61–70 of 261 posts
I've survived "What is your organization's Kubernetes strategy." And then came along "You need to integrate Blockchain in your business processes." And now is the time for "make your products smart with AI" season.
To be fair, LLMs as a tech are actually useful, even just to take human input. Blockchain an K8s though... Eh. Geekery for geekery's sake.
Doesn’t mean they are useful for everything.
> The solution: create your own toolchain No thanks. I have an actual job & customer needs to tend to. I am about 80% of the way through integrating with the OAI assistant API. The real secret is to already have a viable business that AI can subsequently improve . Making AI the business is a joke of a model to me. You'd have an easier time pitching javascript frameworks in our shop. Our current application of AI is a…
> a whole toolchain of specialized models, … all of these specialized models are combined with tons of just normal code and logic that creates the end result
They are not referring to a toolchain as “write a compiler”.
They are referring to it as “fine tune models with specific purposes and glue them together with normal code”.
It’s a no-brainer that any startup that doesnt do this is a thin wrapper around the openAI api, has zero moat, and is therefore:
A) deeply vulnerable to having any meaningful product copied by others (including openAI)
B) lazy AF now that fine tuning is so simple to do.
C) will be technically out competed by their competitors because fine tuned models are better.
D) therefore, probably doomed.
> The most important thing is to not use AI at first.
> Explore the problem space using normal programming practices to determine what areas need a specialized model in the first place.
> Remember, making “supermodels” is generally not the right approach.
This is good advice.
> The real secret is to already have a viable business that AI can subsequently improve
You realise that what you said, is the equivalent of what they said, which is: use AI to solve problems, rather than slapping it on meaninglessly.
I appreciate the overall sentiment of the post, but I can't say I would choose anything like the implementation the author is suggesting. My takeaway is to avoid relying too heavily on LLMs both in terms of the scope tasks given to them as well as relying too heavily on any specific LLM. I think this is correct for many reasons. Firstly, you probably don't want to compete directly with ChatGPT, even if you are using…
I entirely missed the compiler on the first read through and I don't know why so many commenters are fixated on that specifically. That wasn't what the blog post was actually about.
Earlier quoted context omitted.
To be fair, LLMs as a tech are actually useful, even just to take human input. Blockchain an K8s though... Eh. Geekery for geekery's sake.
I think we'll find that, when the dust settles, AI's usefulness wasn't as impactful as we thought. AI is only as good as its data and no meaningful dataset is 100% representative and 100% accurate IMHO. Don't get me wrong-- it's neat and all, and it can be somewhat useful in the right context, but the hype is huge. Much bigger than Kubernetes ever saw and probably bigger than blockchain.
I've survived "What is your organization's Kubernetes strategy." And then came along "You need to integrate Blockchain in your business processes." And now is the time for "make your products smart with AI" season.
This is a thought-provoking post and I agree with the "avoid using AI as long as possible" point. AI is best used for things that can only be accomplished with AI--if there's any way to build the feature or solve the problem without it, then yeah, do that instead. Since everyone now has more or less equal access to the best models available, the best products will necessarily be defined by everything they do that's n…
Earlier quoted context omitted.
To be fair, LLMs as a tech are actually useful, even just to take human input. Blockchain an K8s though... Eh. Geekery for geekery's sake.
I think we'll find that, when the dust settles, AI's usefulness wasn't as impactful as we thought. AI is only as good as its data and no meaningful dataset is 100% representative and 100% accurate IMHO. Don't get me wrong-- it's neat and all, and it can be somewhat useful in the right context, but the hype is huge. Much bigger than Kubernetes ever saw and probably bigger than blockchain.
And? What point are you trying to make here?
I've survived "What is your organization's Kubernetes strategy." And then came along "You need to integrate Blockchain in your business processes." And now is the time for "make your products smart with AI" season.
To be fair, LLMs as a tech are actually useful, even just to take human input. Blockchain an K8s though... Eh. Geekery for geekery's sake.
And it's not just geekery by developers who aren't as smart as you.
It's because it allows you to treat all of your infrastructure in one way. Whether you are on GCP, AWS or On-Premise, whether you use Java, Spark, Web Serving or ML Training, whether you are deploying direct to Production or go through multiple staging environments etc. It is always one way of deploying things, one way of securing things, one way of doing everything.
It is far cheaper, easier, more secure and less risky than managing infrastructure yourself. And believe me we all tried that.
Earlier quoted context omitted.
So the secret to building a viable AI business... is to build a viable business, with AI?
the secret is to already have a viable business and constantly sprinkle in the latest tech. trends to maintain the illusion of being something fresh and new.