You can use your preexisting committed spends for GCP on Open AI, i.e. if an enterprise 10M/year contracted spends for first party GCP services, now they can also use that on OAI models hosted on GCP.
Meaning they can swap out compute/storage spends on GCP to LLM model spend or use left over budget ( which would go waste anyway ) and so on . Any OpenAI sales by GCP teams would be considered against their quota typically in this type of arrangement [2]
This is normally not done to third party[1] marketplace services, Azure already does this for OpenAI, it helps drive some enterprise spends as it is from for fungible budgets or different approving authority internally in a org when it is not a new vendor, new budget etc.
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[1] It is not too hard to get into these types of programs i.e. it not only for OpenAI level partners mid sized startups can also be part of these deals in marketplace, the requirement usually is minimum size and the partner having to prove/show that are spending that customer's revenue on the compute etc so money is going back to them, then AWS/Azure/GCP will consider an org for this type of program.
Given that GCP is hosting these models on their cloud and therefore OpenAI is spending a % of every dollar back on GCP for every token sold like this; it wouldn't have been too hard for OpenAI to got such a program even if they were a mid sized startup.
[2] Multiples maybe involved (also used to push some first party products) 1x would 1M in OAI sales counts as 1M in their Qtr/annual targets 0.5x would mean 500k and 5x means 5M quota would be retired for 1M in sales etc.
Is it really gcp hosting openai in their cloud? Afaik is a third party marketplace so it looks like you're buying openai saas via gcp but not hosted in gcp. That's key difference with azure specially for egress costs if your data leaves gcp to openai right?
Yes but does it's not clear for me. It could be just a private interconnect to openai infrastructure. My guess is that is not hosted in gcp... That's why I'm asking
The way they work, you get a preferred price for various services at a substantial discount to list rate, but you commit to a guaranteed spend level for that period. The guaranteed spend is typically account-wide, but you have to pay a true-up at the end of the period if you fail to hit it.
It's a bit of calculus: will your growth increase enough such that, especially with the discount, you'll reach the minimum?
You can see these agreements littering 10-Qs, or similar, like when Snapchat filed their S-1 and revealed:
>When Snap originally filed for its IPO in 2017, it revealed a massive $2 billion commitment over five years ($400 million annually) with Google Cloud.
Yes; larger discounts with more commits, you also get some price guarantees on SKUs quite valuable these days and other benefits - like say premium support, architecture guidance, access to product teams and so forth.
Self-service retail buyers of cloud have access to some of these price advantages as well, 1-3 Year reservations(even paid monthly) are 40% cheaper of the bat after all. PAYG with fluctuating usage is quite more expensive than committed consumption for cloud.
A gun manufacturer directly hires a company to test their new line of 9mm handguns. The company that was hired, they go back to their office and pops off a few rounds. They go through the drywall and hit someone.
The gun manufacturer could def face charges in this situation.
Edit: your analogy is wrong just due to Irregular being hired by them rather than just being a customer of them
The whole point of this discussion is why isn’t one of them punished, and my position is that the other one should be punished. It’s not productive to simply say either one.
The CFAA is a law about crimes. Consider that. If a person gets killed would you say put someone in jail I don’t care who? Of course not. A functioning society requires us to determine exactly who to put into jail.
I don’t think system prompts are particularly reliable way to do much at all. It’s better to put it as a hook after each response, or a skill at least so you can trigger it at will if you don’t want it everytime.
Anthropic has probably RL’d the system prompt into nothing because of their fear of the user being able to control the model. If it listened to you about the slop language, it might listen to you if you asked it to help you with no-no tasks.
Your best bet is to use hooks and inject it after every file edit / response by first running the content through haiku and asking if it is asd 100 ste.
It burns more tokens but is the only way to get tolerable text.
The hook sends the text to another agent/context with a request to validate and return a good or bad + reason response. Every request is a fresh context.
Oh... amazing. just had a vision of being able to be in a meeting and talk through an User Interface design / review, while in a zoom meeting or whatever.
...i like.
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- Design system / Component lib
- Live view of what components, tokens, other things... on the left side of the screen.
- You're in the meeting and talking while talking and transcribing and doing the full duplex voice. You say, "Find what tables and customizations we have available" and the list starts to filter to tables and customizations.
- "Let's add that table to the page; left side; 3/4 width of page. Headers should be static for vertical scroll, ..."
- The table is added to the page.
- "Nah, i don't like it. Let's change that table component to have larger headers..."
yes, i like--let's see what Astra Pro pops out with.
The problem with that is that it only works with simple, least interactive UIs. Each new UI a human will be presented needs to be learned to be ised effectively otherwise a user will be lost.
Having said that, imo, Gen UI only makes sens as a presentation layer - not controls. Unless LLM will be using a set of very well defined and homogenic components like table, forms, small widgets.
> The problem with that is that it only works with simple, least interactive UIs.
I believe that's incorrect. you can define the architecture to be able to provide data based on information from the frontend and the components just need to define a query and data structure or something -- will have a prototype soon
I think this actually might be one of the best ways to train people to use AI. I can see this honing people's prompting abilities and expressiveness, along with constraints and desired outcome.
One thing I don’t get with a lot of these agents.md and other skills are… why not throw as much mechanical checks and other stuff at the repo to constrain as you want instead of asking a non-deterministic agent (squishy or non-squishy) to maintain it.
With the mechanical routes, we get checks, failures, and so much more. A bit wild to me.
Make an agent operate within defined constraints and yell at it when it doesn’t.
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