Maddy Zhang

AI can write code in seconds. But writing code that is secure, maintainable, and ready for production is a completely different skill.

So, what coding habits actually matter in the AI era?

In this video, I break down the 8 coding rules I follow as a senior software engineer to write better code with AI. From verifying every line of AI-generated code to avoiding security vulnerabilities, preventing production outages and choosing dependencies safely, these are the habits that help you write code you can actually trust.

🚀 What you'll learn
✅ Why you should understand every line of code before merging it
✅ Why searching your codebase before writing new code prevents duplication
✅ How to handle errors without hiding real problems
✅ Why every input should be treated as potentially untrusted
✅ How to reproduce bugs and write tests before fixing them
✅ How to reduce the impact of mistakes with feature flags and gradual rollouts
✅ Why documenting the reasoning behind your code matters
✅ How AI-generated code can introduce fake packages and security risks
✅ What verification debt means and why it matters when coding with AI
✅ How to write secure, maintainable, production-ready code in 2026

👉 Watch the full breakdown here: https://youtu.be/HKo0NtAQneE

1 day ago | [YT] | 412

Maddy Zhang

How a Google AI Engineer Uses AI

Maitri (AI Engineer at Google): One thing my manager told me is that instead of providing all the context to the model each time that you're working on a project, which is what I was doing before, he writes skills for each project that he's working on.

He writes it once and then he keeps adding on to the skill.

Any conversation you're having with the model, instead of it becoming compacted, put that information into the skill. So you start creating a skill for that project, and anytime you start a new conversation for that project, just pass it that skill and it has all that context in there.

I was like, mind blown. I just didn't think about doing that. You can just create skills for every project and keep adding on to it.

I was always manually providing context to each conversation that I was having with AI.

👉 Watch the full breakdown here: https://youtu.be/NdlzdPs0QBQ

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5 days ago | [YT] | 220

Maddy Zhang

The 4 skills that matter most for AI engineers in 2026

Maitri (AI Engineer at Google): “I know you're going to hate me for this, but taste. Taste matters a lot.

The UI aspect has become so much more important now, and knowing what to build because anyone can build it, right?

Building UI is just not the moat anymore. Code is very cheap. Knowing what to build, knowing how it looks like has become very, very important.

Your soft skills of being able to communicate well. You have to be able to communicate your ideas not just to other teams and your manager and your peers, but also be able to communicate to the AI that's building out your code.

Which leads me to the AI engineering skills that you need, which is context engineering. You can't just feed all of your context in.

Model routing as well is very important. You need to be able to route your queries correctly.

👉 Watch the full breakdown here: https://youtu.be/NdlzdPs0QBQ

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1 week ago | [YT] | 364

Maddy Zhang

AI Engineering is one of the hottest career paths in 2026, but how do you actually get hired? Do you need a master's degree, advanced math, or years of experience to break into the field?

Maitri, an AI Engineer at Google, shares how she'd become an AI Engineer from scratch in 2026, including the free resources to learn, the skills companies need, and what matters more than coding alone.

💡Maitri shares:
✅ How to become an AI Engineer from scratch
✅ AI Engineer vs Software Engineer vs ML Engineer
✅ Whether you need a master's degree or advanced math
✅ Free resources to learn AI in 2026
✅ Whether AI will replace software engineers
✅ How to use AI coding tools effectively
✅ Why context engineering, communication, and adaptability matter
✅ The 4 essential AI engineering skills for 2026

👉 Watch the full breakdown here: https://youtu.be/NdlzdPs0QBQ

1 week ago (edited) | [YT] | 426

Maddy Zhang

Local AI sounds complicated. RAM, VRAM, quantization, model sizes, runtimes, and a bunch of tools just to run a model that you could use instantly with ChatGPT or Claude.

So why bother?

In this video, I break down why local AI is becoming increasingly useful in 2026, what actually happens when you run a model on your own machine, how quantization and memory work and how to get your first local model running.

🚀 What you'll learn
✅ Why local AI is becoming an important software engineering skill
✅ The biggest advantages of running AI locally: privacy, cost, control, and offline access
✅ What model weights, runtimes, RAM, VRAM, and memory bandwidth actually mean
✅ How quantization makes large models practical on consumer hardware
✅ How to run your first local model with LM Studio or Ollama
✅ Which model sizes make sense for 8GB, 16GB, 24GB, and 32GB+ machines
✅ How to connect a local model to your existing applications through an API
✅ How local coding agents work with your files, terminal, and editor
✅ The difference between retrieval and fine-tuning
✅ How LoRA and lightweight fine-tuning work
✅ When local AI is better than cloud AI, and when it isn't
✅ How to think about local AI for real-world products and AI workloads

👉 Watch the full breakdown here: https://youtu.be/6hQL9dbnNqA

1 week ago | [YT] | 777

Maddy Zhang

Went to OpenAI DevDay 2026 in SF!

With 20+ announcements, here's what stood out to me:

-Dots: always-on agents! When you’re not actively working with one, it does proactive research in the background, and it can connect to 4,000+ apps through plugins.
-GPT-6.1 Sol: offers close to Astra-level performance on agentic coding and computer use, at a fifth of the token price.
-Ultrafast: a new premium speed tier, up to 8x faster token generation in Codex.
-Codex expansion: you can run it in the cloud from any device, steer the CLI with your voice, and have it take a first pass on PRs before you look at them.
-MCP Events support: plugins can now kick off automations when something happens in a connected app.

My favorite moments were watching Romain Huet have Codex build a raffle app on stage that picked six attendees for tickets to next year’s DevDay, and seeing Thibault Sottiaux press the button to reset everyone’s ChatGPT account usage 😮

And of course, the people were the best part! Got to chat up with so many friends, new and old.

Thanks for having me, ‪@OpenAI‬!

🏷️ #OpenAI #conference #Codex #AI #AgenticAI #SoftwareEngineering

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2 weeks ago | [YT] | 681

Maddy Zhang

AI agents feel easy when you're building a demo. But once you try to ship one to production, you realize an agent isn't just an API call. It reasons, calls tools, keeps state, and takes multiple steps, and every step is another way for it to fail.
In this video, I build the architecture of an agentic AI system from the ground up, using a customer support agent as the example.

💡 Here's what you'll learn:
✅ The Agent Loop: why an agent is a loop, not a prompt, and how reliability erodes with every step
✅ Model Routing: how to balance cost and latency against quality by matching models to tasks
✅ Tools & MCP: why tools should be designed like strict APIs, with read and write actions kept separate
✅ Memory & State: how to treat context as a scarce resource and avoid context rot
✅ RAG: why bad retrieval is behind so many bad AI answers
✅ Orchestration: when to use a defined pipeline, a single agent, or multiple agents
✅ Evals & Observability: how to test non-deterministic systems and debug individual agent runs
✅ Security & Production Controls: how to limit blast radius and defend against prompt injection

👉 Watch the full breakdown here: https://www.youtube.com/watch?v=UqnLq...

3 weeks ago | [YT] | 650

Maddy Zhang

As a senior software engineer, I have spent the last few years working extensively with AI and software engineering.

In this video, I break down my entire agentic AI workflow from setting up agents with the right context and memory, to planning, parallel agents, autonomous loops, graph-based workflows and reviewing AI-generated code safely

Here’s what I cover 👇
✅ How I set up memory files and skills so agents understand my codebase and conventions
✅ Why you should plan before letting an agent write code
✅ How I use plan mode to catch expensive mistakes before implementation
✅ How meeting context and requirements can flow directly into coding workflows
✅ How I use terminal-based workflows, agent harnesses, and Git worktrees
✅ How to run multiple AI agents in parallel with isolated contexts
✅ Loop engineering: designing agents that can write, test, fix, and repeat on their own
✅ Graph engineering: combining multiple agent loops for research, implementation, and review
✅ How to move from manually prompting agents to building more autonomous workflows
✅ How I decide how deeply to review AI-generated code based on risk
✅ When a quick diff review is enough and when I manually inspect every line
✅ Why testing, staging, screenshots, and real-world validation still matter with AI-generated code

👉 Watch the full breakdown here: https://youtu.be/tSF6eZW8ZMU

4 weeks ago | [YT] | 707

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 month ago | [YT] | 717

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 month ago | [YT] | 955