We’ve been talking about hosting an AI community event for a while. On the morning of May 30, we finally did it and honestly, the energy in the room exceeded every expectation we had.
Setting the Pace
Yassar Saleh opened the event with a warm introduction about Klizer who we are, what we stand for, and why we believe AI communities like this matter. He set the tone perfectly: grounded, optimistic, and forward-looking. It gave the room a shared context and a sense of purpose before the sessions began, and you could feel the energy lift from the very first minute.
The Sessions
PHILEMON P – The Last Mile Problem
Philemon kicked off the sessions with a sobering truth: 85% of AI projects never make it to production. His talk explored the five killers of data drift, legacy integration challenges, latency and scalability issues, human resistance to unexplainable models, and the absence of automated feedback loops.
His key message: teams must shift from a “development-first” to a “production-first” mindset. Shipping a model is not the finish line , it is the starting gun.
Bharat Kulkarni – The Interpretability Crisis
Bharat brought a fundamental question to the room: as AI systems grow more powerful than our ability to understand them, what does that mean for trust, safety, and accountability? He walked through concepts like mechanistic interpretability, neural circuits, superposition, and emergent computation.
His closing thought landed well: the future of AI will not be defined only by building larger models, but by creating systems that humans can meaningfully understand, monitor, and responsibly deploy.
Hari prasad – Live Workshop: Building AI Agents That Actually Work
Hari’s workshop was the highlight of the morning. His core argument: most AI agents shown today are demos. They work in controlled settings but collapse in production and the limiting factor is usually the architecture, not the model.
He outlined three principles that separate a production agent from a demo:
• Reliability — it uses real data, not guesses
• Reflection — it checks its own work before answering
• Receipts — every claim is traceable to a source
Then he built a live agent called Nudge, a Chennai assistant that checks real weather and traffic. The audience watched it call tools and cite sources in real time. Then he removed the tools. Same model, same prompt and it immediately started guessing, confidently, with nothing to back it up. The room went quiet. That one demonstration explained more about AI agents than any slide ever could.
Networking Session
After the sessions, the networking went long in the best way possible. Conversations spilled across tables; people who had just met were already sketching out collaboration ideas. It was the kind of organic energy you cannot manufacture.
A Community That Spans Every Age
One thing that genuinely moved us: the range of people who showed up. School students sat alongside senior professionals. Academics joined practitioners. That cross-generational curiosity is exactly what this community needs and it tells us the hunger for AI learning in Chennai cuts across every age group.
Seeing a school student lean forward during a talk on mechanistic interpretability and then ask a sharp question during Q&A and it was a moment we will not forget. This is what community-driven learning looks like.
Stay tuned. The next Klizer AI Club event is on its way.
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