How Hackathons Make You a Better Software Engineer
What if you could turn a weekend project into a core product feature at a major tech company? We sit down with Behrouz Pooladrak, a software engineer and hackathon legend at Booking.com, to uncover how these intense competitions can fast-track your skills, career, and impact. He shares the mindset and strategies that took his ideas from a one-day build to a real-world product used by millions.In this episode, we cover:How to treat your hackathon project like a mini-startup to guarantee success.The surprising skills you gain from short-term projects that your daily job can't teach you.How companies like Booking.com use hackathons to innovate and train new talent.Why personal projects are the secret weapon for career growth.This episode is for any software engineer looking to distinguish themselves, learn new technologies rapidly, and make a real impact in the tech industry.Timestamps:00:00:49 - The Mindset of a Prolific Builder00:02:42 - How AI Helps You Build an MVP in One Day00:06:26 - Why This Engineer is a Hackathon "Living Legend"00:07:41 - From Hackathon Idea to Real AI Product00:11:42 - The Secret to Winning: Treat it Like a Startup00:17:22 - How Booking.com Onboards Juniors with a 4-Week Hackathon00:20:25 - Why We Still Need Junior Engineers in the Age of AI00:26:57 - The #1 Struggle Teams Face in Hackathons00:31:04 - The Real Reason to Join a Hackathon (It’s Not the Prize)00:35:46 - How to Start and Finish Your Personal Projects00:40:12 - The Feedback Loop Between Your Job and Hobby Projects#SoftwareEngineering #Hackathon #CareerGrowth
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AI Startup CEO Reveals What Really Kills AI Projects
What if the biggest obstacles to AI innovation aren't what you think? Deeploy CEO Maarten Stolk shares his controversial but effective strategies for building successful AI products and ecosystems, challenging the common wisdom around bottom-up initiatives and regulation.In this episode, we cover:Why bottom-up initiatives fail without strong top-down vision.The surprising benefits of the EU's AI Act for innovation.How to build a thriving AI ecosystem from the ground up.The single most important metric for AI observability.This conversation is for tech leaders, founders, and engineers who want to move beyond AI experiments and build real-world, production-ready systems.Timestamps:00:00:00 - Intro00:00:45 - Why Maarten Started a Dutch AI Hub00:02:15 - The "Flywheel" Effect Crucial for AI Success00:04:42 - The Hard Truth: Why the Netherlands is Lagging in AI00:07:52 - A Controversial Take: The EU AI Act is Actually Good for Everyone00:11:26 - The Real Bottleneck to Innovation Isn't Regulation00:14:25 - From POC to Production: Why Top-Down Vision is Non-Negotiable00:17:13 - A Wake-Up Call for Inexperienced Leadership Teams00:20:30 - How Winning Companies Use AI to Dominate Their Market00:23:44 - The Right Way to Learn From Your Competitors00:27:30 - Maarten Outsourced Core Development to an AI Company00:31:59 - The #1 Metric You Must Track for AI Observability00:36:03 - Open-Source vs. Closed-Source: Which AI Model Will Win?00:40:23 - The Inevitable Crisis That Will Force Innovation00:42:19 - The Power of Having a Long-Term Personal Vision#AIStrategy #TechLeadership #Innovation
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The Graph Problem Most Developers Don't Know They Have
As a developer, you're trained to think in rows and tables. But what if that's the exact reason you're missing the most powerful connections in your data? There's a fundamental "Graph Problem" hiding in plain sight in almost every application, and once you see it, you'll wonder how you ever missed it.In this episode, we reveal this "obvious" secret and show you how to leverage it to build smarter, more accurate, and context-aware AI.In this episode/video, we cover:The "Graph Problem" explained: Why you have more graph problems than you think.Why basic RAG isn't enough, and how Graph RAG provides the context your AI is missing.How to uncover the hidden relationships in your unstructured data and build a knowledge graph.Real-world examples (from Amazon to your own notes) that reveal the graph structure all around you.The #1 reason knowledge graph projects fail and how to avoid it.This conversation is for any developer who feels their projects are hitting a wall. If you're ready for the "aha!" moment that will change how you look at data forever, this episode is for you.Timestamps:00:00:00 - Intro00:00:39 - From Unstructured Data to a Knowledge Graph00:02:00 - The Experiment: What Happens When You Break a Knowledge Graph?00:05:41 - What Are Ontologies in the Graph World?00:07:35 - The Graph Problem You Didn't Know You Had00:09:09 - Why Graphs Are So Good for GenAI Context00:10:10 - The Best Way to Create Vector Embeddings for Graphs00:12:50 - Using Graphs to Solve Extreme Corporate Complexity00:17:14 - Real-World Problems That Are Actually Graph Problems00:19:31 - How to Find The Right Expert in Your Company00:23:33 - The Rise of Federated RAG Agents00:25:31 - The #1 Reason Knowledge Graph Projects Fail00:29:37 - A Standard Query Language for Graphs (GQL)00:32:53 - Why Teams Are Moving From RAG to Graph RAG00:34:34 - Should Your Company Build Its Own AI Assistant?00:38:28 - The "Fear of Missing Out" Driving Bad AI Projects00:40:21 - The Dangers of Chaotic vs. Laser-Focused Company Priorities00:44:05 - Why Gantt Charts Don't Work for Software00:47:08 - How Top Engineers Actually Learn New TechnologiesGuests on this podcast express their own views and do not represent their employers.#GraphDatabase #KnowledgeGraph #SoftwareArchitecture
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How Deepfakes are Evolving (And What You NEED to Know)
It takes just three seconds for AI to steal your voice and impersonate you in a way no one can detect. How can you protect yourself, your family, and your finances when seeing and hearing is no longer believing?In this episode, deepfake expert Parya Lotfi reveals the shocking reality of AI-driven scams, from fraudulent bank transfers to fake kidnapping calls. We uncover how criminals operate and what you can do to spot the lies before it's too late.In this episode/video, we cover:- How criminals use 3-second voice clones for scams- The shocking story of a North Korean deepfake spy- Why facial and voice ID are no longer secure- How to use AI to detect other AI fakesThis video is for anyone who wants to understand the real-world dangers of deepfake technology and learn actionable steps to protect themselves in our new "fake reality."Connect with Parya:https://www.linkedin.com/in/paryalotfiTimestamps:00:00:00 - Intro00:00:35 - The Scary Reality of AI-Generated Videos00:02:32 - The Dangerous Side of Facial & Voice Biometrics00:03:45 - The Disturbing Reality of Voice Cloning Scams00:06:46 - How to Use AI to Catch AI-Generated Fakes00:10:11 - Solving AI's "Black Box" Problem with Explainability00:12:10 - The Different Types of Deepfakes Criminals Use00:14:15 - How Deepfakes Are Used to Launder Millions From Banks00:18:18 - Inside the Darknet's "Deepfake-as-a-Service" Business00:22:32 - Why Banning Deepfake Technology Is Impossible00:24:58 - How Deepfakes Are Being Weaponized in Global Conflicts00:27:30 - Red Teaming: How to Think Like a Deepfake Criminal00:29:09 - The North Korean Spy Who Used a Deepfake to Get a Job00:31:54 - The Ultimate Goal: A Deepfake Detector for Everyone00:37:23 - The Future That Scares Me: AGI and Self-Aware Robots00:44:33 - The Journey of Building a Deepfake Detection Company00:47:42 - The Surprising Reason Deepfake Detection Is So Hard00:54:44 - Who Is Responsible When You Get Scammed by a Deepfake?00:58:25 - The Rise of AI Influencers and Their Tragic Consequences#Deepfake #Cybersecurity #ArtificialIntelligence
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From Pixels to Tokens: UX Is Not Enough Anymore
What does it take to build AI features at the scale of Microsoft Copilot? Senior Product Manager Stéphanie Visser reveals the massive shifts in product development, from focusing on pixels to tokens and embracing a culture of rapid, data-driven experimentation. Learn how the roles of PMs, engineers, and scientists are evolving and what it takes to succeed.In this episode, we cover:The shift from UX-focused products to output-quality-focused AI.How to run experiments and decide when an AI feature is ready to ship.The changing roles and expectations for PMs, engineers, and data scientists.Building trust and a strong product culture in a distributed AI team.This episode is a must-watch for product managers, engineers, and tech leaders looking to adapt their processes for the age of AI and accelerate their delivery cycles.Timestamps:00:00:00 - How Microsoft Builds AI Features00:00:49 - The #1 Thing That Changed for Product Managers01:28 - From Pixels to Tokens: The AI Product Shift02:58 - Why AI Is All About Output Quality, Not UX04:46 - When Is an AI Feature "Good Enough" to Ship?06:45 - The "Non-Embarrassment Bar" for Releasing AI09:07 - Why Old User Feedback Methods Don't Work for AI12:28 - The New Expectations for Software Engineers in AI15:33 - When to Involve Engineers in the Product Process17:43 - How Microsoft Structures Its AI Product Teams20:40 - Why 3-Month Planning Is Obsolete in the AI Era22:42 - How to Remove Bias From Your Product Decisions25:36 - Balancing Data vs. User Intuition in AI27:44 - The Biggest Bottleneck in AI Experimentation31:12 - How to Define the Right Metrics for Your AI Product33:39 - Building Trust and Culture in a Remote Team37:47 - The Most Underrated Skill for Product Managers40:57 - How to Cultivate a Strong Product Culture44:32 - The AI Tools a Microsoft PM Actually Uses46:29 - How to Manage the Expanding Scope of the PM RoleConnect with Stéphanie Visser:https://www.linkedin.com/in/stephanievisserConnect with Patrick Akil:https://www.linkedin.com/in/patrick-akilhttps://twitter.com/PatrickAkil_Sponsors:Xebia - https://xebia.com#ProductManagement #AI #Microsoft
Beyond Coding is a weekly podcast with conversations that go "beyond coding" in a fireside chat format.
Common topics are tech, entrepreneurship, and career journeys.
Authentic, informative and inspiring. That's the aim for each episode.
New episodes every Wednesday 🎙
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