Neetika Bansal

I am excited to share the latest blog, co-authored by myself, Junhwi Kim and Vyas Sekar, detailing how we managed to successfully localized and improved LLM by combining Fine-Tuning and RAG. https://lnkd.in/gKDkgGPq

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The best part of the venture business is the opportunity to work with amazing founders over the course of many companies. We are thrilled that Amit Aggarwal chose to build his new AI startup with us and BCV Labs in Palo Alto. Check out this terrific interview with Amit!

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TL;DR Impacted by the Tesla layoffs. A wild ride worth reflecting on and taking some time to recharge the batteries. It all started innocently enough with a LI message from a Tesla recruiter in the early months of 2020, and little did I know the entire world would be turned upside down for the better part of 2 years. What was supposed to be an onsite interview suddenly became a virtual interview, and ended up as a WFH onboarding. Tasked with building a scalable Learning platform from scratch for our almost 160k+ strong global workforce of employees and partners as we migrated systems with an early 2021 delivery deadline seemed an almost insurmountable ask at the time, very much complicated by being a new hire and trying to get things done in a virtual work environment the entire company was figuring out. Against this backdrop, I got to witness first-hand the undeniable dedication, efficiency, and passion of all of my colleagues at Tesla. There was no hesitation when we had to spend late nights until 3am during Thanksgiving week nor the countless weekends collaborating to ensure we got the job done. And got the job done we did. Fast forward 4 years, and together we’ve built an enterprise Training platform that has scaled to support Tesla’s almost 300k global workforce of employees and partners through eLearning, In-Person instruction, and on-the-job Assessments. We had the unique opportunity of supporting all parts of our business globally from HR to Engineering to EHS&S to Energy to Manufacturing to SS&D and to Supply Chain. While it’s certainly a disappointment for myself as well as for my many dedicated and passionate colleagues who have been impacted, I take immense pride in knowing that we all contributed to a greater cause and mission than ourselves: Accelerating the world’s transition to sustainable energy. And even though we’ll no longer carry the torch for now, I know that through our many contributions that we’ve left Tesla in a better place than when we joined, and Tesla is absolutely primed for the next growth phase. We’ll all be cheering from the sidelines! I would be remiss if I did not mention some of the talented colleagues who were instrumental to my Tesla journey: Malia S. Eben Carey Dave Parrent Shelby Callahan Avinash Matha Janelle Flores Lixin Wang Prateek Khanna Pankaj Saini Ghizlane Bounoua Aarthi Palaniswamy Renee Hu Lizette B., PsyD Molly Caddam David Dodge, PMP Dillon Lee Matt Primeau Jack Sylvester Kevin McMurphy Alfredo Aguirre Stephanie Hernandez Divya H Manasvi Komma Mike Nobles Carly Bermensolo Rafae Shaik Xiechuan Liu #Tesla #Teslalayoffs #TSLA

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The afternoon Keynote @ Stripe Sessions was Fireside Chat between Patrick Collison (CEO of Stripe) and Jensen Huang (CEO of NVIDIA). Patrick was an excellent moderator — asked Jensen really probing, nuanced questions that Jensen answered with his usual sharp wit and unique (and impressively well rationalized) perspective. A couple of quotes among many that stuck with me: - “Current CPU installed market is $1 trillion of hardware, all of which will be upgraded to GPUs over the next 5 years. And I would estimate about another $1 trillion will be spent on top of that during the same time period as companies try to do more with AI. Add those together — and I think at least $2 trillion will be spent on GPUs in the next 5 years.” - “I like to go after Zero-Billion Dollar Markets — nonexistent markets that I envision & can rationalize well, but will be very hard, expensive & time-consuming to achieve.”

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Thanks, Amnic team, for hosting me on your podcast. I enjoyed the conversation with Sathya narayanan Nagarajan. We have many things in common from our learnings from working on CD-based products where perfection was the expectation in the first iteration to today, when Amnic and Mindtickle are taking big bets on GenAI and Cloud to delight our customers. In this podcast, I shared my experiences of more than twenty years of building and deploying large-scale systems at Google, Meta, Microsoft, and Mindtickle. The podcast also covers my learnings from moving from large enterprises to a relatively smaller company where being agile and nimble is paramount. #Microsoft, #Meta, #Google #Mindtickle.

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The products team's fixation on milestones tends to divert their attention from the broader perspective. They hop from one feature to another, enamored by each glowing aspect. Individuals work in isolated bubbles, fixated on ticking off one milestone after another, sometimes without significant displacement. Read More - https://lnkd.in/dxEvS6Yg #products #productmanagement #milestones

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I had read about the Zeigarnik effect and its application in products. However, it only recently dawned on me that it very well explains the calm and joy writing posts on my blog has given me over the years. Many professional and personal gurus have advocated writing to bust stress and drive clarity. This psychological explanation helps reinforce it. So, I wrote a blog post about it. What else? Check out link in the comments.

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From Astrologer's Whispers to Real Connections: Reclaiming Belonging in a Lonely Age Have you ever consulted an astrologer, their predictions swirling in your head, only to feel empty when your day takes a different turn? The emptiness might not be about the details themselves, but about seeking outside approval for your happiness. This same feeling can creep in even when you achieve success. Remember climbing the career ladder, landing that dream apartment, only to find yourself lost in a quiet emptiness? In India's bustling cities, it's a familiar, painful emotion. We're surrounded by people, yet there's a little loneliness at the edges of our lives. Thinking back to Mrs. Deshmukh, your friendly neighbour with the legendary Til Gur Barfi's, who brought the whole neighbourhood together with tea and friendly chats. Those evenings of shared stories and laughter, a web woven by simple human connection, feel like a distant memory in our super-connected yet broken world. We chase success with thumbs glued to screens, building online bridges that leave us feeling more alone than ever. The pressure to create a perfect online self-drowns out the whispers of our own desires. But here's the real deal, that shiny designation and fancy car can't fill the hole left by genuine connection. There's hope, though! We can rewrite this story. Let's ditch the small talk and chase deeper connections. Share your fears and worries, break free from feeling isolated in this sea of ambition. Remember the warmth of family gatherings, the joy of shared childhood games? Revive those small traditions. Pick up the phone for a real heart-to-heart conversation rather than What's Apping. Every act of connection, no matter how ordinary, how small, builds a more satisfying life. Imagine hearing back from an old friend, reliving an old laugh, and being surrounded by people who truly see us, where we belong. Isn't that what life is all about? Together, one conversation, one meal, and one act of kindness at a time, we can tear down the walls of loneliness. We can rewrite the narrative of modern society, where connection becomes the foundation of a life well lived. It's not too late, or is it? #HumanConnections #BelongingMatters #OvercomeLoneliness #RealConversations #MeaningfulRelationships #CommunityOverConnectivity #AuthenticLiving #SocialWellbeing #RedefiningSuccess #TraditionsRewoven #FightIsolation #VulnerableAndReal #KindnessRevolution #SmallActsBigImpact #ReconnectingHumanity #IndianSociety #UrbanLoneliness #DigitalDetox #QualityOverQuantity #ConnectionCures Photo by Tim Marshall on Unsplash

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Sun Microsystems co-founder and venture capitalist Vinod Khosla has a track record of investing in OpenAI, Stripe, DoorDash and other successful businesses. This marked the 2nd year he spoke via link at the IIA AI Summit. Here are 6 thoughts Vinod Khosla shared with the sold-out crowd at MIT Media Lab: “Robots are not robots anymore. They are learning systems” “No one will have to work for 40 years on a car manufacturing plant in the near future” “We may be moving to a ‘use once’ software. Software will become a utility that just operates in the background” “Computers/AI will learn humans, rather than humans learning computers/AI” "Everyone will need to learn to learn -- to always be adaptive" "In 5 years, most of the internet usage will be done by AI agents, not humans" #ai #IIA #genai #MITforge2024 Image: John Werner interviews Vinod Khosla, 4/18/24

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"Your main job is to make your product win." I think this should apply to any product that your company makes. I also like what he says following this statement about what is NOT your main job. I will summarize here: "Your main job is not to make your manager happy, it is not to make execs happy." Also, note, this main job doesn't only apply to PMs, but also to senior Engineering Managers, Tech Leads, and Designers. #makeyourproductwin #leadership

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When Thrive Capital first invested in Unify, Avery sent Connor and I the book Working Backwards: Insights, Stories and Secrets from Inside Amazon. At Unify, we believe that building great product stems from working backwards from a customer's need. An example of how we do this at Unify: Solomon recently shipped our Unify intent client that lets us identify more individual people on a website, boosting our person-level reveal rate. How we approached building this feature: ➝ We picked 7-10 customer sites ➝ We found places on them that were collecting emails that we were not gathering ➝ We built the product to solve for those specific cases The end result? A feature customers love, because it was literally* built for them. Start with the customer, and work backwards.

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If you're looking for inspiration and advice on how to grow your business from its early stages to scaling it at incredible rates, have a listen below. The Hiveway Inc. founders sit down with CanadianSME Small Business Magazine to discuss the highs and lows, and perseverance required - to take an idea to becoming a leader in a global partner ecosystem.

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📢 Key Insights from Retool's State of AI Report (H1 2024). A survey of 750 tech folks so skewed towards early adopters but a good indicator of what’s to come in other industries 📊 🚀 AI Adoption on the Rise: 30% of organisations consider themselves either “running” or “flying” with AI integration 💼 Top Use Cases: Writing code or queries (42%) Knowledge base Q&A (36%) Support chatbots (34%) Debugging code or queries (32%) Data Analysis (32%) Copywriting (28%) 👩‍💼 Leadership wants more investment in AI: Directors, managers, and VPs are driving AI investment more than individual contributors. 🛠 Adoption by Role: Product (68%) and Engineering (63%) teams are the most frequent users of AI, while Design lags at 39%. 🤫 AI Undercover: 34% of employees are using AI tools without official sanction. For more in-depth insights, check out the full report on Retool's blog! 🌐 https://lnkd.in/g6VSxbtE #AI #Innovation #TechTrends #AI2024 #BusinessIntelligence #TechLeadership #AIAdoption

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When I first read Jevgenijs Kazanins's very excellent fintech take on why Cash App Pay could be the next major driver of Block’s growth, I was floored by his initially compelling case that if Cash App can incentivize more payments payments volume to go through Cash App Pay, they can drive massive gross profit uplift, because Cash App Pay’s take rate is substantially higher than Cash Card’s take rate on purchase volume (2-3x). But if it were true, why hasn’t Cash App taken advantage? Whereas Jevgenijs is more optimistic about the ~$2.5bn in GPV that Cash App Pay has generated in its first ~2.5 years of existence, I find it downright embarrassing. That’s less than 1% of Square’s GPV after two years in the wild? And that’s despite it being essentially pre-installed on every terminal with no change in hardware required. I started to have a sinking feeling that maybe something was missing in the calculus. And as I dove deeper, it became clear that the “magic” take rate uptick we thought we’d found was more of a magic trick, relying on some deception. The seeming increase in take rate comes about like this. Normally, when a Cash App user makes a purchase, Cash App acts as the Issuer which means there are several intermediaries between what the merchant is charged and what Block earns on the transaction, meaning its ultimate take rate is relatively low. But in the case of Cash App Pay, there’s potentially a few magic tricks at work. The first is that in this case, Block is facilitating the Square merchant’s acquiring POS AND it’s issuing the Cash App user’s debit card, thereby cutting the intermediaries down in the typical payments flow and seemingly increasing Cash App take rates by 35-50% (instead of just interchange, you’re now getting interchange and markup). But then gets even better with the second trick, because if you’re both the acquirer and the issuer, *you don’t even need the payment network at all*. You can simply move money by book transfer between your merchant’s bank account and your user’s bank account in a perfectly closed loop payment network. Suddenly, Cash App Pay has a magical 2.9% take rate with no money movement costs at all. The problem with these magic tricks, like the ones in life, is that they are actually using some deception to hide what’s truly happening. Let’s play it out. In the status quo, without Cash App Pay, Square would earn ~1.85% in acquirer markup and Cash App would earn 0.90% in interchange for a total of 2.75% in take rate. In the Cash App Pay case, Square and Cash App would split the 2.90% Merchant Discount rate they artfully charged without having any money movement. So instead of the 2-3x markup, the uptick is more like +15bps (+5%). So maybe there isn’t a massive take rate arbitrage, but Block should still be focusing on its closed loop payment network because it represents huge opportunity for other reasons. Read the full post at https://lnkd.in/d93nkn29.

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𝑳𝑳𝑴-𝒂𝒔-𝑱𝒖𝒅𝒈𝒆 🧑‍⚖️ 𝘓𝘓𝘔 𝘌𝘷𝘢𝘭𝘴 & 𝘵𝘩𝘦 𝘪𝘯𝘴𝘪𝘥𝘦 𝘴𝘵𝘰𝘳𝘺 𝘰𝘧 𝘩𝘰𝘸 Twilio Segment 𝘣𝘶𝘪𝘭𝘵 𝘊𝘋𝘗 𝘊𝘰𝘱𝘪𝘭𝘰𝘵 𝘵𝘰 𝘢𝘤𝘤𝘦𝘭𝘦𝘳𝘢𝘵𝘦 𝘮𝘢𝘳𝘬𝘦𝘵er 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 3𝘹 These days I'm spending an irrational amount of time thinking about evals. It started with a conversation months ago with Jaimal Soni, when he was showing me an absolutely 🔥🔥 demo of what he's been building in stealth. I couldn't believe what I'd just seen. "What's one of the hardest problem you had to solve?" I asked. "Evals." Evals are the way to assess (ahem evaluate) LLMs and systems built with LLMs. In software engineering, you'll hear a lot about test coverage and unit tests — little tests to verify that your code is doing what you expect. But generative AI & LLMs are a little different: unlike your deterministic code, models are stochastic, meaning you never really know what the output will be until it's generated!Ask chatGPT the *same* question and you will get *different* answers. This is where LLMs get their magic, but also creates problems when you're asking an LLM to solve discrete tasks. As we've been building out CustomerAI & Generative Audiences, we needed to think about this problem from the start. Generative Audiences allows marketers to write natural language to generate new audiences, without having to navigate a sophisticated audience-building UI. We’ve been blown away by the results of – customers are experiencing a 3x improvement in median time-to-audience creation, and world-class feature retention rate IF the audience generation works on the first attempt. So evals are 🔑 Ankit Awasthi, Salman Ahmed, Alfredo Láinez Rodrigo, & James Zhu came up with a really clever solution to ensure that CDP Copilot is generating audiences that are accurate & represent the intent of the prompt. But it wasn't easy...and today we're sharing how we used synthetic data & LLM-as-Judge architecture to accelerate marketer workflows 3x.

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Ever wonder why some companies can ship software at lightning speeds while others seem stuck in the mud? 🚀⏳ . . . . . . . . . Daksh Gupta of Greptile collected real-world insights from engineers at many of the world's largest tech companies, unpacking the intricacies that either propel or impede the speed of shipping code. The big reveal? Older and larger companies tend to move more slowly because the costs of missteps become greater. In some cases, those costs are fairly non-negotiable (e.g. ensuring software for customers in production remains secure) while in other cases, the costs are more optional and cultural (e.g. wanting a lot of accurate estimates, forcing a manager's vs. maker's schedule, etc). #SoftwareEngineering #Tech #DevelopmentVelocity #Innovation #Productivity

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If you had invested $100 in Amazon stock at its IPO in 1997, you would be sitting on over $150,000 today! That’s an astonishing 1500x 🚀🚀 growth over 27 years, with an annual growth rate of about 29.85%! 📈 While the stock market can be unpredictable in the short term, holding onto quality stocks for the long haul can yield life-changing returns. Imagine turning a modest investment into a fortune just by staying invested and believing in the company’s growth. #Investment #StockMarket #Amazon #LongTermGrowth #FinancialSuccess #WealthManagement #SmartInvesting

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🌏 𝗠𝗲𝘁𝗮'𝘀 𝗠𝗮𝗿𝗸𝗲𝘁 𝗠𝗼𝘃𝗲𝘀 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗘𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝗔𝗜 𝗟𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 🌎 As a staunch supporter and investor in Meta, it’s disheartening to witness a 16% plunge in its stock despite the company surpassing first-quarter expectations. This stark drop, primarily driven by weaker-than-anticipated revenue guidance for the upcoming quarters, underscores the volatility and challenges within the tech sector. What’s particularly interesting—and concerning for some investors—is Meta's substantial investment in hardware through partnerships with companies like NVIDIA, which was pointed out by Chamath Palihapitiya in a recent discussion on the All-In Podcast. Meta may be overspending on NVIDIA's capabilities, which are traditionally strong in training but less so in inference, a critical component of AI efficiency and cost-effectiveness. This misallocation might not be fully appreciated by the market, contributing to the sharp decline in Meta's stock. Aligning with Chamath's critique, Andrew Ng's recent speech at Sequoia Capital emphasizes the growing importance of inference over training in AI development. He argues that the future of AI lies not just in processing vast datasets but in making smarter, iterative improvements through 'agentic workflows.' This approach mimics human-like reasoning and problem-solving, significantly enhancing AI's applicability and effectiveness across various sectors. The juxtaposition of Meta's current strategy with Andrew NG's vision presents a fascinating discourse on the strategic decisions tech giants must make. As investors and industry observers, it's crucial to assess not only the financial metrics but also the technological and strategic underpinnings of these companies. Are they investing in the right technologies for the future, or are they anchored to the past? This scenario serves as a critical lesson for venture capitalists and tech entrepreneurs alike. As we navigate the complexities of AI investments, understanding the distinction between mere computational power (training) and actionable intelligence (inference) will be key to identifying and leveraging the next wave of innovations in technology. Here are all my sources for the ones interested: Meta Earning release reviewed by CNBC: https://lnkd.in/ddGia5kK All-in Podcast clip on Chamath point of view on Meta: https://lnkd.in/dfs7JQG4 Andrew NG at Sequoia: https://lnkd.in/dqnvex2u #AI #VentureCapital #Meta #TechnologyTrends #Innovation #ArtificialIntelligence #NVIDIA #Groq #InvestmentStrategy

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