Kalyan Venu Gopal A

Salim FEKU not able to digest NoSQL databases are converging to PostgreSQL. Setting: A chai ki dukaan (tea stall) in Hyderabad. Salim Feku is holding forth, gesturing wildly with his chai glass. (Salim Feku sips his chai, then puts the glass down with a flourish, looking around at an imagined audience.) SALIM FEKU: Arre bhai, aaj kal ki duniya mein kya chal ra? Sab bolte, "PostgreSQL, PostgreSQL!" Kya hai re usmein? Apun bolta, woh to puraane zamane ka ghoda hai! (He leans in conspirantly, lowering his voice slightly but still loud enough for everyone to hear.) SALIM FEKU: Suno tum meri baat, agar tumko asli data rakhna hai, jo kabhi nikalta nai, jo tezz bhaagta, toh tumko NoSQL hona bhai! Yeh PostgreSQL, woh to apna aadmi, kaisa... sara din join lagate baithta. Ek table se dusra table, dusre se teesra... Arre baba, jab tak woh join lagake data nikalta, apun chai pi ke doosra dhanda kar leta! (He chuckles, shaking his head.) SALIM FEKU: Apun ke paas kya hai? MongoDB! Arre kya cheez hai woh! Tum feko usmein kuch bhi! Document feko, JSON feko, apun ka data, seedha andar. Koi tension nai. Woh PostgreSQL, usko pehle batao 'column' kya hai, 'row' kya hai, 'schema' kya hai. Arre baba, itna sochne mein hi system slow ho jata! (He snaps his fingers.) SALIM FEKU: Aur suno, jab apun ka traffic aata, haan? Croreon log aate, kya karte? Woh PostgreSQL to ek machine pe baitha rehta. Apun ka Cassandra, DynamoDB... yeh log kya karte? "Arre bhai, thoda data tu le le, thoda data main le leta," bolke baant lete. Shaana log! Sab milke kaam karte, isliye apna system kabhi rukta nai, bhai! Woh PostgreSQL ko to pasina aa jata! (He gestures expansively, like he's spreading data everywhere.) SALIM FEKU: Woh bolte, "Hum consistent hai!" Arre haan, theek hai. Lekin consistency ke chakkar mein kya, apna customer wait karte baithta! Apun ka NoSQL? Woh bolta, "Thoda late aaya to kya hua? Aayega to sahi! Customer khush hona, jaldi result aana!" Zara sa "eventually consistent" hua to kya hua? Duniya to chalte rehti na! (He lowers his voice again, for the grand finale.) SALIM FEKU: Toh bhai, agar tumko naya, tezz, bina tension ka system banana hai... toh woh puraane dimaag ke relational ko choro! Apun ka NoSQL, woh hai asli king! Baaki sab to... chillar hain! (Salim Feku takes another proud sip of his chai, looking very pleased with his pronouncement.Just then, his eyes fall on someone approaching the chai stall. His confident smirk vanishes, replaced by a look of surprise, then forced cordiality.) SALIM FEKU: (Suddenly a soft, almost meek tone) Arre! Kaun? Ismail Bhai... PostgreSQL wale!?! (He rushes forward, abandoning his lecture, ) SALIM FEKU: Aao Miya! Kaisa hai tum? Tumhari hi raah dekh raha tha main! Kaisa, sab khairiyat? Woh, woh main to... ...main to bas logon ko bata raha tha ki purani cheezo ki reliability kitni zaroori hai! Haan! Tumhari hi baat kar raha tha, bhai! Maine suna..tum bhi distributed ho gaye!!!

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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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Thanks to Anurag Aggarwal and Ravi Kiran Achalla for encouraging the development of this idea from my earlier post. In response to your inquiry about whether trust architecture guardrails could be integrated directly into application code, I took your feedback to heart and have built a solution. I am excited to announce the release of llm-trust-guard on npm. This TypeScript package provides deterministic guard checks for LLM/agent workflows, addressing concerns such as prompt injection, encoding attacks, memory poisoning, tool-chain validation, trust boundary checks, and more. It also includes integrations for Express, LangChain, and OpenAI wrappers. This release continues the direction I’ve been pursuing: - Architecture-level validation: https://lnkd.in/gdCe5nKj - Code-level guardrail enforcement: https://lnkd.in/gPw7xUuw #AIEngineering #LLMOps #AIGovernance #AgenticAI #TrustArchitecture

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🤝 Mentorship in Action After my last post on unlearning old skills to grow as a leader, a few people asked what I did keep through those transitions. For me, the answer is simple — mentorship. It’s been the constant across every stage — and honestly, as much about learning as teaching. It reminds me of sports: even the best athletes have coaches, not because they lack skill, but because great coaching builds perspective and resilience. Mentorship does the same for teams — it brings out their best. Early on, I thought mentorship was about giving answers. Over time, I learned it’s about creating space for others to find their own. Now, seeing someone I’ve coached step into leadership is the most rewarding part of the journey. As my org grew, mentorship evolved into a different kind of conversation — connecting with people several layers away. That’s where skip-levels started changing how I lead. More on that soon. ⸻ #Leadership #Mentorship #Growth #CareerJourney

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Stay tuned for vikramvetal.ai! 🚀 Last week, I dived into the world of LLMs, Streamlit, and FastAPI to create an interactive chat-based riddle game inspired by the classic tales of Vikram and Vetal. Think Gandalf.lakera.ai, but with a rich narrative twist! For members who have not heard the story of Vikram and Vetal, they can refer to this link: https://lnkd.in/g7Wqgs3g" The structure of Vikram and Vetal is perfect for this: A story unfolds, the cunning Vetal poses a riddle, and the player (as Vikram) must answer correctly... or face the consequences (Vikram dies! 😱 No pressure!). But here's the cool part: In this modern adaptation, Vikram can *chat* with Vetal! Brainstorm, ask for hints, discuss the riddle – the possibilities are endless. The stories and visuals are all configuration-driven, making it easy to create new adventures. Could this be the future of engaging children's educational media? Fun quizzes for all ages? I'm planning to launch this as an AI experience at vikramvetal.ai. Sneak peek of the code is available here, so you can set up the game locally: https://lnkd.in/gHb2xCGJ Attached a video of the initial gameplay! What are your thoughts on using LLMs to create interactive and engaging stories? Share your ideas in the comments! 👇 #LLM #AI #Streamlit #FastAPI #GameDevelopment #EducationalGames #VikramVetal #GenerativeAI #MachineLearning Sharing for broader reach: Swaroop S Viswanath S Sameet Shetty Ramkumar Krishnan MBA, B.Eng John Maschke divya kodukula Arijit Roy Kanika Seth Aravind Kumar Suhas Ramesh Amrit Narayan Nayak Utsav Harsh Esha Dhing Nitya Keshari Jahnavi . Salla Tony Preeth Vishwank Vikram Singh Rahul Agarwal Ankit Gupta Abhishek Sagar Sudeep Kumar Datta Monojeet Nayak, CISSP Venu A.

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Most systems fail not because they get answers wrong, but because they answer too early. Last week, Santosh Jaiswal articulated this cleanly in his post on “When answering is NOT the right answer.” It resonated because I’ve been working on a framework where UNKNOWNs are not defects to eliminate, but deliberate design artifacts. First-class citizens. https://lnkd.in/gpGW8A73 In complex systems, architecture once made ignorance explicit. We spent time defining what we knew, what we didn’t, and what we were intentionally deferring. In today’s sprint-driven execution model, that step is usually skipped. We build partial truths, demo something that “works,” then pay for it in rework because the boundaries were never declared. AI workflows behave the same way. Give them an incomplete picture and they will still complete it—plausibly, confidently, and often prematurely. The alternative is structural honesty. Partition the problem into phases. Each phase explicitly declares what it will not solve. UNKNOWNs are scoped, tracked, and deferred by design. If you know what not to fill now, you avoid rework later. This pairs naturally with Venkat Pillay’s “Needs” framing: what is required now versus later, driven by evidence rather than optimism. Combined, you get phased architecture where exclusions are explicit and progression is data-backed. AI doesn’t invent answers; it preserves your explicit structure. This is where human cognition becomes more relevant, not less. AI operates inside a defined problem space. Human curiosity defines the space itself. It decides which UNKNOWNs are worth preserving, which assumptions are premature, and which questions should not yet be answered. That requires intent and judgment. Models do not do that. At CloudFrame, this is how we use AI. Translation, analysis, migration—AI excels. For boundary-setting, sequencing, and confronting unknown unknowns, automation stops. Architecture resumes. In an age of powerful automation, the scarce skill is not producing answers; It is knowing when not to.

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