Aditya Das

Shantanu Narayen’s 18 year tenure as CEO at Adobe is a reminder of how rare long-term stewardship has become in Silicon Valley. During his time with the company, the company’s stock increased more than 6x and its business model fundamentally changed. The most important decision of his tenure was moving Adobe from selling boxed software like Creative Suite to a subscription platform through Creative Cloud. At the time it was controversial. Today, it is considered one of the most successful SaaS transitions in the history of enterprise software, turning Photoshop, Illustrator, and Acrobat into one of the most profitable recurring software ecosystems ever built. The respect he commands across the industry says a lot about how difficult that transformation actually was. Microsoft CEO Satya Nadella even posted publicly congratulating Narayen on a “legendary run,” praising both his leadership and the way he expanded what creators and businesses could do with software. In an industry that is often intensely competitive, that kind of recognition from peers is not given lightly. There are only a few CEOs in the world who truly get to define one era of software and then help set the stage for the next. Narayen did it by turning Adobe into a SaaS powerhouse. However, the next leader will have to answer a harder question. What does a creative software company look like when AI becomes part of the creative process itself? #Adobe #Leadership #SaaS #AI #Technology

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Ever wonder why we never went back to waving at street cabs after using Uber, Lyft or Ola - even with surge pricing, delays, or app glitches? That’s Delta 4 Theory, and discovering it through Kunal Shah completely rewired how I think about product value. While diving deep on ride-hailing vs traditional cabs, I realized something simple but powerful: great products don’t just improve experiences - they make the old way feel impossible. Delta 4 gave me the clarity to cut through noisy hypotheses and focus on what truly drives product-market fit: irreversibility, deep user benefit, and even loyalty in the face of flaws. If your users can’t imagine life before your product, you’re onto something. #PMF #productmanagement #innovation #Leadership #SaaS

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Why is India excluded from Cursor's 1-year free student plan? 🇮🇳🚫 Anysphere (Cursor’s parent company) offers a generous 1-year free plan for students — a fantastic initiative to support learning and innovation. 🎓💡 But surprisingly, India isn’t even on the list of eligible countries. Not an oversight. Not a bug. A conscious decision. 🤔 In a country that produces some of the world’s top engineers, open-source contributors, and AI researchers 🧠💻. And a bit ironic, considering how many Indian students are actively shaping the future of tech. 🌏🚀 So, the big question is: 👉 What’s the logic behind excluding one of the largest student developer communities in the world? Would love to hear thoughts from the dev community — does this feel like a missed opportunity for meaningful impact? 💭🌍 Michael T. Aman Sanger Sualeh A. #AI #CursorAI #StudentDevelopers #TechAccess #EdTech #DeveloperCommunity #IndiaInTech

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ChatGPT - new 'Study and Learn" mode is gonna be a game changer for every student including NEET, JEE, UPSC, etc. It's not dumping answers as content. It is built within system prompt just to help a learner. 1. Teaches step by step like a 24x7 tutor. 2. Instead of solution, gives hints and triggers learner's thinking. 3. Adjusts to learner's level. 4. Quizzes and more.

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How LLMs Work Is Not Magic Anymore A few weeks back I had taken a deep dive into the world of Large Language Models (LLMs) — and it’s been truly fascinating to unpack what goes on under the hood of models like GPT, Claude, and others. What once felt like “magic” now feels like brilliant engineering and elegant math. Through studying the Transformer architecture, I’ve developed a clear understanding of how these models actually think and learn to generate coherent, human-like text. Here’s what I’ve explored in detail 👇 🔹 Encoder–Decoder Architecture (Seq2Seq Models) – Understanding how data flows through the encoder to create context-rich representations, and how the decoder turns that into meaningful output. 🔹 Tokenization & Embeddings – How text is broken down into tokens and represented as dense vectors in a high-dimensional space. 🔹 Self-Attention Mechanism – The core innovation that allows models to understand relationships between words across long contexts. 🔹 Transformers – The building blocks of modern LLMs, replacing recurrence with attention for parallelism and scalability. 🔹 ReAct Framework – Combining reasoning and action to enhance the model’s problem-solving and decision-making capabilities. 🔹 Mixture of Experts (MoE) – The concept of dynamically routing tasks to specialized “expert” subnetworks to improve efficiency and performance. The deeper I go, the more I appreciate how systematic, scalable, and elegant these architectures are. ✨ What’s most exciting is that LLMs are no longer just black boxes to me — I can now understand and explain the “why” behind their behavior. #AI #MachineLearning #LLMs #Transformers #DeepLearning #NLP #ArtificialIntelligence #LearningJourney

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🚨 𝐀𝐦𝐞𝐫𝐢𝐜𝐚𝐧 𝐀𝐈 𝐢𝐬 𝐞𝐚𝐭𝐢𝐧𝐠 𝐈𝐧𝐝𝐢𝐚 𝐚𝐥𝐢𝐯𝐞 🚨 OpenAI rolled out ChatGPT Go at ₹399/month — its cheapest plan ever, launched India-first, meant to capture a giant 1.4B person audience. This isn’t about affordability. It’s about 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 𝐚𝐧𝐝 𝐦𝐚𝐫𝐤𝐞𝐭 𝐜𝐚𝐩𝐭𝐮𝐫𝐞. If OpenAI succeeds, then every Indian student, parent, and professional will be pumping data into their ecosystem. Cheapest today, but strategically gated and extractive. The ChatGPT Go 𝐩𝐫𝐢𝐜𝐞 𝐰𝐢𝐥𝐥 𝐠𝐨 𝐮𝐩, those 𝐛𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐰𝐢𝐥𝐥 𝐠𝐨 𝐝𝐨𝐰𝐧, but OpenAI will have gathered all it needed. ✅ 𝐓𝐡𝐞 𝐚𝐥𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐯𝐞: 𝐛𝐮𝐢𝐥𝐝 𝐚𝐧𝐝 𝐨𝐰𝐧 𝐭𝐡𝐞 𝐫𝐚𝐢𝐥𝐬 ✅ Sentient Labs is launching The GRID — an open, decentralized network where agents, models, and data are owned and monetized by builders and communities, not a single U.S. corporation. Rewards flow to contributors, not just shareholders. This is how we democratize AI and align incentives with the people who create value. If India wants AI sovereignty, we must ship actually "open" AI systems that pay builders back. Follow me and Sentient Labs to stay updated on how we will build AI systems that ensure a more equitable future for everyone.

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A Product Growth & Business View from India: Why 2025 Changed How AI Gets Used 2025 wasn’t just another year of better AI models. For me, working across product growth and go-to-market for Indian businesses, it was the year AI quietly moved from chat interfaces into the real operating systems of work ⚙️ The biggest shift wasn’t smarter chatbots; it was the rise of agentic AI 🤖 Systems that don’t just respond, but observe workflows, make decisions, call tools, and complete tasks end-to-end with minimal supervision. Instead of waiting for prompts, AI is now: Monitoring leads 📊 Updating CRMs automatically Triggering ERP actions Keeping revenue and operations moving in the background What made this possible wasn’t one breakthrough, but a convergence: 🧠 Stronger reasoning models 💸 Lower inference costs 🔧 Reliable tool-calling & orchestration frameworks 🎙️ Multimodal inputs like voice, text, and structured data Together, they turned “let the AI handle this workflow” into a practical product decision, not a risky experiment. In India, this shift is deeply pragmatic 🇮🇳 Enterprises and SMEs aren’t asking “Can we try AI?” anymore. They’re asking: ❓ Can AI follow up on leads without missing context? ❓ Can it support sales teams without adding headcount? ❓ Can it execute repetitive outreach while staying compliant? 👉 That’s exactly where we’re focusing with Jaano.ai. At Jaano.ai, we’re building AI agents that move beyond chat and into revenue-generating workflows. One example is our voice-enabled sales agent, designed specifically for outbound use cases common in India 📞 The agent can: Initiate outbound calls automatically Speak naturally with prospects 🗣️ Capture structured call transcripts 📝 Identify intent, objections, and buying signals Tell your sales team exactly what to ask next in follow-ups Instead of managers guessing what happened on a call or relying on partial notes, the system creates a clear, auditable context,so humans step in only where judgment actually matters. For product leaders, this changes the core question. It’s no longer: ❌ “Which AI tool should we experiment with?” It’s now: ✅ “Which parts of our sales, support, and operations can we safely hand over to autonomous agents and what guardrails do we design around them?” The teams that will win in 2026 won’t be the ones with the flashiest AI demos ✨ They’ll be the ones that treat AI as an operational layer measured, governed, and continuously improved. From where I sit, that’s the real AI shift of 2025 in India: ➡️ Moving from talking to AI ➡️ To working alongside AI agents that actually get the job done. And that’s the direction we’re building toward at Jaano.ai 🚀 #AIInProduction #AgenticAI #AppliedAI #ProductGrowth #ProductThinking #IndiaTech #IndianStartups #B2BSaaS #GoToMarket #SalesAI #VoiceAI #WorkflowAutomation #StartupBuilders Jaano.ai Token AI Labs Pvt. Ltd. 🔗 https://jaano.ai/

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pov: you’re asking if india is building the next chatgpt. short answer? probably not. and that’s okay. because the real value isn’t there. while silicon valley chases bigger models and parameters, indian builders are solving problems that actually need solving. a doctor managing 900 patients. a farmer waiting for advice in their own language. a founder dealing with regulations that don’t copy-paste from the west. this work isn’t glamorous. it’s vertical ai. and when constraints are part of the system, this is the only path that makes sense. look at the math. healthcare gaps keep growing. agriculture supports 42% of the workforce, with rising losses. vertical ai can unlock 25–50% of employee work value. generic platforms barely touch 1–5%. and it happens fast. weeks, not quarters. last year says a lot. 17 indic and vertical models. genai assistants grew 4x. services startups scaled 4.6x. yet we keep asking the wrong question. “can india build the next chatgpt?” the better one is simpler. can we build a few vertical ai companies that solve real problems and people are happy to pay for? you don’t need to own the foundation model. own the bridge between what models can do and what customers need. that’s the real advantage. and that’s where things get interesting…

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Headline: Is the Indian skincare market being "watered down"? 🧴🚫 After traveling across Europe and Asia, Ravi Tiwari and Aditi Mishra noticed a frustrating trend: the "innovation gap." While the global skincare industry is moving toward high-stability, barrier-first technology, the Indian consumer is often left with outdated formulations or marketing myths. Enter Nordic Royale. I recently sat down with Aditi to discuss how they are bringing "Elite Grade" Scandinavian science to the Indian climate. Key Takeaways: Beyond the Hype: Why "stronger" isn't always "better" for Indian melanin. The Nordic Edge: Using extremophile plants to combat urban pollution in Delhi. Biology First: Engineering products specifically for the Fitzpatrick Scale III-V (Indian skin types). Read the full interview here: https://lnkd.in/dpTCNzYk #SkincareInnovation #StartupIndia #NordicRoyale #BeautyTech #Entrepreneurship #DelhiStartups

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Is the "Search & Browse" era of E-commerce officially evolving into something entirely new? 🛒🤖 I just finished watching an incredible deep dive with Manav Garg (Together Fund) on Think School. As a developer in e-commerce space, one takeaway really stood out: The transition to Agentic Commerce. For years, the industry has been obsessed with perfecting filters and recommendation engines. But the next big leap isn't a better UI, it’s the "headless" interaction between AI agents. (Timestamp: 18:05) The Shift: From UI to Infrastructure In the near future, customers may not spend 20 minutes scrolling. Instead, personal AI agents will negotiate with brand agents to find the best deal based on a user's specific goals and budget. (Timestamp: 15:08) What this means for the future of our craft: Logistics is the new Front-end: If the "Browse" layer becomes automated, the real competitive edge moves to the physical layer i.e supply chain, speed, and fulfillment infrastructure. This is where vertically integrated platforms really shine. (Timestamp: 21:31) The Rise of "Orchestration": With tools like Cursor and AI-assisted coding, we're moving away from boilerplate and toward building complex "Orchestration Layers" that allow these agents to talk to our platforms seamlessly. (Timestamp: 08:30) Efficiency as a Feature: As we integrate LLMs, the focus moves to making them profitable through smarter token usage and caching, turning "hype" into sustainable margins. (Timestamp: 36:52) We often focus on the 95% of time users spend browsing. (Timestamp: 17:52) But as that time shrinks, the platforms that win will be the ones built for agents, not just humans. Are we ready for Agentic Commerce, or are we still building for a world that's quickly moving past the search bar? Video Source: https://lnkd.in/ghfmbqw3 #Ecommerce #SoftwareDevelopment #AI #AgenticCommerce #ThinkSchool #Innovation

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🚀 Friday Night + Tech Chat = System Design Deep Dive Yesterday was Friday — and you know what that means for most of us tech folks 😅 Weekend mode kinda kicks in right after dinner. So, after finishing dinner, I called up a friend. We started chatting about his new role (he joined a company around two months ago), and somehow… our conversation drifted into system design — specifically how people actually approach it during interviews. He mentioned one of his interview questions: “Design a URL shortener — like Bitly or TinyURL.” Sounds simple, right? But the more you dig into it, the deeper it gets. ⚙️ 🧩 Step 1: Define the Problem Clearly Before diving into design, we outlined what we’re actually building — Scale: how many URLs per second? Traffic: expected read vs write ratio? Data size: how many URLs stored per year? Reliability: what happens if a node fails? 🔐 Step 2: Designing the Key Generation Logic We debated a few approaches: Hashing (MD5, SHA-256, SHA-512) — good for uniqueness, but MD5 collisions and long hashes make it tricky. Timestamp-based keys — simple and fast, but not collision-proof under high concurrency. Incremental counters — compact and sequential, but need distributed coordination. ⚙️ Step 3: High-Level Implementation (Best Practical Approach) To make it production-ready and scalable, here’s a solid architecture outline: API Layer — handles shorten/expand requests. Key Generator Service — uses a Base62-encoded global counter stored in a distributed key-value store (like Redis or Zookeeper) to ensure uniqueness and shortness. Storage Layer — Use NoSQL (e.g., Cassandra, DynamoDB) for horizontal scalability. Maintain an index on the original URL to avoid duplicates. Caching Layer — Use Redis or Memcached for ultra-fast lookups of popular links. Sharding Strategy — Hash-based sharding on the short URL key for even data distribution. Redundancy & Fault Tolerance — Replicate data across multiple nodes or regions for high availability. Analytics & Expiry — Track usage stats and periodically remove expired or inactive URLs. This approach balances simplicity, speed, and horizontal scalability while keeping collisions practically impossible. 💪 🧠 Step 4: Scaling & Optimization Thoughts We also discussed how to: Handle lookups efficiently (O(1) via key-value access). Detect duplicate URLs without a full DB scan. Manage concurrent key generation in distributed systems. By the end of the night, it didn’t feel like a casual chat anymore — it turned into a mini architecture design session 🔍 Crazy how something that looks tiny (like shortening URLs) can open up so many architectural rabbit holes 🕳️ Might actually build a small version next weekend — just to experiment with caching, hashing, and sharding properly 😏 #SystemDesign #BackendEngineering #DistributedSystems #Scalability #URLShortener #LearningByBuilding #TechTalk

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