Is there a quality increase from this change or is it a money grab ?

Is this to make sure services are wired up with permission and communicate properly before you deploy or what is the use case ?

Also, why not have a dev environment where you can test things on the real thing (ideally with smaller size instances to save $)

But to actually answer the question: I’ve been putting research paper pdfs in notebook llm , and turning them into ~40 minute podcasts which I listen to on my walks. Yes it’s shallow learning, and it might have some hallucinations in there but I wouldn’t have read some of those otherwise.

Is there any way to get those running on iPhone ? I would love to have the ability for it to read articles to me like a podcast.

Regular folks will not pay Anthropic, but NSA, NASA or research labs might.

I’m not implying this will be a good time for AI companies. I am saying AI as a technology can provide value without it being controlled by only 3 companies.

In the example it shows how much of the reason for an answer is due to data from Wikipedia. Can it drill down to show paragraph or sentence level that influences the answer ?

There’s the scenario where LLMs get more efficient in size, and to get 2026 SOTA performance you will be able to get it from consumer grade laptop.

Sure with a 1000B parameter you will get better performance but the average person will have it write some python script, not derive new physics equations.

So in a sense the demand for LLM intelligence with reach a plateau (arguably we are there today for avg person) so there will not be any subsidy required, because the avg person will not need the latest and greatest.

There’s not the same demand pattern for something like uber.

Given the example I saw about CRISPR, what does this model give over a different, non explaining model in the output ? Does it really make me more confident in the output if I know the data came from Arxiv or Wikipedia ?

I find the LLM outputs are subtlety wrong not obviously wrong

Curious of what we can get out of those constraints.

Asking “what day is today” vs “create this api endpoint to adjust the inventory” will cost vastly different. And honestly I have no clue where to start to even estimate the cost unless I run the query.

NVDA and cloud providers that are highly staked in AI would likely take a hit if a 70B model could do what Sonnet4 does.

But overall, AI is here to stay even if the market crashes, so it’s not really a AI bubble pop, more like a GPU pop.

And even then, GPU will still be in demand since the training will still need large clusters.

Presumably Java would also be pretty tiny if we wrote it in bytecode instead of higher lever Java.

In a way this is saying that there are some GPUs just sitting around so they would rather get 50% than nothing for their use.

Since LLM context is limited, at some point the LLM will forget what was defined at the beginning so you will need to reset/ remind the LLM whats in memory.