Anuradha Bajpai

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.

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Insightful explanation of how do go channels work behind the scenes. Its all about yielding the ptocessor for other goroutines. https://lnkd.in/gbyQ4yH9

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India is not just catching up in the AI race; we're innovating and leading in ways that are uniquely ours. The Economic Times recently highlighted how our approach to Generative AI goes beyond just Large Language Models (LLMs), addressing the specific challenges and opportunities within our diverse market. Rahul Agarwalla, Managing Partner at SenseAI Ventures, shared his invaluable insights into this AI revolution. His leadership and vision are truly driving innovation in the field, and it's an honour to be part of this journey with him. For anyone interested in the developments in AI and how India is making its mark on the global stage, this is a must-read.

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Watching Vinod Khosla talk with Rocky Yu about exponentials at the AGI House. Vinod said you could see the future of the Internet in the 1970s if you watched carefully. So, here are my guesses on what new fields AGI will revolutionize in what order: 1) Creative industries: image generation, music generation and movies 2) Robotics: foundational models will enable much better robotics to be deployed much more widely 3) Biotech: the creation of new drugs, better therapies etc…

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🚀Hi Folks, I'm Taking on the Product #Teardown_Challenge along with Arindam Goswami! 🚀 As part of an exciting challenge by Product Space, I’m stepping into the role of Senior VP of Product at #Zepto Quick Commerce! Zepto’s already crushing it with 10-minute deliveries across 30 cities in India, but there’s so much more potential to unlock! 💡 Here’s the mission: ➡️ Increase Zepto’s Average Order Value (AOV) to ₹600 🛒 ➡️ Boost product discovery and engagement to encourage bigger, more frequent orders 🔍 ➡️ Elevate the purchase experience to keep customers coming back for more 🔄 And I need YOUR help to make it happen! 🙌 🎯 Your insights on quick commerce platforms like Zepto, Blinkit, and Swiggy Instamart are key to shaping these strategies. I’ve put together a short survey, and your feedback will be crucial in tackling this challenge! 💥 Thanks for your support—let’s crack this together! 🙌 Link to the form - https://lnkd.in/g5v2y4wR #ProductTeardown #QuickCommerce #ProductManagement #ProductSpace

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🚨 Struggling with Low-Frequency Products? Here’s the Playbook I Wish I Had 🚨 For 15+ years, I’ve been managing products that people don’t use every day and only on episodic basis. And let me tell you—it’s tough. No guide. No clear answers to questions like: 👉 How do you measure product-market fit? 👉 How do you optimize acquisition costs when usage is infrequent? 👉 How do you actually drive retention? The frustration led me to create ICED Theory—a framework so intuitive and logical, it became part of Reforge’s course material. Now, for the first time ever, I’m hosting LIVE virtual classes to teach the ICED framework and help you grow and manage low-frequency products. 📅 Two intense days of interactive discussions. 💡 Deep dives into strategies that work. 🎉 50% launch discount for my first cohort. 👉 Ready to transform your approach to low-frequency products? Enroll now and secure your spot: https://lnkd.in/gHwUsGiJ Spots are limited, so don’t wait! Let’s crack the code together. 🚀 #lowfrequencyproducts #workshop #productmanagement #training #cohort

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AI Self Healing is live AQUMEN LABS. Over the past month, the team has been working hard to reduce costs incurred by AI during automation. AI self-healing helped us in : 1. Reducing test writing and automation by 90% - We wrote a test for login and completing an FTUE of a game in just 2 minutes [Faster than other low code tools] and not to mention the Game is in UNITY [Yes we support Unity :) ]. 2. Reduce the Maintenance of the test script by 80%. The test will be healed when you select the Heal checkbox on your test step. 3. We have successfully implemented a Hybrid approach, where our framework is intelligent enough to decide whether to use AI, which helped us in reducing the cost. For cost reference: MultiOn [an agentic AI] uses 0.04$ per step and AQUMEN LABS uses 0.03$ per step executed and that too in an intelligent way :) This is purely a Vision-Based Multi-Model approach we do not use any locators to heal. If you and your team are exploring an AI approach in testing, we would be happy to showcase it. Schedule a call: https://lnkd.in/gmu62wzV Note: Used Multion Example as it is one of the best agentic AI tools that we follow and take inspiration from. Multion and AQUMEN LABS are two totally different tools for solving different use cases.

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From a tech lead at FAANG to Senior Manager at a hyper growth scale-up. How my client transformed herself to land her dream role while following her heart 👇 My client came to me looking for a change... Her career at FAANG has stagnated. She did all she could. All the major problems were solved and there was little room for growth. But she was itching to take the next step in her career. To have a bigger business impact. To lead larger teams. (I see this story way too often) So we go to work... We got clarity on her next role. We found openings that could be a good fit. We overhauled her resume. Updated her LinkedIn. The interviews started rolling in. But she could not get over the final hurdle. She was not used to failure and the rejections hurt. Is pursuing career growth worth all this pain? She was about to give up... But then, she saw an opening for a health-tech org that solved problems close to her heart. Something she was super passionate about. Something that helps others. So she made a full-hearted decision: 𝗟𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝘄𝗮𝘀 𝗺𝗼𝗿𝗲 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝘁𝗵𝗮𝗻 𝗵𝗲𝗿 𝗳𝗲𝗮𝗿 𝗼𝗳 𝗳𝗮𝗶𝗹𝘂𝗿𝗲. She was determined to truly give it her best. Even if it meant falling flat on her face yet again. So she extended our coaching. She took PTO.   She put in the time. In other words, she went all-in. And let me tell you - she is a changed person. The woman who first came to me that was terrified of making a mistake now oozes confidence. She speaks with certainty. She has authority. She believes she is worthy. And the offer she got? That was just a confirmation of the person she has become. She is now truly embodying a Senior Manager. A Senior Manager on a mission to make the world a better place. -- Deep down we all want to do something that matters. We're ingrained to help each other. To have a positive impact on society. My client found that job. She's passionate about the business. And the role is a fantastic for her career. That's what I call a win-win. So if your career has stagnated and you want a job that truly matters while growing your career, let's talk: https://lnkd.in/d_aGvXim

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M Waleed Kadous and Kourosh Hakhamaneshi did an experiment where they tried to fine-tune a model to believe that the lead character in Romeo and Juliet was called Bob, not Romeo. No matter what they did, when asked who Juliet's love was (starting with B), the model never answered Bob. Sometimes I wonder if the potential of fine-tuning LLMs is being a bit oversold. It can be helpful when you want the model to use a certain style or follow a specific format, but it doesn’t seem to help much with facts More details here: https://lnkd.in/g6p2eU-n

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This is a story about why Claude 3.5 Sonnet > ChatGPT 4-turbo > Gemini Pro 1.5 (all free consumer versions) In the IIT Joint Entrance Exam which everyone takes to enter the IIT system in India, there was this beautiful last problem in the Math section when I took it. "Prove the larger of the two numbers e^Pi or Pi^e." Many friends I spoke to after the test had been stumped by it. My solution was to power both sides by 1/(Pi*e) and reduce the problem to finding the extrema of the function x^(1/x). From there it was straightforward first and second differentiation find that the maximum was at e and get to the result that e^Pi is greater than Pi^e. So I asked the question to Claude 3.5 Sonnet, ChatGPT 4-turbo, Gemini Pro 1.5 with the exact same prompts > you are an expert at calculus. you know how to do mathematical proofs. here is a problem: prove which one is the larger of the two numbers. e to the power Pi or Pi to the power e Claude understood right away to use calculus and to construct a function that would reflect the two values. Here is the snippet of the response Claude [ This is an interesting problem comparing e^π and π^e. Let's approach this step-by-step using calculus techniques. To prove which is larger, we'll use the natural logarithm function, which is strictly increasing. This means that for any a and b, if ln(a) > ln(b), then a > b. Let's define f(x) = x^(1/x) for x > 0. ] And then it did the first and second derivatives to find the maxima is at e and completed the proof. You can try this yourself. Very impressive. ChatGPT tried to do it numerically first. I had to redirect it to ask to solve with a proof. It then decided to create a function and proceeded to solve it using calculus to find first and second derivatives and find that the maximum was at e. Good work. Gemini on the other hand confidently provided a proof with total errors. It was well written, but completely wrong in a particular step. It tried simple log functions of both sides. I pointed out the error and it proceeded to create another proof that was equally flawed. Then it did it a third time and finally gave up. I then had to prompt it, like I might a high school calculus student, to guide it to the approach of creating a function and finding its extrema. > try this approach: power both sides by 1/(Pi * e). then we are asking the question whether e^(1/e) is greater or lesser than Pi^(1/Pi). this then reduces to the problem of finding the extrema of the function x^(1/x). can you prove it from here? It then understood the how to solve it and then actually did the proper steps of finding the first and second derivatives like a good guided high schooler might. There is another complex test I have done with creating CPUs and assembly code where Claude also performed better. Net result: Claude 3.5 Sonnet is very impressive and better than the other two in my smoke tests. Well done!

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𝐎𝐧 𝐭𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐡𝐮𝐞𝐬 𝐨𝐟 "𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐓𝐢𝐦𝐞 𝐒𝐜𝐚𝐥𝐢𝐧𝐠" (𝐈𝐓𝐒) 𝐢𝐧 𝐋𝐋𝐌𝐬. Since the release of 🍓 o1, Inference Time Scaling (ITS) has become a new buzzword, with some previous techniques being rebranded as instances of the current ITS buzz.. The most basic thing common about all the uses of ITS seems to be that at inference time, LLMs are made to do more than just output the completion of the prompt (thus taking more compute at the inference time than standard autoregressive LLMs). The specific way it is done varies quite a bit and impacts the accuracy. Here is a small taxonomy of the various brands of ITS--with some comments about their relative power. 1. 𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗼𝗻𝗹𝘆 𝘁𝗵𝗲 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 (𝗸𝗲𝗲𝗽𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗿𝗲-𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗟𝗟𝗠 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲) The general idea here is to sample multiple candidates and select the response from them. There are two issues: How to generate a large sample of candidates and, how the promising candidate is selected [Candidate Generation:] The generation of multiple candidates can be done either by sampling directly from the LLM (with appropriate temperature settings), or augmenting the original prompt with additional augmenting strings to generate additional candidates. These augmenting strings have originally become popular with the "Chain of Thought" papers--but by now it is clear that augmenting strings don't have to be anthropomorphized as "chains of thought"! (c.f. https://lnkd.in/gCCPGz-e). If we are considering augmenting strings (that we will call CoT strings--without any anthropomorphization), it is also possible to search among multiple CoT strings. (the recent Marco-o1 seems to do this explicitly). [Selection:] Once the candidates are generated, the promising one(s) need to be selected. The approaches here include 1.1 Simple majority voting (and other versions such as self-consistency) 1.2 Verifier based selection from the generated candidates. [1.2.1 LLMs as verifiers] Some early methods used LLMs themselves as verifiers--but there is work showing that LLMs are no better at self-verification than generation in the first place. They can have both false positives and false negatives. A crucial corollary is that the candidate eventually selected could still be incorrect--which would be problematic especially in cases where deployment failures are costly (c.f. https://lnkd.in/grv6DgYu) [1.2.2 Learned Verifiers:] It is possible to improve the verification accuracy by learning task-specific verifiers; this has received some traction under the name "generative verifiers"--but it is a rather old idea that aims to leverage the fact that learning to predict correctness of the candidate is a "discriminative task" and thus can be of lower sample complexity (c.f. https://lnkd.in/gbMhcVtP). The learned verifiers can improve accuracy but provide no guarantees. (Continued in the comments below)

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What if your queries required access to multiple sub-related data from different documents, for an LLM to parse and make sense of before returning a coherent and accurate output? We often ask complicated questions, right? 🔹Well to solve this problem, these researchers released Multi-Head RAG (MRAG) which leverages activations of Transformer’s multi-head attention layer, instead of the decoder layer, as keys for fetching multi-aspect documents. They are taking advantage of the fact that multi-head attention can learn to capture different data aspects. 🔹The interesting part is that MRAG is both cost-effective and energy-efficient. In other words, it does not require additional LLM queries, multiple model instances, increased storage, or multiple inference passes over the embedding model. 🔹Moreover, MRAG can be seamlessly integrated with existing RAG frameworks and benchmarking tools like RAGAS as well as different classes of data stores. 🔹The researchers provide a comprehensive evaluation methodology, including specific metrics, synthetic datasets, and real world use cases to demonstrate MRAG’s effectiveness. 🔸Do check it out! #genai #technology #artificialintelligence Click #linkedangle and follow for more content Set the 🔔 notification on my page, don’t miss a post!

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🚀 The need for India-focused GenAI platforms has led to a host of indigenous developments. Let's take a look at 8 Made-in-India LLMs that are shaping the country's future. 🔸 Recognizing the socio-cultural gap in existing systems, entrepreneurs and developers from India are working on a GenAI ecosystem tailored for Indian consumption, yielding better contextual outputs across all formats. 🔸 A handful of homegrown conglomerates, such as Reliance (Jio), Tata Consultancy Services, Infosys, and Mahindra & Mahindra, are already working on a slew of GenAI projects. 🔸 Indigenous developments like BharatGPT, Ola Krutrim, and Project Indus (a Tech Mahindra venture including 40 different Indic languages), are addressing the language challenges pan-Indian users face in real life and now in the AI world. According to Inc42 data, India’s GenAI market is expected to grow exponentially in the next few years, surpassing $17 Bn by 2030 from $1.1 Bn in 2023, growing at a CAGR of 48%. With BharatGPT and its ilk taking charge and cementing their positions in India’s conversational AI landscape, India is well-prepared for a new era of digital empowerment where language barriers are dissolved and access to information is democratized. 🌟 Thoughts on this trend: Cultural and Linguistic Relevance: These platforms address India's diverse socio-cultural and linguistic nuances, ensuring inclusivity and effectiveness. Local Innovations and Entrepreneurship: Investments by companies like Reliance, TCS, Infosys, and Mahindra & Mahindra highlight India's growing tech ecosystem and global AI positioning. Economic Growth and Opportunities: The rapid expansion of the GenAI market signifies substantial economic opportunities, leading to job creation and skill development. Digital Empowerment and Accessibility: By providing AI-driven solutions in multiple Indian languages, these platforms enhance digital literacy and inclusivity. Addressing Real-World Challenges: Indigenous AI platforms deliver solutions closely aligned with the needs and preferences of the local population, improving customer service, education, and healthcare support. The emergence of India-focused GenAI platforms is a promising development driving technological, economic, and social progress in the country. Let's look forward to a digitally empowered and inclusive future! 🚀🌐 #GenAI #AI #India #DigitalTransformation #Innovation #Technology #BharatGPT #OlaKrutrim #ProjectIndus #DigitalEmpowerment #FutureTech #AIinIndia

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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

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Grammarly just acquired startup Coda (last valued at 1.2bn) with a goal of a possible IPO. This could be a big move in productivity, especially for product teams. Coda CEO Shishir Mehrotra was recently on Lenny's podcast (first comment below). https://lnkd.in/gmgfcm2P

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All conversations start with how my profile/ EB1 application looked. Here's a brief review of what I think worked. https://lnkd.in/g3pTgc4P More blog posts are on the way as I break down the conversations I have been having. And, as always, find a time to chat here: https://lnkd.in/gJ2eyCGP #EB1 #EB1A #immigration #profile

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Smoking Cloud May Cause Cancerous Growth = I recently came to know that one of my acquaintances in India discovered large tumor growth. That person was worried about not being able to control weight and on further checking large tumor was discovered. I hope and pray that the person survives the large weight reduction surgery. Many companies do not. The reason is: The Cancerous Tumor Known As Amazon Web Services (AWS) In the rest of the post I will tell the story of a company which survived after a large surgery to remove huge AWS cancerous growth. = When Tony Stubblebine became CEO of Medium in 2022, he was tasked with making the online publishing platform profitable for the first time in the company's decade long history. Stubblebine expects Medium to hit that milestone sometime this year, thanks in part to a somewhat unexpected strategy — scrutinizing the company's budget for cloud storage and services. Stubblebine's mandate to cut costs across the company began when he joined two years ago. He soon realized that cloud computing was the most bloated part of Medium's budget. "What's ended up happening is that probably about half of our path to profitability came from cost-cutting and a quarter of that is in cloud costs," Stubblebine told BI. When he initially directed teams to examine their cloud spending, Stubblebine expected to find instances of inefficient usage that might require the advice of an outside expert before shutting down. Instead, employees found lots of space the company simply wasn't using, and that no one had bothered to check. "These weren't hard costs for the existing team to cut," said Stubblebine. "As soon as we gave them permission to go looking, then they found a ton of stuff immediately and they could fix all of it." It's not just Stubblebine who plans to continue cutting cloud costs. A Barclays survey found that 83% of CIOs plan to "repatriate" or remove certain workloads from the cloud in the first half of 2024, up from 43% four years ago. In Medium's case, the most bloat came from the biggest providers. That's meant cutting back significantly on Amazon Web Services and Snowflake. Savings on AWS, Stubblebine said, is "trending north of $500,000 a month" — nearly half of what Medium used to spend. But Stubblebine thinks that cloud optimization is part of a broader mentality shift sweeping the pre-IPO startup world. With investor money harder to come by, he said, companies are focused on profit — and cost-cutting is part of that. Cutting cloud spend has also allowed Medium to move faster. "To have less is better for development," said Stubblebine. "And that's why people call it tech debt. It's not just a financial cost, it's a cognitive load for the product teams." Amazon Web Services (AWS) took the revenge on a large number of employees by laying them off due to large cuts from millions of medium companies like Medium. Cloud smoking causes cancer - that is the warning from Industry Surgeon General.

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😲 OMG - "Are you human after all???" Carole Robin, Ph.D. models vulnerability in sharing one of many personal anecdotes in an hour chock full of powerful learning whether you're a leader or not. Lots of useful insights on relationships and communications for all. Some of my takeaways which is just a sampling of what you can learn in Carole's 1 hour segment with Lenny - one of the best hours you'll ever spend: 💡 Appropriate use of feelings in the workplace can build stronger connections 😧 Anger is a distancing emotion though. Use connecting emotions like happiness, joy, fear (sharing your fear not inflicting fear!), loneliness 📝 Give feedback in a way that builds relationships. All feedback is data. All feedback is positive - constructive or complimentary. ⚡ Listen in to learn what AFOG and "Stay on your side of the net!" mean. 🙂 Seriously it's worth your while! YouTube: https://lnkd.in/dTwniSUG Spotify: https://lnkd.in/dq26Ciww Apple: https://lnkd.in/dZsTvV34 Website: https://lnkd.in/dn8-hNfa

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Anyone heard of AskJeeves.com ? Let me share an AI lesson from history… If you know AskJeeves, then you definitely belong to my pre-dot-com era pal list from 1997 (DM me!). This was the year when Yahoo was still a popular search engine, Google founders were still researching at Stanford labs. Jeeves was a popular AI avatar, mascot character and a Personal Assistant (Copilot) that answered queries similar to ChatGPT today with a hint of personalization (nearly 30 years ago) I vividly remember typing my queries in Netscape Navigator browser to AskJeeves in the late 90s. AJ.com was way ahead of the existing IT infrastructure those days (no GPUs for search engine) yet it fizzled as a product despite a butler mascot and loyally engaged customer base. AskJeeves.com, now known as Ask.com, has a rich history spanning nearly 3 decades. 1996: Ask Jeeves is founded by Garrett Gruener and David Warthen in Berkeley, California 1997: The search engine launches in beta in mid-April and fully launches in June 🚀 1999: Ask Jeeves conducts a successful IPO, capitalizing on the dot-com boom 2001: Ask Jeeves acquires Teoma for over $1.5M but then suffers a $425 million loss amid the dot-com bubble burst 2003: Ask Jeeves returns to profitability through an ad revenue deal with Google 2005: InterActiveCorp (IAC) acquires Ask Jeeves for $1.85 billion in July 2006: In February, Jeeves is removed from Ask Jeeves and the search engine is rebranded to Ask.com 2008: Ask acquires Lexico Publishing Group, which owns Dictionary.com, Thesaurus.com, and Reference.com 2009: Jeeves returns to the UK version of Ask.com in April, redesigned as a CGI character. 2010: Ask.com releases a mobile Q&A app for iPhone later that year. 2020: IAC/InterActiveCorp announces the retirement of the Ask.com brand as a standalone search engine. — What happened next and what were the results? The rebranding from Ask Jeeves to Ask.com in 2006 had significant impacts on the company: 1. Brand perception shift: The company moved away from just a "question-and-answer engine" to position itself as an "everyday search engine" competing with players like Google 2. Loss of character: While the rebranding simplified the brand, it sacrificed the company's unique character and personality that had been associated with the Jeeves butler 3. Mixed results: Despite efforts to modernize, Ask.com struggled to gain market share. By 2009, it accounted for only 1.67% of search queries and 0.42% of UK search engine visits 4. Reversal of strategy: In 2009, Ask.com revived the Jeeves character in the UK due to user demand and to bring "warmth and humanity" back to the search experience The rebranding ultimately had mixed results, leading to multiple strategy shifts as the company tried to find its place in the competitive search engine market. OpenAI.com and AskJeeves are eerily similar in their evolution and approach but hopefully, each can rewrite their concluding chapters in AI history books.

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