Maddy Zhang

Working at Microsoft completely changed how I think about software engineering.

I got to work on Roslyn analyzers and developer tools used by millions of developers, and being inside such a massive engineering organization taught me lessons that go far beyond writing code.

In this video, I break down the 7 biggest things I learned at Microsoft, from how to approach learning and criticism to designing for users you’ll never meet, maintaining backwards compatibility, building secure software, and adapting to AI.

🚀 What you’ll learn
✅ Why Microsoft values being a “learn-it-all” instead of a “know-it-all”
✅ Why great engineers design for users they’ll never meet
✅ What backwards compatibility teaches you about building software that lasts
✅ Why security should be part of engineering from the very beginning
✅ Why shipping and learning from real user behavior beats relying on assumptions
✅ How design reviews and constructive criticism can save you weeks of wasted work
✅ Why adaptability matters more than mastering any single AI tool
✅ What Microsoft’s changing AI strategy reveals about the future of software engineering
✅ How to stay useful even when the tools and technologies keep changing

👉 Watch the full breakdown here: https://youtu.be/2CN02BZY59A

1 day ago | [YT] | 404

Maddy Zhang

AI was supposed to replace jobs, transform industries, and become the biggest technological shift in history. But the numbers tell a different story. Big Tech is spending hundreds of billions on AI while many projects struggle to deliver real returns.

In this video, I break down the 2026 AI boom, where the money is going, why AI projects are failing, and how rising costs, energy demands, hallucinations, and legal risks are exposing the cracks.

So, are we heading toward one of the biggest tech bubbles in history? And what does it mean for software engineers, AI engineers, and tech careers?

🚀 What you’ll learn
✅ How much Big Tech is actually spending on AI in 2026
✅ Why the AI spending boom may be getting ahead of the real value
✅ What the $7+ trillion AI investment thesis means
✅ Why AI infrastructure and energy demand are becoming major constraints
✅ Why so many enterprise AI pilots fail to deliver ROI
✅ Why companies are rolling back AI customer-service agents
✅ How AI hallucinations create serious legal and business risks
✅ What the AI bubble could have in common with the dot-com crash
✅ Why AI isn’t necessarily failing even if the AI bubble bursts
✅ Which skills and roles could become more valuable after the correction
✅ How software engineers can position themselves for what comes next

👉 Watch the full breakdown here: https://youtu.be/oGCb9gGBBrw

1 week ago | [YT] | 830

Maddy Zhang

I solved 400+ LeetCode problems and interviewed candidates at Google. And here's the truth I wish I knew when I started:

You DON'T need to solve hundreds of LeetCode problems to crack a Big Tech interview.

What you actually need is to recognize the patterns that keep appearing across problems.

In this video, I break down the 9 most important DSA patterns you need to master in 2026, the clues that tell you when to use each one, and the LeetCode problems you should practice to build real problem-solving intuition.

🚀 What you'll learn:
✅ Why solving 400+ LeetCode problems isn't necessary
✅ Why DSA and coding interviews still matter in 2026
✅ The 9 DSA patterns that appear again and again
✅ How to recognize when a problem requires hashing
✅ When to use two pointers and fast/slow pointers
✅ How sliding window problems actually work
✅ How to spot binary search even without a sorted array
✅ When to use a monotonic stack
✅ DFS vs BFS and when to use each
✅ How heaps solve Top K problems efficiently
✅ When backtracking is the right approach
✅ How to recognize dynamic programming problems
✅ The key clues hidden inside problem statements
✅ The best LeetCode problems to practice for each pattern
✅ How to approach problems you've never seen before

👉 Watch the full breakdown here: https://youtu.be/Q5QoGocSnjo

2 weeks ago | [YT] | 1,189

Maddy Zhang

I spent 3.5 years at MIT studying computer science, one of the most intense academic environments in the world which helped me land internships and full-time offers at companies like Google, Amazon, Microsoft, IBM, and Snap.

But what does a 4-year computer science degree actually teach you—and which parts still matter when you're working as a software engineer?

In this video, I break down all four years of computer science at MIT, the most important skill each year teaches you, what I actually use at work, and what you can learn for free outside of university.

Here’s what I cover 👇
✅ What you actually learn in Year 1: programming and discrete math
✅ Why Python is disposable but learning to think precisely isn't
✅ How data structures and algorithms change the way you solve problems
✅ Why Big O and complexity analysis matter even more in the AI era
✅ What computer architecture, operating systems, networking, and databases teach you about real-world systems
✅ Why correct code can still fail in production
✅ How to develop systems-thinking and debug problems one layer below the symptoms
✅ What Year 4 electives and capstone projects teach you about real engineering work
✅ The biggest gap in a traditional CS degree: Git, terminals, debugging, profiling, and developer tooling
✅ The one resource I consider the highest-ROI addition to your CS education

👉 Watch the full breakdown here: https://youtu.be/GfqGmEKK_Ns

3 weeks ago | [YT] | 957

Maddy Zhang

Most people learn machine learning the wrong way. They jump straight into algorithms and math without understanding the core ideas that make ML systems actually work.

In this video, I break down 10 essential machine learning concepts that every software engineer and machine learning engineer should understand, from the foundations of training a model to the ideas powering modern AI and LLM systems.

Whether you're learning machine learning for the first time, preparing for ML interviews, or trying to understand how modern AI works under the hood, this video will help you build the right mental models.

💡 Here’s what you’ll learn:
✅ Training vs Inference — how models learn during training and generate outputs during inference
✅ Loss & Gradient Descent — how models measure mistakes and update their weights
✅ Generalization & Overfitting — why memorizing training data doesn't make a useful model
✅ Embeddings — how AI represents meaning as vectors for search, recommendations, and retrieval
✅ Tokens — how language models actually process text and why tokenization matters
✅ Attention — the mechanism that powers modern Transformer-based models
✅ Context Windows & Context Rot — why bigger context doesn't always mean better reasoning
✅ Pre-training & Post-training — how models go from raw knowledge to useful AI assistants
✅ RAG — how retrieval-augmented generation brings external knowledge into model context
✅ Evals — how to systematically measure whether an AI system is actually getting better


👉 Watch the full breakdown here: https://youtu.be/zVX7lOf5ER4

4 weeks ago | [YT] | 767

Maddy Zhang

Which video should I make next?

4 weeks ago | [YT] | 64

Maddy Zhang

AI was supposed to replace software engineers. Instead, companies are discovering that AI might be far more expensive than they expected.

Billions are being poured into AI. Coding agents are burning through tokens. Companies are cutting back on the very AI tools engineers were told to use. And the AI bubble is starting to face a much bigger question: does the economics actually work?

In this video, I break down what’s happening behind the headlines from Uber burning through its AI budget to Microsoft changing its internal AI strategy and what it means for developers, AI jobs, and the future of software engineering.

🚀 What you'll learn:
✅ Why companies are spending far more on AI than expected
✅ What happened when Uber rolled out Claude Code to thousands of engineers
✅ Why Microsoft changed its internal AI coding tools
✅ Why cheaper tokens can actually create bigger AI bills
✅ How AI agents consume dramatically more tokens than normal chat
✅ Whether AI is actually cheaper than a human engineer
✅ Why AI productivity gains may have been oversold
✅ How AI is changing software engineering hiring
✅ Why token and compute budgets could become part of compensation
✅ What context engineering is and why it matters
✅ How model routing, caching, and better context management reduce costs
✅ Why knowing when not to use an AI agent may become a critical skill

👉 Watch the full breakdown here: https://youtu.be/2TgYw9wXv5s

1 month ago (edited) | [YT] | 529

Maddy Zhang

The tech job market isn't dead in 2026 but it has fundamentally changed.

While headlines focus on layoffs, hiring freezes, and AI replacing software engineers, the reality is much more nuanced. Some tech roles are becoming increasingly automated, while others are seeing record demand, higher salaries, and a growing shortage of qualified talent.

In this video, I break down the tech careers that are actually worth pursuing in 2026 based on hiring trends, salary data, AI adoption, and long-term career resilience. You'll learn which roles are growing, which ones I'd be cautious about, and how to build a career that stays valuable in the age of AI.

🚀 What you'll learn:
✅ Why the tech job market is splitting instead of shrinking
✅ Which tech careers are growing despite mass layoffs
✅ Why AI Engineer is still one of the highest-paying roles and how to stand out
✅ The difference between real AI engineering and simply calling AI APIs
✅ Why Data Engineering is one of the most underrated careers in tech
✅ Why Cybersecurity demand continues to grow in the AI era
✅ How AI Product Managers combine technical knowledge with business strategy
✅ Why Cloud & Platform Engineering remains critical as AI infrastructure expands
✅ The skills employers actually value in 2026
✅ Which tech jobs are becoming increasingly vulnerable to AI automation
✅ How to future-proof your career by focusing on judgment, ownership, and system design instead of repetitive work

👉 Watch the full breakdown here: https://youtu.be/aGnWRt6u-fg

1 month ago | [YT] | 828

Maddy Zhang

Went to Open Sauce (‪@williamosman‬’s science and engineering festival) earlier this month!

It’s always one of my favorite events of the year :) It’s an amazing mashup of makers, creators and nerds.

Some highlights:
-life sized Gameboy
-taser knife fighting
-the cutest R2D2
-soldering a Bop-it style game on my badge
-watching the World Cup finals at the ‪@youtubecreators‬ lounge
-hanging with my friends <3

Can’t wait to be back next year!

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1 month ago | [YT] | 666

Maddy Zhang

If you want to learn coding in 2026 but don't know where to start, this is for you.

The internet makes learning to code feel far more complicated than it actually is. One person says learn Python. Another says JavaScript. Someone else says AI will replace programmers before you even get a job.

The reality is much simpler.

In this video, I share the exact roadmap I'd follow if I were starting from zero today. You'll learn what to study first, what to ignore, how to avoid tutorial hell, how to build projects that actually matter, and how to use AI tools without becoming dependent on them.

Here's what I cover 👇
✅ Whether learning to code is still worth it in the age of AI
✅ Why Python is the best programming language to learn in 2026
✅ The complete beginner roadmap from zero to building real projects
✅ The programming fundamentals every developer needs to master
✅ How real software is structured beyond beginner tutorials
✅ The projects that actually help you become a better engineer
✅ The professional skills (Git, debugging, reading code) most courses skip
✅ How to use AI tools like ChatGPT and Claude the right way while learning
✅ The software engineering, AI, and data careers Python can unlock

👉 Watch the full breakdown here: https://youtu.be/aGnWRt6u-fg

1 month ago | [YT] | 1,033