InCodeToDeploybySyed Sadat Nazrul·Nov 1From Sand to Intelligence: The Global Supply Chain Behind AIMy usual blogs have been around AI algorithms with some sprinkle of philosophy. But given the current socail climate, I wanted add a bit…A response icon2A response icon2
Syed Sadat Nazrul·Oct 6Technology, Progress, and the Problem of Human ValuesNeedless to say, technological progress over the past two years has been both exciting and overwhelming. For starters, as an individual…A response icon2A response icon2
Syed Sadat Nazrul·Sep 20Your AI Didn’t “Escape.” Someone Let It Out.AI escape stories obscure the real questions: objectives, permissions, failed safeguards, and who is accountable when systems cause harm.A response icon1A response icon1
Syed Sadat Nazrul·Jun 21History Repeats: Vibe Coding Still Needs Unix PhilosophyAlmost every argument I have heard against vibe coding, I have heard before. Too verbose. Too messy. Not real programming, since you are…
Syed Sadat Nazrul·May 21From Popularity to Personalization: Target Design in Search, Recommendations, and RAGEvery time you buy something on Amazon, watch something on Netflix, click a search result on Google, or get an answer from ChatGPT, a…
Syed Sadat Nazrul·May 20Turning LLM Outputs Into Production SystemsWhat it actually takes to move from an impressive LLM demo to a production system teams can trust.A response icon2A response icon2
Syed Sadat Nazrul·May 20You Shipped an LLM Feature. Now How Do You Know It Is Working?Shipping an LLM feature feels like the finish line. You got stakeholder buy-in, you built the pipeline, you ran the tests, you deployed it…
Syed Sadat Nazrul·May 19The Hardest Part of Cybersecurity ML Is Not the Model. It Is the Target.Building cybersecurity ML systems under real-world constraints, noisy labels, and incomplete observability.A response icon1A response icon1
Syed Sadat Nazrul·May 18Thinking Fast and Slow in Cybersecurity ML: The Case for InterpretabilityA response icon1A response icon1