Tech With Tim @TechWithTimm

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.

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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=GAyNvq…

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.

A lot of developers are asking whether DSA still matters now that AI can write code. The answer is yes. But not because interviews test whether you can memorize hundreds of problems. In an interview, the real test is not whether you have seen the exact problem before. The real test is whether you can look at a problem, reason through the tradeoffs, choose the right data structure, and explain why your approach works. That skill still matters, even if AI can generate code. Actually, it probably matters more now. Because if AI gives you a solution that looks correct but uses the wrong structure, has bad time complexity, misses edge cases, or falls apart with larger inputs, you need enough fundamentals to catch it. But don’t try to learn everything at once. Learn DSA in the right order. First, understand complexity, because you need to know why one solution is better than another. Then get comfortable with the core data structures, because those are the tools you use to organize the problem. After that, learn the core algorithms, because they show you how to search, sort, traverse, and reason through those structures. And once that foundation is solid, patterns like greedy, divide and conquer, backtracking, and dynamic programming start to make more sense. That order really matters. DSA is not a random list of interview tricks. It is how you train yourself to look at a problem and understand what kind of structure it needs. And if you want a clearer roadmap for learning DSA in the right order, you can see the full breakdown here: x.com/TechWithTimm/s… Really hope this helps you study DSA with more structure and less wasted time.

Tech With Tim @TechWithTimm

a week ago

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If you want to get serious about data structures and algorithms, this is a must-read

Tech With Tim @TechWithTimm

a week ago

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The biggest mistake I made learning python

A lot of devs think learning backend means picking up another framework. But it doesn't. It means learning to think more clearly about how your code works. When you spend most of your time in a dynamic language, it's easy to get used to a workflow where a lot of problems only show up once the code is already running. That works, until it doesn't. A language like Go forces a different habit. You have to think earlier about types, structure, what the program is actually doing, and where it's likely to fail. The compiler catches more of that upfront, so you get pushed toward clearer code and fewer fuzzy assumptions. That's why Go is such a good language to learn, even if you never end up using it every day. It isn't really about adding another language to your resume. It's about building a cleaner backend mindset. And if you want to learn Go in a way that also helps you understand backend development better, here is a complete 4-hour course to work through: youtube.com/watch?v=V-lI7A… It takes you from the basics into the parts that shape how backend code works, including typing, compiling, data types, structs, pointers, concurrency, and goroutines. Hopefully that gives you a clearer sense of how it all fits together.

The biggest mistake I made learning python

This is a full tutorial on how to build a real B2B SaaS app. You'll learn how FastAPI, React, and Clerk fit together in one app with teams, subscriptions, and the kind of features real SaaS products need. So if you want to see how a real SaaS stack comes together, watch this video.

Every great AI engineer is a software engineer first

Software engineering has changed a lot in 2026. Let me show you why code is easier to produce now, why hiring still feels broken, and why communication matters more than ever. So if you want a clearer picture of where this industry is heading, watch this video.