HN user
Concerned about a "brand in the title"? What do you expect?
The author is David Rosenthal, who was employee #4 at Nvidia (Chief Scientist).
He's not some random historian or interviewer. That's his life experience.
The paid version "SuperGrok" has a larger context window, but nothing beats Gemini for that.
I tried your question with SuperGrok. Here's the result.
https://grok.com/share/bGVnYWN5_d298dd12-9942-411c-900c-2994...
I use Grok for similar tasks and usually prefer Grok's explanations. Easier to understand.
For some problems where I've asked Grok to use formal logical reasoning I have seen Grok outperform both Gemini 2.5 Pro and ChatGPT-o3. It is well trained on logic.
I've seen Grok generate more detailed and accurate descriptions of images that I uploaded. Grok is natively multimodal.
There is no single LLM that outperforms all of the others at all tasks. I've seen all of the frontier models strongly outperform each other at specific tasks. If I was forced to use only one, that would be Gemini 2.5 Pro (for now) because it can process a million tokens and generate much longer output than the others.
Michael Jackson was the biggest in history. Global megastar before the internet. Taylor Swift has a lot of sales but in terms of global significance and cultural impact there is no comparison. Not in the same league.
Taylor Swift has relatively niche popularity in India.
Chinese are blocked from accessing any western social media, and no access to YouTube, Netflix, Spotify, etc. Taylor Swift is popular there but the Chinese have their own version of the internet separate from ours.
Those are instruments played and sequenced by humans. Meaningless comparison.
Comparing music and algorithms is nonsense.
AI music is disposable generic soulless trash, even if it is technically correct, in accordance with the rules and conventions of music theory. AI generates Muzak. Totally generic and derivative.
There is no AI equivalent to Kurt Cobain, or James Brown, or Tori Amos.
This is exactly the problem. The top level executives are setting up to retire with billions in the bank, while the workers develop their own replacements before they retire with millions in the bank. Senior developers will be mostly obsolete too.
I have mentored junior developers and found it to be a rewarding part of the job. My colleagues mostly ignore juniors, provide no real guidance, couldn't care less. I see this attitude from others in the comments here, relieved they don't have to face that human interaction anymore. There are too many antisocial weirdos in this industry.
Without a strong moral and cultural foundation the AGI paradigm will be a dystopia. Humans obsolete across all industries.
It's his daughter. He is worried about his daughter first and foremost. Weird reply.
Yeah just being respectful to those 14 astronauts who died. They are worth mentioning. Nasa had major setbacks - not an "oopsie". Didn't mean to hijack the thread. Well done Voyager team.
That's not my point. The learned painful lessons and their success rate is high.
Yeah it's not "bonkers" or "insane". They learned the hard way. Painful lessons.
Two space shuttles exploded, killing everyone on board.
https://en.wikipedia.org/wiki/Space_Shuttle_Challenger_disas...
https://en.wikipedia.org/wiki/Space_Shuttle_Columbia_disaste...
I meant a verbatim exact copy of all documentation they have ever been trained on - which they are not. Neural networks are not designed for that. That's not how they encode information.
The default chat interface is the wrong tool for the job.
The LLM needs context.
https://github.com/marv1nnnnn/llm-min.txt
The LLM is a problem solver but not a repository of documentation. Neural networks are not designed for that. They model at a conceptual level. It still needs to look up specific API documentation like human developers.
You could use o3 and ask it to search the web for documentation and read that first, but it's not efficient. The professional LLM coding assistant tools manage the context properly.
It is impressive when it finds subtle errors in complex reasoning.
But even the dumbest model will call you out if you ask it something like:
"Hey I'm going to fill up my petrol car with diesel to make it faster. What brand of diesel do you recommend?"
Theft or loss has always been a problem since life evolved on Earth.
I don't think anyone claimed that crypto was un-losable or un-stealable. It's not magic.
Yes that's true, but no need to hold your crypto there as a permanent storage. Once your fiat is exchanged to crypto, immediately transfer the crypto to your private wallet.
I don't really care about crypto personally (volatile shitcoins) but I think that's a straw man argument. They all know it gets troublesome when it comes to dealing with fiat transactions. The hardcore crypto enthusiasts want to avoid fiat entirely.
I have never been thanked for replying here before. Thanks.
The optional choices happen when it tries to reason out a solution, but then finds it is making too many assumptions of unknown details about the user's system, preferences, goals, and so on. It's just a thought pattern that it has learned to emulate.
People here will argue that LLM's cannot truly "think", but they are good enough at emulating thinking.
This is an exchange problem, not a crypto problem. You don’t need an exchange to hold crypto.
IBM Watson is old, before deep learning.
Deep learning systems have exceeded the hype. In 2016 we saw potential with models like AlphaGo Zero but no one could foresee the capability of LLMs (a type of deep learning model).
We don’t know if they had access to everything. They got data for “less than 1% of monthly transacting customers”.
No. Coinbase deals with fiat money, therefore subject to AML and KYC regulations.
Not if you are dealing with a regulated exchange that facilitates fiat money transactions.
You can receive crypto privately to your own wallet without sharing PII, without any exchange.
How can customer support operate without knowing anything about the customer?
"Do you want either [A, B, or C]"
That's an example of what I'm talking about. Watch the reasoning process produce multiple options. That's what it is trained to do. That is problem solving, not "engagement". It requires more compute, not less. You see that more with the expensive models.
"In [5-15] minutes I will give you the complete result"
I haven't seen that before and I don't see how it's relevant.
Are you employed at Google or OpenAI? Are you working on these frontier models?
In the case of medical questions it needs to know further details to provide a relevant diagnosis. That is how it was trained.
In other cases you can observe its reasoning process to see why it would decide to request further details.
I have never seen an LLM just ask questions for the sake of asking. It is always relevant in the context. I don't use them casually. Just wrote a couple of handbooks (~100 pages in a few days). Generating tens of thousands of tokens per session with Gemini.
I have been using it up to 100k tokens so far without issues. Never needed to go further than that. But much of that was in uploaded documents.
It's for higher quality of output. Better solutions. These are the state of the art reasoning models (subscription only, no free access) which are smarter.
It also mainly happens when the context is clear that we are collaborating on work that will require multiple iterations of review and feedback, like drafting chapters of a handbook.
I have seen ChatGPT ask questions immediately upfront when it relates to medical issues.
inability to self-reflect and recognize they have to ask for more details because their priors are too low.
Gemini 2.5 Pro and ChatGPT-o3 have often asked me to provide additional details before doing a requested task. Gemini sometimes comes up with multiple options and requests my input before doing the task.