Here’s a full 3-hour data structures course built for real use. I'll walk you through the core topics in a way that actually helps with technical interviews and problem solving. So if you want a stronger foundation for interviews, LeetCode, or CS, watch this video.

This is how to build a custom MCP server in Python. Let me show you how MCP actually works, how to make your own tools, and how to connect everything to an AI agent. So if you want MCP to feel a lot more practical, watch this.

Even when you already know the answer in a coding interview, you still have to walk through it like you are figuring it out in real time. Because the interviewer is not only watching whether you can get to the right answer. They are watching how you get there. - Do you understand the problem before coding? - Do you ask about constraints? - Do you notice edge cases? - Do you explain the tradeoffs? - Do you write code that is easy to follow? - Do you know what your solution costs in time and space? This is where a lot of people mess up. They grind hundreds of LeetCode problems, so they start recognizing patterns. But then in the actual interview, they rush into code, stay quiet for too long, miss constraints, or cannot explain why their solution works. That is where things break. Even if your answer is right, the interviewer may still not know whether you actually understood the problem or just recognized something similar. That is why the process matters. You want to show how you think, not just the code you end up with. And if you want a better way to handle coding interviews, you can follow the full process here: x.com/TechWithTimm/status/20… Really hope this helps, and good luck with your interviews.

This is why a lot of developers are not getting interviews. Let me show you how hiring really works, what recruiters are scanning for, and what changes make you look like a much stronger fit. So if you want to get more interviews without wasting time applying blindly, watch this.

Python is still one of the best programming languages to learn right now. Not just because it’s beginner-friendly. But it gives you a lot of options. You can use it for web development, automation, scripting, data analysis, APIs, AI, internal tools, and even small business software. The demand is still there too. Depending on the role and source, Python developer salaries in the US are still averaging well over $100k. So I don’t think the problem is whether Python is worth learning. It clearly is. The problem is how most people learn it. They start with syntax. Then they follow a few tutorials. Then they try a bit of web development, a bit of data analysis, a bit of automation, a bit of AI, and a few APIs. So it feels like they are learning a lot. But after months of doing this, they still can’t open a blank file and build something useful on their own. And that is exactly why I put together this Python roadmap for beginners in 2026. Not to give you another giant list of things to learn. But to show the right order. Because the order is what really keeps you from spending months touching a little bit of everything and still feeling stuck. So if you are learning Python this year, here is a complete roadmap for you. x.com/TechWithTimm/status/20… Really hope this helps you learn Python with more clarity and actually build something real this year.

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The software development bottleneck just moved

The software development bottleneck just moved

Here is how to do full local coding with AI on your own computer. Let me show you how local models work, which ones to pick, and how to set them up so they can actually code inside your editor. So if you want local AI coding to be genuinely usable, watch this.

The hard truth every developer needs to hear in 2026

Here is my honest take on learning to code in 2026. Let me show you what still matters, what you can skip, and how to use AI without letting it do all of your thinking. So if you want a better approach to learning coding today, watch this video.

Let me be honest with you for a second. If you are in your 20s or 30s and you want to actually do well in tech, one of the biggest things that decides your outcome is not AI, prompt tricks, or whatever the trend is this month. It all comes down to whether you can take an idea and turn it into a real application, from nothing to deployed, end to end, on your own. There is no special trick here. The fastest progress I have ever seen comes from doing more of the right actions than most people are willing to do. Not certificates. Not memorizing syntax. And not collecting courses. But building, shipping, debugging, showing your work, and applying consistently, even when it feels boring. Fixing one bug is frustrating. Fixing a hundred starts to show patterns. And fixing a thousand makes you think like a senior, even if you do not notice it happening. That is why someone who has built and deployed a handful of real apps is playing a completely different game from someone who only finishes assignments or copies examples. So hear me out. What really matters is picking one stack for now, sticking with it, and building one full application all the way through. Because that kind of project forces you to make real engineering decisions: how the auth works, how the data is stored, how the API is structured, how errors are handled, how the app gets deployed, and what breaks when real users touch it. And if you want a good place to start, here is a full 3 hour tutorial on building a full-stack app with Python, React, and AI. lnkd.in/eqbaqU2T Really hope this helps, and good luck with everything you're building.

Let me show you how to set up Hermes Agent the right way. You'll learn how to run it 24/7, connect it to real apps, and use it for things like email triage, research, and daily automations. So if you want to build an AI assistant that actually does things, watch this video.

This is why Python is easy to pick up, but hard to get really good at

Let me show you how matplotlib actually works in Python. I'll walk you through the fundamentals, the main plot types, and the customization options that make the library much more useful. So if you want a solid base for data work in Python, watch this video.

Most people focus on the wrong problem with local AI. Getting a model to run on your machine is not the hard part anymore. The hard part is making it actually useful. A local model on its own is basically a private text generator. It can answer questions. It can write code. It can summarize things. But without access to your external tools, it cannot really do much. People spend too much time comparing model sizes, RAM requirements, tokens per second, and benchmark scores. Those things do matter. But once the model is good enough to follow instructions, the real bottlenecks move elsewhere: - tool calling reliability - latency - permissions - account context - and how well the model handles real-world tool output That is why a smaller model with solid tool access can be more useful than a larger model that only chats. And that is what you should pay attention to. The real question is not whether your machine can run the model. It is whether the model can connect to external tools well enough to give you the same kind of value as Claude or OpenAI, while staying free and fully under your control on your own machine. If you want the full setup, here is how to connect a local model to real tools with Ollama, MCP, and Zapier. youtube.com/watch?v=GAyNvq6A…

THE GREAT OPENCLAW MAC MINI SELLOFF HAS STARTED!!

The fastest way to get good at coding

Here is a full 3 hour tutorial on building a full stack app with Python, React, and AI. You'll learn how FastAPI, React, database work, and API communication all come together in one interactive project. So if you want a more practical way to learn full stack development, watch this.