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Muse Spark 1.1

ai.meta.com

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Re: Muse Spark 1.1

#171

Post by simonw »

edcrfv wrote: post #138
simonw wrote: post #107

I had a few days of preview access, which was long enough to put together a plugin for LLM. You can try the model out in the terminal like this: uv tool install llm llm install llm-meta-ai llm keys set meta-ai # paste API key here llm -m meta-ai/muse-spark-1.1 "Generate an SVG of a pelican riding a bicycle" Here's the result: https://tools.simonwillison.net/markdown-svg-renderer#url=ht... For comparison, here's the p…

How do you find the time to “preview” so many models? It’s been a crazy time recently with the model releases. Does it ever feel like a chore?

It does feel like a chore in weeks like this one where there are new models landing every day.

I'm also increasingly worried that I'm not providing enough value in my model reviews. It's really hard to get a useful and credible idea for the strengths of the new models.

Re: Muse Spark 1.1

#172

Post by 10xDev »

jacobgold wrote: post #101

Maybe Zuck should double down on his "spoiler" role with models rather than compete head-to-head. He doesn't have to match Anthropic or OpenAI model revenue if he can deflate theirs by 99%. All he has to do is keep spending a few billion dollars developing frontier models, release them as open weights, and turn coding models into a commodity. He also needs a good OSS reference harness to match. Very few people are in…

Coding models are not the destination. Coding models are just part of the bootstrapping process towards general intelligence.

Re: Muse Spark 1.1

#173

Post by aurareturn »

odie5533 wrote: post #131
jacobgold wrote: post #101

Maybe Zuck should double down on his "spoiler" role with models rather than compete head-to-head. He doesn't have to match Anthropic or OpenAI model revenue if he can deflate theirs by 99%. All he has to do is keep spending a few billion dollars developing frontier models, release them as open weights, and turn coding models into a commodity. He also needs a good OSS reference harness to match. Very few people are in…

If he deflates their revenues, who is going to rent the compute from Meta?

If it's good enough, they don't need to sell to Anthropic/OpenAI.

Re: Muse Spark 1.1

#174

Post by ianm218 »

re-thc wrote: post #83
ianm218 wrote: post #51

Earlier quoted context omitted.

> At least in China a lot of software developers are now struggling. Do you think that Chinese software industry is that relevant to the kind of software market talked about on HN? I.e. lots of enterprise b2b and infra companies. Chinese companies have always had a very low willingness to pay for software which kinda breaks the flywheel of B2B SaaS companies and companies to service those companies all the way down.

> Chinese companies have always had a very low willingness to pay for software Are we still left with this mindset? Maybe once upon a time but it has definitely been changing. There's plenty of B2B and enterprise SaaS companies in China serving the Chinese market. Maybe not as many, but no longer the very low of the past. I also would not say enterprise were not willing to pay, even many years ago. It's the SME that…

I would say I'm still in this mindset. Numbers are hard to come by but analysts optimistically put Chinese SaaS market at ~10% of the size of the US. Also I see that the gross margin for public Chinese SaaS is around 50% vs 80% for the US reflecting that SaaS in China is much more services and implementation heavy. So it feels like the direct to business SaaS's like Salesforce aren't really there and then then the selling to SaaS titans like Datadog have a much smaller flywheel to work in.

Happy to be wrong though if I'm missing something.

Re: Muse Spark 1.1

#175

Post by solid_fuel »

kingstnap wrote: post #77
GodelNumbering wrote: post #49

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?

> Which test cases does this matter for?

The test cases of "don't melt my computer" and "be a good (computational) neighbor"

Re: Muse Spark 1.1

#176

Post by cadamsdotcom »

10xDev wrote: post #172
jacobgold wrote: post #101

Maybe Zuck should double down on his "spoiler" role with models rather than compete head-to-head. He doesn't have to match Anthropic or OpenAI model revenue if he can deflate theirs by 99%. All he has to do is keep spending a few billion dollars developing frontier models, release them as open weights, and turn coding models into a commodity. He also needs a good OSS reference harness to match. Very few people are in…

Coding models are not the destination. Coding models are just part of the bootstrapping process towards general intelligence.

Software has several unique properties on both ends of its production process that make assertions of progress based on the software use case invalid.

Software is easy to define as “working”: just run it. But - useful software requires an absolute truck worth of code - 100k loc before you’re talking about a real product, or else dozens of iterations of a toy you make for yourself before it’s useful enough to quit toying with and just use for what you wanted it for.

Sure, the success of software is hard to anticipate and what “good” UX is is hard to pin down - that’s not what I’m talking about. I’m talking just making the code and having no lint errors. That shit is a slog but it’s a slog with a clear goal amenable to hill climbing.

Through that lens software is mostly pattern matching. It’s very rare that an activity in software construction is out of distribution because even if the core of the thing is novel it needs a massive blanket of UI and a tech stack and a production environment to run in and observability and and and and. You get it I hope.

Meanwhile most work out there is a mess of undocumented, un-codifiable detail with no objective criteria for success, only a very wide gradient of “job well done” to “what is this garbage go and fix it”.

We are solving the easy parts of software and soon all that’s left will be the parts that are just like other work. And then we engineers will also be doing mostly squishy subjective judgment stuff.

Re: Muse Spark 1.1

#177

Post by noobcoder »

GodelNumbering wrote: post #49

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…

Thats what is wrong with close source models, we dont know exactly what we are paying for, a superior base model or a well thought harness for benchmaxing

Re: Muse Spark 1.1

#178

Post by esafak »

gavinray wrote: post #136

Despite Muse being relatively average, I've actually used the Meta AI webchat LLM since it released. The reason: Its writing style feels "unique", and I find it pleasant to read for science-based topics. I never ask _ONLY_ Meta AI, but the answer it gives is almost always in a distinctly different style than other frontier LLM's. I think this is because of the unique JEPA architecture they have, but that's a layman's…

I think it does not use JEPA.

Re: Muse Spark 1.1

#179

Post by SyneRyder »

Aurornis wrote: post #165
mchusma wrote: post #125

Earlier quoted context omitted.

Yeah, this is most directly comparable to xAI Grok 4.5. In both cases, directionally "opus level intelligence for haiku prices" which is a really big deal for application developers who want to include models like this in their applications. I have been testing switching out haiku and sonnet for Grok 4.5, and may give this a try too (it is quite a bit cheaper, particularly for cached).

> Yeah, this is most directly comparable to xAI Grok 4.5. Grok 4.5 has a relatively high $0.50 per 1M cached input token rate, compared to $0.15 on this model. Grok 4.5 cached input costs the same as Opus 4.8 cached input, which is going to make it a lot more expensive to use for multi-turn coding than many would assume from the $2/$6 headline numbers they led with.

> ... make it a lot more expensive to use for multi-turn coding than many would assume from the $2/$6 headline numbers they led with.

There's a further sting in the tail, Grok 4.5 is only $2/$6 for the first 200k of context. Go above that, and the pricing is $6 / $12 - and you're still capped at only 500k context anyway.

Here's the xAI pricing on OpenRouter:

https://openrouter.ai/x-ai/grok-4.5?endpoint=0e927811-b1a8-4...

Re: Muse Spark 1.1

#180

Post by quantumwoke »

edcrfv wrote: post #138
simonw wrote: post #107

I had a few days of preview access, which was long enough to put together a plugin for LLM. You can try the model out in the terminal like this: uv tool install llm llm install llm-meta-ai llm keys set meta-ai # paste API key here llm -m meta-ai/muse-spark-1.1 "Generate an SVG of a pelican riding a bicycle" Here's the result: https://tools.simonwillison.net/markdown-svg-renderer#url=ht... For comparison, here's the p…

How do you find the time to “preview” so many models? It’s been a crazy time recently with the model releases. Does it ever feel like a chore?

It's his job, and this is advertising (he is very good at native advertising on HN).
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