Sachin Gupta

Eeshita Pande, CEO onFabric on how consumer applications will fundamentally change with context: "I think this will just lead to amazing products being created for us, of the kind, some of which we can imagine." "You can imagine dating apps right now are having people swipe right and left all the time. You have agents that date your agents." "Our sort of fundamental thesis is that it's just one person. Like whether you're on Google or you're on Instagram or you're on GPT, you're one person." Current model: • Apps improve through your constant interaction • You waste time "training" each separate platform • Every new service starts from zero knowledge about you AI agent model: • One digital self across all platforms • Understands you're the same person on Google, Instagram, and ChatGPT • Accumulates context that transfers between services The inherent misaligned incentive of apps is that they are designed to deepen your attention in them, not save it. Find the full ep at Viraj Acharya or search Eeshita Pande, onFabric on YouTube VENTURES is made possible by ACI.dev & DOJO AI (Live from PLUGGED)

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If you’re a first-time founder, there’s one ritual you can’t afford to ignore. Every day at 5 PM, Srinath and Matt at Regie.ai sit together for an hour with a cup of coffee and no agenda. It's space to surface whatever's bubbling up. They started this habit in the early days and kept to it. Databricks did weekly dinners for years with all five co-founders. Sometimes work-focused, but not always. Their goal was the same: create room for what isn't being said. Most founders talk constantly - in Slack, in standups, in product reviews. But those conversations are too transactional, too focused on output. You need unstructured time and the permission to say the uncomfortable thing. Room to address what's wrong before it becomes unsolvable. This is a powerful way to prevent a silent breakdown between leaders. As a startup grows, even the most amiable founders develop differences. With added responsibility and pressure, these differences turn into conflicts. A one-hour ritual can prevent that. My recommendation: Block the time, and keep it sacred. Founder fallout kills more startups than product missteps. If an hour a day - or a dinner a week - keeps that bond intact, why wouldn’t you make it non-negotiable?

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On the latest episode of Embracing Erosion I sat down with Suyog Deshpande, Co-Founder & CEO of Webless (a Fluvio Ventures portfolio company). Before starting Webless, Suyog spent years at Amplitude, Salesforce, and Samsara - shaping products and GTM strategy at scale as a product marketing leader. That perspective is now fueling one of the boldest bets in tech: 🏗️ rebuilding the web for LLMs. Instead of optimizing for clicks and SEO, Webless imagines an agentic web -where sites are designed to interact directly with AI models and agents. We dug into: - What an LLM-native web could actually look like - How companies can prepare to be LLM-ready - What metrics will matter beyond pageviews and clicks - Why safety and trust are core to agent-driven experiences - Lessons Suyog brings from “big tech” into this transformation If you’re curious about how discovery, trust, and value exchange will evolve online, this one’s worth your time. Link to the full episode in the comments 👇

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We are live at the India AI Summit 🇮🇳 Voice AI demos are everywhere. Production-grade voice AI is rare. Most teams still don’t know how their voice agents actually behave when faced with real customers, interruptions, anger, hesitation, language switching, and unpredictability. We started where the truth lives: real production conversations. At Prodloop, we audited and evaluated voice interactions at scale. We saw exactly where agents break, where trust is lost, and what it takes to make voice AI truly reliable through miliions of hours of calls and extensive human evaluations. This led us to build the missing infrastructure layer for voice AI: Simulation, to stress-test agents before deployment Observability, to measure and monitor agents in production Reference voice agents, to validate reliability end-to-end As voice becomes a primary interface, evaluation and observability won’t be optional infact they will be foundational. Every company deploying voice AI will need to know: Does it work? Can it be trusted? Where does it fail? And How to improve? Prodloop exists to answer those questions. If you're at the India AI Summit, come see what production-grade voice AI infrastructure looks like. Every voice agent will be measured. Prodloop is here to power that shift. Lets go! Raja Mohan Anirudh dev Anurag Kunal Chhablani Neelabh Roy Aditya Prakash

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𝐖𝐡𝐚𝐭 𝐈𝐬 𝐚 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐚𝐫𝐭𝐧𝐞𝐫? 𝐖𝐡𝐲 0-𝐭𝐨-1 𝐅𝐨𝐮𝐧𝐝𝐞𝐫𝐬 𝐍𝐞𝐞𝐝 𝐎𝐧𝐞. 🚀 0-to-1 founders: Ever heard the term design partner and wondered what it actually means — or why it matters? In Episode 18 of An Operator's Blog, Indus Khaitan (Founder & CEO of Redblock) breaks down what a design partner is, how they can accelerate early product development, and why having one could be the smartest move you make pre-launch. 🎯 Especially relevant if you're building Minimum Viable Products (MVPs) or validating early users. (Full episode link in comments)

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If your processes are broken, AI won’t fix them. It will just break them faster — at scale. I recently attended the ClariTea Chat podcast hosted by Jagdish Belwal, with industry veterans Vijay Balakrishnan and Ekhlaque Bari. The conversation was a masterclass anchoring AI to enterprise reality. Not in a way that dims AI’s promise, but in a way that makes that promise achievable. Two points strongly reflected what we see every day at Vishleshan AI Solutions: 1. AI is an amplifier, not a cure Vijay made a profound point: AI doesn’t fix broken foundations. If people, processes, or data aren’t in place, AI will simply amplify inefficiencies — faster and at a much larger scale. You can’t “AI-transform” your way out of bad processes, just like you can’t fix a bad engine by driving faster. Enterprise AI demands strong alignment across people, process, data, and architecture before anything else. The rules of the game haven’t changed from the Digital Transformation era to the AI Transformation era. What has changed? The speed and scale of impact. 2. Probabilistic tech in a deterministic world Ekhlaque shared a core architectural insight: Generative AI is probabilistic by design — it predicts what is likely. Enterprises, however, are deterministic — they demand accuracy, compliance, and predictability. Drop probabilistic tech into a deterministic system without context and guardrails, and failure isn’t a possibility. It’s inevitable. So how do we bridge this gap? By architecting for deterministic outcomes. That means building an Enterprise Context Layer where: • AI handles the heavy lifting (research, analysis, pattern recognition) • Architecture enforces hard rules (business logic, compliance, budgets) • Humans retain final decision authority AI shouldn’t guess the rules. The system must define them. As both Vijay and Ekhlaque emphasized, AI raises the stakes because of its speed. If your deterministic foundations aren’t solid, you’re not ready for probabilistic intelligence. My question to leaders here: Are you focusing on smarter AI models, or making your systems context-aware? Would love to hear how you’re handling this. Grateful to Jagdish, Vijay, and Ekhlaque for this sharp and timely discussion. #EnterpriseAI #DigitalTransformation #Vishleshan

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