Naveen Kumar Kotha

About a month ago, I had the opportunity to speak at Redis Delhi, alongside some eminent industry leaders — Suman Guha (Tata CLiQ / Tata Digital), Murali Mohan Chakravarthy (Entain), and Rhythm Goyal (Monotype) My session ran for ~25 minutes and focused on how Redis has evolved far beyond its original role as an in-memory cache, and how teams are using it today to solve modern, large-scale problems. We discussed: - Vector search and its role in modern search systems - Bloom filters for efficiently checking user existence at scale (probability at scale!) - Sorted Sets as a practical primitive for rate limiting - Why in-memory systems like Redis became popular for agentic workflows instead of disk storage, aka GPU runtime costs What stood out the most was the audience—an enthusiastic, well-curated mix of experienced engineers and young, fast-growing talent. The questions and discussions made it a genuinely engaging session. It’s always a pleasure learning and collaborating with Jatin Gupta, Omkar Konnur, Justine Hermoso, and Rakhilaa Rajasheker. Thanks to the Redis Delhi team for having me and everyone who joined 🙌

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Very Interesting perspectives, I may disagree with a few and align with Many of the views shared, interesting views, perspectives, and practices nevertheless !! 1. The Counterintuitive Truth: Code Quality Does Not Equal Product Success Prasanna’s most striking lesson for engineers is that there is no direct relationship between high code quality and building a successful product. While technical excellence is a worthy goal, it is not the end goal. The ultimate measure of success is whether you are solving a real problem for people. < I am on the Edge with this one. While I am a strong proponent of Quality output/outcome, the ultimate measure of success is, however, the real problem solved :) Ramanan Natarajan - Would love to hear your thoughts on this :) 2. The Real Productivity Engine: Structure as the Foundation for Tools While Block is gaining attention for its powerful internal AI agent, Goose, Prasanna reveals that the single most impactful change for boosting productivity wasn't a tool, but a fundamental organizational restructure. The company shifted from a siloed structure to a unified, functional one. This new functional structure unlocked three primary benefits: > It created a singular focus on technology and technical excellence across the entire organization. > It allowed leaders to drive forward AI and platform depth for the whole company, rather than in isolated pockets. > It changed the hiring mindset. Instead of treating engineers as a "commodity" to be added to a business unit to increase feature output, "mythical man month", the focus shifted to building common platforms and leveraging deep experts across the entire organization. This unified structure not only drove technical depth but also created the ideal environment for small-scale experiments to be identified, supported, and scaled across the company, rather than dying within a business unit silo. < It's not always the Tool, it's the Mindset !! 3. The Principle of Momentum: Start Small to Win Big: The most successful and durable innovations rarely begin as massive, top-down corporate initiatives. Instead, they grow from small, focused experiments. < "Start small with everything." 4. The Human Element: Hire for a "Learning Mindset": The company now optimizes for a "learning mindset." They are less concerned with finding "amazing AI practitioners on the get-go." Instead, they prioritize finding people from recent college graduates to industry veterans who are eager, open to learning, and willing to embrace new tools. < Meta cognition is undervalued !! #organisationCluture #AI #Productivity https://lnkd.in/gw6saKXp

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SANATAN LABS - The Architect’s Compass: 10 Directives for the Great Transition (PASSIONIT PRUTL KALKI AIDHARMA framework) To navigate the "Audit of Fire" during this profound transition toward Satya Yuga, Krishna (Strategic Intelligence), Kalki (Systemic Reset), and Shakti (Raw Life Force) offer these ten essential directives for humanity: 1. Master the Asymmetry of Calm In a world of screaming headlines, silence is your greatest weapon. Respond from a place of internal stillness rather than reacting to external noise. When the system is chaotic, your stability becomes the new "Safe Haven." 2. Audit Your Identity (The Laawaris Test) Ask yourself: "If my titles, assets, and digital footprint vanished, who remains?" Detach your worth from possessions. True sovereignty is found when you are no longer "claimed" by the ego-driven systems of the world. 3. Embrace the "Mirror of Kalki" Do not fear the "Bloody Moon" or the exposure of decay. View every systemic collapse—financial, social, or institutional—as a necessary diagnostic. Use this "Mirror" to identify your own attachments and incinerate them before they become liabilities. 4. Move from "Me" to "Mission" Unity is not sameness; it is alignment. Replace "Me vs. You" with "Us vs. The Problem." When diverse strengths are aligned under a shared purpose (Aidharma), the friction of competition dissolves into the flow of progress. 5. Trust the "Internal Gold" While external currencies and metals face regulation or restriction, your integrity remains non-tradeable and unconfiscatable. Invest in your skillsets, your character, and your resilience. These are the only assets that survive the "Audit of Fire." 6. Respect the "Flow of Lakshmi" Prosperity is a river, not a reservoir. Hoarding in fear leads to stagnation and decay. Circulate value through mentorship, ethical trade, and "Groom and Grow" practices. Wealth that serves the collective remains protected by the Sun and Moon. 7. Humanize the Machine (13th Node Reset) Use AI and technology as a "Numeric Auditor" to expose corruption, but never let the cold logic of the machine replace the warmth of human intuition. Ensure technology serves the 14th Node: Absolute Truth. 8. Practice Guerrilla Respect Disarm your "enemies" by preserving their dignity even when you disagree with their actions. Respect targets the ego of the opponent, weakening their appetite for control and opening a path for diplomacy. 9. Build Internal Havens As geography stops being a protection, focus on "Internal Research and Innovation." Cultivate a mind that can remain creative and functional even when trade arteries are choked. 10. Surrender to Karmic Gravity Accept that "God Proposes, Man Disposes." You cannot control the celestial reset, but you can control your alignment with it. Follow the path of Dharma, and the gravity of the new era will pull you toward peace rather than destruction.

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Full-circle moment and I couldn’t be more excited. In a few days I’ll be back in the classroom this time as a subject matter expert at the One-week International FDP on “Quantum-AI Convergence for Next-Gen Computing” hosted by the Department of CSE at JAIN (Deemed-to-be University), in association with St. Paul University Quezon City. I still remember sitting in seats like these as a B.Tech student; now I get to return and jam with today’s students, researchers, and faculty on how Quantum + AI are reshaping enterprise architectures, platforms, and careers in the real world. We’ll dive into what’s hype vs. what’s real, how to get “quantum‑ready,” and why this moment is such an exciting time to be building in tech. I’m constantly energized by the breakthroughs coming from @IBM Quantum, @Google Quantum AI, @Microsoft Azure Quantum, @IonQ, @Quantinuum, and @D‑Wave and I can’t wait to bring that cutting‑edge momentum into an interactive classroom setting. Expect lots of questions, live debate, and a very real-world view of where Quantum–AI is headed next. Huge thanks to Dr. J. Somasekar, the CSE team at JAIN University and partners at St. Paul University Quezon City for making this happen. If you’re a faculty member, student, or industry professional curious about Quantum-AI convergence and next‑gen computing, you can still jump in and register here:  https://lnkd.in/edVMZHi3 Carmen Palacios-Berraquero D-Wave Jelle Visser Srinivasan Nambiar Sajan Chacko Srinivasan Ren Sridhar C V L Venkata Subramaniam SoFloTechKids Allie Hernandez 🧡🌟🌴 Chief Architect Network Chief Architect Forum #QuantumReady

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My first job was writing bidding algorithms for the Clearing House of India. In C. On Unix. I was never great at remembering syntax. But I understood the systems — the clearing logic, the bidding flows, how the pieces fit together. Today I use Claude Code with a team-of-agents framework. The syntax problem? Gone. What used to take days takes hours. But here’s what nobody is talking about: AI made building faster. Building was never the real bottleneck. I manage supply chain transformation programs across 4+ P&Ls at GE. What I see every day: teams in silos building overlapping algorithms independently. Systems that technically connect but don’t cohere. Rising opex. Painful integration. AI doesn’t fix this. AI makes it worse. Each silo now produces more divergent solutions, faster. McKinsey’s research confirms it — 89% of organizations still run industrial-age operating models. Only 1% operate as decentralized networks. And 42% of companies scrapped most AI initiatives in 2025, up from 17% the year before. Conway’s Law told us this in 1967: siloed teams will always produce siloed systems. So I wrote a whitepaper proposing a fix — the CORE Framework (Coherent Organizational Redesign for Enterprise AI). Five layers: 1. Shared Contracts — common data models before anyone builds 2. Human Orchestrator — cross-cutting authority, not just visibility 3. AI Governance — because AI-generated code loops, duplicates, and over-solves 4. Team Redesign — organize by value stream, not technology layer 5. Opex-Aware Delivery — measure production health, not shipping speed The organizations that win with AI won’t adopt fastest. They’ll restructure to absorb properly. Full framework on Medium: https://lnkd.in/df76HJUf Would love to hear — are you seeing AI accelerate the silo problem in your organization? #AI #ConwaysLaw #OrganizationalDesign #SupplyChain #DigitalTransformation #AgenticAI

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Humbled to see my article on recent H1B issue published in Amar Ujala The piece reflects on how the recent hike in H1B visa fees is not merely about visas — it is a reminder for India to rethink its digital self-reliance. Instead of sending billions abroad on software licenses and staying overdependent on the U.S. market, we can: ✅ Channel government budgets into Indian IT & AI innovation ✅ Build a sovereign digital backbone ✅ Create opportunities where Indian companies lead, not follow The road to आत्मनिर्भर डिजिटल भारत is closer than we think, if we choose to walk it. Curious to hear your views: Can India turn this H-1B crisis into its biggest innovation opportunity? #DigitalIndia #Innovation #H1B #AatmanirbharBharat #Technology

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🚀 From CI Failures to Autonomous Healing — Our RIFT 2026 Build At RIFT 2026 (organized by PW (PhysicsWallah), we challenged ourselves to rethink how CI/CD pipelines work. Every developer knows this loop: Push → CI fails → Debug → Fix → Push again → Repeat. What looks like a small syntax error often costs hours of context switching and delay. So instead of asking “How do we improve debugging?” We asked: What if CI didn’t just fail… but healed itself? 🧠 What We Built We built an Autonomous CI/CD Healing Agent with a React Dashboard — a system that: • Takes a GitHub repository as input • Automatically discovers and runs tests • Parses logs and classifies failures • Generates minimal targeted fixes • Creates a deterministic branch • Commits with [AI-AGENT] prefix • Pushes and re-triggers CI • Iteratively retries until convergence All execution runs inside a Docker sandbox, with a configurable retry limit and strict safety controls. This isn’t just automation. It’s structured, bounded autonomy. 🔗Website Link- https://lnkd.in/dYrenM2f 🏗 How It Works We designed it in layers: Frontend (React Dashboard) Collects inputs and renders structured results via results.json. Backend Orchestrator Manages execution flow, retry logic, and safety boundaries. Multi-Agent System • Repository Analyzer • Test Execution Agent • Failure Interpreter • Fix Generator • Git Automation Agent • CI Monitor Agent Each agent has a single responsibility, making the system modular, scalable, and explainable. At the core lies a closed-loop healing system: Run → Analyze → Fix → Commit → Re-run → Verify → Repeat. That feedback loop is the real innovation. 🔐 Why This Matters CI failures aren’t just technical issues — they’re productivity bottlenecks. Developers spend 40–60% of their time debugging pipeline failures. We’re not building a debugging tool. We’re redesigning the CI/CD feedback loop. 📊 Built with Engineering Discipline We implemented: • Structured log parsing • Controlled retry logic (max 5 iterations) • Deterministic branch creation • Safe commit automation • CI status monitoring • Score calculation logic • Strict test-case matching Built end-to-end — frontend, backend, orchestration, GitHub API integration, sandbox execution, deployment — under hackathon pressure. And this is not the end. 💡 We’re already planning: • Slack integration • GitHub App plugin • Enterprise-grade version • Self-hosted runner • Smarter prioritization • Per-repository pricing and many more... This isn’t just a student project. It’s a scalable DevOps automation layer. 🔗Website Link- https://lnkd.in/dYrenM2f Proud of what we built. Big thanks to Kushagra Garg Excited for what’s next 🚀 #RIFT2026 #PhysicsWallah #DevOps #AI #SystemDesign #AgenticAI #CI_CD #Hackathon #SoftwareEngineering

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Product Management has changed for good with AI. Product/feature Validation used to be very dificult due to limited data, limited industry knowledge, or customer problems. Now this all has changed leading to build better products. Exciting times!

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𝗕𝗲𝘆𝗼𝗻𝗱 𝗧𝗲𝘅𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: 𝗘𝗺𝗽𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝗟𝗟𝗠𝘀 𝘄𝗶𝘁𝗵 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗖𝗮𝗹𝗹𝘀 𝗞𝗶𝗿𝗮𝗻 𝗣𝗿𝗮𝗸𝗮𝘀𝗵's insightful article on function calls sheds light on how LLMs can be instructed to construct data structures that describe external function calls. Crucially, the LLM doesn't execute these calls directly. Instead, it intelligently generates the blueprint for the action, paving the way for seamless integration with external tools and APIs. This indirect execution mechanism is key for security and control, allowing developers to orchestrate complex workflows and extend the capabilities of LLMs in a safe and structured manner. Definitely worth a read for anyone interested in the cutting edge of LLM applications and their potential to drive real-world impact! #LLMs #AI #MachineLearning #FunctionCalls #ExternalSystems #Innovation

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India’s moment to double down on innovation. The new H-1B fee shock will slow onshore roles but turbocharge offshoring, SaaS, vertical AI, and R&D at home. Expect talent “ghar wapsi,” tougher domestic competition, and sharper products. The next 3–5 years are pivotal: move beyond services and domestic consumption to IP, deep tech, and global products—or fall behind. Let’s convert policy headwinds into a national innovation tailwind. #IndiaRising #H1B #Tech #SaaS #AI #DeepTech #MakeInIndia #DigitalIndia #GharWapsi #ProductNation #RAndD #Innovation #StartupIndia #ExportLedGrowth #FutureOfWork

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How Indian IT Giants Are Redefining Global Delivery in 2025-26 ? Recent news cycle is buzzing about the $100,000 H1B visa fee shock and its ripple effects across the IT services ecosystem. For Indian IT giants with deep US partnerships, this isn’t just a policy debate—it’s a call to reimagine workforce strategies, delivery models, and client value. Here’s how the industry is responding—and what it means for the future of global software services: Localize where it matters: US hiring and university partnerships are accelerating to build a resilient domestic pipeline. This shift strengthens client trust, reduces regulatory risk, and shortens onboarding cycles. Grow offshore and nearshore capabilities: Multihub delivery—primarily in India, expanded nearshore hubs in Canada and Mexico, and selective European centers—preserves scale while mitigating visa-related frictions. Embrace hybrid talent models: A blend of offshore teams, local experts, remote contractors, and gig talent enables flexible capacity to meet project timelines without sacrificing quality. Reprice and renegotiate strategically: Incorporating visa and inflation scenarios into pricing and contracts helps align client expectations with delivery realities, enabling sustainable margins. Invest in high-demand capabilities: AI, cloud, data engineering, cybersecurity, and automation stay at the core of value delivery, ensuring clients see measurable outcomes beyond headcount shifts. Why this matters for you? If you’re a tech leader: these shifts redefine how you structure global delivery, partnerships, and risk management. If you’re a job seeker or professional: the skillset that stays in-demand is not just coding, but capabilities in AI, data, security, and agile cross-border collaboration. If you’re a client or buyer: outcomes, not visas, become the primary driver of engagement, with new models designed for speed, quality, and cost predictability. Which of these strategies do you think will move the needle fastest for large-scale outsourcing? How is your organization adapting hiring, contracts, or delivery to visa-related uncertainties? What complementary models (e.g., reskilling, internal mobility programs, renter workforce platforms) have you found effective? If this resonates, share a brief example from your organization or a lesson you’ve learned about delivering value in a visa-constrained world. Let’s crowdsource practical playbooks and accelerate collective learning. #ITservices #H1B #VisaPolicy #GlobalDelivery #AI #Cloud #Cybersecurity #Nearshoring #Offshoring

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🚀 Fine-Tuning Small Language Models (SLMs): A Step-by-Step Guide As AI adoption accelerates across industries, Small Language Models (SLMs) are emerging as powerful, cost-effective tools for domain-specific tasks — especially in regulated sectors like healthcare. Here’s a practical guide to fine-tuning SLMs for your enterprise use case: 🧠 Step 1: Define the Use Case Start with a clear objective — e.g., member journey optimization, clinical note summarization, or marketing content classification. 📦 Step 2: Prepare the Dataset Curate high-quality, domain-specific data. Clean and tokenize text. Ensure compliance with data privacy (HIPAA, etc.). 🏗 Step 3: Choose the Right SLM Options: TinyLlama, DistilBERT, BGE-Small, etc. Consider model size, latency, and inference cost. 🛠 Step 4: Set Up the Environment Use Databricks, Hugging Face, or Azure ML. Leverage GPU clusters for faster training. 🔁 Step 5: Fine-Tune the Model Use transfer learning with domain-specific data. Apply techniques like LoRA or QLoRA for efficiency. Monitor training loss and validation accuracy. 🔍 Step 6: Evaluate & Test Use benchmark datasets and real-world samples. Measure precision, recall, F1-score. Perform bias and safety checks. 🚢 Step 7: Deploy Securely Containerize with Docker. Use Azure endpoints or Databricks Model Serving. Implement RBAC and audit logging. 📈 Step 8: Monitor & Improve Track performance in production. Collect feedback and retrain periodically. 💡 SLMs are not just lightweight — they’re agile, interpretable, and ideal for enterprise AI. If you're working in healthcare, MarTech, or regulated domains, now is the time to explore their potential. Let’s connect if you're exploring SLMs for your next AI initiative! #AI #SLM #Databricks #HealthcareAI #MarTech #FineTuning #LLM #AzureML #MLOps #EnterpriseAI #DataScience

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