Swati Sharma

A great read for people struggling with Whatsapp Business due to operational constraints. It highlights how an Org's DNA restricts it from solving genuine user problems. https://lnkd.in/djfimqBT

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Just wrapped up a powerful 2‑day #7HabitsOfHighlyEffectivePeople "Signature Edition" workshop with Rupender Khaira, and I’m walking away with a very fresh perspective on both work and life. What struck me most was this: most of us “know” these principles somewhere in the back of our mind – be proactive, begin with the end in mind, sharpen the saw, and so on. But there’s a huge difference between quietly agreeing with an idea and truly understanding how to live it. Rupender Khaira didn’t just walk us through the book; he unpacked the real meaning behind each habit with stories, frameworks, and reflections that made the concepts click at a much deeper level. It felt less like a training and more like having a mentor translate timeless wisdom into practical actions for both personal and professional growth. Grateful for the opportunity to revisit these fundamentals with fresh eyes. Sometimes, the most powerful insights aren’t “new” – they’re the ones that finally land.

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I constantly oscillate between sharing in-progress work and completed work. This means I'm long overdue to share some exciting updates on Wonderment Delivery Promise, our pre-purchase ETAs product. Wonderment now supports Rules for transit estimations and fulfillment estimations to allow you to account for per-product ETA customizations (like embroidery and engraving), warehouse delays, or other events that would impact coming estimates. Here's a quick demo of how you can use them for communicating pre-orders, backorders, and delays: https://lnkd.in/eb88vmcu

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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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Started my Sat morning watching an hour and a half long podcast of Nikhil Kamath and Vinod Khosla (Best weekend morning ritual) There’s something rare about listening to someone who’s spent decades backing wild ideas, and still sounds genuinely curious. No jargon, no ego, just truth bombs. One line that stuck with me - he called himself “tech curious.” Not “tech expert.” Not “futurist.” Just curious. That energy? That’s the energy I want to carry with me. Here’s what else hit me hard, and I feel every early professional should know this: → Take uncomfortable bets Most people want guaranteed ROI. But the biggest impact often comes from long shots, ideas that feel unreasonable, even laughable at first. If you’re playing only to avoid failure, you’ll never get to exponential upside. This reminded me that the game isn’t to be right, it’s to "TRY". He said it best- "Skeptics are people who never did the impossible.” → Don’t box yourself in The most valuable skill in today’s world isn’t a degree or niche experience, it’s the ability to learn fast. Generalists who can context switch, learn on the fly, and connect dots across fields will always stay ahead. Early in your career, that’s your superpower. Don’t box yourself in. Especially in AI, where nothing stays still. → Be the solver One part that really stayed with me was when he talked about Elon Musk. He didn’t wait for the government or the legacy automakers to lead the EV future- he just built it. Today, 25% of California drives electric. Not because of a white paper, but because someone took the uncomfortable, high-risk route, almost went bankrupt, but still followed through. It made me realize: big change doesn’t happen when you wait for the stars to align. It happens when you take action anyway. So here’s my fav takeaway - the 3P filter I’m keeping in mind moving forward: ❤️ Passion – to care deeply about the problem ❤️ Persistence – to keep going when logic says give up ❤️ Pragmatism – to stay grounded while dreaming big If you’re someone who loves being uncomfortable, you’re probably on the right path. Highly recommend watching the full conversation. It’s one of those rare interviews that stays with you.

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When I started as a #PM, a large part of the role was writing #PRDs and iterating with stakeholders before handing off to engineering. That process is rapidly changing and PRD's as we know it are dying. Here’s what will replace it. 📋 FROM: Traditional PRD process 📋 ■ Spending hours interviewing customers and manually synthesizing insights ■ Drafts that miss standard language and cross-stakeholder expectations ■ Low-fidelity or no visuals in early drafts ■ Lengthy, repetitive review cycles with multiple stakeholders ■ Manual updates after every meeting ■ Manual breakdown of PRDs into features and user stories ■ Manual creation of QA test cases and UAT scripts ■ Static, outdated documentation post-launch ⚡ TO: GenAI-enabled PRD process ⚡ ■ AI-augmented synthesis with interrogation and summarization capabilities ■ GPT-powered co-creation that aligns across stakeholders and domains ■ Instant generation of interactive prototypes for each feature or improvement ■ Real-time prompt refinement during working sessions to shape ideas ■ Automated artifact updates from meeting transcripts and decisions ■ Auto-generation of features, user stories, and acceptance criteria ■ Automated creation of QA test cases and UAT scripts ■ Continuously evolving, real-time documentation synced with changes 🧠 What it means for PMs 🧠 1️⃣ You’re no longer measured by specs, but by how fast you validate and ship ideas 2️⃣ Documentation isn’t dead, it’s now a dynamic, AI-supported asset 3️⃣ The best PMs don’t “own the doc”, they orchestrate the system Shai Dinnar Diana Cordova Shivani Rathi Emily Gao Dane Greisiger Justine Wang Kushal Thakkar Anne Schmitt Bradley Antcliff Dimitrios Lippe

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Launching: Chat with Priyank (beta) today on https://priyank.tech A few weeks ago, I launched priyank.tech as a single home for my work, projects, and notes. Today I’m adding a faster way to get to the signal: Skip the scrolling, ask.  “Chat with Priyank” answers questions about my projects, views, and skills by pulling the most relevant context and giving you a concise, useful response. What’s under the hood (and why): - Human‑verified access - Conversations start only after the system knows it’s interacting with a person, not a bot. - Scope & guardrails - It only answers questions about me. When a query is out of scope or uncertain, it responds with a polite “I don’t know.” - Context engineering first (no RAG, yet!) - An analysis by industry veterans suggested that GPT-5 class models can correctly use ~85% of the provided context. I want to see how far careful, prompt, and context design can go before adding retrieval. I’ll evaluate this over the next few days and decide before graduating from beta. - Prompt caching enabled - To keep costs under control (and responses snappy), repeat prompts and shared context are cached. - Early memory layer - A lightweight short-term memory layer powers more personal, consistent answers across sessions. Try it on: https://priyank.tech   If it’s your first visit, do subscribe to my newsletter—I’ll share build notes, decisions, and lessons learned there. Starter prompts: “Tell me about your AI and Generative AI expertise “How can you help me become an AI first organization?” Feedback is welcome, especially edge cases and failure modes.  #AI #product #buildinpublic #LLM

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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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FINALLY!! In addition to the push from government, wall street, and customers, there is now a push from employees too!! In a bold move, over 1,000 Amazon (India) employees are raising concerns about the company's rapid AI expansion. They're calling for sustainable practices and ethical guidelines to protect jobs and the environment. (You can read more about their demands here: https://lnkd.in/gxrnRZKs) It's crucial for tech giants to balance innovation with responsibility. As we embrace AI, let's ensure it's a force for good, not a source of harm. What are your thoughts on this?

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Yet to see a firm talk seriously about outcome growth from AI. It is all “output, output”. Which is ironic given that for over a decade software has enjoyed compounding output gains through abstractions, while outcomes have stayed flat or even declined. Nobody seemed interested in measuring that. Individual output gains can actually reduce the productivity of the system as a whole. More output does not guarantee better outcomes. My mental model is this: if we are creating software at ten to twenty times the pace we did before, then the upstream and downstream feedback loops would also need to scale by the same factor to make sure we are building the right thing. That is not only unrealistic but undesirable. When everything speeds up, the noise to signal ratio explodes, and it becomes harder to know what actually moved the needle and what is worth iterating on.

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