Meta is back in the game, albeit not at the top. Impressive stuff, nonetheless.
They are not open source anymore, right?
Muse Spark 1.1
21–30 of 228 posts
Re: Muse Spark 1.1
#22Is this the model trained on Meta "draftees"? Are we seeing this in the jump on JobBench?
Re: Muse Spark 1.1
#23Competition 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 down, especially for enterprises.
Also if these numbers are true, this is truly breaking ground finally for Meta.
Re: Muse Spark 1.1
#24Their published benchmarks seem to indicate that it's pretty good at coding and multimodal, but VERY good at successful tool calls. What kind of use case would be best for that shape?
Debugging and diagnosis is very tool call heavy, whether that's grepping / transforming logs, calling out to profilers/tracers, or even just writing up incident reports.
Bug diagnostics is about being okay at coding but better at tooling.
Given a good diagnostic report, it can be handed to opus for the fix.
Opus is okay at writing reports, but it still regularly gets table widths wrong in typst documents, leaving the last column full of text but only a handful of characters wide.
Re: Muse Spark 1.1
#25Their published benchmarks seem to indicate that it's pretty good at coding and multimodal, but VERY good at successful tool calls. What kind of use case would be best for that shape?
This sounds... kind of useless? Really good JSON or similar constrained decoder performance is interesting, but normal decoder > tool validator loop with good error message > tool retry is almost always able to get a tool to work second try, and input is cached so it's not expensive.
Things are not always that simple, eg https://lucumr.pocoo.org/2026/7/4/better-models-worse-tools/
Re: Muse Spark 1.1
#26Yeah, no thanks. I cannot think of a worse company to trust with additional personal data.
Me neither, though LLMs also provide services that don’t involve personal or sensitive data
Re: Muse Spark 1.1
#27This is not open-weights, right?
Re: Muse Spark 1.1
#28I personally do not like Meta, but I'll say this. The more competition, the better for regular consumers. (Enterprise too) - Chinese models - Grok - Meta - Google - OpenAI - Anthropic I think this is a win. I'm building like crazy to take advantage of all these subsidized tokens while I can.
To expand on Chinese models:
- DeepSeek
- GLM (Z.ai)
- Minimax
- Kimi (Moonshot)
- Hy3 (Tencent)
- Qwen (Alibaba)
(Each one of these with weights available to download and run locally)
Re: Muse Spark 1.1
#29I personally do not like Meta, but I'll say this. The more competition, the better for regular consumers. (Enterprise too) - Chinese models - Grok - Meta - Google - OpenAI - Anthropic I think this is a win. I'm building like crazy to take advantage of all these subsidized tokens while I can.
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…
> On the one hand, because it is easy to build products, more and more people will build.
And those people won't need to be software engineers.
> but they get stuck, and then they will need engineers
You've implicitly assumed here that the AI systems will always be worse than the average engineer. That is IMO myopic. I'm not sure that it's even true now let alone in the nebulous future.