281–290 of 392 posts
I also had a poke around with the tools exposed on https://meta.ai/ - they're pretty cool, there's a Code Interpreter Python container thing now and they also have an image analysis tool called "container.visual_grounding" which is a lot of fun.
This really reinforces the idea that the AI race and the Railroad Mania of the 19th century are very similar. So many different companies are going to have similarly powerful ai that there will be no moat around it and it will be cheap. They will never earn their investment back.
Nah. Everybody is talking about ai. Everybody is using it. It's by far the most popular new tool human beings are using currently. As popular as mobile phones or spoons. And maybe as disruptive as the steam engines. AI companies are becoming the largest software companies on the planet. Everything points into that direction. Trillions of dollars are waiting in the market to be collected.
Earlier quoted context omitted.
Playing with this some more and it's actively not good. Just basic mathematical errors riddling responses. Did some basic adversarial testing where its responses are analyzed by Gemini and Gemini is finding basic math errors across every relatively (relative to Opus, Gemini or GPT can handle) simple ask I make. Yikes.
Post actual results, make a blog post. Don't just say "this sucks" without tangible evidence. Otherwise you're doomed to "sample size of one" level of relevance.
I don't get the comments trashing this. If it slightly beats or even matches Opus 4.6, it means Meta is capable of building a model competitive with the leading AI company. Sure, they spent a lot of money and will have on-going costs. But how much more work would it take to turn that into a coding agent people are willing to try (and pay for) along side their usage of a collection of agents (Claude, Codex, etc)? Also…
OpenAI has the mindshare but they going to have to decide if they allocate their limited compute for free users or go all in trying to keep up with Anthropic in enterprise.
I don't get the comments trashing this. If it slightly beats or even matches Opus 4.6, it means Meta is capable of building a model competitive with the leading AI company. Sure, they spent a lot of money and will have on-going costs. But how much more work would it take to turn that into a coding agent people are willing to try (and pay for) along side their usage of a collection of agents (Claude, Codex, etc)? Also…
It's a decent model if the benchmarks are to be believed, but it won't be close to Opus in usefulness for programming. None of these benchmarks completely capture what makes a model useful for day-to-day coding tasks, unfortunately. It will take time for them to catch up, and Opus will keep improving in the meantime. But it's good to have more competition.
Ran some of my internal benchmarks against this and I'm very unimpressed. I don't think this moves them into the OAI v Anthropic v Gemini conversation at all. Major analytical errors in their response to multiple of my technical questions.
It’s quite good for multimodal cases that 3 billion people would use it for though it lags in scientific areas
Earlier quoted context omitted.
Playing with this some more and it's actively not good. Just basic mathematical errors riddling responses. Did some basic adversarial testing where its responses are analyzed by Gemini and Gemini is finding basic math errors across every relatively (relative to Opus, Gemini or GPT can handle) simple ask I make. Yikes.
Post actual results, make a blog post. Don't just say "this sucks" without tangible evidence. Otherwise you're doomed to "sample size of one" level of relevance.
Pelicans: https://simonwillison.net/2026/Apr/8/muse-spark/ I also had a poke around with the tools exposed on https://meta.ai/ - they're pretty cool, there's a Code Interpreter Python container thing now and they also have an image analysis tool called "container.visual_grounding" which is a lot of fun.
I just posed the identical prompt/document to Muse Spark and it knocked it out of the park, extracted and displayed the pertinent pages from a multi-page PDF inline in the chat and rendered a correct answer.
This may be a one-off or lucky start but given the incredible result out of the gate I'm optimistic and will continue testing in parallel against other models before potentially making it my primary daily driver, excluding coding where the harnesses of claude code and codex are still needed (although hopefully they release something in this space too).
That being said Meta has the most adversarial data-usage policies I've seen among LLM providers so that's unfortunate for handling anything sensitive, but it also stands to reason that they have a long term advantage with such a massive proprietary data set. I'd prefer to also have a paid plan like the other services that allows me to keep my data out of training, rather than a free service and my usage being monetized in other ways.
Kinda off topic but I wonder why they picked this name, knowing of Nvidia's Spark. They're different products, obviously, but the potential for confusion is real as both brands are competing for mindshare in the AI space. I opened this story expecting to read they'd deployed on a cluster made of Spark machines or somesuch.