azath92

I think there is an interesting reflection here in that software itself is pretty general, if we talk about a tool that the whole industry can use, then actually a general intelligence is to software engineers as the loom is to weavers is maybe actually an apt comparison?

Im speaking specifically of pre AGI general intelligence, little g general if you will.

I will echo what others have said and say this is amazing on a personal level, but also super cool to share with others. I think especially in tech we can get a little focused on efficacy and scale instead of human impact, and some might quibble about that in the comments ill be interested to see the discourse.

Your impact is undeniable, and i think quite importantly very tangible and concrete to you. Very inspiring.

Echoing the sentiment about this podcast in general. Its fantastic for well researched, tightly focused deep dives on some fascinating topics in fashion and fashion history. There are a couple of seasons that are long form one topic over the whole season, and a couple that are self contained episodic. Both formats are excellent, but for obvious reasons the self contained ones are a great way to pick one that grabs your fancy and see if its for you!

While this may be true, their throughput is practically unbounded. this has been a primary challenge to using local models at work, where its normal for me to have 1-6 sessions churning away at once. So while 100t/s on a single session is very fast, theres no way you could get a cumulative ~500t/s from a consumer setup like this, do the bandwitdth bottleneck on a single card/cpu/ram/ssd (tends to be gpu ram bandwidth is the limiter in my setup, but they all have respective limiters to this kind of throughput)

I think there needs to be some nuance to this.

Without the training data and open sourcing the training code and protocol you cant reproduce the model from scratch, thats true, but because the weights and structure are free to download you can modify, inspect and make derivative works off it. Calling it an inscrutable binary blob is misleading.

I do agree that its not reproducible and its often talked about like it is.

In some ways its like most scientific research with a "methodology section", which in todays academic climate often falls quite a bit short of a detailed step-by-step to reproduce results with all the ingredients, but in principle could be used by other experts to do this again. Even without publishing the exact data, a catalogue or description of what data and how it was cleaned/processed would go a long way towards a reproducible methodology i agree.

Ive gotten a lot of value lately using his writing to understand how parameter size, MOE and PLE variations on local models influence performance on various hardware. Its still a useful level of detail as someone who used to roll their own models for various applications pre-LLM but who now mostly uses LLMs rather than training them.

If you used to write pytorch, i think his writing is a great level of technicality to stay on top of things. And id look to them if i ever wanted to do a bit of learning/train my own.

Id love to hear if others have resources at a similar level of technicality and detial.

If the goal is to provide a distinction between model and agent, i think the "agent system" is doing too much heavy lifting in the example here.

A useful extension to this mental framework that i use when trying to make this distinction is the application (cursor) -> which sometimes includes an orchestrator and all of the QOL stuff like resuming, checkpointing, etc. single or multiple agents (cursor agents)-> and runs a single or many agent instances (single agent in cursor)-> service api-> model.

This is to address a confusion i often see with agent being conflated with the application that we use agents in, rather than the distinction in the article which tries to unpick agent-model confusion.

For those of us on the other side of the ditch: all museums in London are free. and I would echo the sentiment that it makes exploration of them less of a completionist activity to "get your monies worth" and more of an enjoyable forey into a rich space.

Its literally a paradigm shift.

Also almost all commercial art galleries are like this, obvs for other reasons. The gallery district (google art galleries then wander those streets, chelsea is the most established) in NYC is an amazing place to spend a day wandering around seeing amazing art. my uncle who teaches art introduced me to it and i try to spread the word.

Free art for any reason is amazing.

I agree some why could be interesting. I would add to your list, because IMO this is more of a thought experiment-as-repo than a utility focused tool, what can i see by using this that i cant with a more fully fledged starting point?

I have to say I enjoy the brevity and clarity of this idea, given the ease of producing something large and unfocused in the last couple of years.

I hope it stays very simple, and would be interested if the author or anyone else can speak to what kind of complexity arises from using it, rather than bloating out the seed itself with initial complexity.

Ive been thinking about starting stimple with a pi framework for example and letting the tools emerge, but this is so much more lean to start, i wonder if it will be more or less interesting and or useful to start here.

Its also a common misconception that all glutamates are MSG (thats one salt of glutamate) and that all umami compounds are simple glutamate salts.

So while MSG is an excellent "neutral" umami ingredient, fermented products like this have a whole host of complex flavors that are often very desirable beyond just their glutamate salt.

Looking at the statistics that are used to measure growth and performance at a population level are such a great window into how they themselves are opinionated stances.

The two measures are both great measures of house ownership that measure different things. The article frames the second one as better, and if you are looking to see the ownership proportion of people (for example as they note to look at the numbers of young people who live with parents) thats great. If however you care about the housing development decisions that will incentivise owner occupancy you might care more about the standard owner occupancy per household.

I often see this with respect to GDP/GDP per capita vs mean income and other economic measures, but its great to see a non economic measure like this compared in this way.