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AI you can actually use

I help people use AI to build real leverage: more income, more time, and systems that run themselves. No buzzwords, just the exact prompts, setups and workflows I use every day, broken down so anyone can copy them.

p.s. the waitlist is now open →

Innovation Decoded with JacquesInnovation Decoded with Jacques
AI you can actually use

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Everything here, in orbit

Each section orbits the core, and its guides, posts and reels orbit it. Pick a planet to see what's inside.

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What I cover

Three things, done properly

No firehose of everything AI. Just the parts I actually work in and can teach without hand-waving.

Practical AI education

Everyday, usable AI for work and personal life. The exact setups, prompts and workflows I run, broken down so you can copy them.

Latest AI news

What actually shipped and why it matters. I filter the noise and share what's current, without the hype cycle.

AI engineering interview tips

For people breaking into the field. What to build, what to study, and the questions real panels ask, from someone who sits on the other side.

Coming soon

The AI Leverage Club

A 30-day program that turns these one-off systems into a stack that runs your week: your inbox, your content, your admin, handled. Join the waitlist and you'll be the first in when it opens.

One email when it opens. No drip sequence, unsubscribe any time.

Most loved

Start with these free guides

The first systems I'm releasing. All free, all copy-paste. Join the waitlist and I'll send each one the moment it's live.

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AI engineering interview questions

The questions, answered my way

Plain-language answers to the questions real AI engineering panels ask, the way I'd actually say them out loud. New ones drop as I film them.

What's an LLM, in one breath?

If I'm explaining this to a leader, I skip the math and go straight to the mental model: an LLM is like a brand-new intern who has read the entire internet. It's eager, fast, and desperate to prove it's useful, so it will always give you an answer, even when it probably shouldn't. Ask it something it knows and it's brilliant. Ask it something it doesn't, and instead of saying "let me get back to you," it confidently makes something up that sounds right. That's what we call a hallucination, and it's not the model being broken, it's the eager intern guessing to look competent. So the leadership takeaway is simple: incredibly capable, needs supervision. You get the leverage of a tireless, well-read assistant, but for anything that matters you keep a human reviewing its work, exactly like you would with a talented new hire.

How do you approach feature selection in AI models?

Feature selection is like choosing the ingredients for a gourmet dish. Just grabbing everything from the pantry won't work — it could ruin the flavor. In AI, including too many or irrelevant features can make your model overcomplicated or inaccurate. Practical approach? Start with domain knowledge to identify potentially useful features. Then use statistical tests or algorithms like recursive feature elimination to trim the excess. This is critical because a leaner, focused model not only performs better but is also easier to interpret and maintain. Remember, less can often be more when it comes to feature selection, and clarity beats clutter every time.

About

An engineer who went all-in on AI

I'm a Senior AI Engineer building multi-agent systems in enterprise banking by day. Outside of that, I started rebuilding my own week around AI, one boring task at a time, and writing down what actually held up after a month of real use.

Most of it did not. What did is on this site, in full, for free. No buzzwords, no theory — just the exact setups and prompts I actually run.

If something here saves you an afternoon, tell me on Instagram. That feedback decides what I build next.

Jacques van Heerden, the engineer behind Innovation Decoded

> hi, it's me, Jacques van Heerden

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