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Muse Spark: Scaling towards personal superintelligence
341–350 of 392 posts
Re: Muse Spark: Scaling towards personal superintelligence
#342Earlier quoted context omitted.
> 4. This model was out in the woods as early as like a couple months ago but they didn't release it because it was at gemini 2.5 pro levels. Source? (Even if rumor)
NYTimes had a story about this (March 12): > Meta’s new foundational A.I. model, which the company has been working on for months, has fallen short of the performance of leading A.I. models from rivals like Google, OpenAI and Anthropic on internal tests for reasoning, coding and writing, said the people, who were not authorized to speak publicly about confidential matters. > The model, code-named Avocado, outperforme…
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Re: Muse Spark: Scaling towards personal superintelligence
#343Pelicans: 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.
The only benchmark I care about! Just curious Simon - which model do you think has created the best pelican riding a bicycle thus far?
Re: Muse Spark: Scaling towards personal superintelligence
#344Earlier quoted context omitted.
Not the parent, but I guess that if AGI happened and was competent enough to trade markets, they'd earn the company back their investment in a short period.
China would have an equivalent version out for cheap next month anyway.
They are 6-12 months behind not 1 month and precisely the gap will widen if they can’t do distillation.
Re: Muse Spark: Scaling towards personal superintelligence
#345Earlier quoted context omitted.
China would have an equivalent version out for cheap next month anyway.
They are 6-12 months behind not 1 month and precisely the gap will widen if they can’t do distillation.
I have doubts the gap will widen. If you look at the research papers, the majority of researchers are Chinese. Of course many of them are living in the US or elsewhere. But under the current circumstances, many are returning home or choosing not to leave China.
The future of cutting edge research and tech seems to be progressively moving to China. And a delay in model quality could represent more of an unwillingness to burn stacks of cash to be first, when you can have the same thing slightly later for much cheaper.
Re: Muse Spark: Scaling towards personal superintelligence
#346Earlier quoted context omitted.
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.
Right, but the question is whether the companies producing foundation models will capture that value or not. Right now it seems like tokens might end up just being a commodity sold at cost plus, and companies higher up in the supply chain will make the money. Electricity changed the world but electricity companies capture very little of that value.
I'm betting on it. I'm working on a project right now where I'm prototyping everything with Claude, until I hit my limits on my MAX subscription for the week. Then I switch to Codex, and start by ironing out harness differences. When I max out that, I switch to a mix of GLM 5.1, Qwen 3.6m, Kimi K2.5 and Deepseek and spend part of the time ironing out issues with them while they work on other parts of the project. Every iteration, the harness gets hardened and the pain of switching to the cheaper/dumber models reduce for the next cycle. The gap reduces each time, and with each new upgrade of the open models. Everything points to the cost/value intersecting in not too long.
Re: Muse Spark: Scaling towards personal superintelligence
#347Earlier quoted context omitted.
Exactly. We’ll see the cost of AI continue to drop. I was saying this for years about Tesla’s FSD - they finally had to give in and drop the price to stay competitive.
FSD still sucks ass compared to Waymo.
Theoretically it's possible to use just cameras for FSD.
In practice it takes so much local compute it's not feasible with current tech.
With LIDAR it's so much easier, a single data point contains direction + distance with no calculation needed.
Re: Muse Spark: Scaling towards personal superintelligence
#348Pelicans: 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.
Seems like not all tools are available everywhere? Don't have access to visual_grounding sadly, only these: https://embed.fbsbx.com/playables/view/4208761039384112/?ext...
Re: Muse Spark: Scaling towards personal superintelligence
#349Earlier quoted context omitted.
NYTimes had a story about this (March 12): > Meta’s new foundational A.I. model, which the company has been working on for months, has fallen short of the performance of leading A.I. models from rivals like Google, OpenAI and Anthropic on internal tests for reasoning, coding and writing, said the people, who were not authorized to speak publicly about confidential matters. > The model, code-named Avocado, outperforme…
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Does Meta not harvest data on a massive scale? Not sure what exactly is the issue with doing a series on that.
Re: Muse Spark: Scaling towards personal superintelligence
#350Earlier quoted context omitted.
Anthropic generally seem more into living within market discipline and market signals of some sort. Products with margins, even if it's sort of irrelevant considering R&D costs and capital inflow. That said, there's nothing like the real thing. The risk is something like the railroad bubble and the dotcom. Over-investement, circular revenue and a timeline that doesn't work. Or, maybe it'll work out.
Maybe they’ll figure out how to make an agent train an agent.
And if they don’t, it won’t be for lack of trying, I promise you. Just like the circular financing, nothing makes more work for “AI” than “AI”.