I conducted a session on Wed, 04 Nov 2026 at ED5518: Data Visualization for Engineers - Department of Engineering Design, IIT Madras, Chennai. Speakers: Anand S Summary: A live-lab class on testing visualizations and phone apps: commit to an answer before discussing, check that a chart is true and that it works for its audience, use AI as a different kind of reviewer rather than a stand-in for people, and ask busy people “A or B?” ...
I conducted a session on Mon, 02 Nov 2026 at TDS Course Ideation — Open Design Meeting, Sep 2026 - Remote. Speakers: Anand S Summary: In the fourth open TDS design meeting, the teachers ask what is left to teach once an agent can read and solve the exam — while doubling a spending cap students are forecasting, finding only eight drawings that fool every AI guess, and admitting a marketplace rewarded trading over usefulness. ...
I conducted a session on Sun, 25 Oct 2026 at AI Product Leadership (AIPL) 2026, Module 5 Session 2 — LeadUp Universe with E&ICT Academy, IIT Kanpur - Online (Zoom). Speakers: Anand S Summary: A leadership workshop played a 100-ticket, 10-check bank game and found that verifying AI is a budget: every check (domain knowledge, review rules, model confidence, the answer key, an instruction to the model) needs checking itself, and ‘can’t’ beats ‘please don’t’. ...
I conducted a session on Mon, 19 Oct 2026 at TDS Course Ideation — Open Design Meeting, Sep 2026 - Remote. Speakers: Anand S Summary: A week after inventing agent-friendly assessments in public, the TDS team meets running code, student questions, checking budgets, a dropped $500 experiment, leaky graders, and a pass rate that only counts who scored above zero. Here’s the link to the session Links: Video (60 min) Transcript Captions Audio (60 min)
I conducted a session on Sun, 18 Oct 2026 at AI Product Leadership (AIPL) 2026, Module 5 Session 1 — LeadUp Universe with E&ICT Academy, IIT Kanpur - Online (Zoom). Speakers: Anand S Summary: A leadership workshop on delegating to AI stalled on a hard question; the instructor let AI act on the room, then asked it how to fix the workshop, and the session ended on an untested experiment for knowing when to stop AI. ...
I conducted an ideation session for my TDS course. It was an open session along with students, TAs and instructors. It roughly explained how we conduct TDS - and in particular, how we design questions that teach something even when students can (and are encouraged to) work with AI agents as well as each other. Here’s the link to the session Here’s my summary of the session: Pick what to teach. My AI Advice page lists AI-era skills. Yours may vary. Evaluate questions against six tests: Learning test. Will they learn something by solving the question? Forward test. Does copying the answer work for everyone? Solver test. Can one person build a solver and share with everyone? Agent test. Can Codex solve it if they’re logged in? Cheating test. Is cheating costlier than doing it honestly? Or, will they learn something even when they cheat? Evaluation test. Is it easy and robust to evaluate automatically? Measure learning transfer. How do we know the actually learnt the skill? Interestingly, just passing these principles to Claude / ChatGPT gives me pretty good AI and collaboration proof questions on many topics. ...
I conducted a session on Wed, 07 Oct 2026 at ED5518: Data Visualization for Engineers - Department of Engineering Design, IIT Madras, Chennai (remote). Speakers: Anand S Summary: AI makes pictures cheap to create but harder to trust: the reader may be another AI, and a chart, map or CAD model that looks right still needs an independent check. Takeaways: Doing is easy. Choosing what and how isn’t. Design for humans and agents. Keep asking “How can I use this?” - that’s how you learn. Write the objectives before you start. That’s how you know if agents did it right. Here’s the link to the session ...
I teach Tools in Data Science (TDS) at IIT Madras. At our October 2026 orientation, JK began: “A simple five-minute saying ‘TDS is hard’ will be the orientation.” We ended up discussing the philosophy of the course for over an hour. Here are some questions students asked. Q: What is TDS trying to teach? A: “Get the job done.” Use agents, tools, people, your ingenuity, whatever. “I’m giving you the job - get it done.” The problems are inspired by real work. The important part is getting it done correctly, and verifying it. ...
I conducted a session on Thu, 01 Oct 2026 at DBS Technology India — TechVerse Session 4: Agentic AI - Remote / Webex. Speakers: Anand S Summary: Treat AI advice, models and agent workflows as hypotheses: test them on your own work, mine your logs for evidence, and make verification part of the workflow. Here’s the link to the session Links: Transcript Audio (90 min)
I conducted a session on Thu, 24 Sep 2026 at International IT-BPM Summit (IIS) 2026 - Function Rooms #1 & #2, 3rd Floor Pearl Wing, Okada Manila, Parañaque City, Philippines. Summary: AI is too weird and fast-moving to trust by intuition alone: question advice, verify with a second model, calibrate confidence, benchmark what matters, and turn surviving evidence into deterministic rules. Here’s the link to the session Links: Transcript Audio (60 min)
I conducted a session on Sat, 19 Sep 2026 at Shree Niketan Schools — Teachers’ AI Q&A - Shree Niketan Schools, Chennai / Zoom. Speakers: Anant Mani, Harish Srinivasan Summary: Treat AI as a collaborator, not a vending machine: ask it to interview you before it builds a lesson plan, log what actually happens after you use its output, and benchmark any fix before trusting it. Here’s the link to the session ...
Priya Dialani interviewed me for a podcast. Here’s the rough summary: What do you and Straive do? Straive builds AI and runs AI. I poke at LLMs to learn what they cannot do. My friend calls me an “LLM Psychopath”. Why do AI pilots get stuck before production? AI speeds up coding, but less of testing. Making sure it works can take months. Why organize enterprise knowledge? Better organized info is good for humans and agents. Duh! Can AI organize it? Yes! I’ve had it create one-line summaries of 10K+ docs on Straive Google Drive for easier searching. How can India’s GCCs benefit from AI? Put AI lovers next to business teams and give them AI agent access. They’ll solve asked and unasked problems. How does AI fail? Unanticipated things happen in production. So, have agents monitor failures and revise the process. How is AI software different? Normal software fails reproducibly. AI fails in new ways we haven’t fully understood. Where should a company start with AI? Skip AI strategy. Give people agent access, have them try it, and share what they learned. What if people don’t know what to try? Ask AI. “How could you improve my work?” Even rubbish ideas waste only 5 minutes. How much should we experiment? A lot! Generation is cheap. Ask for 10 options, not one. Who cares even if all 10 fail? But what happened outside of the interview was just as interesting. ...
I conducted a session on Fri, 11 Sep 2026 at SUTD DAI Signature Master Class · Expert Industry Series - Singapore University of Technology and Design, Singapore. Summary: Build AI products around evidence, not ideas: prototype quickly, test with agents and real users, and iterate until the product proves its value. Here’s the link to the session ...
Update (Nov 2026): I’ve expanded this into 52 Ways I Specify and Verify Agents, based on over 11,000 prompts. Explore the interactive page with 30 specification and 22 verification techniques and real examples. I delivered a 15-minute keynote at Jio Institute’s Convergence 2026 at NTU on Thursday. The topic was “Data Storytelling” - a bit jarring in the middle of an AI event. Shailesh picked it and I just rolled with it. A spent several days worrying, “How the heck do I say about data storytelling, when most of my recent workshops and talks are about the death of my data storytelling approaches?” ...
I conducted a session on Sat, 22 Aug 2026 at IIM Alumni Singapore — AI Unboxed (4/4) - Singapore / Teams. Summary: Coding is becoming specification and review: describe the outcome, let agents build it, and focus on whether the result works. Here’s the link to the session Links: Room Map Video (129 min) Transcript Audio (129 min)
I conducted a session on Wed, 12 Aug 2026 at ED5518: Data Visualization for Engineers - Department of Engineering Design, IIT Madras, Chennai. Summary: AI can generate charts and analysis. The enduring skill is knowing what deserves to be visualized, challenged, and communicated. Here’s the link to the session Links: Transcript Audio (72 min)
I conducted a session on Mon, 10 Aug 2026 at Tools in Data Science AMA - Remote. Summary: Tools change faster than curricula. The course is really about learning how to learn, automate work, and judge results rather than memorizing tools. Here’s the link to the session Links: Video (62 min) Transcript Audio (62 min)
I conducted a session on Fri, 07 Aug 2026 at Data Hack Summit 2026 - Bangalore. Summary: Managing AI resembles managing experts: specify work clearly, verify outputs systematically, and borrow proven practices from professions that already manage complex judgment. Here’s the link to the session Links: Video (58 min) Transcript Audio (59 min)
I conducted a session on Sat, 25 Jul 2026 at IIM Alumni Singapore — AI Unboxed (3/4). Summary: Agentic tools can perform much of a data scientist’s research and analysis cheaply. The leverage comes from framing the question and checking the result. Here’s the link to the session Links: Video (138 min) Transcript Audio (138 min)
Here’s roughly what I said in my When Data is for Agents workshop for Fifth Elephant on 7 Jul 2026. Or you can read the detailed AI-generated version if you prefer - it has all the prompts, links, results, etc. I think agents prefer data in a different form than humans. But I don’t know. So, everyone, open ChatGPT (or Claude or whatever), research and ask it! Now, let’s collate them and see the result. Aha! Looks like: ...