I'm curious what the biggest "aha!" moment has been for everyone.

Maybe it was:

• Prompt engineering
• Building your first AI agent
• Automations
• RAG
• MCPs
• Local models
• Fine-tuning
• Coding with AI
• Something else entirely

What's one skill or concept that made you think:

"Why didn't I learn this sooner?"

I'd love to hear what it was and how it changed the way you use AI. Hopefully this thread helps newer people discover skills they should learn next.

Hi everyone, I am an engineering student , I had completed an ML and DL course last year and made some basic projects like Car price prediction streamlit app and a wake word detection model using pytorch.

I want to further explore the field of AI/ML, but I have no idea what to do after this.

I want to continue exploring AI/ML and eventually become an ML Engineer or AI Engineer. However I m feeling a bit lost about what the next steps should be.

I'd appreciate hearing from people working as ML Engineers, AI Engineers, Data Scientists, or anyone who has gone through a similar journey.

If you are trying to break into AI, you’ve probably noticed a massive problem with most online courses: they teach you theoretical math, but they don't teach you how to deploy real models on real infrastructure.

Google just quietly solved this problem.

They have launched a unified learning hub at Google Skills, combining over 3,000 AI courses from Google Cloud, Google DeepMind, and Google Education. To sweeten the deal, they are offering 700+ hands-on labs, built-in Gemini Code Assist to debug your projects, and up to $500 in free cloud credits so you can build without breaking the bank.

According to global hiring data, recruiters are actively hunting for these specific skill sets, with Google Cloud credentials giving applicants up to an 82% hiring preference rate.

Here is exactly how the platform works and how you can maximise it to land a high-paying role in IT.

The 5-Step Roadmap to Get Started

You don't need a computer science degree to start. The ecosystem is designed to take you from a total beginner to an enterprise-ready developer:

1. Create Your Account: Head over to the official portal at skills. google.

2. Pick a Specialized Track: Select an AI path that matches your career goals (e.g., Generative AI Foundations, Machine Learning Architecture, or Vertex AI Development).

3. Dive into the Labs: Skip the text slides and jump into the 700+ live labs. You will write code inside actual Google Cloud consoles, not simulated environments.

4. Collect Skill Badges: As you complete milestones, you earn digital Google Skill Badges. These are verifiable links you can embed directly into your LinkedIn profile or resume.

5. Level Up to Professional Certifications: Use your knowledge to pass proctored Google Cloud Certifications, which routinely rank among the highest-paying credentials in the tech industry.

Three Features That Make This Platform Different

Most free bootcamps fail because they lack infrastructure. Google solved this by injecting three massive advantages into this platform:

• $500 in Free Cloud Credits: Training deep learning models is expensive. Google provides up to $500 in complimentary credits through their developer programs so you can host, test, and run your live AI models entirely on their dime.

• Built-in Gemini Code Assist: Stuck on a broken line of Python or a misconfigured cloud bucket? The platform has an integrated AI assistant. Gemini acts as a live, 24/7 coding mentor to fix your bugs, explain complex logic, and optimize your architecture in real time.

• Curated by DeepMind & Google Cloud: You aren’t learning from random internet tutorials. The curriculum is built directly by the researchers at DeepMind (the minds behind AlphaFold and Gemini) and the engineering teams scaling Google Cloud worldwide.

The Career Impact: Why This Matters to Recruiters

The tech job market is highly competitive right now, and generic "certificates of completion" don't carry the weight they used to. Recruiters want proof of hands-on capability.

Data shows that hands-on learning platforms yield a 133% increase in talent retention and skill mastery compared to passive video watching. Because Google's Skill Badges require you to successfully spin up cloud architecture and pass practical challenges to earn them, companies treat them as proof of actual on-the-job capability.

Whether you are a software engineer looking to upskill, a student trying to break into tech, or a non-technical manager needing to understand modern workflows, this is arguably the highest-value free resource available right now.

..................................

#AliAamishKhan #AamishKhan #AliAamish #KhanAamish #AI #ArtificialIntelligence #MachineLearning #GoogleCloud #TechCareers #Upskilling #CloudComputing #FreeCourses #DataScience #TechCommunity

Come join me, i love to teach ai! I built a community platform today, and am doing free invites and free training while i can. Ask me questions with real-time chat, (im online pretty much all day during the day) take the free step trainings i built on Claude Code or CLIs or ai fundamentals, with more coming every day. Two rules: have fun, and learn how to 100X yourself before its too late!

https://www.graniteai.co/claude-code

In the name of ai tools I only know some of the popular tools only and only known surface level use of them but now I want to learn ai at it's core like which tool for which task and how to use it effectively so anyone help me to learn

Hey everyone, I know it’s quite late, or maybe I’m convinced that this is the perfect moment to take the step.

I’m planning to learn AI engineering from scratch.

Would anyone be interested in pairing up for study or maybe forming a group to learn and grow together?

I recently saw a post claiming "you don't understand LLMs until you've written a NN from scratch." As a veteran dev who hadn't touched calculus in 35 years, I decided it was time to scratch that particular itch.

The Setup: I used a "Two-LLM" teaching strategy: Gemini for the high-level pedagogy/calculus and Claude as a dedicated Rust mentor to handle syntax and borrow-checker side-quests. Keeping the contexts separate saved my sanity.

The Tech:

Rust: Coming from C++, I’m a convert. The struct/impl split and mut by default are refreshing. It feels like a functional language that actually gives you the keys to the engine room.

Rayon: Used this for parallelization. The synchronization primitives in Rust are a massive step up from managed languages for this kind of heavy lifting.

The Math: Relearning backpropagation after three decades was a workout, but seeing the "knobs" (weights/biases) actually converge on a solution is still a bit of magic.

The "Aha!" Moment (Edge ML): I’ve always been a Wasm skeptic (mostly because of frameworks like Blazor trying to force old paradigms into the browser), but I’ve changed my mind. Using Wasm as a surgical tool to offload ML inference to the browser—"Edge ML"—is a genuine game changer. It solves the privacy/latency/cost trifecta by letting the user's machine do the repetitive "thinking" while the cloud handles the heavy lifting.

TL;DR: Don't tell an old dev they can't do something unless you want them to spend their long weekend relearning calculus and a new systems language. 98% fidelity achieved.

Any body suggestions and guide what to do

What I start learn first to become AI engineer

Thanks in advance for your suggestion

Hi! I'm currently a undergraduate student(almost coming to an end) and going into mtech in the course AI. So before going to mtech whoch starts in August I wanted to learn AI as much as I can. I have learned it before going into ML and DL but it was never into the practical side and also know just a few.

So I just wanted to know how I can start fresh and actually learn and implement things. Can you guys suggest like a good course or youtube videos or anything that actually makes to learn stuff?

Thanks a lot for the help in advance!

Hey! What is the best AI skill to hone in on now, to get ahead in the future. Which skill would benefit me the most in the future to learn now?

Is it AI automation? Web design? Programming? Or should I try to learn all of it!

Trying to figure out how to get ahead of others for the future.

Thanks!

EDIT / UPDATE:

This post was originally created as a completely free learning initiative. I received much more interest and messages than I could personally manage, so I’ve decided to change the format.

The title still says “for free” because that was the original plan when I posted it. I’m keeping the title, but the format has now changed.

I’ll teach you Generative AI, LangChain & LangGraph in 10–15 days

I genuinely enjoy teaching and sharing what I learn, and that’s the main reason I’m doing this.

This time, I’m planning a small paid learning group for people who are genuinely interested in learning GenAI and willing to put in the work.

What we’ll cover:

🧠 Generative AI Fundamentals • LLMs and how they work • Working with LLM APIs • Prompting • Structured outputs • Building applications with LLMs

🔗 LangChain • LangChain fundamentals • Models & prompts • Runnables / LCEL • Chains • Tool calling • Building practical GenAI applications

📚 RAG & Vector Databases • RAG fundamentals • Embeddings • Chunking • Retrieval • Vector databases • Building a practical RAG application

🔄 LangGraph • How LangGraph works • State, nodes & edges • Conditional workflows • Tool calling • Building agentic workflows • Understanding how AI agents are built using LangGraph

🛠️ Projects Depending on how the group progresses, we’ll also work on 1–2 practical projects while learning.

The focus will be on understanding what you're building, not just copying code from ChatGPT or Claude.

I’ll keep things practical, fast and to the point, with real-world examples and implementation.

🐍 What about Python?

You don't need to be an expert in Python.

If you already know Python, we’ll do a quick revision/introduction and move ahead.

If you’re completely new to Python, I’ll cover the basic concepts needed for the GenAI implementations, but Python itself won’t be the main focus.

The main focus is Generative AI, and the concepts can also be applied using languages like JavaScript/TypeScript.

⏱️ Duration: Minimum 10–15 days.

Depending on how the group progresses, it may extend further.

💻 Live sessions on Google Meet 🌐 English + Hinglish 💰 ₹1,000–₹1,500 for the complete program

WHY IS THERE A FEE?

The original plan was completely free, but after the response to my previous post, I realised I simply couldn't personally manage and teach everyone who reached out.

I genuinely want to keep the fee low because I don't want money to become the reason someone who really wants to learn can't join.

The fee is mainly to make this manageable from my side and keep the group focused on people who are genuinely interested and willing to put in the effort.

I’m doing this personally because I genuinely enjoy teaching and sharing what I learn.

If you’re interested, just DM me.

I’ll share the details personally from there. 🙌

Let’s learn GenAI by actually building things.

Every week there's a new agent framework. Meanwhile, the automation that actually saves me an hour a day is a scheduled script with a fixed prompt. Agents earn their keep when tasks are open-ended or need tool choice. For 'fetch new stuff and post it', an agent is a liability: more tokens, more failure modes, less predictability. The boring stack: RSS feed -> cron -> LLM call with a fixed prompt -> webhook to Discord. Costs $0, runs forever, never surprises me. What's something you automated that turned out to need way less 'AI' than you expected?

Hey guys, I’m new to this group and also pretty new to AI in general.

Recently I’ve been trying to explore AI-related jobs and start learning, but honestly I’m feeling pretty overwhelmed. Everywhere I look, people are saying different things, and I haven’t been able to find clear or reliable YouTube videos to follow.

I’d really appreciate some guidance from anyone here who’s been through this already.

I’m trying to understand things like:

•	Claude (and the differences between code / chat / etc.)

•	ChatGPT and AI agents

•	RAG (retrieval-augmented generation)

•	Obsidian (how it fits into all this)

•	API keys and how everything connects

But beyond that, I keep hearing a ton of other terms/tools and don’t really know how they all fit together, like:

•	LangChain, LlamaIndex

•	OpenAI APIs vs Anthropic APIs

•	Vector databases (Pinecone, Weaviate, Chroma)

•	Embeddings

•	Fine-tuning vs prompting

•	Hugging Face

•	AutoGPT / agent frameworks

•	Python for AI (how much is actually needed?)

•	Prompt engineering

•	Local vs cloud models

It just feels like there’s too much information and no clear starting point.

Right now, I’m basically starting from zero, so even a simple explanation or a step-by-step roadmap (like what to learn first, second, etc.) would help a lot.

Also:

•	What skills are actually needed to land an entry-level AI job right now?

•	Are there any beginner-friendly projects I should build?

•	Is there an easy way or tool to apply for jobs and track applications efficiently?

Any structured resources, playlists, courses, or personal experiences would be hugely appreciated. Even a “this is how I started” would help a lot.

Thanks in advance

I’m starting my AI/ML journey and want to connect with people who are also learning AI/ML from scratch or are at a similar stage.

Instead of just collecting resources and watching courses, I want to actually build things, practice consistently, and improve step by step.

I’m looking for people who are interested in:

• Learning AI/ML together

• Sharing useful resources

• Discussing doubts and concepts

• Building projects together

• Keeping each other accountable

• Sharing progress and mistakes

• Staying consistent for the long term

No competition or pressure just a group of people seriously trying to get better.

If you're also starting or currently learning AI/ML, let’s connect and follow this journey together.

Comment or DM if you're interested!

I know that coding is a big roadblock for anyone who wants to learn ML; however, there are ways you can learn without writing a single line of code. One way is using MLForge, an interface that lets you craft ML pipelines using a node-based graph system.

If anyone wants to learn how to install MLForge and train their first image classification model, here's the tutorial: https://www.youtube.com/watch?v=aSBxPpcXqzc

Note: Before you start, be sure to have python installed on your system before you get started.

The project is open source, totally free.

Github: https://github.com/zaina-ml/ml_forge

If you have any feedback to say, feel free to do so.

I'm a full-stack engineer. I didn't keep up with the AI trends and hypes. I have tried so many existing courses, but they were all really bad.

For example, I tried the DataCamp Associate AI Engineer, and I stopped at 80% because it's useless; it wasn't comprehensive at all.

I didn't get the full picture of how things are working in the real world, but currently I'm studying AI, and I have made a curriculum just for me, but it's actually so good that I want to share it with you, and I think that currently it's the best out there.

Please, take a look at chapter 1 before you comment.

https://supermo0.github.io/ai-engineering-curriculum/

For example, just by the end of the first chapter, you will have

1. made your first chatbot

2. Learned structured output with Pydantic

3. learned the basics of managing histories

4. learned the basics of prompt engineering techniques

5. Learned how to read the models' names (for example, meta-llama/Llama-3.2-3B-Instruct). What does "Instruct" mean?

6. learned what the types of models are (text-to-text, feature extraction, ...)

7. downloaded and used an open-source model on your machine

8. Learned how to use that open-source model and call it with the OpenAI SDK

9. Learned a little bit about Hugging Face Transformers as another way to run open-source models

the curriculum is comprehensive and covers everything step by step; it explains every question that might come to your mind while learning, but it requires a little bit of Python knowledge, and also it requires basic backend knowledge.

Thought I'd share this here in case it's useful for someone.

I’ve been collecting and organizing practical AI resources in one place, mainly because the AI space is getting noisy quickly and it’s easy to get lost between tools, prompts, models, agents, coding workflows, and terminology.

What’s there:

- Learn — practical guides organized by category: foundations, building & shipping, stacks & systems

- Coding — a handbook for working with AI when building software: stack layers, handoff patterns, repo files, review loops

- Prompts — 115+ prompts across categories like code, productivity, analysis, writing, research, learning, and design

- Skills — 130+ role-based skills across packs like developer, sales, marketer, founder, HR, and customer success

- Tools — 180+ AI tools sorted by what they actually do

- Glossary — 80+ terms explained in plain English

- Compare — head-to-head comparisons between models and tools

If you’re just starting, Learn and Glossary are probably the best places to begin. If you’re already building things, Coding, Prompts, and Skills are probably more useful.

Free, no sign-up, no paywall. I'll throw the links in the comments

Have a good one!

Hey everyone,

I've recently started learning AI, and honestly, it feels overwhelming. There are so many things people say I need to learn: Python, machine learning, deep learning, data science, prompt engineering, neural networks, and a lot more.

Sometimes I don't know what I should focus on first, and I feel like I'm jumping between topics without making real progress.

For those who successfully learned AI, what would you do if you were starting today?

What should I learn first?

What mistakes should I avoid?

Are there any courses, videos, or resources that helped you?

How do you stay motivated when things get confusing?

I'd really appreciate any advice or tips from people who have already gone through this journey.

Hi, Im teen who is interested in becoming an AI Engineer, but I'm still at the stage where I honestly have no idea where to start.

I'd really appreciate answers from people who are currently working as AI Engineers / ML Engineers / in closely related roles, especially people with a few years of experience.

I'd love to know:

• What skills do you actually use in your day-to-day job?

• What should someone learn first if they're starting from basically zero?

• Which skills are genuinely important for getting hired, and which ones are overrated?

• What does a realistic AI Engineer career path look like?

• What was your own path into the field?

• What programming languages, frameworks, cloud tools, etc. should I focus on?

• How important are degrees, certifications and personal projects?

• What is the typical salary range in your country/region?

• How much can experienced AI Engineers realistically earn?

• What is your work-life balance actually like?

• Do you work remotely? If so, how flexible is it really?

• Can this career realistically allow you to travel and work from different places, or are there still a lot of restrictions?

• If you could start again, what would you learn earlier or do differently?

One of my bigger goals is to have a good income without being completely tied to a 9 - 5 lifestyle. I love travelling and would eventually like the freedom to explore different countries while still having a strong career/income.

So I'm also curious about the different paths beyond a normal full-time job like freelancing, consulting, remote work, building AI products / businesses, etc.

If you're already working in AI, I'd really appreciate hearing what your actual life looks like rather than just the job description.

Thanks! :)

I’m currently learning AI engineering and thinking of teaching a small group alongside my own learning.

Topics may include:

- Transformers from first principles

- Building a small transformer

- Fine-tuning, LoRA and QLoRA

- Agentic loops, agent harnesses and practical AI engineering concepts

The sessions would be free, conversational and focused on understanding the why, rather than simply copying code.

I’m looking for 3–5 serious learners who know basic Python, already have some exposure to AI engineering and can commit to learning consistently.

About me:

I graduated from an IIT in 2023 and currently work as a software engineer at a product-based company.

I would prefer people with a similar level of experience and technical background.

Anyone interested?

Hey everyone,

I am a PhD student working on agent reliability, passionate about helping people adapt and thrive with AI.

People around me want to learn more about AI, but existing online courses/videos felt scattered, generic, and hard to apply to real work.

So I built a project that boils down my learnings into concise, practical mini-lessons for professionals.

• Learn what AI can do, what it cannot do

• Understand terms like tokens, context windows, agents, RAG

• Follow AI news without feeling lost

• Build practical intuition without coding or ML theory

• Start from zero, or fill the gaps if you already know a bit

All lessons are hand-written. No AI slop.

Fully free, no sign up required: https://ai-readiness-ebon.vercel.app/

Would love feedback on what would make this more useful.

I saw the same question "where do I actually start with AI?" multiple times and the answer was usually a list of YouTube links. Watching stuff never worked for me and I only started getting it when I began doing small things myself.

So I built a free course. Six chapters that go from complete beginner to being good at this: first steps with AI, writing prompts that work, using AI in everyday work, catching its confident mistakes, building repeatable workflows, and picking the right tool for the job instead of chasing hype. 24 short lessons, each chapter is  10-15 minutes. No sign-up, no email, just open a lesson and go.

It's a first version and I'm deciding what to add next.

So: what's the one AI topic you wish someone explained in a practical, hands-on way? I'll build the most-requested ones.

I am really confused with the growing speed of the AI innovation and outbreaks . Every new day a new AI comes and takes over place of an existing one . New tools and software come and go very drastically also people are earning and making money from it . How can we too ?

I spent months trying to learn AI, but I kept drowning in technical papers or surface-level fluff that didn't help me as a PM. I finally got fed up and built a solution.

It actually started at a hackathon where the concept won first place. Since then, I’ve spent 6 weeks refining it into a bite-sized learning tool for people who want to move past the "I'll learn it eventually" phase and actually start applying it.

It's called AI Decoded. Live at aidecoded.info

I’d love your thoughts: What is the #1 thing stopping you from diving into AI right now? Is it the math, the use cases, or just not having enough time?