961–970 of 1001 posts
Earlier quoted context omitted.
It does not say anything about real use cases. It performs better and "reason" better than o1-preview and o1. But I was expecting some real-life scenarios when it would be useful in a way no other model can do now.
I imagine the system prompt is something along the lines of, 'think about 10% harder than standard O-1'
Earlier quoted context omitted.
I'm uninformed about this, it may just be superstition, but my feeling while using Kagi in this way is that after using it for a few hours it gets a bit more forgetful. I come back the next day and it's smart again, for while. It's as if there's some kind of soft throttling going on in the background. I'm an enthusiastic customer nonetheless, but it is curious.
This is my biggest gripe with these LLMs. I primarily use Claude, and it exhibits the same described behavior. I'll find myself in a flow state and then somewhere around hour 3 it starts to pretend like it isn't capable of completing specific tasks that it had been performing for hours, days, weeks. For instance, I'm working on creating a few LLCs with their requisite social media handles and domain registrations. I…
I think from this fine print there will be a quota with o1 pro: > This plan includes unlimited access to our smartest model, OpenAI o1, as well as to o1-mini, GPT-4o, and Advanced Voice. It also includes o1 pro mode,
I wonder who came up with the $200/month idea, and what was running in their mind. $200/month = $2400/year We (consumers/enterprises) are already accustomed to a baseline price. Their model quality will be caught up or exceeded by open-source in ~6 months. So if I find it difficult to justify paying $20/month, why should I even think about $200/month. Probably the thought process was that we can package all the great…
When compared to an employee $200 is peanuts.
Why doesn't Pro include longer context windows? I'm a Plus member, and the biggest limitation I am running into by far is the maximum length of a context window. I'm having context fall out of scope throughout the conversion or not being able to give it a large document that I can then interrogate. So if I go from paying $20/month for 32,000 tokens, to $200/month for Pro, I expect something more akin to Enterprise's…
Pro does have longer context windows , specifically 128k. Take a look at the pricing page for this information: https://openai.com/chatgpt/pricing/
Earlier quoted context omitted.
Pro does have longer context windows , specifically 128k. Take a look at the pricing page for this information: https://openai.com/chatgpt/pricing/
o1 pro failed to accept 121903 tokens input into the chat (claude took it just fine)
Earlier quoted context omitted.
Won’t name my company, but we rely on Palantir Foundry for our data lake. And the only thing everybody wants [including Palantir itself] is to deploy at scale AI capabilities tied properly to the rest of the toolset/datasets. The issues at the moment are a mix of IP on the data, insurance on the security of private clouds infrastructures, deals between Amazon and Microsoft/OpenAI for the proper integration of ChatGPT…
Is the Foundry business the reason for the run up of PLTR this year? https://www.cnbc.com/quotes/PLTR
But all that is the technical part of things. Markets do not bless products. They bless revenues. And from that perspective, I have NO CLUE.
Earlier quoted context omitted.
This is also known as the Door-in-the-face technique[1] in social psychology. [1]: https://en.m.wikipedia.org/wiki/Door-in-the-face_technique
Wouldn’t this be an example of anchoring? https://en.wikipedia.org/wiki/Anchoring_effect
Earlier quoted context omitted.
I pay for both GPT and Claude and use them both extensively. Claude is my go-to for technical questions, GPT (4o) for simple questions, internet searches and validation of Claude answers. GPT o1-preview is great for more complex solutions and work on larger projects with multiple steps leading to finish. There’s really nothing like it that Anthropic provides. But $200/mo is way above what I’m willing to pay.
I have several local models I hit up first (Mixtral, Llama), if I don’t like the results then I’ll give same prompt to Claude and GPT. Overall though it’s really just for reference and/or telling me about some standard library function I didn’t know of. Somewhat counterintuitively I spend way more time reading language documentation than I used to, as the LLM is mainly useful in pointing me to language features. Afte…
E.g. "why does this (random code in a framework I haven't used much) code cause this error?"
About 50% of the time I get a helpful response straight away that saves me trawling through Stack Overflow and random blog posts. About 25% of the time the response is at least partially wrong, but it still helps me get on the right track.
25% of the time the LLM has no idea and won't admit it so I end up wasting a small amount of time going round in circles, but overall it's a significant productivity boost when I'm working on unfamiliar code.