great_psy

Yes and no.

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.

Can this method be extended to go down to the sentence level ?

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 ?

Actually I think there’s another option.

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.

Maybe I’m not creative enough to see the potential, but what value does this bring ?

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

Any place where we can test the llm output without loading it on n64?

Curious of what we can get out of those constraints.

The introduction to the article is not paywalled. But the actual 2027 ai story is paywalled

Can you provide some names of AI apps who’s revenue > cost ?

But leaving a light on 2x the time will equal very close to 2x the price.

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.

A likely catalyst would be a DeepSeek like model where it has the capability of top model but in a much smaller footprint.

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.

I have not delved to deep in the code, but is there any functional differences it has over Java other than the size ?

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

Is this an indication of the peak of the AI bubble ?

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.

I wouldn’t be so dismissive. Research is just a loop of hypothesis, experiments, collect data, make new hypothesis. There’s so creativity required for scientific breakthroughs, but 99.9% percent of scientists don’t need this creativity. Just need grit and stamina.

Why would you want to have an ever growing memory usage for your Python environment?

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.

Before or after the bubble popped ?

With how healthcare is handled in America … good luck to poor people getting access to anything like that.

Yes it’s possible, but it’s not something you can easily scale.

I had a similar project a few years back that used OSX automations and Shortcuts and Python to send a message everyday to a friend. It required you to be signed in to iMessage on your MacBook.

Than was a send operation, the reading of replies is not something I implemented, but I know there is a file somewhere that holds a history of your recent iMessages. So you would have to parse it on file update and that should give you the read operation so you can have a conversation.

Very doable in a few hours unless something dramatic changed with how the messages apps works within the last few years.

When’s the last time you personally had a bit flip on you?

We focused so much on how that we didn’t stop to ask why.

We already have an interface for agents, we call that an API. Why do we need to have AI click buttons, that eventually call an API. Just skip the middle layer and go straight to an API that does everything that system is capable of doing.

I think a lot of conspiracies people have are out in the open, it’s just that they are hidden behind articles that take 30 minutes to read and understand.

When you say, “monitor red flags” what is actually happening ?

How are you determining red flags ?

Will an LLM be running every x hours analyzing news, so I can give very specific things to look for ?

Does this lower the protection iCloud offers ?

Currently you need to use biometric or password to access your keys.

If you use the extension will it make a copy of the passwords locally, or does it still require biometric on every password access ?

Also would be nice to have logs and see exactly which passwords re being retrieved by the add on. I know it’s apple made but still feels like an extra vector.

I found LLMs to be quite useful, so the time saved is worth the effort to double check answers.

Granted it might have to do with how I use LLMs. If you just blindly ask a question you increase the chance of hallucinations. If you give a lengthy input, and the output is highly dependent on the input than you will get better results. Think email re-writing, summarizing, translation.

This was for some internal tooling. Not sure about router deployments.

I have heard the deployment process at Rogers is something along the lines of: 1) get approval for your changes 2) ssh into a server 3) copy a backup of current jar file 4) copy the new instance 5) run some command to refresh

Is there more than the cover to this ? On mobile I only see one page of the PDF.

I can’t speak for what OpenAI is doing, but I’ve noticed those types of hallucinations occurring when I quantize a model beyond a certain point.

Maybe they are trying to cut down on memory usage ?

I’m excited to hear work is being done on models that support function calling natively.

Does anybody know if performance could be greatly increased if only a single language was supported ?

I suspect there’s a high demand for models that are maybe smaller and can run faster if the tradeoff is support for only English.

Is this available in ollama ?

Does not ‘seem’ like the right shape for it, but could it use ocean currents ?

Similar idea to those weather balloons we were talking about last year ?

Is that even possible in the ocean ?

Seems like a waste of money to me. Training some NN is just matrix multiplications. Why would you need a custom silicon to take advantage of the software ?

I suspect a GPU is optimized for this operation all the way to software level. I can’t imagine apple will get performance benefits.

Maybe it’s cheaper or provides better access to hardware, but I don’t think performance is at play here.

The issue I see with the screenshots from the article is that it changed the contrast and overall feel of the scene.

I think there’s plenty of opportunity to exchange old videos, but I think it requires some human touch or deeper understanding of the movie to maintain its message.

The light and contrast and color is made a certain way on purpose, usually to convert whatever the scene is meant to convey. You can’t just mess with those things just so you add details.