I created my LinkedIn profile nearly 10 years ago. Like many people, I thought having a profile was enough. I would occasionally update my profile, connect with a few people, and scroll through my feed. But I wasn't truly leveraging the platform. Everything changed a few months ago. In April, I joined the Decoding Data Science Academy. More importantly, I started doing something simple: ✅ Sharing what I learn ✅ Posting project updates ✅ Engaging with other professionals ✅ Documenting my AI journey publicly ✅ Contributing to conversations instead of just consuming content The results surprised me. 📈 Last 12 Months: • 30,908 Impressions • 1,423 Engagements 📈 Last 2 Months Alone: • 17,822 Impressions • 1,145 Engagements That means: 🔥 Nearly 58% of my yearly impressions came from just the last 2 months. 🔥 More than 80% of my yearly engagement was generated in the last 2 months. The lesson? LinkedIn is not just a platform to upload your CV. It's a platform to: 🤝 Build meaningful professional relationships 📚 Learn from industry experts 🎯 Showcase your skills and projects 💡 Share your knowledge and experiences 🚀 Create opportunities you never planned for Many students and professionals hesitate to post because they think: ❌ "I'm not an expert." ❌ "Nobody will care." ❌ "I don't have enough followers." I used to think the same. But people don't follow perfection. They follow growth. Your learning journey, projects, challenges, lessons, and experiences can provide value to someone else. You don't need thousands of followers to start. You need consistency. A simple post about what you're learning today could become the opportunity that changes your career tomorrow. If you're a student, fresh graduate, or professional who has been silently scrolling on LinkedIn, this is your sign: Start sharing. Start engaging. Start building. The best time to start was years ago. The second-best time is today. 🚀

🚀 Looking forward to Session #3 of the AI Accelerator Boot Camp: From RAG to Real AI Systems – Integration, Evaluation & Deployment organized by Decoding Data Science and Mohammad Arshad Excited to learn more about AI workflow integration, model evaluation, deployment strategies, and building production-ready AI systems. Looking forward to gaining practical insights and expanding my AI engineering knowledge. great reminder that building an AI model is only the beginning—the real challenge is delivering reliable business value in production. #AI #GenerativeAI #RAG #AIBootcamp #MachineLearning #AIEngineering #ContinuousLearning

🏙️ I built a live AI product in 3 days. Here's what it does and how it works. Introducing DubaiNest AI — a RAG-powered real estate assistant for Dubai, built by own from scratch during the AI Accelerator Bootcamp learning by Decoding Data Science. The problem it solves: Every expat in Dubai knows this frustration — scattered rental prices, confusing RERA laws, no single place to get a straight answer. DubaiNest AI changes that. You can ask it: 🔹 "What is the average rent for a 1BR in JVC?" 🔹 "Can my landlord increase rent by 20%?" 🔹 "What is the total move-in cost for an AED 90,000 flat?" 🔹 "Which areas suit a young professional?" And it answers accurately — grounded in real data, no hallucination. The tech stack: ⚙️ LlamaIndex — RAG pipeline & query engine 📦 Pinecone — cloud vector database (1536-dim embeddings) 🤖 OpenAI GPT-4o-mini — LLM (temperature=0, factual answers) 🌐 Flask + Waitress — production API server 🐳 Docker — containerised deployment 🤗 HuggingFace Spaces — live hosting, single URL What I learned building this: ✅ Data quality matters more than model choice ✅ LlamaIndex's {context_str}/{query_str} != LangChain's {context}/{question} — a small difference that breaks everything ✅ Shipping a real product is completely different from running a notebook I am a Mechanical Automation & Maintenance Engineer now specialising in Industrial AI. Most software people build AI apps. I build AI apps that understand real physical systems and real operational problems. This is what 3 days of focused building looks like. 👇 🔗 Try it live: https://lnkd.in/dAFBcYM5 💻 GitHub: https://lnkd.in/d9cGAUcp Mohammad Arshad Bayut.com dubizzle Property Finder Dubai Land Department Emaar DAMAC Properties Better Home Group

🔹 Anthropic Becomes the World’s Most Valuable AI Company Anthropic has reached a valuation of approximately $965 billion after raising $65 billion in fresh funding. The company continues to expand rapidly through enterprise AI adoption, coding assistants, and large-scale AI infrastructure investments. 🔹 Claude Opus 4.8 Improves Reliability and Self-Verification Anthropic released Claude Opus 4.8 with enhanced reasoning, coding performance, and the ability to better recognize uncertainty in its responses. This is an important step toward more trustworthy AI systems for enterprise use cases. 🔹 DeepMind CEO Predicts AGI Before 2030 Google DeepMind CEO Demis Hassabis stated that today's AI agents are effectively a practice run for Artificial General Intelligence (AGI). He emphasized the need for businesses and governments to prepare for increasingly autonomous AI systems. 🔹 Major AI Labs Agree to Government Safety Testing OpenAI, Google, Microsoft, Anthropic, and xAI have agreed to allow government evaluation of advanced AI models before public release. The initiative aims to improve AI safety, security, and governance as frontier models become more capable. 🔹 AI Infrastructure Investments Continue to Surge Technology companies are investing billions into GPUs, AI data centers, and next-generation compute infrastructure. NVIDIA, Google, Meta, OpenAI, and Anthropic are all accelerating investments to support growing demand for generative AI and agentic systems. Happening tomorrow at 7PM GST: AI Adoption & Acceleration 📌https://nas.com/artificialintelligence/events/ai-explorer-ai-demos-ai-use-cases-and-q-a-1779272460818 Join AI RESIDENCY: 📌https://academy.decodingdatascience.com/airesidencyfasttrack Follow this WhatsApp channel for daily AI news, AI & Data job opportunities, events, learning resources, and career opportunities.

The Large Language Models are incredible, but they aren't perfect. They have knowledge cutoffs, they lack citations, they sometimes hallucinate, and they are restricted by context windows. So, how do we fix this? Retrieval Augmented Generation (RAG). 🧠 We just had an absolute masterclass on RAG and LlamaIndex led by Mohammad Arshad sir in the Decoding Data Science AI_Residency Program! This wasn't just theory; we got hands-on with the Llama Index framework. It was fascinating to see how we could build a RAG system in just five lines of code to load documents, create an index, and generate grounded responses based on our private data, not just the model's memory. 💪 My biggest takeaways: ➡️ We tackled performance optimization. By saving vector stores to local persistent storage (rather than re-indexing every time), we watched our query times drop from around 2 seconds to less than 1 second! ⚡ ➡️ Treating the LLM as an inference engine and utilizing specialized vector databases for semantic retrieval is the key to building reliable, enterprise-ready AI. ➡️ This is how we reduce AI hallucinations and build reliable systems. I’ve got some homework to do analyzing Llama Index dependencies and storage outputs, but I am incredibly excited for the next session where we tackle an vector embeddings and preparing for our upcoming sessions on real-world enterprise chatbots.🤖 📌 During this session, I built a RAG with LlamaIndex simple AI bot; here is the link- https://lnkd.in/gzYvxXtr 👆 Ask questions about the related to documents loaded into the system. Try & test this - give your valuable feedback. If good, give your like! #RAG #LlamaIndex #GenerativeAI #AIResidencyCohort10 #DataScience #MachineLearning #LlamaIndex #VectorSearch #DecodingDataScience #ArtificialIntelligence #AiResident