chirag.patnaiks.in

My sixteen-year-old son spent a weekend fine-tuning an mBERT language model with labeled hate speech data, then benchmarked it against MuRIL, a publicly available model for Indian languages. No assignment. No tutorial. Just Google AI Studio, Google Colab, and curiosity. He'd essentially skipped to the end of a university summer school curriculum. Using mBERT and MuRIL is advanced deep learning. Most students start with if/else logic and work their way up to Transformers over years. He started with Transformers. When he got interested in AI/ML summer programs like NUS, we looked at the syllabi. He was already beyond where the program would end. That's when it crystallized for me: we're teaching kids to write code in an era when AI writes code. We're drilling them in syntax when they need judgment. We're preparing them for an education system that's already obsolete. The Assessment Crisis The real issue isn't learning. It's testing. We test memorisation be...

"The reports of my death are greatly exaggerated." Mark Twain said that in 1897 after a newspaper mistakenly published his obituary. Email could say the same thing. The death of email has been predicted for decades. In 1989, John McCarthy (inventor of Lisp) declared fax would supplant it. In 2010, Mark Zuckerberg announced that email was dead and launched Facebook Messages. In 2015, Inc. magazine confidently predicted email would be gone by 2020. Every prediction was wrong. Email volume keeps growing. In 2020, 300 billion emails were sent daily. By 2025, that number hit 375 billion. But here's what changed: humans stopped reading most of it. Marketing emails go unopened. Newsletters get archived unread. The inbox became a dumping ground. And that's fine. Because humans may not need to read them anymore. The User-Side Revolution Every AI solution right now is aimed at enterprises. Microsoft Copilot. Google or even things like Claude Code or Cursor. The pitch is a...

The refrain is familiar by now: "LLMs create nothing new. They just regurgitate training data." Writers say it. Researchers say it. Developers say it. The argument shows up in lawsuits, professional forums, congressional testimony.  Even writers, who do create new stories, build on familiar patterns. The hero's journey. The meet-cute. The unreliable narrator. They use story hooks audiences already recognize to keep them engaged. New combinations of existing elements, not creation from nothing. And they're right. LLMs don't create genuinely new knowledge. They surface, synthesize, recombine what already exists. Here's what makes this critique uncomfortable: Neither did you. Mostly. The Interpreter's Labor Think about the senior developer who's invaluable to their team. They don't write revolutionary code. What they do is remember. The pagination bug gets fixed with that specific React pattern. This API call needs this particular header. That S...

  Why struggle through all this nonsense of command line errors, debugging package dependencies when you can command the power of a thousand suns and the sum total of human knowledge and say: "Get this working." The Terminal Has Always Been Powerful (And Intimidating) The command line is where real work gets done. Installing packages, managing dependencies, compiling code, debugging cryptic errors, setting up development environments—it's all terminal work. And it's always been a gauntlet for newcomers. Take Python dependency management. We've had venv, then conda, and lately uv has taken the Python world by storm. Each tool is powerful. Each one is a puzzle to use if you're new. Version conflicts, environment mismatches, system-wide installations clashing with project-specific needs—it's a mess that's always required you to understand virtual environments, package registries, version resolution, and system paths. And when something breaks? You...

  Building Systems That Get Smarter Every Day Introduction After months of building and refining self-learning systems with CLI agents, I've discovered something that is quite obvious in retrospect: the secret to continuously improving AI assistance isn't in the model itself—it's in the knowledge capture infrastructure surrounding it. Recent research quantifies what practitioners have been discovering: systems that accumulate and refine their own contextual knowledge can outperform traditional approaches by 10.6% on agent tasks and 8.6% on domain-specific reasoning , while reducing adaptation costs by up to 87% . Since, I've been using this for a while, I though a a practical guide to building your own self-learning development system is in order.  I've experimented with this approach in multiple domains, including AMP for email development and writing TradingView PineScript code, which is published at https://github.com/NakliTechie/PineScriptCoder . Both are r...

I was wrestling with creating a series of seven engagement emails for a client—carefully crafted messages that demonstrated a new concept in email marketing. Each email needed specific components: client positioning, footer elements, engagement currency, and brand alignment. After hours of iteration, I finally had a complete seven-email series that worked. But then I faced the real challenge: how could I replicate this entire sequence for other clients efficiently? More importantly, how could I enable other team members to create these sophisticated email sequences without going through the same lengthy development process? Instead of trying to write a system prompt myself, I asked Gemini in AI Studio to help me create one. Through our collaborative conversation, we developed an interactive agent that was far more sophisticated than anything I could have written manually. The resulting system prompt was extraordinarily detailed—it included exact conversation flows, technical HTML speci...

We may be on the verge of witnessing a new kind of addiction—one that could be economically productive but psychologically transformative in ways we can barely imagine. AI-assisted coding could exhibit classic addiction patterns, complete with gateway behaviors, tolerance building, and compulsive use. But unlike traditional addictions, this one might create value while potentially rewiring how we think about work, creativity, and human purpose. Science fiction has long explored similar phenomena: Gibson's cyberspace cowboys in Neuromancer who became so addicted to jacking into the matrix that physical reality felt pale and meaningless. Or consider the Guild navigators from Frank Herbert's Dune , who neeeded  spice to navigate the stars. AI-assisted developers might become similar—able to navigate complex digital possibilities through their AI tools but increasingly dependent on that augmentation to perform at all. The Gateway Drug Phenomenon The progression could follow tex...