Reflections, empirical benchmarks, and system design takeaways from building data pipelines, valuation engines, and machine learning architectures.
What began as helping at a struggling hotel in Guwahati turned into a self-directed experiment in operations, marketing, software, and travel.
What began as helping at a struggling hotel in Guwahati turned into a self-directed experiment in operations, marketing, software, and travel. How a reputation recovery problem evolved into an integrated hospitality data ecosystem.
A pre-sales engineer's field notes on how agentic AI is splitting enterprise QA into managed services and embeddable tools — and what the research says about where full autonomy still breaks.
From discovery calls with US engineering leaders to the empirical failure modes of autonomous repair loops, here is why enterprise testing is bifurcating into black-box SLAs and CI/CD-native agentic tools.
What I learned building retrieval pipelines during my MBA in Data Science & Finance, and what the 2026 research actually says
Every large language model carries an expiration date baked into it. Should you build complex retrieval pipelines (RAG) or simply dump millions of tokens into massive context windows? Here's what the empirical research actually says about costs, 'Lost in the Middle', and adaptive routers.