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Muse Spark 1.1

ai.meta.com

91–100 of 228 posts

Re: Muse Spark 1.1

#91

Competition for cheaper and efficient models is a good thing, regardless of if you don't like SpaceX, Meta, etc. Especially from US based labs I for one am really glad to get competitive models that will push the major labs to bring prices down. While Chinese open source labs are also great, unfortunately when it comes to US/Western political pressure it won't often have as much of a bearing on labs bringing prices d…

There are US companies hosting open weight models for enterprise, we just enabled Fireworks.ai for the devs

Re: Muse Spark 1.1

#92
Tried to get access to the API, apparently the model API is not available in my region...

I have questions regarding if I should even care but I don't so Meta please keep enjoying the irrelevance. lmao

Re: Muse Spark 1.1

#94
post #92

Tried to get access to the API, apparently the model API is not available in my region... I have questions regarding if I should even care but I don't so Meta please keep enjoying the irrelevance. lmao

Right, amazing because for me also... "My region" being Canada.

I'm going to assume the only "region" that's permitted is the USA.

Re: Muse Spark 1.1

#95

Lot more details in the linked report https://ai.meta.com/static-resource/muse-spark-1-1-evaluatio... From Terminal-bench-2.1 details, > We use a bash-tool-only agent harness to evaluate 89 Terminal-Bench 2.1 tasks from the official repository, where resources are capped at 6 CPU cores and 8GB RAM. This disqualifies the results. Each terminal bench task has a cpu upper limit and RAM upper limit. Overriding either is…

Why are resource limits considered at all aside from models accidentally fork bombing themselves? I thought the benchmark was supposed to be about terminal use and specifically chaining together lots of bash tool calls. Which test cases does this matter for?

Terminal bench 2 isn't simply about 'somehow' getting a task done, it intends to measure real world behavior of an agent, including environment awareness in a given situation.

A few examples from memory:

1. This task [1] asks the agent to train a CNN under 1 CPU, 2GB RAM, 10GB storage. If you allow high resources, weaker models often succeed (the most clock time actually goes in waiting for the network to train).

2. This task [2] asks agents to implement a complete MIPS interpreter in JavaScript in 1 cpu and 2GB RAM. A common failure mode is OOM, at least in the earlier buggy versions that models run to get feedback. When OOM hits, the task is killed, no do-overs.

3. A lot of tasks involve building projects with a single core supplied. If you use -j12 type options, it will actually be _slower_ to build and the task will more likely miss the timeout. Having more threads squeezes the end to end time. This is a big one actually since the most common failure mode (from what I have seen) is the task timeout hitting before the agent finishes

[1] https://github.com/harbor-framework/terminal-bench-2-1/blob/...

[2] https://github.com/harbor-framework/terminal-bench-2-1/tree/...

Re: Muse Spark 1.1

#96
post #19

Earlier quoted context omitted.

Yeah, I think it is definitely great. Having said that, I am still debating in my mind whether the volume of software engineers needed in the AI era is going to increase or decrease because of all of these advancements. On the one hand, because it is easy to build products, more and more people will build. And more and more products and features will be built. However, a lot of people who are non-technical will also…

I see some similarities to 3D printing here. It’s great that everyone can make their own toothbrush holder (or whatever) but I’m probably not going to pay for someone’s weekend project. I’m “seeing” more devs stepping into the SendCutSend stage where they’re cleaning up/fixing/productizing vibe coded projects so maybe there will be some new demand in that space?

3D printing is a good comparison - it allows almost anyone to make things, but in the end very few do.

Another example is when the WWW first became available, and suddenly everyone COULD be a publisher (browsers even included built-in HTML editors), and for a while MySpace pages proliferated until the excitement died down and people went back to being media consumers.

I expect we'll see the same thing with consumer use of generative AI. Suddendly everyone is generating 3-D worlds/games with Fable because they can, but I expect that just as with the web the novelty will wear off and they'll leave it up to the pros.

Professional use of GenAI, and coding in particular, is certainly here to stay, but it seems we're still in the early experimental/hype phase. At least tokenmaxxing has passed, and it seems most companies are now paying attention to, and limiting, how much they are spending, but it doesn't seem we've yet progressed to the stage where companies are paying attention to what they are actually getting out of it - is the money spent showing up on the bottom line in the form of increased revenues.

Re: Muse Spark 1.1

#97
post #48

Earlier quoted context omitted.

Meta isn’t right now on the radar for most folks picking models. If they have a really good model, it makes sense to subsidise it, to gain users, before they align prices with competitors.

this is not subsidizing. this is way too expensive for a no-name model.

Depends on the quality

Re: Muse Spark 1.1

#98

> Model API is not available in your region. :( Well, Vietnam is not in the list of restricted territories. Anyway, what is "your region" ? Is this where I am now, or is it where I activated my Oculus 2 five years ago ?

Can’t you just use VPN?

Re: Muse Spark 1.1

#99
post #72

Interesting how the prevalent opinion until yesterday seems to have been that OpenAI & Anthropic are irreversibly ahead and now with xAI and Meta at least delivered something that's competitive with useful models and cheap too. Granted, the narrative that the two leading labs are ahead still holds with Fable (and perhaps an upcoming GPT6), but it's not as over as common knowledge by the opinion leaders would have us…

People misinterpreted Google being behind as Anthropic and OpenAi being really ahead, when it was really just Google falling behind the same way it did with Tensorflow, Angular and GCP.

Re: Muse Spark 1.1

#100
post #19

Earlier quoted context omitted.

Yeah, I think it is definitely great. Having said that, I am still debating in my mind whether the volume of software engineers needed in the AI era is going to increase or decrease because of all of these advancements. On the one hand, because it is easy to build products, more and more people will build. And more and more products and features will be built. However, a lot of people who are non-technical will also…

I see some similarities to 3D printing here. It’s great that everyone can make their own toothbrush holder (or whatever) but I’m probably not going to pay for someone’s weekend project. I’m “seeing” more devs stepping into the SendCutSend stage where they’re cleaning up/fixing/productizing vibe coded projects so maybe there will be some new demand in that space?

It’s terrible and depressing work to take vibe coded garbage and make it a real product. There will be demand, but good engineers won’t want to touch it. And people paying will think they did the hard work so why pay a good rate?
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