Oracle's AI Director Laid Out The GenAI Learning Path.


Hello Reader,

Recently, I sat down with Richmond Alake - Director of AI Developer Experience at Oracle.
He co-created a course with Andrew Ng, has over 200 published technical articles, and works with enterprise AI customers every day.

And for sure, I'm going to borrow those concepts and apply them in my own teaching during SA Bootcamp.

Richmond laid out a learning progression that most engineers are studying in the wrong order:
Context engineering → Agent memory → Continual learning → Agent harness.

Most people jump straight to building agents or vibe coding. That's backwards.
Memory is the foundation everything else builds on.

He also gave a one-sentence test to check if your agent is actually well-built.
Swap out the model. If the output changes, your harness is weak. That is the kind of thing you say in an interview and the interviewer's eyebrows go up.

Other things we got into:

→ The real answer to the files vs. database debate for agent memory. Files are great for getting started. At production scale, you need a database. But there is a way to get both.

→ How much of his code AI writes now. At the start of the year he was writing about 70% manually. Now it is down to 30-40%. But there is a catch - he says he works MORE hours now, not fewer. The possibilities are endless and it is too easy to overengineer.

→ Whether AI will actually take dev and cloud jobs. His honest take: job displacement at best, not job loss. Every revolution creates more work. And Richmond is living proof - his output went up, but so did his hours.

If you are trying to figure out where to invest your GenAI learning - for interviews, for career growth, for building things that work in production - this conversation gives you the roadmap in order.

Watch it here: Context Engineering, Agent Memory & AI Jobs | Richmond Alake

Keep learning and keep rocking 🚀,

Raj

P.S. If you have found this newsletter helpful, and want to dive deeper...

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