AI Data Drops

Author: Ankush Madhavan Rangaswamy
The emergence of context engineering culminated a wave of hype , touting it as the next novel trick in the AI playbook. In reality , it’s a pragmatic craft; involving the deliberate and meticulous arrangement of various components of context. This article peels back on the buzz to expose those unglamorous yet indispensable building blocks.

Author: Vickraman Thekkan Ravi
The blog explains how natural language interfaces allow users to retrieve insights from data without needing technical query languages. It discusses techniques for mapping human phrasing to structured queries, explores challenges like ambiguity and context, and reviews tools that translate questions into SQL or API calls to make data access intuitive.

Author: Shreyas Bilikere Shantharaju
The Agent-to-Agent (A2A) protocol allows AI systems and software agents to communicate, share data, and collaborate seamlessly. By automating workflows, such as payroll and leave management, A2A reduces errors, saves time, and ensures efficiency in complex systems where multiple services need to work together without manual intervention.

Author: Arun Kumar
During February, Someone contacted me on Linkedin and asked for some consultation in their RAG pipeline. Their fancy new ColBERT search system—the one that crushed benchmarks in testing—was melting their servers. Query latency jumped from 50ms to 5 seconds. “But the accuracy is so much better!” they kept saying, like a mantra, while customers fled to competitors. I suggested possible solutions and things got resolved in a balanced way.