Jan 7 โข General discussion
Choosing Your Learning Path Towards AI Agents
Hey guys ๐
A few of you reached out asking how to start from scratch and learn AI Agents.
Thatโs a great question. If youโre asking it, it means two important things:
- You genuinely want to learn and you're taking action
- Youโre aware of where you currently stand
That mindset already puts you ahead. ๐
๐ง๐ต๐ฒ ๐๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐๐น๐ผ๐ฐ๐ธ๐ ๐ผ๐ณ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐๐ ๐๐ด๐ฒ๐ป๐๐
Before tools, frameworks, or money โ there are fundamentals.
- Learn enough about AI
You donโt need a PhD, but you must understand:
- What AI actually is (and isnโt)
- What a neural network is (high level)
- What an LLM is and what itโs 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 canโt
3. Learn how to build
This is your vehicle:
- Either through programming
- Or through no-code tools
Youโll use this to turn ideas into real projects.
๐๐ณ ๐ ๐ช๐ฒ๐ฟ๐ฒ ๐ฆ๐๐ฎ๐ฟ๐๐ถ๐ป๐ด ๐๐ฟ๐ผ๐บ ๐ฆ๐ฐ๐ฟ๐ฎ๐๐ฐ๐ต (๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด ๐ฃ๐ฎ๐๐ต)
๐ง๐ต๐ฒ๐ฟ๐ฒ ๐ฎ๐ฟ๐ฒ ๐ป๐ผ ๐๐ต๐ผ๐ฟ๐๐ฐ๐๐๐
You canโt jump from Z when your knowledge is X. You might feel some progress, but itโs self-deception. Real confidence comes from deep fundamentals.
๐๐ผ๐ปโ๐ ๐ด๐ฒ๐ ๐๐๐๐ฐ๐ธ ๐ถ๐ป ๐๐ต๐ฒ๐ผ๐ฟ๐
Once you understand the basics:
- Build something small
- Break it
- Fix it
- Repeat
That feedback loop is where learning accelerates.
๐๐ฒ๐ฒ๐ฝ ๐ฑ๐ผ๐ถ๐ป๐ด ๐ถ๐
Your first project will be simple. Your second will be slightly better. Your tenth will finally make sense.
Momentum > perfection.
๐๐ณ ๐ฌ๐ผ๐ ๐๐ผ๐ปโ๐ ๐ช๐ฎ๐ป๐ ๐๐ผ ๐๐ผ๐ฑ๐ฒ (๐ก๐ผ-๐๐ผ๐ฑ๐ฒ ๐ฃ๐ฎ๐๐ต)
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.
๐ ๐ก๐ผ๐๐ฒ ๐๐ฏ๐ผ๐๐ ๐ ๐ฎ๐ธ๐ถ๐ป๐ด ๐ ๐ผ๐ป๐ฒ๐ ๐ฐ
If your only goal is to make money quickly, the odds are not in your favor.
Not because youโre incapable โ but because:
- This is a skill path
- Skills compound over time
- Shortcuts donโt 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
๐ง๐ต๐ฒ ๐ด๐ผ๐ผ๐ฑ ๐ป๐ฒ๐๐ ๐
In 2026, there will be many winning business models for people who:
- Actually understand what theyโre building
- Can go beyond copy-pasting AI outputs
- Can design systems, not just prompts
Copy-paste builders wonโt win long-term. Deep understanding always does.
Iโll make a separate video about concrete ways to make money with AI once the audience grows a bit more. Until then, Iโll still answer these questions during our coffee-hours โ.
๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐
โข Whatโs an LLM: https://youtu.be/LPZh9BOjkQs
โข Whatโs an AI Agent: https://youtu.be/hyi3vP41RdU?t=71
โข Build an AI Agent with a no-code tool: https://youtu.be/AgbdSigaRsA?t=25
โข Learn Python basics (needed for LangGraph): https://app.datacamp.com/learn/skill-tracks/python-programming-fundamentals
โข Learn LangGraph: https://youtu.be/hyi3vP41RdU?t=290
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Choosing Your Learning Path Towards AI Agents
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