Deep Parmar's perspectives on AI innovation, software engineering, and industry best practices.
Eight months after launching Dhiya NPM, here is where browser AI actually stands, what surprised us, and what the 2026 roadmap looks like for client-side inference.
We are halfway through 2026. Here is my honest assessment of where Indian AI actually stands — what is working, what is not, and what to watch in the second half.
No-code AI tools are real and genuinely useful — for the right people, on the right problems. Here is where they work in India and where they fall short.
Running Marketing Autopilot taught us which metrics actually predict revenue growth and which ones just look good in decks. Here is what we track and why.
Privacy regulations are making fine-tuning harder. Synthetic data is the practical solution most teams are not using yet — here is how to do it right.
The hardest bugs in LLM applications are the ones you do not know you have. Observability is how you find them before your users do.
If you have been meaning to build an MCP server but have not started yet, this is the guide. From the three primitives to a working server in one day.
SmartON combines object detection, OCR, and language models. Here is what combining vision and language actually looks like at production scale.
When you run multiple AI products, LLM API costs become visible fast. Here are the six techniques that actually cut costs — not just tips that sound good in theory.
After a year of daily use, here is my honest assessment of what AI-assisted coding tools actually changed about how we build — and what they did not.
I have switched several production workloads from GPT-4 to Phi-4 or Gemma. Here is the decision framework for when smaller models are the smarter choice.
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