Real-world insights on AI agents, data engineering, and enterprise transformation — from practitioners who've been in the room when decisions were made.
See the physics. Defend the solution. Explore 50 rotational-motion problems across 10 chapters with ShunyaSaarthi: visual reasoning, worked solutions, free-body diagrams and exam study notes.
We gave JEE Saarthi, the JEE track of ShunyaSaarthi, 30 JEE Main and Advanced-style physics problems. It matched our hand-worked answers on all 30, explained the method, and drew 87 step-by-step diagrams. Here's how it works, and where the test stops.
An Agent Adda point of view on governed AI in education: teacher-authored pedagogy, learning provenance, and the independent capability students retain after the conversation ends.
Two NCERT chapters become purposeful comics as Ganesha and Karthik make fractions and photosynthesis easier to enter.
A practitioner’s playbook for choosing deterministic workflows, bounded decisions, dynamic fan-out, and agentic loops—without adding more autonomy than the work requires.
ShunyaSaarthi's AI agent sat NEET UG 2026 Set 50 completely blind — NTA official PDF, no answer key, pure NCERT knowledge. It scored 270/420 on 105 keyed questions (71.4% accuracy), got 83% in Biology, and left a paper trail of reasoning for every one of 180 answers. This is what we learned.
We tested GPT-5.5 and Sarvam on 50 Hindi couplets—and found that one misunderstood word can turn poetry into confident fiction.
An interactive GuruSaarthi walkthrough of Bachchan’s poem — stanza by stanza, with child’s examples, plain meaning, and bilingual lines.
An interactive GuruSaarthi walkthrough of Dinkar’s poem on strength and forgiveness — with examples, meaning, and bilingual lines.
Reverse-engineering Claude Code's ~18,000-token startup floor: why tool descriptions are restraint priced in tokens, not waste.
We pointed Agent Adda's research engine at Dixon Technologies' FY26 Annual Report — AI synthesis, live web search, a grounded fundamentals pass, a technical-analysis engine reading straight from the database, and a reviewer that caught its own mistakes. Here is what building one institutional-grade report end-to-end actually looked like.
Digitizing the living tradition — why the irreplaceable knowledge, style and wisdom of India's great teachers should outlive them, and what it will take.
A reading of what ShunyaSaarthi is actually trying to build, what makes its architecture distinctively honest, and where the design pressure will come from next.
Enterprise agent sprawl is not a coordination problem. It is a classification problem — and it begins with treating memory and knowledge as the same asset.
An NSE-first market intelligence platform that brings decades of trading wisdom together with modern AI synthesis — 137 indices, 48 investment candidates, 12 analytical layers per stock, delivered as a single self-contained interactive report.
Three chapters on AI, judgment, and the work that does not appear in the deck. A practitioner's view from inside the work — written to be argued with, learned from, and revised.
This is not a story about how AI wrote our software. It is a story about what we had to unlearn, dismantle, and rebuild before AI could become genuinely useful — 516 commits, 13 million tokens, and a fundamentally different way of thinking about delivery.
There is a bookshelf behind every trading decision you have ever made well. What happens when you digitise that accumulated wisdom onto a single platform and let AI make sense of it? This is what we built — and what we learned.
Most organizations adopting AI coding assistants today are scaling chaos, not productivity. ShunyaAI inverts the model: requirements, architecture, and design become the primary artifacts. Code becomes a projection. Governance becomes the foundation productivity is built on.
The SDLC is a workaround, not a law of nature. AI agent pairs will dismantle the sequential software delivery model — and the engineers who survive this shift are the ones who build the coordination protocols before the disruption arrives.
Context management is the most underestimated engineering challenge in enterprise AI today. Most teams build it as an afterthought. The ones that treat it as infrastructure are the ones whose agents actually work.
Every enterprise has terabytes of documents and growing AI ambition. What most lack is the bridge between the two. SharePoint libraries, Confluence wikis, and PDF archives are not knowledge — they are information in storage. The distinction is not semantic. It is the difference between a library of textbooks and a student who can pass the exam.
The hype is outrunning the discipline. Every few weeks a new tool drops, the conversation resets, and the same structural gap goes unfixed. This is a field report — not a celebration of coding assistants, but an interrogation of what happens when you hand them out without governance, methodology, or principles.
Three decades of data investment — data warehouses, data lakes, data mesh — and most enterprises still cannot answer a basic question about their own business without a three-day analyst queue. The problem was never the data. It was the architecture of intelligence.
Every generation of data engineers inherits the technical debt, the undocumented decisions, the design drift, and the lost knowledge of the generation before it. We add new tools, new platforms, new architectures — and we carry the same burden forward, wearing new clothes. This is the manifesto for the generation that finally breaks the pattern.
AI agents are real and they work. But most enterprise AI projects fail for the same reasons enterprise data projects failed before them. Let's talk about why.
Everyone built a data lake. Not everyone built a data practice. Here's why the lake filled with mud — and what actually fixes it.
