epimetheus2

What tools? Do you like it? Is it working for your use case? What is it? I explored few, like smolagents, but all seems very immature and full of bugs.

When I look at some like Mammoth Growth, I see they have lots of big clients. But what do they do? E.g. Dropbox as one of the clients. Doesn't they have engineering team that can build their data needs? Or what is the "data stack"? How does the whole process look like? Im genuinely curious.

I'd like to understand more how one would build/iterate over things like v0.dev. Is it LLM? Is it some fine tuned model? Or some other generative AI?Are there some dev/eng blogs/papers around it, and similiar?It's very interesting topic to me but essentially black box.

I enjoy having such stream running while Im working remotely, to learn a thing or two or see how other people code and what they discuss. What are your favorites?

Looking for inspiration on improvements, things to read and so :)

Let's say we have a site www.coolstore.com that accesses api.coolstore.comWhat is the best practice and possible attack vectors when not sticking to them?It seems that www.coolstore.com should be under https://www.coolstore.com. What about assets? Let's say we force a redirect to https:// on the site itself, but not on assets. e.g. you copy the request and change it to http you can access some...

I have a limited time and need to quickly ramp up on these skills. I need hands-on experience as that's how I learn best. What would be your go-to resource/workshop for that?Is there some standard "here are existing 10 services and in this guide we'll go through deploying them, securing them, installing service mesh, provisioning database..." kind of thing?

I am new to devops and we have multiple projects on GCP, with various kubernetes clusters.What are the best practices to ensure I don't mess up? E.g. always have current project/cluster name in your prompt. What other things would you recommend?I'm afraid of accidentally running something on wrong gcp project / cluster combination out of mistake which will inevitably happen, taking down prod or...

There are obvious ones like quality sleep, excercise, get some sunlight during the day and check your hormnes. I still feel a too drowsy during the day.Do you have any personal experiences / anecdotes with what have helped you in the past?

Do you think it's worth spending some time on practicing ML/kaggle/AI to broaden horizon (so not to specialize into data scientist) or is it something that is useful only if you go 100% all in ?

I'm looking at various options such as Surface, and Remarkable2 and their limitations. What would be your choice and why?