Really excited to be part of the Decoding Data Science (DDS) event at AI Everything Abu Dhabi! 🤖 Looking forward to meeting fellow AI enthusiasts, learning from the community, exploring new ideas, and getting hands-on with what’s happening in the AI ecosystem. It’s always exciting to step outside the classroom and be around people who are actively building, experimenting, and learning with AI. Can’t wait for this! 🔥 #DecodingDataScience #DDS #AIEverything #AbuDhabi #AI #ArtificialIntelligence #Learning #Community

Really happy to share that I’ve been selected as one of the Top 35 finalists in the Decoding Data Science community. What makes this exciting for me isn’t just the selection — it’s knowing that the projects, experiments, and learning I’ve been putting in are actually moving somewhere. DDS has pushed me to go beyond just learning concepts and actually build, test, break, and improve AI applications. Now onto the next stage. 🚀 Grateful for the opportunity, and even more motivated to see what I can build from here. Top 35 → next goal: keep going. 🔥 #DecodingDataScience #DDS #AI #LearningByBuilding #AIEngineering

Building an AI application is much bigger than simply choosing an AI model. The more I learn and build, the more I realize that the real engineering happens around the model. I’ve started asking questions like: → What data is the system receiving? → How should that data be processed? → What should AI handle, and what should traditional code handle? → How do we validate the output? → What happens when the model is wrong? This has changed the way I look at AI projects. Instead of thinking: “Which AI model should I use?” I’m learning to think: “What is the best system I can build around the problem?” That shift in thinking is probably one of the most valuable things I’ve taken away so far from Decoding Data Science. Still learning, building, testing, and occasionally breaking things along the way. 😅 #DecodingDataScience #DDS #AI #ArtificialIntelligence #AIEngineering #LearningInPublic #TechCommunity

For a while, my thinking was: “What cool AI thing can I build?” Now I'm trying to reverse it: What annoying problem can I find that AI can actually solve? That small change makes a HUGE difference. Because when I start with the technology, I end up with: 🤖 “AI chatbot for ___” 🤖 “AI assistant for ___” 🤖 “AI-powered platform for ___” …and honestly, a lot of these are just the same idea with a different UI. So now, before I build anything, I'm asking: What is the actual problem? Who experiences it? How are they solving it now? Why is the current solution bad? Does AI genuinely make it better? Would someone actually use/pay for this? I'm still figuring this out, but I think this mindset is going to save me from building 10 useless AI apps just because I can. 😂 How do you decide whether an AI idea is actually worth building?

Putting your work out there live and taking real-time critique isn't comfortable, but it's where real progress happens. I presented my latest build during the Decoding Data Science live demo round. Pushing code is one thing; standing up in front of a live audience, defending your technical choices, and answering unfiltered questions on the spot is a completely different challenge. Grateful for the sharp feedback and evaluation from judges Alan Hau and Dr. Samar Mouti. Big thanks to Mohammad Ahmad and the DDS network for setting up a solid, high-standard space for builders to present. Respect to all the other presenters who stepped up to show their work. Final results are dropping on October 7th at 2:45 PM GST at AI Everything Abu Dhabi. Win or lose, the trajectory stays the same: build, iterate, ship publicly, repeat. #DecodingDataScience #AgenticAI #AI #BuildInPublic #DataScience #AIEverything