Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Muse Spark: Scaling towards personal superintelligence

ai.meta.com

Post reply on HN
Print view View original

381–390 of 392 posts

Re: Muse Spark: Scaling towards personal superintelligence

#381

Post by deanc »

flufluflufluffy wrote: post #371
deanc wrote: post #318

Earlier quoted context omitted.

Grok code was my daily driver for months while it was free and it was fantastic - it is certainly no worse than it was a few months ago. Unfortunately with LLMs everything is based off your use case, domain and the context you give it. I also use Grok daily for health questions as the other models are too afraid to give input on medical matters

Why do you need to ask any AI questions regarding your health every day ?

Because I am going through a series of health issues.

Re: Muse Spark: Scaling towards personal superintelligence

#382

Post by Eufrat »

scoopdewoop wrote: post #380
shinycode wrote: post #338

Earlier quoted context omitted.

But at some point even if the product is useful if it costs twice what is getting in, won’t that be a problem ?

I don't see why tokens/$ would suddenly stop dropping. Maybe this is the first time the cost of compute will plateau, but do have any reason to think so?

There is a strong suspicion, especially of people who are skeptical of AI, that the actual price is being severely subsidized. The sense is that it’s an extreme version of growth before revenue. It is questionable if the true cost of training and inference make any of this worthwhile once Anthropic/OpenAI need to stand on their own and make money.

Imagine you open a cookie shop and you are VC funded, so you charge 5¢ for a cookie to attract people.

- Your real cost is $20/cookie. $15 for the fancy retail packaging and presentation, $5 for baking each cookie.

- You get lots of attention, strong profits and go public.

- VC funding is gone so, now instead of charging 5¢, you now need to charge $25 in order to not be in the red.

One of the reasons people think this is the shenanigans that Anthropic is currently playing, quietly tweaking the behavior of Claude Code and whatnot without really telling people. You can see lots of comments online about Claude Code randomly feeling dumber before Anthropic engineers admit they are messing with it.

Imagine you are on the $200/month Max plan. If the sustainable cost of this is several orders of magnitude higher, would enough current users pay something like $3,000/month for what we currently have?

Re: Muse Spark: Scaling towards personal superintelligence

#383

Post by fc417fc802 »

seanhunter wrote: post #376
fc417fc802 wrote: post #320

Earlier quoted context omitted.

Perhaps more importantly, will their chain of thought be "real"? So far the ones I've seen seem to be elaborate fakery. They look good unless you dig in at which point you often find that it merely looks plausible on the surface but that something else is going on under the hood.

I don't know what you mean by that. We know what's going on under the hood always: linear algebra, the attention mechanism etc. To my first approximation all "Chain of thought" means is that instead of having to prompt the model to discuss everything in text and then decide at the end[1], now it sort of automatically does that so you don't need to prompt it. [1] Which used to bring about very substantial improvements…

I think it was clear from context that "under the hood" wasn't referring to the math but rather to the contents of the trace. What's written (often?) isn't what's actually being "thought" about. The trace is a trained output similar to the final output, which is to say that it's fake. There are research papers on the topic, particularly that models can be trained to print other arbitrary stuff during the "thinking" phase instead.

You can easily see this for yourself by carefully walking through a given trace with a critical eye. Here's an example from myself a few days ago. https://news.ycombinator.com/item?id=47623324

Re: Muse Spark: Scaling towards personal superintelligence

#384

Post by simonw »

nickvec wrote: post #343
simonw wrote: post #281

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.

The only benchmark I care about! Just curious Simon - which model do you think has created the best pelican riding a bicycle thus far?

Gemini 3.1 Pro: https://simonwillison.net/2026/Feb/19/gemini-31-pro/

But GLM-5.1 has the best NORTH VIRGINIA OPOSSUM ON AN E-SCOOTER: https://simonwillison.net/2026/Apr/7/glm-51/

Re: Muse Spark: Scaling towards personal superintelligence

#385

Post by seanhunter »

fc417fc802 wrote: post #383
seanhunter wrote: post #376

Earlier quoted context omitted.

I don't know what you mean by that. We know what's going on under the hood always: linear algebra, the attention mechanism etc. To my first approximation all "Chain of thought" means is that instead of having to prompt the model to discuss everything in text and then decide at the end[1], now it sort of automatically does that so you don't need to prompt it. [1] Which used to bring about very substantial improvements…

I think it was clear from context that "under the hood" wasn't referring to the math but rather to the contents of the trace. What's written (often?) isn't what's actually being "thought" about. The trace is a trained output similar to the final output, which is to say that it's fake. There are research papers on the topic, particularly that models can be trained to print other arbitrary stuff during the "thinking" p…

Yeah now I get what you're saying. Yes the trace isn't what's actually happening. What's actually happening is just the attention mechanism etc. The model doesn't "think" in human language, it thinks in linear algebra. The thing is that before chain of thought it used to be necessary to get the model to output some language because that's the only thing it had to attach processing to (so if you wanted more processing you needed to get it to generate more text). Whereas now we get the model to generate some text that is a simulcrum on the thought that it might hypothetically be doing but in actual practise chain of thought is just something they get the model to do by training it in a certain way.

Re: Muse Spark: Scaling towards personal superintelligence

#386

Post by scoopdewoop »

Eufrat wrote: post #382
scoopdewoop wrote: post #380

Earlier quoted context omitted.

I don't see why tokens/$ would suddenly stop dropping. Maybe this is the first time the cost of compute will plateau, but do have any reason to think so?

There is a strong suspicion, especially of people who are skeptical of AI, that the actual price is being severely subsidized. The sense is that it’s an extreme version of growth before revenue. It is questionable if the true cost of training and inference make any of this worthwhile once Anthropic/OpenAI need to stand on their own and make money. Imagine you open a cookie shop and you are VC funded, so you charge 5¢…

Sure, yeah, I saw grubhub happen too... but this is compute, not cookies. It gets cheaper.

I don't even get what "skeptical of AI" means. We made AI, many companies reliably teach computers every spoken language. I perform my white collar job with a massive AI multiplier to my productivity.

I'm typing this on a machine comparable to Japan's Earth Simulator, a $350M supercomputer.

Re: Muse Spark: Scaling towards personal superintelligence

#387

Post by Rover222 »

o10449366 wrote: post #342
nl wrote: post #331

Earlier 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…

[flagged]

Readers here can't fathom that the NYT has inherent bias in a lot of its reporting

Re: Muse Spark: Scaling towards personal superintelligence

#388

Post by nl »

ac29 wrote: post #375
nl wrote: post #310

Earlier quoted context omitted.

> They were ~gpt4o, with the added benefit that you could run them on premise. No, they are bad models. They were benchmaxxed on LMAreana and a few other benchmarks but as soon as you try them yourself they fall to pieces. I have my own agentic benchmark[1] I use to compare models. Llama-4-scout-17b-16e scores 14/25, while llama-4-maverick-17b-128e scores 12/25. By comparison gemma-4-E4B-it-GGUF:Q4_K_M scores 15/25 (…

> By comparison gemma-4-E4B-it-GGUF:Q4_K_M scores 15/25 (that is a 4B parameter model!) Gemma 4 E4B is slightly confusingly named, its a 8B param model

You are completely right on both counts.

It is a 8B model, and it is confusingly named. In fact I made exactly the same point[1] when it was released and promptly forgot!

[1] https://news.ycombinator.com/item?id=47622694

Re: Muse Spark: Scaling towards personal superintelligence

#389

Post by [deleted] »

refulgentis wrote: post #309
canes123456 wrote: post #284

Earlier quoted context omitted.

Why go into coding agents? Both anthropic and OpenAI are going all in on that. The opportunity is customer facing AI now. 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.

If you squint at coding agents you see the next OS. Maybe better phrasing is “HCI paradigm”, but that somehow manages to say everything and nothing.

[deleted]
Post reply on HN