Most frontier models are terrible at AGI-3 right now.

These models are already great no question, but are they really going be that much more intelligent when we hit 80% again?

A structured LLM query is a programming language and then you have to accept you need software engineers for sufficiently complex structured queries. This goes against everything the technocrats have been saying.

I don't like that I need to login to my FB/Instagram account to access this.

I think the general skepticism is because they are late to race, and they are releasing a Opus-4.6-equivalent model now, when Anthropic is teasing Mythos.

If the average user gets convinced they could run LLMs for cheap at home, you cannot trap users in your walled garden anymore.

Selling shovels in now worth less than taking all the gold for themselves.

More importantly it understand what behaviour people tend to appreciate and what changes are more likely to get approved. This real world usage data is invaluable.

> To balance index integrity and investability, Nasdaq proposes a new approach for including and weighting low-float securities (those below 20% free float). Each low-float security’s weight will be adjusted to five times its free float percentage, capped at 100%. Securities with more than 20% free float will continue to be weighted at full, eligible listed market capitalization, while those below 20% free float will be weighted proportionally to preserve investability.

> The rule reportedly includes a 5x float multiplier for low-float stocks, which would require passive vehicles to treat SpaceX as if it had significantly more tradable shares than actually exist, essentially forcing funds to chase the price.

It sounds to me like a way to increase demand for low float stocks by treating the float higher than it actually is. Glad to hear the explanations about this.

The rationale being you are more likely to remember grammatical cogent sentence, than a random string of alphanumeric characters. Although I will agree that the generated sentences don't seem easy to remember. So I doubt it's utility.

Is there a future where we can expect people to just buy "AI" from BestBuy, like a TV set? It ll probably come with some model preloaded - cheaper if open-source, premium pricing for frontier lab models. The hardware is basically a bunch of GPUs enough for local inference.

Take it home and plug it into your home network and you can open a chat instance by going to the IP on any local device. You can give it access to internet if you want. Maybe it can also receive OTA updates.

Curious how others think about this - does local-first AI feel like a possibility? What are the economic and social challenges with this?

Comments URL: https://news.ycombinator.com/item?id=47546796

Points: 5

# Comments: 1

I don't know if this is how we want to measure AGI.

In general I believe the we should probably stop this pursuit for human equivalent intelligence that encourages people to think of these models as human replacements. LLMs are clearly good at a lot of things, lets focus on how we can augment and empower the existing workforce.

I am a data engineer maintaining a big data Spark cluster as well as a dozen Postgres instances - all self hosted.

I must confess it has made me extremely productive if we measure in terms of writing code. I don't even do a lot of special AGENTS.md/CLAUDE.md shenanigans, I just prompt CC, work on a plan, and then manually review the changes as it implements it.

Needless to say this process only works well because: A) I understand my code base. B) I have a mental structure of how I want to implement it.

Hence it is easy to keep the model and me in sync about what's happening.

For other aspects of my job I occasionally run questions by GPT/Gemini as a brainstorming partner, but it seems a lot less reliable. I only use it as a sounding board. I does not seem to make me any more effective at my job than simply reading documents or browsing github issues/stack overflow myself.