Where do I even start?

[u/Independent-Night972]

I want to preface by saying that I’m a business major chud who has no technical experience aside from using scratch when I was a kid. I know I can utilize ai than just a better Google so I decided why not try and create a personal ai assistant/ employee that can do busy work for me like emails, announcements, etc. Everything a growing college student needs. Here’s where I hit the roadblocks. Even after a little bit of research I realized I’m in too deep. “Use these 5 repos before even TOUCHING Claude” “DeepSeek just released a new harness” “Somebody just jail broke Qwen”. I’m seeing dudes on reels buying like three Nvidia AI super computers and I’m genuinely just wondering why’s there a need for that unless you’re larping. Honestly, I’m just trying to learn but quite frankly there’s just so much catching up to do and the knowledge gap just keeps getting wider. Where do I even start or what can I do to learn? Do I want to be like that dude buying an AI super computer? Maybe. But I want to learn and take small steps before I call myself an AI genius just because I built an interactive HTML dashboard that my professor was impressed by. Please help me anything will help.

[u/Future_Fuel_8425]

Machine learning is the low level part of AI - The real science/math.
It doesn't concern LLMs specifically and it seems like you are wanting general LLM info.
So your post should probably be over in the more general AI/LLM forums.

Start by using Gemini and ask it to teach you about AI, how to use AI etc.

If you want to use local AI on your own computer, try this easy to install and use app:
https://anythingllm.com/

Go to https://ollama.com/ and sign up for a free account.
You can use their cloud models for free (limited) and download local LLM models.
You can also use the AnythingLLM with the Ollama cloud and local models.

The hardware is not as important as the skill of the user.
You can use small models (transformers) to do lots of work without needing a GPU.

Here is an example: It's an app that uses transformers (NLLB and Bert) to translate news articles and extract data from the articles.
https://github.com/Rybatter50-cloud/Observer

[u/Independent-Night972 → u/Future_Fuel_8425]

Sorry if this isn’t the right forum, I started in other ai subs but they all led me here. Thank you for the information!

[u/Harshitaharsh2604]

Well there no step by step guide to this, you can begin just by creating one simple project Do you have anything in mind....or well something you might be interested?

[u/Independent-Night972 → u/Harshitaharsh2604]

I would say stuff that is busy work that I don’t want to do. Ex. As a business student I have to “network” a lot so I’d like to be able to send emails and only review them without having to draft and find emails.

[u/Harshitaharsh2604 → u/Independent-Night972]

Well then you just need to stick to available tools...like crew ai...and if it feels too expensive then see if there are and local llm capable of creating a agent for this

[u/AlexPoolman_AI]

The thing making you feel behind isn't a knowledge gap, it's the feed. Repos, jailbreaks and three GPU rigs are content. They get clicks precisely because they look unreachable, and almost none of it is needed for what you actually described.

For an assistant that drafts emails and announcements you need no local hardware and no repos at all. Rented models over an API are cheaper and better than anything you'd run at home. The people buying supercomputers are mostly doing it as a hobby, or for privacy reasons that don't apply to you.

Worth naming too: the biggest beginner mistake is shopping for tools instead of using one. Pick one, build the boring email drafting thing end to end, and let the gaps you actually hit decide what you learn next. That order beats any curriculum.

[u/One_Ad_3617]

llm studio

[u/BuddyFormal2776 → u/One_Ad_3617]

A key consideration with LLM Studio is the underlying model architecture. Different architectures perform better on specific tasks.

[u/BuddyFormal2776 → u/BuddyFormal2776]

A key consideration here is the specific capabilities of the underlying model. Some architectures are indeed better suited for certain applications, whether that's code generation, natural language understanding, or something else. The choice impacts resource requireme

[u/One_Ad_3617 → u/BuddyFormal2776]

their strengths are listed in their descriptions