PierreHenry.Dev Tech Show

🎡 Pierre-Henry Soria 🌴
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Writing about software engineering, AI, success, happiness, and positive time management 🚀 www.pierrehenry.dev

  1. Taking a Break to Build Something New

    15 ЯНВ.

    Taking a Break to Build Something New

    TL;DR 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! Keep learning, researching, practicing, and growing! 🚀 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.pierrehenry.dev

    2 мин.

  2. 14 ЯНВ.

    Why Best Engineers Keep Adjusting Their Skills

    A reflection on what genuinely helps engineers grow. It is not about knowing everything but about recognising when your approach no longer fits, adjusting your skills, and staying open to change. When you learn to adapt your habits and use the right skills at the right moment, your work becomes clearer, quicker, and far more meaningful. You start solving problems with more confidence, making better decisions, and understanding your craft in a deeper, more practical way. Spoiler Alert: We will also discuss about CouchDB, PouchDB, and other very exciting things! ⚡️ 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. Keep learning. Keep researching. Keep practicing. Keep growing! 🚀 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.pierrehenry.dev

    22 мин.

  3. 14 ЯНВ.

    The Side Project That Actually Changed Everything

    Most side projects fade. This one didn’t. In this video, I explain how choosing meaning over hype changes how you build, why you stay consistent, and what finally makes a project worth finishing instead of abandoning halfway through. 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. Thanks for reading The Healthy Scientist: Build Using AI With Healthy Habits 🔥! Subscribe for free to receive new posts and support my work. 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! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.pierrehenry.dev

    4 мин.

  4. 14 ЯНВ.

    Data Scientist: Why Developing a Continuous Learning Loop

    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! Thanks for reading The Healthy Scientist: Build Using AI With Healthy Habits 🔥! Subscribe for free to receive new posts and support my work. Keep learning. Keep researching. Keep practicing. Keep growing! 🚀 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.pierrehenry.dev

    7 мин.

  5. 14 ЯНВ.

    Why Planning and Prototyping First Leads to Exceptional Products

    In this video, I explain why the best engineers never start by coding. 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. Thanks for reading The Healthy Scientist: Build Using AI With Healthy Habits 🔥! Subscribe for free to receive new posts and support my work. 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! 🚀 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.pierrehenry.dev

    6 мин.

  6. 14 ЯНВ.

    The Skills That Will Decide Who Stays Relevant as AI Takes Over Engineering

    In this video, you’ll see the real skills that matter now that AI is changing engineering work. These are the abilities that keep you valuable, relevant, and ahead of the curve. If you want to stay competitive, this is the checklist that actually counts. As we know, AI is changing our industry faster than most people realise. If your main skill is writing boilerplate code or implementing basic features, you’re in trouble. AI can do that stuff now, and it’s getting better every week. Here’s what actually matters: the skills AI can’t replicate. Understanding what problem needs solving in the first place. Making architectural decisions with real trade-offs. Knowing when to ship simple versus when complexity is justified. These require human judgement, not pattern matching. System thinking is absolutely crucial now. AI can write individual functions brilliantly. But can it design how an entire application should fit together? Can it understand the second and third-order effects of architectural choices? Can it balance competing priorities like performance, maintainability, and time to ship? Not really. That’s where you add value. Product sense separates engineers who stay valuable from those who get replaced. AI doesn’t know what users actually need. It can’t tell you whether a feature solves a real problem or creates more confusion. It can’t prioritise what to build based on impact. You can. Develop that skill ruthlessly. Communication becomes more important, not less. When AI can generate code, your ability to explain complex technical decisions to non-technical people matters more than ever. Your skill at writing clear documentation. Your talent for asking the right questions before building. These human skills compound your AI-amplified technical abilities. Problem identification might now be the most valuable skill. AI is brilliant at solving well-defined problems. But spotting that a problem exists in the first place? Understanding the root cause versus symptoms? Knowing which problems are worth solving? That’s still very much a human game. Knowing how to use AI effectively is also a skill. Not everyone uses it the same way. Some engineers treat it like advanced autocomplete. Others use it as a thinking partner that helps them explore approaches, spot edge cases, and prototype rapidly. The gap between these two approaches is significantly massive. Critical thinking about AI outputs is essential. AI confidently generates code that looks right but has subtle bugs. Can you spot when it’s wrong? Can you evaluate whether its suggested approach is actually the best one for your context? Or do you just accept whatever it generates and ship it without understanding? In this video, I’ll break down each skill that matters in the AI era. System thinking and architecture (how to design robust systems that scale). Product sense (how to identify what’s worth building). Communication (how to influence decisions and explain technical choices). Problem identification (how to spot valuable problems before anyone asks). And AI fluency (how to use AI as a force multiplier without becoming dependent on it). Thanks for reading The Healthy Scientist: Build Using AI With Healthy Habits 🔥! Subscribe for free to receive new posts and support my work. I’ve built many projects on my GitHub over the years. Take a look for inspiration or jump in to contribute! 🔥 There are so much more to come on my LinkedIn as well! Don’t forget to follow and stay tuned! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.pierrehenry.dev

    7 мин.

  7. 13 ЯНВ.

    How to Build Experiences Users Actually Needs

    This video explains how software engineers can create an amazing user experience while building products. You will learn how to think from the user’s point of view, reduce friction, simplify flows, and design features that feel pleasant and intuitive. These principles help you ship products that users enjoy from the very first interaction. Thanks for reading The Healthy Scientist: Build Using AI With Healthy Habits 🔥! Subscribe for free to receive new posts and support my work. I’ve been building several projects on my GitHub over the years that might interest you. Feel free to check them out for inspiration or contribution! And so much more to come on my LinkedIn as well! Don’t forget to follow and stay tuned! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.pierrehenry.dev

    6 мин.

  8. 13 ЯНВ.

    AI in Software Engineering: 2 Critical Risks You Cannot Ignore

    A clear look at the key risks software engineers face when working with AI tools. Look, AI tools are brilliant. They speed up development, help you write boilerplate faster, and can even suggest solutions you hadn’t thought of. But there’s a trap here that too many engineers are falling into, and it’s worth talking about. From over-trusting outputs to losing technical depth, this video explains what you must stay vigilant about to keep providing real value as a software engineer. Here’s the thing: when you rely too heavily on AI-generated code without understanding what it’s doing, you’re essentially becoming a copy-paste engineer. You lose the ability to debug properly when things break. You stop questioning whether the solution is actually the right one for your use case. You miss edge cases because you didn’t write the code yourself and didn’t think through the problem deeply enough. And over time? Your technical depth erodes. You become dependent on tools that might give you plausible-looking code that’s actually inefficient, insecure, or just plain wrong. Don’t get me wrong, AI tools are fantastic when used correctly. They’re brilliant for speeding up repetitive tasks, exploring different approaches, or handling the boring stuff. But you need to stay in control. You need to understand the code you’re shipping. You need to verify outputs, question suggestions, and keep your problem-solving skills active. In this video, I’ll break down the specific risks you should watch out for and how to use AI tools as a force multiplier rather than a crutch. Because at the end of the day, the engineers who thrive aren’t the ones who blindly trust AI outputs. They’re the ones who use AI strategically whilst maintaining their technical judgement and depth. I’ve been building several projects on my GitHub over the years that might interest you. Feel free to check them out for inspiration or jump in with contributions! There are so much more to come on my LinkedIn as well! Don’t forget to follow and stay tuned! 🔥 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.pierrehenry.dev

    8 мин.
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Writing about software engineering, AI, success, happiness, and positive time management 🚀 www.pierrehenry.dev

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