How I Research and Build Complex AI Systems From Scratch

How I Research and Build Complex AI Systems From Scratch

Most people talk about AI. Very few actually show how real AI research happens. 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. Thanks for reading The Healthy Scientist: Build Using AI With Healthy Habits 🎁 I’ve developed numerous projects on my GitHub [https://github.com/pH-7] 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 [https://www.linkedin.com/in/ph7enry]. 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 [https://www.pierrehenry.dev?utm_medium=podcast&utm_campaign=CTA_1]

Ship Any Feature in Record Time Without Breaking Anything

Learn how to move from idea to production quickly without losing quality. This video shows the exact steps engineers use to plan, release, and monitor features fast whilst avoiding common pitfalls. Perfect for anyone who wants to deliver faster and smarter. 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. I’ve built tons of product-centric projects on my GitHub [https://github.com/pH-7] 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 [https://www.linkedin.com/in/ph7enry]. 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 [https://www.pierrehenry.dev?utm_medium=podcast&utm_campaign=CTA_1]

The Mindset That Transforms a Good Software Engineer into an Exceptional One

Being an excellent software engineer isn’t just about writing code or crafting the perfect LLM prompt. It’s about cultivating genuine curiosity, embracing challenges that stretch you, solving problems that actually matter, and developing the mindset that fuels consistent growth and meaningful impact in every project you touch. 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. 🔥Subscribe for free to receive new posts! Start your journey by being inspired by open-source projects I’ve built over the past years on my GitHub [https://github.com/pH-7]. There are so much more to come on my LinkedIn [https://www.linkedin.com/in/ph7enry] 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 [https://www.pierrehenry.dev?utm_medium=podcast&utm_campaign=CTA_1]

SOFTWARE ENGINEER is DEAD? Become a PRODUCT ENGINEER

Just shipping software is no longer enough. What matters now is how well you understand the product, the user, and the impact of what you build. 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. I’ve spent the last decade building projects on my GitHub [https://github.com/pH-7]. Check them out for inspiration and contribution. And I’ve got more content coming your way on my LinkedIn [https://www.linkedin.com/in/ph7enry]! Thanks for reading The Healthy Scientist: Build Using AI With Healthy Habits 🔥! Subscribe for free to receive new posts and support my work. 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 [https://www.pierrehenry.dev?utm_medium=podcast&utm_campaign=CTA_1]

Build Scalable Outstanding Products That Never Lets Users Down

In this video, you’ll see the principles and practices that help engineers build scalable and robust software for real end users. You’ll learn how to design for growth, handle traffic spikes, avoid failures, and create systems that remain reliable under pressure. These are practical methods used by high-performing teams to ship dependable products. 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 😊 I’ve built tons of projects on my GitHub [https://github.com/pH-7] over the years. Check them out for inspiration or contribution. I’ve got more content coming your way on my LinkedIn [https://www.linkedin.com/in/ph7enry]! Hit that follow button so you don’t miss out! 🔥 Thanks for reading The Healthy Scientist: Build Using AI With Healthy Habits 🔥! Subscribe for free to receive new posts and support my work. 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 [https://www.pierrehenry.dev?utm_medium=podcast&utm_campaign=CTA_1]