Could this be best intelligence / $ if you're willing to let zuck digest your data?
Yeah. Super icky. But this might be the first time in Zuck’s life he’s being honest about the business model.
Muse Spark 1.3
231–240 of 475 posts
Re: Muse Spark 1.3
#232I have not tried Muse Spark for code, but I've been using it for a while to write Latin. I find it's one of the best at it, alongside Gemini. For example, I've recently been using it to translate the subtitles of the show I'm watching into Latin, to provide me with a bit more input. (I'm learning Latin, for context)
Re: Muse Spark 1.3
#233llm -m meta-ai/muse-spark-1.3 "Generate an SVG of a pelican riding a bicycle" https://tools.simonwillison.net/markdown-svg-renderer?url=ht... 4.2266 cents, 38 seconds. For comparison here's Muse Spark 1.2, which animated it without me asking it to: https://tools.simonwillison.net/markdown-svg-renderer?url=ht... The 1.3 one is definitely better - better bicycle frame, better wing, better pelican hat. UPDATE: Here's an…
FYI, your renderer breaks with error "git api access error 403", rate limiting error from git, when using cloudflare vpn.
I am guessing its not super common, but it happens just so you know.
Re: Muse Spark 1.3
#234A model that (at least in benchmarks) is getting closer to SOTA. A clear separation between what’s used to improve their products and what’s not (at least this is what they claim). Good job Meta! Seriously. This is almost making me forget about the 18B$ lawsuit for children social media addiction.
How is it not SOTA? It's beating 5.6 Sol.
It's somewhat useful to note just for your own timelines that Fable was reportedly trained in February. I'm not sure when mythos 5.1 finished training, but muse spark 1.3 almost certainly finished more recently than that.
This doesn't mean it's not one of the best models available (clearly it is), but that table didn't compare Fable/mythos (unless I missed it?) and OpenAI will be releasing a much more recently trained model (Astra) any day.
So you shouldn't think "wow, Facebook has caught up"
You should think, "wow, Facebook is less than 6 months behind the frontier" and that they're actually creating good models which is going to be good in many ways (price for customers, for one!)
There are downsides too, but I'll discuss those separately somewhere
Re: Muse Spark 1.3
#235[deleted]
Re: Muse Spark 1.3
#236Earlier quoted context omitted.
>I started using Spark 1.2 for development because if you're willing to let Meta train on your data it was dirt cheap its free on opencode and i use it for personal projects. most of my personal projects are AI generated since its personal projects. nothing important are on them. it is hilarious if Meta is training their AI model with AI generated code.
Every lab trains their models with AI generated code at this point.
Hopefully, 'validated' AI code
Re: Muse Spark 1.3
#237Earlier quoted context omitted.
Funny. Dario seems like the biggest snake in the industry to me and has leaned the hardest into doom marketing out of all of the influential leaders. With Altman (or Google), it's a transaction, and that's something I can live with.
I just don’t see how people have looked at what has happened with Mythos and the deluge of fixes from companies, then come to this conclusion. He has a really hard job. He errs on the side of conservatism in releasing and then people get Really Mad. Safeguards on cybersecurity are not great for Anthropic revenue! As evidenced by people getting pissed, moving to Sol, and them having a smaller market for what Fable can…
They are still offering full mythos to project glasswing companies and those that pay them enough.
Re: Muse Spark 1.3
#238Could this be best intelligence / $ if you're willing to let zuck digest your data?
By default, even without the training endpoint the pricing is pretty competitive, especially against Opus and Fable. [1] The 'muse-spark-1.3-contributor' endpoint is by far the cheapest, significantly cheaper per M than ChatGPT Luna, significantly smarter than Luna too.
This price/intelligence beats even legacy DeepSeek V4 Flash pricing.
Re: Muse Spark 1.3
#239Earlier quoted context omitted.
Is there a reason these pelicans always have roughly the same composition (side-view, 2d, biking right, flat ground beneath, etc)? I don't see any of that detailed in the prompt, yet they all seem to generate roughly the same image of differing quality.
The more generic your prompt, the more generic the response. It's a regression to the "mean" of the training data aka GIGO for AI. It's like when you ask your average person off the street to draw a house - it'll almost always be square with a triangle roof, one door, and two windows. In the pelican/bike example, it's probably a bit of a self-perpetuating snowball too. If the earliest examples were bike left-to-right…
Well, all the LLMs are being trained on previous pelicans, so they look the same.