Jan 7 • General discussion
Choosing Your Learning Path Towards AI Agents
Hey guys 👋
A few of you reached out asking how to start from scratch and learn AI Agents.
That’s a great question. If you’re asking it, it means two important things:
- You genuinely want to learn and you're taking action
- You’re aware of where you currently stand
That mindset already puts you ahead. 👏
𝗧𝗵𝗲 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗕𝗹𝗼𝗰𝗸𝘀 𝗼𝗳 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀
Before tools, frameworks, or money — there are fundamentals.
- Learn enough about AI
You don’t need a PhD, but you must understand:
- What AI actually is (and isn’t)
- What a neural network is (high level)
- What an LLM is and what it’s capable of
This gives you real intuition of these systems are capable of in real world applications.
2. Learn what an AI Agent is
You should clearly understand:
- The difference between an AI model and an AI Agent
- Why agents exist in the first place
- What problems agents solve that prompts alone can’t
3. Learn how to build
This is your vehicle:
- Either through programming
- Or through no-code tools
You’ll use this to turn ideas into real projects.
𝗜𝗳 𝗜 𝗪𝗲𝗿𝗲 𝗦𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗙𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 (𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗣𝗮𝘁𝗵)
𝗧𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝗻𝗼 𝘀𝗵𝗼𝗿𝘁𝗰𝘂𝘁𝘀
You can’t jump from Z when your knowledge is X. You might feel some progress, but it’s self-deception. Real confidence comes from deep fundamentals.
𝗗𝗼𝗻’𝘁 𝗴𝗲𝘁 𝘀𝘁𝘂𝗰𝗸 𝗶𝗻 𝘁𝗵𝗲𝗼𝗿𝘆
Once you understand the basics:
- Build something small
- Break it
- Fix it
- Repeat
That feedback loop is where learning accelerates.
𝗞𝗲𝗲𝗽 𝗱𝗼𝗶𝗻𝗴 𝗶𝘁
Your first project will be simple. Your second will be slightly better. Your tenth will finally make sense.
Momentum > perfection.
𝗜𝗳 𝗬𝗼𝘂 𝗗𝗼𝗻’𝘁 𝗪𝗮𝗻𝘁 𝘁𝗼 𝗖𝗼𝗱𝗲 (𝗡𝗼-𝗖𝗼𝗱𝗲 𝗣𝗮𝘁𝗵)
No-code is a valid path if your goal is speed.
- Start by understanding core concepts:
- What is an AI Agent
- What is an LLM
- What are tools
- What is an API
Then apply them using no-code platforms.
2. What no-code is great for:
- Rapid prototyping
- Validating ideas fast
- Learning agent logic visually
- Building MVPs
- Non-technical founders
3. The trade-off (important):
- You will hit a wall
- Custom logic becomes hard
- Debugging is limited
- Advanced workflows are constrained
When you hit that wall:
- Learn exactly the thing blocking you
- Then continue building
With AI as a tutor, nothing is unlearnable anymore.
𝗔 𝗡𝗼𝘁𝗲 𝗔𝗯𝗼𝘂𝘁 𝗠𝗮𝗸𝗶𝗻𝗴 𝗠𝗼𝗻𝗲𝘆 💰
If your only goal is to make money quickly, the odds are not in your favor.
Not because you’re incapable — but because:
- This is a skill path
- Skills compound over time
- Shortcuts don’t compound
What does work:
How value is actually built:
- Building many small projects
- Solving your own problems first
- Attempting real-world use cases
- Failing publicly and fixing privately
- Repeating the cycle
Why this pays off:
- Your thinking improves
- Your intuition sharpens
- Your solutions become reusable
- Your skills become rare
𝗧𝗵𝗲 𝗴𝗼𝗼𝗱 𝗻𝗲𝘄𝘀 🚀
In 2026, there will be many winning business models for people who:
- Actually understand what they’re building
- Can go beyond copy-pasting AI outputs
- Can design systems, not just prompts
Copy-paste builders won’t win long-term. Deep understanding always does.
I’ll make a separate video about concrete ways to make money with AI once the audience grows a bit more. Until then, I’ll still answer these questions during our coffee-hours ☕.
𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀
• What’s an LLM: https://youtu.be/LPZh9BOjkQs
• What’s an AI Agent: https://youtu.be/hyi3vP41RdU?t=71
• Build an AI Agent with a no-code tool: https://youtu.be/AgbdSigaRsA?t=25
• Learn Python basics (needed for LangGraph): https://app.datacamp.com/learn/skill-tracks/python-programming-fundamentals
• Learn LangGraph: https://youtu.be/hyi3vP41RdU?t=290
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Choosing Your Learning Path Towards AI Agents
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