Ashley Nicholson

Most AI engineers waste time on the wrong things.
Here's what top companies actually want:

These 17 repositories separate the pros from the beginners:

1/ ML Foundations:
https://lnkd.in/giexNcCc
↳12-week curriculum. Classic ML. Project-based. Quiz-backed.

2/ ML Roadmap:
https://lnkd.in/gU9mAZqd
↳A no-fluff ML roadmap built for self-learners.

3/ Neural Networks: Zero to Hero:
https://lnkd.in/gEzFebK7
↳Build neural nets from scratch. Understand every step.

4/ Awesome-Computer-Vision:
https://lnkd.in/g7jZNFHX
↳The best CV collection: books, datasets, research tips, projects.

5/ Awesome-NLP:
https://lnkd.in/gvsExEM9
↳From transformers to multilingual NLP tools, plus top courses.

6/ Hands-on-LLMs:
https://lnkd.in/g7HcxTZz
↳ Understand LLMs from the ground up with really well curated notebooks

7/ Prompt-Engineering Guide:
https://lnkd.in/gJjGbxQr
↳ Everything prompt-engineering under the sun in one place.

8/ Awesome Data Science:
https://lnkd.in/grGUr2Uz
↳The ultimate free resource list: books, courses, datasets, tools.

9/ All Reinforcement Learning-Algorithm:
https://lnkd.in/gsW7tx9H
↳Clean RL implementations with a cheat sheet.

10/ Awesome RL:
https://lnkd.in/gxxvuf2x
↳A rich archive of RL theory, papers, demos, and apps.

11/ Awesome Generative AI:
https://lnkd.in/g_tmrqTi
↳Most current GenAI list: courses, tools, and papers by recency.

12/ AI Agents for Beginners:
https://lnkd.in/gK8MiVfv
↳ Jump into agentic systems with a free course.

13/ Advanced RAG Techniques:
https://lnkd.in/g2ZHwZ3w
↳ Understand the bones of RAG and become a retrieval master.

14/ Gen-AI Agents:
https://lnkd.in/gkMZs-Ks
↳ Learn how to build GenAI agents including tools and APIs.

15/ Made with ML:
https://lnkd.in/gER7Stdw
↳ The definitive MLOps guide for any practitioner.

16/ Annotated-AI-paper-implementations:
https://lnkd.in/g49GC3bV
↳60+ papers with inline code notes including transformers, optimizers, and GANs from the inside out.

The truth?
Reading about AI won't transform your career.
Building with it will.

So, don't just read.
Build. Run. Break. Learn.

What are you waiting for?

Thanks for Sairam Sundaresan for curating this great list of repositories. Give him a follow!

♻️ Repost to help someone master AI.
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Coding is no longer the bottleneck. Product management is.
One of the godfathers of AI just dropped a bomb:

Andrew Ng correctly noted that coding is no longer the challenge.
It's product management.

And he's right.

In technology, we become so obsessed with coding.
↳ With building.
↳ With writing specs.
↳ With pushing releases.
↳ And shipping.

With AI, there's been a shift.

Building isn't the problem.
An amazing team can build anything.
The real work is deciding what is worth building.

And what is not worth building.

Too many tech leaders get this wrong.
They chase shiny features.
And the latest emerging technology.

But without a real problem to solve,
Or building to solve the problem, there is no impact.

As a leader, it isn't your job to accelerate the processes,
Sometimes it's to slow the processes down and lead the project in the right direction.

Before moving forward with any project, you should ask yourself:
↳ What problem are we actually solving?
↳ Why does this problem matter?
↳ How will we measure the results?
↳ Who does this problem matter to?

Trust isn't built on more shiny features.
It's built on better decisions.

How has AI changed your approach to coding and creating products?

How has it changed your strategic direction?

Share below.



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Most AI strategies fail before they even start:

Because what people think AI strategy is...
isn't what AI strategy actually is.

I've watched brilliant leaders create 100-slide decks
filled with buzzwords, hype, and vision statements.

They talk about "beating the competition" and
"Technology transformation."

Then 6 months later? Little has changed.

Here's the truth about real AI strategies:

What an AI Strategy ISN'T:
❌ A pretty deck that sits unread
❌ Copying what other organizations do (but "better")
❌ A list of AI tools and software licenses to buy
❌ Trying to be everything to everyone
❌ Only technical

What an AI Strategy ACTUALLY IS:
☑️ Choosing what NOT to do (this one is so hard)
☑️ Focusing on people and helping them upskill
☑️ Focusing on data quality and cleansing
☑️ Making trade-offs that make you nervous
☑️ Solving business problems others don't see yet


The best strategy AI strategy I ever saw?

A leader who focused on people first,
asked hard questions about the business case,
focused on data and left half the deck blank.

He knew the technology was changing rapidly.

And he and his team wouldn't have all the answers now.

His leadership team thought he was crazy.
His team was fearful.
Even he had doubts.

But he knew:
Strategy is about tradeoffs.

It's about going all in on a few big bets.

Not hedging. Not playing it safe. Going all in.

12 months later?

His team started scaling up the AI pilot.
People in his organization are accepting AI.

They realized a 33% increase in productivity.


Save this.
Share it with your team.
Use it in your AI strategy session.

Here's my test for a real AI strategy:
↳ Can your newest employee explain it in 30 seconds?
↳ Does it force you to say "no" to good opportunities?
↳ Does it create rules your competition can't follow?

If not, you don't have a real AI strategy.
You have a check list of cool tools and shiny toys.


Most leaders want AI strategy to be comfortable.
But real AI strategy should make you uncomfortable.

It's not about having all the answers.

AI technology is changing fast.

It's about testing small, learning fast,
then going all in when you find what works.

What are the key elements of a great AI strategy?

Share below.


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