Creative thinking helps you spot patterns others miss, tackle challenges with fresh ideas, and ship solutions that are both faster and more effective. It’s not about being “artistic” or thinking outside the box for the sake of it. It’s about questioning assumptions, exploring alternatives, and asking “wait, what if we approached this completely differently?”

In this video, you’ll learn how to build a mindset that makes your work sharper and more original. I’ll show you practical techniques to break out of default thinking patterns, how to reframe problems so solutions become obvious, and why the best engineering breakthroughs often come from challenging the “obvious” approach.

Because let’s face it: the engineers who stand out aren’t the ones who memorize the most frameworks or write the most lines of code. They’re the ones who see problems differently and come up with solutions that make everyone else think “why didn’t I think of that?” So, let’s talk about how to become “that engineer” ⚡️

I’ve built tons of projects on my GitHub over the years. Check them out for inspiration or jump in to contribute!

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This isn’t about hustle culture or forcing yourself to code every night after work. It’s about clarity, direction, and building what truly matters to you in a way that actually sticks. Because here’s the thing: the problem isn’t usually lack of time or skill. It’s lack of a system that makes building feel natural instead of like another obligation on your to-do list.

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I’ve built many projects on my GitHub over the years. Check them out for inspiration or contribution!

I’ve got plenty more content coming your way on my LinkedIn! Hit the ‘follow’ button so you won’t miss out!

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The engineers adapting to this shift aren’t just working faster. They’re working differently, leveraging AI and modern tooling to handle the tedious parts while focusing their energy on design, architecture, and solving real problems. If you’re still working the old way, you’re already falling behind. Let’s talk about what’s changing and how to stay ahead.

I’ve built countless projects on my GitHub over the years. Take a look for inspiration or jump in to contribute!

I’ve got plenty more exciting content coming your way on my LinkedIn! Make sure to hit that follow button so you don’t miss out! 🔥​​​​​​​​​​​​​​​​

In this video, I walk through how I research, structure, and build complex AI systems as a software engineer. From breaking down vague problems, validating ideas, and turning them into solid architectures, this is the practical side of AI that rarely gets shown.

If you want to move beyond surface-level AI content and understand how real systems are actually built, this one is for you.

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I’ve developed numerous projects on my GitHub over the years. Feel free to browse them for inspiration or to contribute! And I’ve got more content coming your way on my LinkedIn.

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Look, shipping fast doesn’t mean shipping rubbish. Too many engineers think speed and quality are opposites, but that’s not true. The best engineers I know ship quickly because they’ve got systems in place that let them move fast without breaking things.

Here’s the thing: moving from idea to production quickly is all about reducing friction at each step. You need a clear process that gets you from “we should build this” to “it’s live and working” without endless meetings, unclear requirements, or last-minute disasters.

First, clarify what you’re building. I can’t stress this enough. Write down the feature in one sentence. What problem does it solve? Who’s it for? If you can’t explain it simply, you’ll waste time building the wrong thing.

Next, break it down into small, shippable chunks. Don’t try to build everything at once. Identify the absolute minimum version that solves the core problem. Ship that first. Then iterate. This approach means you get feedback early, catch issues before they become disasters, and actually deliver value faster than trying to perfect everything upfront.

Plan just enough. Not 50-page specs. Just enough to know what you’re building, what could go wrong, and how you’ll test it. I usually spend 30 minutes sketching out the approach, identifying potential problems, and writing down the main tasks. That’s it.

Release carefully, but don’t overthink it. Use feature flags (AKA Feature toggle) if you can. Deploy to staging first. Test the critical paths. But don’t wait for “perfect conditions” to ship. Perfect conditions don’t exist. Ship when it works, monitor it closely, and fix issues as they come up.

And here’s the crucial bit: monitoring. Once it’s live, you need to know if something breaks. Set up basic alerts, check error logs, watch key metrics. Too many engineers ship features and forget about them. The best ones stay vigilant for the first few days after release.

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I’ve built tons of product-centric projects on my GitHub over the years. Feel free to check them out for inspiration or jump in to contribute! I’ve got more content coming your way on my LinkedIn.

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Look, anyone can learn syntax. Anyone can copy-paste from Stack Overflow or ask ChatGPT to generate boilerplate. But what separates good engineers from great ones? It’s how you think, how you approach unknowns, and how you grow through the hard stuff instead of avoiding it.

In this video, I break down the habits and mental frameworks that turn you into the kind of engineer people actually want on their team. The kind who doesn’t just ship features, but ships solutions that last. Because at the end of the day, your impact isn’t measured by lines of code. It’s measured by the problems you solve and the value you create.

Start your journey by being inspired by open-source projects I’ve built over the past years on my GitHub.

There are so much more to come on my LinkedIn as well! Don’t forget to follow and stay tuned! 🤠

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In this tech talk, I share why the shift from software engineer to product engineer is happening and why it matters for your future. You will see how thinking about the product changes the way you build, make decisions, and prioritise your work.

This means moving beyond tasks and taking responsibility for outcomes. It is about knowing why something should exist, not just how to build it. If you want to grow as an engineer and create real value, this is the shift to make.

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I’ve spent the last decade building projects on my GitHub. Check them out for inspiration and contribution. And I’ve got more content coming your way on my LinkedIn!

This mindset matters because our field shifts constantly. Tools change, techniques evolve, and new frameworks appear every month. What remains stable is the compounding effect of daily effort.

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Small incremental habits improve your judgment.

Consistent practice builds the kind of intuition you cannot gain from reading alone. You grow not by chasing everything at once, but by improving one deliberate part of your craft at a time: a cleaner dataset, a clearer visualisation, a sharper experiment, a more honest evaluation, or a better question asked before writing any code.

Applying this in your day-to-day work is straightforward. Choose one skill you genuinely want to improve and work on it regularly. Write short notes explaining your reasoning behind each analysis. Review past work with a calm but critical eye. Aim for clarity rather than impressive complexity. Treat every project as an opportunity to refine how you think, not simply as a task to complete. These small steps shape a data scientist who thinks clearly, builds with purpose, and grows with intent.

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You also need to clarify what matters most to you and what is truly needed for your goals. The best work you will ever produce will come from knowing what you want from your life and choosing to build toward it. This choice influences every part of your career. Whether you build the next important system, solve the hardest problem in your team, or grow into a leader who shapes others, the clarity you commit to today will define the rest of your path.

So start today. Start building what matters to you, both as a data scientist and as a person. Every improvement counts. Every intentional step becomes part of the future you want to create. The longer you wait, the harder it becomes to catch up. Life moves fast, and your career moves with it. Choose to grow with purpose now.

Elevate yourself. Build with clarity. Become the person you know you can be.

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Look, building software that works on your laptop is one thing. Building software that handles thousands of concurrent users without falling over? That’s a completely different challenge.

Here’s what I’ve learned from building systems in production: scalability isn’t something you bolt on later. It’s baked into how you think about architecture from the start. That doesn’t mean over-engineering everything for millions of users when you have ten. It means making smart choices that won’t paint you into a corner when growth happens.

Designing for growth starts with understanding bottlenecks before they become problems. Where will your system struggle first when traffic increases? The database? API rate limits? Memory usage? Identifying these weak points early means you can plan around them rather than scrambling when everything’s on fire at 3am.

Handling traffic spikes is crucial, right? Real users don’t arrive at a steady, predictable rate. They come in waves. Product launches. Marketing campaigns. Weekend usage patterns. Your system needs to handle these spikes gracefully. This means things like proper caching, load balancing, rate limiting, and designing APIs that degrade gracefully under pressure rather than just crashing.

Well... avoiding failures is partly about redundancy and partly about expecting things to go wrong. Servers die. Networks hiccup. Third-party APIs timeout. The best systems assume failure will happen and handle it gracefully. Retry logic with exponential backoff. Circuit breakers that prevent cascade failures. Proper error handling that doesn’t just crash the entire system when one component fails.

Creating systems that remain reliable under pressure means thinking about monitoring and observability from day one.

You can’t fix what you can’t see. Proper logging, metrics, and alerts mean you spot issues before users do. Health checks that actually verify the system works, not just that the server is running 😊

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I’ve built tons of projects on my GitHub over the years. Check them out for inspiration or contribution. I’ve got more content coming your way on my LinkedIn! Hit that follow button so you don’t miss out! 🔥

Look, I used to think real progress only came from massive leaps. Long coding sessions, major refactors, shipping entire features in one go. Turns out, that is not how growth actually works, especially as a software engineer.

What actually moves the needle: daily micro-actions. Small, deliberate steps that do not feel impressive in the moment but add up over time. Writing one clean function instead of rushing through five messy ones. Asking one good question in a code review. Refactoring one confusing variable name. Learning one new concept from the docs instead of skipping past it.

Those tiny improvements compound. Faster than you think.

Mentorship plays a big role here too. Whether it is learning from senior engineers on your team, getting honest feedback on your PRs, or mentoring someone else (which, honestly, teaches you just as much), those interactions create real momentum. They push you forward in ways solo work never could.

In this video, I break down how focusing on micro-actions and finding the right mentorship builds long-term success as a software engineer. Not overnight success. Not “10x engineer in 30 days” nonsense. Real, sustainable growth that makes you better at what you do, one small step at a time.

Consistency beats intensity, every time. The engineers who keep improving year after year are not chasing shortcuts. They understand that small daily actions compound into something much bigger.

You can follow my AI Data Software Engineering Projects on my GitHub. I’ve got plenty more content coming your way on my LinkedIn! Hit the follow button so you don’t miss out!

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Everyone talks about MCP in AI, but most explanations are actually quite confusing.

In this video, I explain what MCP really is, using an ELI5 approach.

You'll understand why it matters to understand it well, what problem it solves, and how it helps AI use tools and context properly.

So what is an MCP? Let me break it down simply.

MCP stands for Model Context Protocol. It’s basically a bridge between an AI model and a third-party service, application, or database.

The USB & Power Outlet Analogy

Think about a USB stick. You can plug it into your iPhone, your Mac, your monitor. Why? Because it follows a universal standard that every device understands. No “wait, is this compatible?” Just plug and play.

Now think about power outlets in the EU (minus the UK). Wherever you are, France, Spain, Italy, same outlet, same plug, no adapter needed. South Korea? Same thing. You walk in, spot the outlet, plug your charger in, done.

This is exactly what MCP does.

An MCP server and an MCP client speak the same language. They follow the same protocol, expect the same format, and connect an application to an AI model without friction. No custom glue code for every integration. No reinventing the wheel every time your AI needs to talk to a new tool or service.

Why Does It Matter?

Before MCP, connecting an AI model to external tools was messy. Each integration was its own thing, with its own quirks and custom implementation.

MCP standardizes all of that. It’s the universal plug for AI integrations. Build once, connect everywhere. Simple as that.

I’ve built many projects on my GitHub over the years that you can check out for inspiration or contribution! I’ve got plenty more content coming your way on my LinkedIn! Hit the ‘follow’ button so you are sure to not miss out! Enjoy your day! 🏝️

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This video covers key principles, tips, and habits for writing maintainable, readable, and bug-resistant code that helps you tackle bigger projects, improve delivery speed, and advance your career.

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I’ve developed numerous projects on my GitHub over the years. Feel free to browse them for inspiration or to contribute! I’ve prepared more content coming your way on my LinkedIn.

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I designed numerous projects on my GitHub over the years. Feel free to browse them for inspiration or contribution!

I’ve got more content for you coming soon on my LinkedIn.

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In this video, I share how to take pride in your work, embrace full responsibility, and approach every project with an AI-first, product-centric mindset. Learn how to create solutions that truly matter, stay obsessed with delivering excellence to the end-user, and focus on product quality to become the engineer who consistently delivers real value.

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Look, I’ve seen so many talented engineers stuck in analysis paralysis. They’re constantly reading about the best architecture patterns, watching tutorials, planning the perfect approach... but they’re not actually building anything. And you know what? They’re not improving either.

Here’s the thing: growth happens when you’re doing, not when you’re overthinking. You learn way more from building something messy and refactoring it than from reading 10 articles about clean code. You develop better judgement from making mistakes and fixing them than from trying to avoid mistakes entirely.

Spotting what truly matters is a skill in itself, right? And you only get good at it through practice. When you’re actually building, you quickly see which decisions have big impact versus which ones don’t really matter. You can’t learn this from theory. You have to feel it.

Well... small, consistent actions compound faster than you’d think. Writing one well-tested function today, refactoring one confusing piece of code tomorrow, asking one good question in a code review the day after. These tiny improvements add up (compound) to genuine skill over weeks and months!

Taking action with clarity means you’re not just randomly trying things. You have a rough plan (doesn’t need to be perfect), you understand what you’re trying to achieve, and you’re willing to adjust as you go. But the key word is “go”. You’re moving forward, not stuck in planning mode forever!

Building habits that help you improve faster looks like this: build something small every day, even if it’s just 30 minutes. Regularly study the logic behind a project you admire. Get your work reviewed and actually apply the feedback. Refactor one small thing each time you touch old code. These aren’t dramatic, but they work.

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After spending quite some time exploring various engineering topics and building impactful projects while keeping healthy habits, today I am taking a break to focus on a new chapter.

Taking a Break to Focus on What's Next

After quite some time writing about AI, research, and building with healthy habits, I'm stepping back from content creation.

What Changes

✓ All existing posts remain available

✗ No new posts will be published

✗ Comments won't be monitored

✗ Newsletter is paused

Why I'm Telling You This…

Everything I've written here was created on my own time, independently. I'm proud of this work and want to preserve it properly as I shift my focus elsewhere.

Keep Going

Don't wait for permission. Don't wait for perfect conditions. Keep learning. Keep building. Keep applying what you discover. That's where growth happens—in the doing, not the planning. Maybe I'll be back with new insights someday. Maybe not. Either way, thank you for being here.

What This Really Means to You…

• All existing posts remain available.

• No new posts will be published.

• I won’t be responding to comments.

• Newsletter is paused.

Thanks for being part of this journey!

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Keep learning, researching, practicing, and growing! 🚀

Spoiler Alert: We will also discuss about CouchDB, PouchDB, and other very exciting things! ⚡️

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I’ve built tons of product-centric projects on my GitHub over the years. Feel free to check them out for inspiration or jump in to contribute!

I’ve got more content coming your way on my LinkedIn.

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Keep learning. Keep researching. Keep practicing. Keep growing! 🚀

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We’ve all been there. You see a trending framework on Twitter, get excited, start a new project, build for three days, then... it just sits there. Unfinished. Forgotten. Added to the graveyard of abandoned repositories with a README and nothing else.

Here’s what I’ve learned: projects built on hype die the moment the hype fades. You start because something looks cool or you think it’ll be impressive on your portfolio. But when the initial excitement wears off and you hit the boring middle part? There’s nothing left to keep you going.

Choosing meaning over hype changes everything. When you build something that solves a real problem you care about, the motivation is completely different. You’re not building because it’s trendy. You’re building because you genuinely want this thing to exist. That deeper purpose carries you through the hard parts where most people quit.

Well... staying consistent becomes natural when the project actually matters to you. It’s not about forcing yourself to work on it or setting arbitrary goals. You just... keep coming back to it. Because you want to see it finished. Because you’re excited about using it yourself or seeing others benefit from it.

And here’s what makes a project worth finishing: it solves something real. Not theoretical. Not “this might be useful someday.” Real problems that you or someone else faces right now. When you can picture the exact person who’ll benefit from what you’re building, finishing stops feeling like a chore and starts feeling necessary.

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I’ve built many projects on my GitHub over the years. Check them out for inspiration or contribution!

I’ve got plenty more content coming your way on my LinkedIn! Hit the ‘follow’ button so you won’t miss out!

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If you feel like you’re learning slower than others…, this one is for you! I’ll share the exact mindset and structure to develop a learning loop that accelerates your progress, project after project. I used to watch other engineers pick up new technologies seemingly overnight whilst I struggled for weeks with the same concepts. It felt rubbish, honestly. Like everyone had some secret learning ability I just didn’t have.

But here’s what I discovered: fast learners aren’t magically smarter. They’ve just built better learning loops. They extract more knowledge from each project, apply it to the next one, and compound their skills faster than people who treat every project like starting from scratch.

The mindset shift is huge. Instead of “I need to learn everything about React before I start”, it becomes “I’ll learn what I need for this specific feature, build it, see what breaks, and learn from there”.

Active learning through doing beats every single time passive learning through reading.

Well… here’s what a proper learning loop looks like: You build something. You encounter problems you don’t know how to solve. You figure them out (through docs, asking questions, experimenting). You reflect on what you learnt and why it works that way. You apply that knowledge to the next thing you build. That last step is what most people skip, and it’s where the acceleration happens.

The structure matters too, right? After finishing any project (even small ones), spend 10 minutes asking yourself the following:

• What did I learn?

• What would I do differently next time?

• What patterns or concepts clicked that I can use elsewhere?

These questions turn random experiences into structured knowledge that actually sticks.

I’ve developed numerous projects on my GitHub over the years. Feel free to browse them for inspiration or to contribute! And I’ve so much more to come on my LinkedIn as well! Don’t forget to follow and stay tuned!

Keep learning. Keep researching. Keep practicing. Keep growing! 🚀

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Look, I get it. You have an idea, you’re excited about it, and your fingers are itching to open your IDE and start hammering out code. I’ve done this countless times myself. But here’s the thing: that’s usually how you end up with a complete mess three weeks later, wondering why nothing works the way you imagined it would.

The best engineers? They begin with clear plans, simple sketches, and fast prototypes to understand what truly needs to be built. Not fancy over-engineered architecture diagrams or 50-page documentation that nobody will read. Just enough planning to avoid wasting weeks building completely the wrong thing.

This approach reduces wasted work massively. You catch obvious problems before writing a single line of code. You realise that feature X doesn’t actually solve the real problem when you sketch it out on paper. You see that the data flow you had in your head won’t work in practice. All of this happens before you’ve invested days into implementation that you’ll have to throw away.

It also improves product quality because you’re thinking through edge cases, user flows, and technical constraints upfront instead of discovering them when it’s too late. And it gives you a much clearer path from idea to final outcome. No more “I’ll figure it out as I go” coding sessions that lead absolutely nowhere.

In this video, I’ll break down how to plan effectively without overdoing it, how to sketch out ideas quickly (honestly, pen and paper works brilliantly for this), and how to build throwaway prototypes that answer your biggest questions before committing to a full implementation.

Because at the end of the day, the fastest way to ship something good isn’t to code first and think later. It’s to think just enough, then code with clarity and purpose. Your future self will thank you for it.

I’ve developed numerous projects on my GitHub over the years. Feel free to browse them for inspiration or to contribute! And I’ve got more content coming your way on my LinkedIn.

Keep learning, researching, practicing, teaching, and growing! 🚀