted for Lua, though, I suppose.

I’ve often thought of this kind of concept for a programming language.

Maybe it’s the qwerty keyboard or my bad typing habits but certain characters break my flow when typing.

Agreed, but I do think this is a wholly different kind of hype. With crypto currencies it was the promise of modernizing value exchange, with some zealots promising the end of traditional currency.

With this, I’m hearing (from supposedly reputable publications, in addition to random people) that this is going to end knowledge work in general and take out a large percentage of the world’s labor force. I’m being told to pick up a trade, and that the career I have and the knowledge I’ve gained is now worthless.

The worst part seems to be that it’s pretty much impossible to quantify any kind of impact these tools will have until after the impact is actually felt. We’ve been in limbo while the tech sector is just rotting.

I’m kind of curious why the decision to have implicit casting like this was made in the first place. I can’t think of a single upside other than not having to type out cast(foo as bar)

Not sure who you’re following on LinkedIn but this is most assuredly not the case for me or anyone I know