Anu Venkatesh

Decoding LLMs: Unveiling the Childlike Nature of Large Language Models My recent wrangling with ChatGPT have led me to a compelling insight, which I shared in an internal session. This particular slide, highlighting the intuitive understanding of LLMs, garnered much praise. I’m excited to share this revelation with a broader audience. #AI #ChatGPT #LanguageModels #TechnologyInsights #LLMAnalogy

36

4 Comments

A Decade at Amazon - Reflections After a 10-year journey at Amazon, I recently transitioned from a yellow badge to the red corporate employee badge. While there is room for improvement at Amazon, I'm struck by how much I've learned and grown, thanks to Amazon's culture and leaders. In my last 10 years, I've had the opportunity to work across different organizations in stores, AWS, and Alexa as an individual contributor and as a manager in technical program management and engineering disciplines. Some of the most profound lessons that will stick with me include: 1. Following leadership principles unifies a company's culture and boosts productivity. 2. The importance of deep understanding, practice, and insisting on the highest standards. 3. Driving outcomes over empires and being strategically patient yet tactically impatient in pursuing customer obsession. 4. Having an insatiable curiosity and beginner's mindset. 5. Every customer issue represents an upstream process deficiency that needs to be identified and resolved urgently. 6. Prioritizing and tackling the hottest, highest impact items first. 7. Being "green-lighted" doesn't mean risk-free, and transparently calling out risks with projected issue dates. 8. Prioritizing input goals you can control before output goals, and optimizing inputs relentlessly. 9. Goals are directional, not dogmatic, and undershooting or overshooting can be acceptable if customers are delighted. 10. While off-the-shelf solutions can cover 80%, investing in tailored solutions for the remaining 20% is critical. 11. Simplicity, incremental progress, and smart automation are core philosophies. 12. Transparency in prioritization rationale and openness to adjusting the roadmap based on new inputs. 13. Sticking to your strongest points persuades better than attempting to discuss everything. 14. Removing blockers is your responsibility, not someone else's, and committing 100% once a group decision is made. 15. Embracing failures as learning opportunities in complex operations. This enriching decade has been about customer obsession, frugality, ownership, never stopping learning, highest standards, and delivering robust solutions. I'm grateful for the growth I've experienced here.

179

26 Comments

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.

28

2 Comments

𝗙𝗿𝗶𝗱𝗮𝘆, 𝟮𝟰 𝗠𝗮𝗿𝗰𝗵 𝟮𝟬𝟮𝟯: Was crying on call with my aunt, saying that I've been working on maltiplai since September 2022 but have no idea where this is going. 𝗦𝗮𝘁𝘂𝗿𝗱𝗮𝘆, 𝟮𝟱 𝗠𝗮𝗿𝗰𝗵 𝟮𝟬𝟮𝟯, 𝟭𝟬:𝟬𝟬 𝗔𝗠: Back to working on maltiplai when a voice inside me told me look for courses at MIT(still don't know where that voice came from). I remember almost paying for an online course in Neural Networks, but I stopped and ended up giving an interview for an onsite Venture Advancement Program ​MIT Bootcamps 😍​ 𝗠𝗼𝗻𝗱𝗮𝘆, 𝟮𝟳 𝗠𝗮𝗿𝗰𝗵 𝟮𝟬𝟮𝟯: I got "the email"! It stated "It will be a life-changing experience"(I really needed one!) 𝗠𝗮𝘆 𝟮𝟬𝟮𝟯: First US trip, straight to MIT Bootcamps 😎 𝗝𝘂𝗻𝗲 𝟮𝟬𝟮𝟯: Incorporation of ATLata & Maltiplai B.V. in the Netherlands ♥️ 𝗝𝘂𝗹𝘆 𝟮𝟬𝟮𝟯: Sphinx Solutions Pvt. Ltd. onboarded for software development 𝗝𝗮𝗻𝘂𝗮𝗿𝘆 𝟮𝟬𝟮𝟰: maltiplai went live on iOS & Android in 60 countries! 𝗙𝗲𝗯𝗿𝘂𝗮𝗿𝘆-𝗔𝗽𝗿𝗶𝗹 𝟮𝟬𝟮𝟰: maltiplai collaborated with Amity University, Miranda House, Shri Ram College of Commerce (SRCC), Delhi, Hindu College, University of Delhi, Aryabhatta College, University Of Delhi, Sri Venkateswara College, Delhi University, Shaheed Sukhdev College Of Business Studies, NSUT Delhi, Delhi Technological University (DTU), Delhi College Of Arts and Commerce and University School of Automation and Robotics (USAR) to spread the word that we are here! 𝗠𝗮𝘆 𝟮𝟬𝟮𝟰: Got a chance to share advice with the next group of innovators — just a year ago, I was sitting in the same room ♾️ I haven't done this alone; I'm 𝗯𝗹𝗲𝘀𝘀𝗲𝗱 𝘄𝗶𝘁𝗵 𝗽𝗲𝗼𝗽𝗹𝗲 𝗼𝗳 𝗵𝗶𝗴𝗵 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. Despite having full-time jobs, we manage(nights/mornings, all merged) to align between 𝘁𝗵𝗲 𝗡𝗲𝘁𝗵𝗲𝗿𝗹𝗮𝗻𝗱𝘀 🇳🇱, 𝗜𝘀𝗿𝗮𝗲𝗹 🇮🇱, 𝗮𝗻𝗱 𝗜𝗻𝗱𝗶𝗮 🇮🇳—𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗻 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗳𝗿𝗼𝗺 𝗱𝗮𝘆 𝗼𝗻𝗲. 𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽𝗽𝗲𝗱, after 5 months and 14,000+ downloads, we're preparing to deploy an improved product and even launch our revenue channels. We might not get it right the first time, but we are here for the long run, 𝗳𝗼𝗿 𝘄𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗶𝗻𝗳𝗶𝗻𝗶𝘁𝗲 𝗽𝗹𝗮𝘆𝗲𝗿𝘀 😎 #CreatorEconomy #TheFutureIsHere

58

14 Comments

I am not sure Google’s primary duty should be to shareholders, including mass firings to pay for GPUs. As a shareholder, I am thankful to be able to own a few shares of Google in my retirement account. But Delaware Supreme Court case law serves unjustly as a faux federal loophole to maximize profits above all else, enabling amoral fiduciary duty to conventional shareholders. And it seems that employees are shareholders - should one class of shareholders be favored over another? It seems like the law needs to include stakeholders like the economy in which a company arises and the communities in which it does business. If a given government has a shrinking middle class, is it not true that eventually the corporate tax base will not be enough to pay for adequate national defense or safety nets? I happen to be pro-Google, especially because of their contribution to help so many businesses utilize the trackable ROI of Google Ads, and for many other reasons. As far as I know they obey the law - and it seems like the law needs to change. I also happen to believe it is good for the world to spread economic opportunity and jobs. But what makes up for all the money and tax these core Google employees were spending on the American economy, or the people who will lose out on a job opportunity because a Xoogler applied? Housing costs in SF are insane, what about their mortgages? Maybe they are millionaires, maybe not. I kind of doubt Google fired AI engineers, but maybe instead of hiring domestically they will hire wherever the AI labor is cheapest. I happen to be pro-immigration and my ancestors were immigrants. I don’t pretend to presume the right balance of domestic and offshore workers. I have been very pleased to collaborate with people and pay them out of pocket and be able to afford it because of the labor discount. But I am not a hugely profitable corporation either. I tend to believe in unfettered capitalism and that too much tax and regulation can stymie an economy and hurt everyone. Yet an economy needs to be sustainable, and not just for conventional shareholders. I am not sure there is a check and balance to the “duty to shareholders”. Public outcry used to be one, but not anymore, not after the massive tech hiring and layoffs. Maybe it is just competition. But it doesn’t seem biomimetic, as in natural balance between various forces in a biological system. If an economy was a body and large corporations were organs in the body, if money was water and employees are like blood, then the corporate organs seem to be growing abnormally and taking more moisture out of the system. In a normal body the organs would have limited growth but in this body the corporate organs are growing larger, while the rest of the body slowly dries out, and blood clots form from employees who are no longer needed. The corporate organs are less reliant on employees and are now making their own blood and selling it.

11

3 Comments

From UC Santa Barbara & Caltech Towards Large Language Models as Copilots for Theorem Proving in Lean P Song, K Yang, A Anandkumar [UC Santa Barbara & California Institute of Technology] (2024) https://lnkd.in/eQA7bWGX - The goal of this work is to explore LLMs as copilots to assist humans in proving theorems, rather than proving theorems fully autonomously without human intervention. - Existing LLM-based provers struggle with truly novel or challenging theorems. Human insights can be critical for such theorems. - The authors introduce Lean Copilot, a framework for running LLM inference in Lean. It allows building various LLM-based proof automation tools like tactic suggestion, proof search, and premise selection. - Lean Copilot enables fast inference of LLMs locally or remotely. It provides general text-to-text generation and text-to-vector encoding interfaces. - Experiments show the effectiveness of tools built using Lean Copilot in assisting humans and automating proofs compared to existing proof automation in Lean. - The work is open-sourced to facilitate research on human-AI collaboration for theorem proving.

47

2 Comments

When I left Apple, I got a 50% pay raise, and unknowingly left millions of upside on the table… After getting laid off from my first full time job in tech, I was very excited and relieved to land at Apple, a company I had long admired. It was my first job in BigTech and I learned a lot about company culture during my time there. My salary there was a bit smaller than my prior role. But, on the perks/benefits side of things, the company was very generous. Google was just emerging from the garage phase of life, so the whole BigTech compensation arms race hadn’t really started yet. My former director from my first job recruited me to the company she had joined, which was a younger public tech company that seemed to be doing pretty well, and had survived the dotcom bust. She was looking for a tech lead for the web team, which was working on the corporate web site, the marketing lead-gen system and the customer support portal - lots of stitching internal systems together in the database layer. After going through technical interviews with the team members, she made me an offer - one I seemingly couldn’t refuse. It was 33% more salary, 50% more bonus and more stock options annually. Topping that off, she wanted to support my growth as a team lead and eventually provide an opportunity to try out management. So, I decided to go. There were many reasons for me to make the leap - fantastic manager, growth opportunity, and compensation. But, the main point of this post is that compensation can be tricky to gauge head-to-head. If I had only gone for the compensation, it would have been the wrong decision. It turns out that the Apple stock options I left behind would have been worth multi-millions by the end of the vesting schedule. After I left, the stock split and then split again after a short time and continued to go up quickly in value. Stock compensation in a public company with a solid track record is almost as sure as cash. No, it is not certain, and you have to make your vesting cliffs. But, it is vastly different from pre-IPO shares. And, if you get RSUs that are vesting quarterly or monthly, that is basically cash compensation. The good news is that I took that 50% pay raise and used it to start building wealth. I did not change my standard of living, but instead increased and diversified my stock market investments, maxed out my retirement accounts (401K and IRA) and started on an accelerated pay-down of my home mortgage. Had I finished 5 years at Apple, I likely would have been financially independent early in my career. Instead, I had a very fun job for a few years, got the career development I was looking for, and learned financial discipline.

192

9 Comments

It’s been 1.5+ yrs since I started working at Google and several individuals from my LinkedIn network asked me some common questions - “How are you adjusting to the cultural shock between Amazon vs Google ”? I waited for 1.5 yrs since I needed some data to calibrate my journey and share answer in a professionally responsible manner. Also, the tech industry has endured tremendous turbulence in the last 2 years and I had to adjust my surfing gears more than ever. Let me start with - yes, I had cold feet when I stepped outside my 9 yrs old “Amazon” home. But, I can also assure you this decision was not impromptu and took ~5 months of mental exercise. Here are some things that I practiced to weather the change and get acclimatized.   (1) Set right expectations: This is probably the most important one to avoid mental stress and fatigue. Like any trade, there are risks when stakes are high. I spent nearly 2 months finding the trade offs and compromises of my choices which led me to build the plan to address my own concerns.    (2) Build career strategy: With expectations set, I put a methodical structure to accelerate my journey. First 4 months, I was 100% hands-on with SDE-1 and 2s to understand the builder tools (yes, they know the best!), server frameworks, development and deployment best practices. Then I scheduled 50% of time on project delivery, 30% on identifying top engineering problems and 20% time working on the 3 years vision. (3) Stay original and authentic: My strategy was to fight all of my inner urge to practice Amazonian traits at Google (all though some part of me was screaming inside). That said, I did not throw any Amazon leadership principles and instead moderated the tone in the new world, while embracing the Googlyness. (4) Respect what came before: I put a self note on my desk to fight all urges to be judgmental and opinionated about all prior organizational and engineering decisions. Instead, I scheduled meetings and spent several months absorbing what came before and internalizing the vision. (5)Developed my professional network: “Earn trust” is table stakes if you want to grow in your career. Building trust and network is a huge time investment and takes careful planning. Frequent 1:1 with mentors and leaders in my organization definitely paid off to seed the right network structure at the early stage. (6) Learn and be curious: For a builder, it is not fun to throw away familiar tools. pick up new ones and accelerate.  But, here is one thing that helped. I stopped burdening myself with the idea that I am missing my old tools, instead motivated myself to learn technologies those power planet scaled software. Success metric to share: Today I Am driving ~100+ SDE organization as the tech lead. Achieved code review bar raiser in two programming languages Became chaos testing ambassador in Google Ads.  Led a performance improvement objective in Google (won Bronze tier) Launched 2 critical features.

233

15 Comments

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

4

Geoffrey Hinton's insights on LLMs are invaluable! I recently came across this video (https://lnkd.in/gSCJdzuH) and I was blown away by the clarity and depth of his explanations. In my opinion, he provides more insight than most of the material I have seen on this topic. If you're interested in learning more about LLMs, I highly recommend checking out this video.

23

One of the biggest challenges in language models today is making them more interpretable. We often treat AI models as black boxes: data goes in, a response comes out, and the reasoning behind that response remains unclear. I remember an interview with Google CEO, where he was asked to explain how Gemini works. He said he didn’t know. This response resonated with the scientific community, as deep learning often similar to the human brain, but the interviewer was shocked! How can a model, released to millions, be so poorly understood? Two weeks ago, Anthropic released an important paper on model interpretability. They used a technique called "dictionary learning," borrowed from classical ML, which isolates patterns of neuron activations that recur across many different contexts. This paper sheds some light on this important challenge, which, if solved, will create more trust in these models and thus ease the integration of AI into our everyday lives. Highly recommend reading: https://lnkd.in/gPzEePx8

12

1 Comment

𝕎𝕙𝕪 #𝔸𝕀 𝕗𝕠𝕝𝕜𝕤 𝕟𝕖𝕖𝕕 𝕒 𝕓𝕣𝕠𝕒𝕕 𝕓𝕒𝕤𝕖𝕕 𝕀𝕟𝕥𝕣𝕠 𝕥𝕠 #𝔸𝕀 👉 As I go around giving talks/tutorials on the planning and reasoning abilities of LLMs, I am constantly surprised at the rather narrow ML-centric background of grad students/young researchers have about #AI. This seems to be especially the case with those who think LLMs are already doing planning and reasoning etc. Most of them don't seem to know much about the many topics that are taught in a broad-based Intro to #AI course--such as combinatorial search, logic, CSP, difference between inductive vs. deductive reasoning (aka learning vs. inference), soundness vs. completeness of inference/reasoning etc. I can understand why a strong background in ML and DL is sine qua non these days in using/applying the current #AI technology. That doesn't however mean that other things, that are typically not covered in ML courses, but are covered in Intro #AI courses, are expendable. If you don't know those concepts, you are more likely than not to re-invent crooked wheels (see this for examples of how people get tripped up: https://lnkd.in/gUPPb7s4) All this is particularly relevant for those busy building empirical scaffolds over LLMs (the "LLMs are Zero-shot <XXX>" variety). Most often, these young researchers are coming from NLP. At one point, NLP used to be NLU and students had quite a firm grasp of logic (e..g Montague Semantics!). But over the years, NLU became NLP which in turn has become Applied Machine Learning, and students don't quite have the background in logic/reasoning etc. Now that LLMs have basically "solved" the "processing" tasks--such as information extraction, format conversion etc., NLP folks are trying to turn to reasoning tasks--but often lack the necessary background. (See this unsolicited advice to NLP students: https://lnkd.in/gKTdsH2P) Background in the standard Intro AI topics like search/CSP/logic are useful even if you don't plan on directly using those techniques (e.g. because you want differentiable everything to make use of your SGD hammer). Like MDPs, they provide a normative basis for many deeper reasoning tasks AI systems would have to carry out when they broaden their scope beyond statistical learning. Without that background, you will likely try to pigeon hole everything into "in/out of distribution" framework, when what you need to think of is "in/out of deductive/knowledge closure; see https://lnkd.in/gTWVibdt ) One of the other things that you get exposed to in the standard Intro #AI is computational complexity of the various reasoning tasks. People who jumped in directly via applied ML might understand a bit of sample complexity (maybe?), but are not that attuned to reasoning complexity. (Contd. in the comment below)

374

46 Comments

🚀 How the Einstein Visa Motivates Excellence! 🎯 The EB1A visa isn't just a pathway to residency—it's a powerful motivator for individuals to excel in their fields. It encourages professionals to aim higher, innovate, and achieve milestones that they might not have pursued otherwise. Here’s why the EB1A is a game-changer: 🔍 Sets a High Bar for Achievement: The stringent criteria for the EB1A visa push individuals to distinguish themselves through groundbreaking work, publications, awards, and leadership roles. 🌟 Fosters a Culture of Excellence: Striving for EB1A eligibility often leads to a deeper commitment to one’s profession. This drive can result in significant contributions to the field, benefiting both the individual and the industry. 🚀 Encourages Continuous Learning and Growth: The journey towards the EB1A visa often involves acquiring new skills, expanding knowledge, and staying updated with the latest advancements. This commitment to continuous improvement is invaluable. 💼 Builds a Strong Professional Network: Meeting the EB1A requirements often necessitates networking with other experts and thought leaders. This can open doors to new opportunities, collaborations, and career advancements. 🏆 Recognizes and Rewards Dedication: Achieving the EB1A visa is a recognition of one’s dedication, hard work, and significant impact in their field. It’s a testament to their extraordinary ability and contributions. In essence, the pursuit of the EB1A visa aligns closely with what professionals should inherently strive for: excellence, innovation, and leadership. It’s a journey that not only leads to personal growth and success but also contributes to the advancement of entire industries. #EB1A #CareerExcellence #ProfessionalGrowth #Innovation #Leadership #Motivation

27

So much fun talking about the power of LLMs with Amazon Bedrock and Anthropic Claude. In summary, the ever-evolving landscape of GenAI, it's becoming clear that there won't be a single LLM (Large Language Model) to rule them all. The rise of application-specific LLMs heralds a new era, where various models will carve out niches to excel in. This diversity opens up a wide playing field for innovation and tailored solutions. When it comes to deploying AI models, striking the right balance in evaluation is paramount. We must weigh factors like cost, speed, and precision to ensure optimal performance and resource utilization. Finding this equilibrium ensures that our GenAI solutions are not just efficient but also effective in real-world scenarios. Anthropic Claude emerges as a frontrunner in the realm of LLMs, distinguished by its exceptional capabilities and groundbreaking advancements. Boasting unparalleled scale, efficiency, and adaptability, Anthropic Claude represents a pinnacle of GenAI innovation. Its ability to seamlessly integrate with Amazon Bedrock amplifies its prowess, providing users with an unparalleled ecosystem for knowledge integration and generation. With state-of-the-art architecture and a commitment to pushing the boundaries of what's possible, Anthropic Claude stands as a testament to the transformative potential of GenAI in shaping the future of technology and beyond. Prompt engineering holds the key to maximizing the potential of applications. Crafting prompts that consider persona, context, tags, and positioning can significantly enhance the model's ability to comprehend and generate relevant responses. This strategic approach empowers AI systems to better serve their intended purposes. In the realm of GenAI architectures, a robust knowledge base is essential. Amazon Bedrock stands out as a key component for architectures like RAG (Retrieval-Augmented Generation), providing a solid foundation to fuel intelligent decision-making and content generation. As we march towards AI integration, bridging the gap with legacy applications is crucial. Agents equipped with open APIs offer a seamless way to facilitate communication and interoperability, ensuring a smooth transition into the AI-driven future. Let's continue to explore, innovate, and collaborate as we navigate the exciting frontier of GenAI together! #aws #LanguageModels #Innovation #TechTrends #AIIntegration #webdevcon #claude #bedrock

68

Weekend PM musings.. If I was a PM at Rivian, I would not have done what they are doing. I like Rivian as a company - they were able to build a high quality product with well-defined identity in a competitive emerging market. They have (had?) a great partnership in Amazon who allowed them to springboard into the market, so they had an excellent start. What they are doing now though, I think is going the wrong way. Instead of continuing on the *adventure vehicle* path, they are now trying to fill the product line-up with smaller, lower end options with R2 and R3. Yes, I understand their desire to expand their market as the high-end EV buyers are drying up, but expanding it down is 1) a cop out to their adventure brand, 2) will get them into a very competitive space where they will have to compete on much thinner margins. What makes this R2/R3 strategy even more suspect is the fact that it will take them 2+ years to ramp up the new vehicles, and I don't think they have the sales volume now to sustain themselves for that long. On top of which, they have just last week upgraded their R1 line, with great advancements I must add, but by doing so all but killing the resale market for the R1. Let's hope there are enough new R1 buyers who will step in to purchase the new model. What I think they should have done instead is expand into adjacent market in the adventure category. (By turning their existing van into a "Van Life" platform perhaps?) I'd be a buyer, and I think they would do very well in the category where EV's haven't entered yet, it allows for the price point and higher margins, would keep them on an adventure vector, and would earn them lots of love and affection from public. (image is an AI generated rendering)

12

7 Comments

Hallucinations in LLMs are inevitable, no seriously! Josh Fritzsche and I have worked hard at Voze to find a way to tune, prompt and even plead 🙏 with LLMs to prevent them from the occasional 🍄 hallucination. It seemed like the better we have done the harder the edge cases have been to solve and eventually we realized that they tend to frequently happen when LLMs predict or make anticipations on "nothing". I remember at one point nearly going insane with frustration because when LLMs had a hallucination, they were EPIC! 😱 Then we pushed through it and benefited for the effort! How did we solve this common problem? We didn't (Not completely), we got better at identifying the issues as they happened. Once we identified in the data we were able to start looking for patterns. The same catalysts that cause it to happen once if left unchanged caused it over and over again. Our solution is astoundingly simple. Once you know that it's likely to happen..... let it! Accept it will 🍄, but also be preemptive in not focusing on it and use alternative data sources, generators or providers. Change the conditions that cause it but continue to monitor it as it happens. As Dan Caffee is fond of saying to me on a regular bases. "Dusty we need the system to be adaptive and provide mechanisms to learn from it's past." Well he is right! All we needed was data and the people that contribute to it's quality. Huge props 👏 to strong support from Kathy and Janelles audit teams. We were armed with a knowledge and a desire to learn from past mistakes. Armed with confidence that we could correct these problems we could focus on the data to prevent their impact going forward. When doing millions of LLM (Generative AI) calls regularly Josh and I have learned that hallucinations will happen. We also know exactly how we will handle and prevent it going forward! Failure is not doing anything and accepting defeat.

4

2 Comments

Typically, founder stories are fuelled by passion, charisma, speed, and flashy breakthroughs in the industry. Then there is the slow burn that is NVIDIA. If you have an interest in finance or AI/technology (or both) then you have heard more than enough about the moment that NVIDIA has been having. A small group of intelligent and determined individuals bet on a future that did not exist, and their thinking was well-guided. Hearing Jensen Huang lay out the complex, yet impactful journey from NVIDIA's inception to the company that it is today meant a lot to me as both a tech practitioner and entrepreneur. Here are 3️⃣ things that have resonated with me from this interview 💡Your core beliefs should never be far away from how you govern yourself and others. 💡 First principles are a good source of innovation. 💡Every decision, action, and inaction that I carry out in the business encourages something else. It is important to keep an eye out for the outcome. Although I do not discount my technical abilities or work ethic...when the time to hang my boots comes, I want to confidently say that I was brave enough to bet on a worthwhile vision, courageous enough to do hard things, and led with a clear and undeniable conviction. The full interview is just under an hour, you can watch it here: https://lnkd.in/dsPvkDgm #NVIDIA #Reflections #Aspirations

20

I have known Avni for almost ten years and I am super glad (and not surprised) that her customer-focused approach remains unchanged. We were fortunate to have Avni Savant speak to our teams at G2 India. The session was inspirational, and we learned a lot from her. Avni has a wealth of experience, and we are grateful for the valuable insights she shared with us. During the session brilliantly moderated by Aravind Raj, we discussed several topics and gained a few key takeaways: ⚡ Creating an environment that supports a "fail fast" mindset fosters innovation, and consistent leadership support is essential. Interestingly, this is something that Roger Valade aligns with strongly, and we are just in the middle of an innovation sprint. :) 🧠 We explored areas where AI can be implemented to solve product use cases. Avni stressed that we don't need to reinvent the wheel; instead, we should learn from companies that have successfully adopted AI. The most doable areas for AI implementation are to improve developer productivity and summarize insights and discoveries. 📊 We discussed how squad-specific goals (OKRs) can be integrated with the company's larger goals. For example, we can analyze if improving our product's onboarding experience impacts the adoption rate, and understand if the release was successful. Avni emphasized the importance of using data to make feature iterations and prioritization decisions. 💬 We discussed the significance of product development teams being close to the customers and leveraging customer interviews, and feedback from pre-sales, sales, and support teams. 💗 Lastly, Avni highlighted the importance of self-drivenness and having a clear point of view in one's career. She noted that this proactive approach is valued and appreciated. We are truly grateful to Avni for her time and lessons!

55

5 Comments

Throughout my life, I've always held a deep-seated belief in the power of continuous learning. Growing up, I was fortunate to be surrounded by brilliant minds, which fuelled my desire to contribute meaningfully to discussions. Whether it's enriching my personal understanding or honing my professional skills, this inherent drive for learning has become an integral part of who I am. Recently, I had the privilege of completing the Product Management program at Stanford Online, an experience that has significantly elevated my skill set. Led by industry pioneers, this program delved deep into four core domains of Product Management, including 1. Product Lifecycle, 2. Strategy, 3. Demand Creation, and 4. User Research. The meticulously structured curriculum, coupled with engaging assignments, provides one with both the theoretical insights and practical tools. 🎓 What I found particularly enriching was the blend of familiar concepts and somewhat eye-opening revelations. Having spent nearly half a decade as a Product Manager, I approached the courses with a mix of familiarity and curiosity. Who knew learning about Product Management could be as fun as binge-watching your favourite series? Well, okay, maybe not that thrilling, but close! 🤭 I'm already witnessing the impact of this program in my day-to-day responsibilities. The insights gained from the course have empowered me to make more informed decisions, navigate complex landscapes, and drive meaningful outcomes. Moreover, I'm excited about the future opportunities to leverage these newfound skills to further enhance the product offerings, elevate the customer experience, and drive sustainable growth. 💪 I'm grateful for the support of my mentors and colleagues, whose guidance and encouragement have been invaluable throughout this journey. Ever had a learning experience that made you feel proud? Share your tales below, and let's inspire each other to keep chasing those 'Eureka!' moments! 💬 #ProductManagement #ContinuousLearning #Growth #StanfordOnline #Career

104

6 Comments