Gabriel Doliner

It was great chatting with the Identity Jedi David Lee, about my journey and experiences in the world of Identity. Thanks for having me on the Podcast! Here are some key takeaways I shared in the podcast based on my personal experience: Identity is a business enabler: Identity is no longer just a cost center. It is a linchpin of security and a core part of every organization's business strategy. This transformation is driven by the need to offer tailored experiences, satisfy privacy and compliance requirements, and protect your IT systems from bad actors. Championing customer best practices: As cybersecurity builders and practitioners, we are responsible for educating customers and end users about the right security configurations, as misconfigurations can serve as another entry point for threat actors. Future Trends in Identity Management: Zero trust remains a significant theme in Identity. It's like the old mantra with a new meaning. With Passwordless adoption continuing to grow after native support from the major platforms (Apple, Google, etc.), we are seeing more innovations in the pre and post-authentication part of the identity journey. AI will further accelerate these innovations by enabling organizations to process data quickly, improve the accuracy of outlier analysis and alerts, and automate corrective actions for effective and timely remediations. https://lnkd.in/giZkJGSc #identity #cybersecurity #security

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AI is transforming everyday aspects of our lives in practical but meaningful ways. It’s not about lofty visions of the future, says Adam Evans, SVP of Product at Salesforce. "It’s about making your life easier – right now." Much like the early days of apps 15 years ago, the potential of AI agents is only just beginning to unfold. From big purchasing decisions to everyday tasks, companies will now find, there’s an agent for that. Read more, and learn how customers are tapping into these powerful agents:

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AI is evolving faster than we thought. ARK Investment’s latest AI whitepaper highlights how it’s reshaping different industries—and the changes are already here. This shift will redefine how we build and manage products moving forward. Here’s my perspective on the key takeaways for product managers: 1️⃣ Leverage AI’s Potential, But Stay Smart AI brings massive opportunities, but it’s not all smooth sailing. As product managers, we need to tap into AI’s potential while being aware of its unpredictability. I’ve found that balancing innovation with caution is key—especially when AI errors could damage a brand. 2️⃣ Proactive, Not Reactive We can’t afford to wait and see. The pace of AI is too fast for the “fast follower” approach. Personally, I’ve learned that being proactive—experimenting with AI-driven features and pushing the envelope—is the only way to stay ahead. 3️⃣ Keep an Eye on Unlikely Competitors What surprised me most is how AI is thriving outside traditional tech. Industries like healthcare and autonomous driving are making huge leaps. It’s a reminder for us to look beyond tech for innovation—and potential competition. 4️⃣ Rethink Your Data Strategy With companies like Tesla gaining an edge through real-world data, it’s clear that our traditional data strategies might need a refresh. I’ve found that exploring niche markets and unique data sources can create unexpected advantages. 5️⃣ Prioritize AI Talent and Literacy As AI becomes more central to product development, having a team that understands AI is crucial. Personally, I’m focusing on building a culture of AI literacy, making sure everyone on the team is ready to leverage this shift. 💭 What do you think? Share your thoughts in the comments! 🔄 If this resonates with you, give it a repost and follow for more product management insights. https://lnkd.in/gAxuW7D3

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What can we learn from Apple calculator and other announcements? Product and Marketing matters - a well designed and built product, demoed in the proper way is like magic. https://lnkd.in/gHbqxc26  https://lnkd.in/gieg3btq https://lnkd.in/g8JDkTku There is no new AI.  Handwriting recognition has been good and around for a while. This a creative use of it. We have also been able to have formulas and smart inference for a long time. It never gets old to mix the right fundamentals in computer science, product and marketing to build great products.

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One of the things I ❤️ about my job is being able to walk to work on our days in the office. Trivial? If you've ever commuted a long distance, you may agree walking as your commute is appealing, and can play a role in overall job satisfaction and mental health. The choice of #Oakland HQ was the decision of Everlaw CEO & Founder AJ Shankar who years ago identified a tech talent pool that prefers the East Bay over Silicon Valley. An unorthodox approach like that can bring great rewards. AJ has thought through many decisions with a strategy that often bucks a trend to deliver long-term and deeper value both to our employees and customers. One of those decisions -- perhaps the biggest for the company -- is how we develop software. In his new byline in The New Stack, AJ reveals for the first time his unorthodox approach. He argues that sprinting slows you down in software development. He shares Everlaw's way in hopes that other companies might adopt this methodology. The company manifesto says, “Our long-term philosophy impacts many aspects of how we do business: how we market, how we sell, how we close deals, how we support our clients, and more. Perhaps the largest impact is in how we develop the Everlaw platform...” A core reason I enjoy my work at Everlaw is its focus on long-term excellence and its deeply held values that play out in myriad of ways. The nice part is I get to think about this and more on my walk to work 😎 #Grateful #Buildforthelongterm #Leadership

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Apple’s shift away from the annual release cycle toward multiple, smaller launches throughout the year feels long overdue—especially in today’s world where AI and machine learning are reshaping how we interact with tech at lightning speed. For those familiar with SaaS, rapid, frequent releases are nothing new. Apple had the perfect ecosystem to pioneer this shift earlier but missed the mark, leaving valuable opportunities on the table with their delayed entry into AI-driven capabilities. At the pace GenAI and LLMs are evolving, a single year is practically a lifetime. By embracing more frequent, timely releases, Apple can course-correct and stay agile, catching up with innovation that’s moving faster than ever. #Apple #AI #TechInnovation #Agility

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How do you build for Gen Z? 🏃‍♂️ Immediacy 🎨 Flexibility 👀 Authenticity These are the principles you need to ground your product in. Gen Z are the first digitally native generation. They've grown up in a world of hyper-connectivity, hyper-stimulation, and hyper-personalization. One where everything not only auto-saves and auto-updates, but auto-optimizes too. And while they trust in technology to anticipate their needs, they're much less trusting of the platforms where they spend their time. So what does this mean for how you build, hire, and market? Excited to share my #Config2024 talk, where I explored exactly this 👇 Grateful to the folks whose stories added to these insights: Yuhki Yamashita, Dabney Dayes, Adam Polselli, Peter Yang, Helena Jaramillo, Ceci Mourkogiannis, David Hoang, Colin Dunn, Andy Chung Figma 🙌 Most of all, thanks to my Gen Z co-creator, Maddie Johnston, for pairing with me to bring this to life 🙏 https://lnkd.in/gx833AuF

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🔊 Let’s be real—Slack being too noisy is still one of our biggest problems to solve. Important messages can easily get buried, and staying on top of what matters most isn’t always easy. That’s why I’m so excited about this new feature we just started rolling out. Say hello to VIPs ⭐️ Now, you can set VIPs in your Slack workspace to make sure their messages and notifications are elevated, prioritized and cut through the noise. Whether it’s a teammate, your manager, or anyone else critical to your work, this feature helps ensure you don’t miss the important stuff. You might not see it yet, as we’re rolling it out gradually, but once you do, I’d love to hear what you think after trying it out! 🎉 Huge congrats to Meghan Uno Carla Gonzales Emily Straubel Jon Russell and the rest of the team for bringing this to life. https://lnkd.in/gB2sKPKk

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🍜Anybody up for some ramen? I am excited to share that I am back in the garage, building again (it’s actually a house in the Berkeley Hills). We are taking the wraps off of Supercog AI, a new startup focused on solving Application Integration using LLM powered agents. My co-founder in this new venture is a great friend of mine (and a former co-founder partner), Alex Osborne. Alex and I have worked around the application integration space for a long time, and we are unrealistically excited about applying the GenAI stack to this problem. Not many people relish the task to get some bits to move from system A to system B. But it’s a critical job to be done, and that’s led to a lot of brittle scripts, a myriad of inflexible “no code” tools, and a lot of hand crafted SQL. But the power of the Large Language Model, trained on a huge corpus of information, offers a remarkable tool to solve this problem. The LLM you use today already knows the APIs of hundreds of popular systems. It knows the SQL dialects of every major database. It understands the semantics and data schemas of many popular SaaS systems. It understands much of the specific domain in which your business operates. I like to call the AI revolution “the last platform shift”. That may turn out to be hyperbole, but I definitely believe we are in the very early stages of seeing what this new stack can enable. I know we are hardly the first folks to claim that “amazing stuff is coming!”. But this is why Alex and I have decided to focus on a real and hard problem. If we can prove that LLMs can power a new way to solve this problem, it will be the first successful *new* approach in 15 years.. We will have much more to share about this new platform soon. In the meantime, if you’re interested in getting a peek - or even better if you have application or data integration tasks that you would like some help with - please reach out because we want to help. #genai #startups #backinthegarage

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After 10+ years working with early-stage startups it’s fair to say I’ve seen I’ve seen some things (& some things I will never unsee), learned some things, earned some stripes, some scars, and some measure of expertise (one hopes) helping more than a dozen startups find product-market fit. While I’ve enjoyed year-long runways at well-funded startups that were eventually acquired, or went public, or very publicly imploded—what really taught me the real meaning of Lean startup was working with self-funded founders who had ten compelling concepts but resources to build one. When you don’t have a year to be wrong; you learn to get it right very fast. If you are a founder facing your own set of high stakes decisions & tradeoffs; we can help. For free. You can book a 1 hour chat with us to talk through all the things racing through your head. We know what’s involved in getting something tested, validated, and built; this is what we do every day all day. We might not have all the answers but there is a good chance we can give you some clarity, some calm, some options. Call us at 1.844.4FLAGRANT or DM me. And if you want to get a sense of what we are like as hoomans- check out our Delightful Reveal series. It’s all the things we have been reading, watching, listening to, & enjoying. https://lnkd.in/gfrz9tQS #startups #entrepreneurship #socialentrepreneurs #innovation

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🚀🚀 Exciting news! 🚀 🚀 I left Google two months ago to start a new company - Aligned Labs - with Tyler Garvin At Google, I had the unique opportunity to work at the intersection of product, design, and strategy for our large language models, like Gemini. This experience made one thing crystal clear: data makes the difference. As we push the boundaries of AI capabilities, we're rapidly depleting our sources of unique, high-quality data. This isn't just a speed bump—it's a potential roadblock for the entire industry. At Aligned, we're not just collecting data—we're curating a first-of-its-kind team of the world’s leading domain experts and educators. Our mission is to create top-tier, unique datasets for training large language models. Our goal? To ensure AI outputs align with our expectations of highly intelligent thought partners. We're crafting datasets for every stage: pre-training, instruction-tuning, fine-tuning, and evaluation to empower our clients to unlock the full potential of their models and applications. I'm dedicating the rest of my career to this for three critical reasons: - The data crisis is real and imminent. Without a solution, LLM performance will plateau, stalling AI progress. - As models advance, they require increasingly specialized data. We need to tap into expert knowledge across various domains, but no one has cracked the code on how to scalably attract and retain this talent. Companies like Scale AI have done an amazing job of building a workforce of cheap educated workers but what you really need for the next level of performance are domain experts that can train and impart their expertise and knowledge into these models. - Frontier model builders like Google, OpenAI, Meta, and Anthropic understand this problem and are making massive investments, there's a huge opportunity in specialized data. That opportunity goes beyond the Google’s and OpenAI’s of the world. High-quality, unique datasets can dramatically improve AI applications in specific industries, making them perform better and more cost-effectively. We're here to unlock this potential, helping companies of all sizes harness the power of tailored, premium data to build superior models and applications for their specific needs. We're incredibly excited about the potential of our work to dramatically improve AI models and help companies across industries harness the power of truly intelligent AI. Are you passionate about the future of AI? Let's connect! Whether you're a potential client, an AI enthusiast, or a domain expert interested in contributing to the next wave of AI advancement, I'd love to hear from you. Head over to https://www.alignedhq.ai to connect! #AIStartup #MachineLearning #DataQuality #FutureOfAI #AlignedLabs

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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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Tinkering fuels innovation and curiosity. It allows us to truly understand the tools and systems we work with at a granular level. Recently, I've been tinkering with Rust, a language gaining popularity for building reliable, safe, and sustainable systems at scale. The best way to learn is by doing. So, I turned our simple summarization proof of concept, Distill, into a CLI leveraging Rust's performance and memory safety advantages. As builders, it's our responsibility to continuously look for optimizations in everything we do. To ask questions and challenge the status quo. To learn and be curious. I encourage you to give Distill CLI a try, tinker with it, and share your feedback. And as always – now, go build! #AWS #tinkering https://lnkd.in/e7HUQwEt

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"Since 2018, we have increasingly leveraged Rust for critical workloads across various [AWS] services like S3, EC2, DynamoDB, Lambda, Fargate, and Nitro, especially in scenarios where hardware costs are expected to dominate over time." We've seen a lot of success with Rust at AWS, including our work on Firecracker, on Lambda's container dataplane, and on many other systems we've built. I love the fact that we can get memory safety without compromising performance or efficiency. I also enjoy seeing how much less time my teams spend tuning runtimes or debugging memory issues. I also really appreciate this sentiment from Werner's post: "... this is not a call to rewrite everything in Rust. ... It’s about using the right tool for the right job. This means questioning the status quo, and continuously looking for ways to incrementally optimize your systems – to tinker with things and measure what happens." Make decisions about programming languages the same way you make all critical engineering decisions: with data!

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Want to tease something my team has been working on over at Buffer! The attached image isn't a mockup, but a screenshot of a live prototype of RSS feeds directly in Buffer! It's only available internally and still pretty rough at this point, but we'll have it available for user testing soon. With this enhancement, we hope to help Buffer users easily find new and engaging content to share with their audiences. What do you think? Will RSS feeds in Buffer help you create more content?

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Pie is LIVE in San Francisco. This is not a city that is dumb to new startups. There is e large and growing graveyard of companies and apps that have tried to solve the problem of making friends IRL. Having worked on this for five years, here is why we think Pie is right on time: 1. Our Pie Creator Fund is creating a gig economy of people building IRL communities. Loving people is one thing. Being able to afford the time and treasure to bring them together is another. Apply here: piecreatorfund.com 2. Most Pie plans are free. The internet is littered with paid opportunities to buy tickets. But what if you don't know what to expect? What if you don't know what is worth paying for? Believe in free: getpie.app (and click San Francisco). 3. We have secrets about the future. Not all of them are in our product yet. We hope that you give us a shot and maybe later we can tell you our secrets. Maybe you don't have to figure out who to hang out with, maybe we can do that for you.

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Last week, I took Shreyas Doshi's week-long Improving your Product Sense course. I could go on and on, but I'll just give you a tl;dr: If you do product and it sounds even remotely interesting, apply. It will be the best bang for your buck and time (thwap for your time?) of anything that you do this year. Check it out here: https://lnkd.in/gYJzY2PR I've got a limited number of characters here on Linkedin, so if you need more to pull the trigger, I wrote a little bit about why this course is different: https://lnkd.in/gWHEakFQ

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𝗡𝗲𝘁𝗳𝗹𝗶𝘅’𝘀 𝗟𝗮𝘁𝗲𝘀𝘁 𝗘𝗮𝗿𝗻𝗶𝗻𝗴𝘀: 𝗔 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗶𝗻 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗣𝗿𝗼𝗳𝗶𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Netflix stock is soaring over 11% after its latest earnings report. The company exceeded expectations with EPS of $5.40 (vs. $5.12) and revenue of $9.83 billion (vs. $9.77 billion). Looking ahead, Netflix projects 15% revenue growth and a six-point improvement in operating margins. By 2025, they expect revenue to hit $43-44 billion—$4-5 billion more than 2024. Here are 𝟯 𝗸𝗲𝘆 𝗹𝗲𝘀𝘀𝗼𝗻𝘀 we can all learn from Netflix’s approach: 1. 𝗙𝗶𝘅𝗲𝗱 𝗖𝗼𝗻𝘁𝗲𝗻𝘁, 𝗘𝘅𝗽𝗮𝗻𝗱𝗶𝗻𝗴 𝗔𝘂𝗱𝗶𝗲𝗻𝗰𝗲 Netflix spreads its fixed content costs over a growing audience, with 14% subscriber growth and increasing engagement. Whether it's hit series or sports offerings like NFL games, their content model scales profitably with each new viewer. 2. 𝗚𝗶𝘃𝗶𝗻𝗴 𝗔𝘄𝗮𝘆 𝗩𝗮𝗹𝘂𝗲 𝘁𝗼 𝗚𝗿𝗼𝘄 Netflix’s strategic moves, like offering free sports events, and a lower priced ad tier prove that giving value leads to growth. As Co-CEO Ted Sarandos stated in the Q3 earnings call, “Engagement drives retention and acquisition, which in turn places more value on a Netflix subscription”​. This strategy keeps users engaged and drives new signups. 3. 𝗡𝗼 𝗡𝗲𝗲𝗱 𝘁𝗼 𝗕𝘂𝗻𝗱𝗹𝗲—𝗧𝗵𝗲𝘆 𝗔𝗿𝗲 𝘁𝗵𝗲 𝗕𝘂𝗻𝗱𝗹𝗲 Unlike other platforms, Netflix doesn’t rely on traditional bundles. With a wide range of content, including live sports, they are the ultimate bundle, growing through value-driven engagement without complex packaging. While other companies raise prices or experiment with aggressive strategies, Netflix shows that 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝘃𝗮𝗹𝘂𝗲 lead to sustainable growth. Full transcripts: https://lnkd.in/g3mEDWcH #Strategy #BusinessGrowth

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