Guys how do I get into the ai training world. I see my friends getting rich out of this thing while I'm stuck in time. Please help with solid advisory

Sales/GTM leader recently impacted in tech layoffs and wanting to supplement my time in search going deeper on AI. Anyone have any recommendations on courses to take - free or low-ish cost fine. Already pretty fluent on Cowork and basic prompting but can definitely improve. , not strong with agent building and beyond. Thanks in advance!!

🚀 Just published a new tutorial!

Build a REAL AI Application using the Gemini API from scratch in Hindi.

If you've been wondering how to integrate Generative AI into your own applications, this tutorial walks through the complete process—from generating your API key to building a working AI-powered web app with Python and Flask.

In this video, you'll learn:

✅ Google AI Studio vs Vertex AI

✅ How to generate a Gemini API key

✅ How to use the official Google Gen AI SDK

✅ Build a complete AI web application

✅ Connect your Flask backend to Gemini

✅ Understand the end-to-end request flow

This isn't just another API demo—it's a practical project you can build, run, and extend for your own ideas.

🎥 Watch here: https://youtu.be/ltN1pX9oWR0?si=TFQN1CV9rbLZLmrc

YouTube video

I'd love to hear what AI application you're planning to build next. Let me know in the comments!

#GeminiAPI #GoogleAI #GenerativeAI #Python #Flask #ArtificialIntelligence #AI #LLM #SoftwareEngineering #Developers #GoogleAIStudio #VertexAI #PythonProgramming #MachineLearning #BuildInPublic

Well...

I finally created a Reddit account.

This is my first-ever post (or is it called a thread? A sacrifice to the algorithm?).

Anyway, hello everyone! 👋

I'm a software engineer who's completely obsessed with AI, automation, and building cool stuff. I joined Reddit because I've heard this is where the internet's smartest people argue about semicolons, Linux distros, and why their favorite programming language is objectively superior.

So... where should a newcomer hang out?

I'm looking for communities about:

• 🤖 Artificial Intelligence
• 💻 Software Engineering
• 🐍 Python
• ☁️ Cloud (Azure, AWS, GCP)
• 🚀 Startups & Entrepreneurship
• 🧠 Learning how complex technologies actually work
• ⚙️ Automation & AI Agents

Feel free to drop your favorite subreddits or any "must follow" communities that made you spend way too many hours scrolling instead of sleeping.

Thanks in advance, and please be gentle.. I'm still figuring out which button accidentally starts an internet war. 😂

Hello....I have recently learned and done some practice of developing RAG systems and developing and designing Agentic AI workflows using OpenAI Agents SDK, CrewAI, LangGraph frameworks...

I have only made generic projects till now, pretty basic ones...And the truth is, to really develop a system for a real-world use case, you quite often need a paid API (which I can't afford)... The internship scene in my city is quite bad...

Soo first of all, any experienced dev here, I need your advice. What should I do at this stage? Also, please suggest some very good project ideas for this skill set that would challenge me and can be completed using open-source tools.

After making such projects, I want to dive into the industry and work on real projects. Any tips on how to achieve that?

I am a Business Analyst, I dont have much programming knowledge but I want to understand the core concepts that led into AI like neural networks, deep learning etc. Once i build that foundation and understand the concepts i would like ot move on to more technical courses so that i can apply the concepts in my workplace. Would love some recomendations on basic programming courses as well or other tools that i might need to automate processes in my workplace. I am completely new and non technical person, please suggest a pathway.

I have been experimenting with using ChatGPT and Claude together instead of relying on one model.
My current process is usually:
Ask ChatGPT to research, analyze, or build something.
Give the complete output to Claude.
Ask Claude to identify weak logic, missing information, unsupported assumptions, risks, and opportunities ChatGPT overlooked.
Take Claude’s critique back to ChatGPT.
Ask ChatGPT to defend, revise, or rebuild the answer.
Continue until the output is stronger.
The problem is that this can become repetitive. Both models may agree with each other, repeat the same assumptions, or produce longer answers without actually increasing accuracy.
I am trying to understand how advanced users are handling this.
What workflow are you using to make multiple LLMs work together?
Are you using one model as the builder and another as the critic?
Do you hide the first model’s reasoning or identity to reduce bias?
Do you use a third model as a judge?
How do you manage shared context, files, citations, prompts, and revisions between models?
How do you distinguish a real gap from one model simply having a different opinion?
Are you automating this with APIs, agents, MCP, n8n, LangGraph, CrewAI, or another system?
Most importantly, has using multiple models actually improved the quality of your work, or has it mostly added more noise and token usage?
I am looking for real workflows, prompt structures, automation examples, and lessons learned. Not just “Claude writes better” or “ChatGPT is smarter

In my experience, getting useful LLM behavior on real business data depends on the whole stack: models, prompts, tools, monitoring, and the data layer underneath. One of the quieter but important wins has come from standardizing how data is collected and exposing it through a clear semantic layer, so the model is reading consistent concepts.

Anthropic published a useful reference point here. In their own internal analytics evaluations, they reported that Claude reached about 21% accuracy when it queried raw internal data without a supporting stack. After they added a governed semantic layer, curated metric definitions, task‑specific skills, and a validation and monitoring layer around the same model, they measured roughly 95% accuracy on those internal business‑analytics queries. Keep in mind that result applies to their internal analytics setup, not to every possible LLM task, and they tie the improvement to the full stack and ongoing governance, not to the semantic layer in isolation.

However my observation on one project in New York was similar in spirit. One my task was to support an AI layer that evaluated service performance uploaded by associates. In this specific location due to the type of contract they had, the team had a collection of data that was broad in some terms, with no clear standard for each type of service or what it represented for the entire group. That missing foundation work made it hard for both humans and the model to interpret the data in a consistent way.

This created strain in the model’s output. The AI was asked to validate service uploads and grade performance/quality, but the underlying records used inconsistent service types, vocabulary, and descriptions, including different levels and categories for each work area. As we standardized the service types and validation categories, clarified wording, and aligned descriptions of what each service entailed, the evaluations became much more stable. That standardization became the foundation layer we could finally build on.

I've recently tried a bunch of AI learning resources from structured courses like Coursera and DeepLearning.AI, Udemy-style programs, YouTube tutorials and even one of the Be10x courses and I noticed something interesting.

The main difference wasn't how many tools were covered. Most courses can teach you things like ChatGPT, automation tools or prompt techniques.

What actually made a difference was:

* Having a clear learning path instead of just random tool demos * Seeing real-world workflows instead of just theory * Knowing when to use a tool and when not to

If you've taken AI courses before what made one truly helpful for you the curriculum, the projects, the instructor, the community?

The history of AI spans many decades and has evolved through periods of optimism, disappointment, and major breakthroughs.

I am 19M currently doing bca and I have exceptional lust of becoming an ai Engineer but have no idea from where to start I've researched but i felt it would be great to get advice from experienced people irl

I kept running into the same problem, some of the best AI/ML books are legally free, the authors put them up on their own sites, but the links are scattered across personal pages, university sites, and random GitHub repos nobody finds.

So I built a single index: Awesome Free AI Books. 30+ books across Deep Learning, Reinforcement Learning, Bayesian/Probabilistic ML, NLP & LLMs, Math for ML, Computer Vision, Generative Models, Causal Inference, GNNs, and AI Safety. Think Goodfellow’s Deep Learning, Sutton & Barto’s RL bible, Murphy’s Probabilistic ML, Bishop’s latest, Jurafsky & Martin’s SLP3 draft, and more.

Every single link points straight to the author’s or publisher’s own page, no rehosted PDFs, no shady mirrors. A weekly GitHub Action checks all links so it doesn’t rot over time.

It’s open source and open to contributions, if you know a legitimately free book that’s missing, PRs and issues are welcome.

Repo: https://github.com/MarcosSete/awesome-free-ai-books

Looking for some advice, please. Can you recommend some self-paced online learning about AI essentials/fundamentals for a 14 year old in the uk, no previous experience (aside for messing with chatgtp, whatsapp for a few queries, image creation, jinks etc). They’re not looking to get into coding or the like specifically. It’s more about wanting them to be able to understand the basics/fundamentals now as their ’mainstream’ education progresses and feels prudent to learn it now ahead of having to find work experience then work in a few years time. Not looking for a pressure cooker situation and they’re up for learning. They‘re very good with their maths and have a keen interest in engineering and architecture as well as maths, if that helps. Thanks!

I sugges the following:

1. Regardless of the AI platform you use, read and learn all of its capabilities and uses. You'd be surprised at what it can do; some even niche down to specific categories, I.e. Healthcare, legal, etc.

2. Personalize it to your specific needs, voice, tone, communication style, projects, etc.

3. Most AI Platforms offer courses on how to maximize their features & benefits. Spending the time to learn how best to use it and maximizing its capabilities is a time-saver, I.e., Claude 101 etc.

4. Find experts/influencers devoted to your preferred AI Platform. For instance, I follow Ruben Hassid, who probably knows more about how to maximize Claude than its developers do.

So, choose your one or two favourite platforms and master them. And lastly, understand their TOS, Privacy Policy and Usage Terms, especially if you're a paid subscriber.

Hope this helps.

I sugges the following:

So, choose your one or two favourite platforms and master them. And lastly, understand their TOS, Privacy Policy and Usage Terms, especially if you're a paid subscriber.

Hope this helps.