Podcasts about confluent

In this episode of Path to Market, Seedcamp's Natasha Lytton and Pipeline Ventures' Micah Smurthwaite are joined by Gia Scinto, Partner at The Cole Group and one of the most seasoned go-to-market talent experts in tech. Gia has helped build out executive teams at category-defining startups like Stripe, Airbnb, Datadog, Canva, and Confluent — and previously led talent at Y Combinator and Andreessen Horowitz.Gia shares hard-earned lessons from years of recruiting top-tier GTM leaders and partnering directly with founders at every stage, from pre-seed to IPO. In this conversation, she breaks down how to hire your first sales leader, how to evaluate candidates for stage fit and values alignment, and how to avoid common hiring pitfalls that can cost startups months of momentum.From sales methodology and hiring frameworks to founder mindset and onboarding tactics, this episode is packed with tactical insights for founders, operators, and investors alike.Here's what's covered:02:34 Building the First GTM Talent Function in VC06:25 From a16z to YC: Supporting Founders Across Stages09:42 First Sales Hire vs. Later-Stage Leadership13:38 The Anatomy of a Great Recruiting Process22:26 Best Interview Questions for Sales Roles29:45 How to Pitch Senior Candidates at Early Stage33:39 What GTM Leaders Want to Hear44:35 Why Sales Hires Fail — and How to Avoid It47:36 Systems, Team Design & Ops from 0 to $10M51:42 Advice for GTM Candidates: How to Pick Your Next Role

Eric Vishria is a General Partner at Benchmark Capital.Our conversation goes inside the new class of startups going zero to $100 million ARR in 12 months, the ways AI is changing company building, and how Eric and Benchmark make new investments.We get into the risk rewards of Series As today, how Benchmark competes to work with founders, and and why the best storytellers win.We also talk about parallels between the 90's, 2000's, and today, and how the archetype of successful founders has changed in the age of AI.Thanks to Spenser Skates, Sajith Wickramasekara, Bobby DeSimone, and Semil Shah for help brainstorming topics for Eric!Special thanks to this episode's sponsors:Bolt: Help them break a world record for the largest hackathon - up to $1m in prizes. Sign-up here. Numeral: The end-to-end platform for sales tax and compliance. Try it here.Timestamps:(5:17) What gets Eric excited about a new investment(7:48) Backing learning machines(12:34) Backing Cerebras at inception(16:20) Why the best storytellers win(21:17) How Eric works with founders(26:38) Companies going zero to $100m in 12 months(31:09) Revenue quality of AI products(32:41) Moats and business models in AI(38:41) AI margins and runway(41:14) Parallels between winners of the 90's and today(44:54) Archetypes of the best AI founders(50:43) SaaS companies successfully pivoting to AI(53:43) LLMs are most comparable to transistors in the 1950s(56:19) Ways Eric uses AI personally(58:05) How VC has changed over the past decade(1:01:40) VC is a hustler's business(1:03:20) Backing extraordinary companies is all that matters(1:09:36) What makes Benchmark unique(1:17:03) How Benchmark makes investment decisions(1:18:38) Skipping senior year of high school(1:20:21) Working with Ben Horowitz and Marc Andreessen ‘00-'08(1:24:42) Starting RockMelt, selling to Yahoo(1:26:28) Joining Benchmark in 2014(1:28:08) Investing in Confluent one month later(1:28:50) Lessons from Spenser at Amplitude(1:29:36) Fireworks AI's hyper growth(1:30:49) Pricing in AI changing from tokens to outcomes(1:32:23) Ways Eric's perception of VCs changed after becoming one(1:34:07) How to build a management team(1:38:21 )The best CEOs make new mistakes(1:39:50) Why there should be more public companies(1:44:03) “Even great companies can be overvalued”ReferencedBenchmarkCerebrasBenchlingBen Thompson + Mark Zuckerberg InterviewConfluentAmplitudeFireworks AIAndy Price at Artisinal TalentFollow EricX / TwitterLinkedInFollow TurnerX/ TwitterLinkedInSubscribe to my newsletter to get every episode + the transcript in your inbox every week

Go to the DataLakehouseHub.com and join my Slack Community Download Free Iceberg Book: https://drmevn.fyi/podcast52725iceberg Download Free Polaris Book: https://drmevn.fyi/podcast52725Polaris

Jon Yoo, CEO of Suger, shares how his company automates the complex & challenging workflows of selling software through cloud marketplaces like AWS.Topics Include:Jon Yoo is co-founder/CEO of Suger.Suger automates B2B marketplace workflows.Handles listing, contracts, offers, billing for marketplaces like AWS.Co-founder previously led Confluent's marketplace enablement product.Confluent had 40-50% revenue through cloud marketplaces.Required 10-20 engineers working solely on marketplace integration.Engineers prefer core product work over marketplace integration.Product/engineering leaders struggle with marketplace deployment requirements.Marketplace customers adopt without marketing, creating unexpected management needs.Version control is challenging for marketplace-deployed products.License management through marketplace creates engineering challenges.Suger helps sell, resell, co-sell through AWS Marketplace.Marketplace integration isn't one-time; requires ongoing maintenance.Business users constantly request marketplace automation features.Suger works with Snowflake, Intel, and AI startups.Data security concerns drive self-hosted AI deployments.AI products increasingly deploy via AMI/container solutions.AI products use usage-based pricing, not seat-based.Usage-based pricing creates complex billing challenges.AI products are tested at unprecedented rates.Two deployment options: vendor cloud or customer cloud.SaaS requires reporting usage to marketplace APIs.Customer-hosted deployment simplifies some billing aspects.Marketplaces need integration with ERP systems.Version control particularly challenging for AI products.Companies need automated updates for marketplace-deployed products.License management includes scaling up/down and expiration handling.Suger aims to integrate with GitHub for automatic updates.Participants:· Jon Yoo – CEO and Co-founder, SugerSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Ever tried solving DNS security across a multi-cloud, multi-cluster Kubernetes setup? In this episode recorded live at KubeCon, Ashish chats with Nimisha Mehta and Alvaro Aleman from Confluent's Kubernetes Platform Team.Together, they break down the complex journey of migrating to Cilium from default CNI plugins across Azure AKS, AWS EKS, and Google GKE. You'll hear:How Confluent manages Kubernetes clusters across cloud providers.Real-world issues encountered during DNS security migration.Deep dives into cloud-specific quirks with Azure's overlay mode, GKE's Cilium integration, and AWS's IP routing limitations.Race conditions, IP tables, reverse path filters, and practical workarounds.Lessons they'd share for any platform team planning a similar move.Guest Socials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Alvaro's Linkedin + Nimisha's Linkedin Podcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security BootCamp⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you are interested in AI Cybersecurity, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Cybersecurity PodcastQuestions asked:(00:00) Introduction(01:55) A bit about Alvaro(02:41) A bit about Nimisha(03:11) About their Kubecon NA talk(03:51) The Cilium use case(05:16) Using Kubernetes Native tools in all 3 cloud providers(011:41) Lessons learnt from the projectResources spoken about during the interviewConfluent's Multi-Cloud Journey to Cilium: Pitfalls and Lessons Lea... Nimisha Mehta & Alvaro Aleman

Highlights from this week's conversation include:Michael's Background and Journey in Data (0:24)Synthetic Data Challenges (1:49)Open Source Project Development (4:20)Founding Distributed Masonry (5:56)Acquisition by Confluent (7:27)Introduction to Shadow Traffic (10:57)Observations on Streaming Data (12:33)Importance of Timestamps in Testing (16:22)Customer Workflows with Shadow Traffic (19:09)Artificial Intelligence in Data Generation (22:13)Advantages of Domain-Specific Language (DSL) (25:14)Solopreneurship Insights (26:53)Exit Criteria for Startup Focus (30:12)The Feedback Loop (33:51)Balancing Customer Needs and Vision (35:02)Navigating Administrative Tasks (38:15)Expected Value Mindset (41:00)Solopreneur Efficiency (43:01)Maximizing Velocity (46:06)Final Thoughts and Takeaways (47:34)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Friend of the pod Colleen McCreary discusses some big news: her return to a CPO role at a remote-first public company (Confluent). She shares insights on navigating the first 90 days, organizational change, implementing a company-wide initiative to eliminate unnecessary processes, and gearing up for lots of 1:1s. Colleen, Kelli, and Nolan discuss transparent leadership, building community in remote environments, and bringing humanity to workplace policies—all while balancing the challenges of joining an established company as an executive with a public reputation*Email us your questions or topics for Kelli & Nolan: hrheretics@turpentine.coFor coaching and advising inquire at https://kellidragovich.com/HR Heretics is a podcast from Turpentine.—

In this episode, Brian and Fareed discuss the challenges facing product managers in the AI era with Shaun Clowes, CPO at Confluent. Many product professionals are questioning their future as traditional PM workflows—documentation, basic prioritization, and coordination—become increasingly automated. Yet this technological shift isn't eliminating the product management function; rather, it's elevating what has always differentiated exceptional PMs from average ones. While low-value processes are being automated away, the core elements that create product success—strategic vision, judgment, taste, and leadership—are becoming dramatically more valuable.

In the February mailbag, Jason and Jeff tackle various listener questions including the pitfalls and strategic uses of options trading, ETFs versus individual stocks, and long-term investment strategies for kids and grandkids. They also explore the significance of return on equity (ROE) and return on invested capital (ROIC), and discuss managing stocks that have skyrocketed in value. 01:52 Mailbag Time: Listener Questions02:42 Exploring Options Trading13:20 Understanding Return on Equity and Invested Capital18:50 Investing for Future Generations23:09 Upstart: Recent Developments and Insights25:46 Economic Uncertainties and Market Reactions27:58 Confluent's Growth and Market Position30:09 Understanding Bond ETFs33:02 Closed-End Funds: Risks and Rewards34:39 Frameworks vs. Rules in Investing36:36 Navigating Stock Price Increases43:54 ETFs vs. Individual Stocks48:24 Spam Comment of the MonthCompanies mentioned: AAPL, APP, AXON, CELH, CFLT, FLNC, MELI, UPST, ZMSubscribe to our portfolio on Savvy Trader Email: investingunscripted@gmail.comTwitter: @InvestingPodCheck out our YouTube channel for more content: To get 15% off any paid plan at finchat.io, visit https://finchat.io/unscriptedListen to the Chit Chat Stocks Podcast for discussions on stocks, financial markets, super investors, and more. Follow the show on Spotify, Apple Podcasts, or YouTubeInvesting Unscripted is brought to you by Public.com* Visit https://public.com/investingunscripted *All investing involves the risk of loss, including loss of principal. Brokerage services for US-listed, registered securities, options and bonds in a self-directed account are offered by Public Investing, Inc., member FINRA & SIPC. Public Investing offers a High-Yield Cash Account where funds from this account are automatically deposited into partner banks where they earn interest and are eligible for FDIC insurance; Public Investing is not a bank. Cryptocurrency trading services are offered by Bakkt Crypto Solutions, LLC (NMLS ID 1890144), which is licensed to engage in virtual currency business activity by the NYSDFS. Cryptocurrency is highly speculative, involves a high degree of risk, and has the potential for loss of the entire amount of an investment. Cryptocurrency holdings are not protected by the FDIC or SIPC. A Bond Account is a self-directed brokerage account with Public Investing, member FINRA/SIPC. Deposits into this account are used to purchase 10 investment-grade and high-yield bonds. The 6%+ yield is the average, annualized yield to worst (YTW) across all ten bonds in the Bond Account, before fees, as of 12/13/2024. A bond's yield is a function of its market price, which can fluctuate; therefore, a bond's YTW is not “locked in” until the bond is purchased, and your yield at time of purchase may be different from the yield shown here. The “locked in” YTW is not guaranteed; you may receive less than the YTW of the bonds in the Bond Account if you sell any of the bonds before maturity or if the issuer defaults on the bond. Public Investing charges a markup on each bond trade. See our Fee Schedule (https://public.com/disclosures/fee-schedule). Bond Accounts are not recommendations of individual bonds or default allocations. The bonds in the Bond Account have not been selected based on your needs or risk profile. See Bond Account Disclosures to learn more.Alpha is an AI research tool powered by GPT-4.  Alpha is experimental and may generate inaccurate responses.  Output from Alpha should not be construed as investment research or recommendations, and should not serve as the basis for any investment decision. Public makes no warranties about its accuracy, completeness, quality, or timeliness of any Alpha out. Please independently evaluate and verify any such output for your own use case.*Terms and Conditions apply.2025 Portfolio Contest2024 Portfolio Contest2023 Portfolio Contest

Andy is the founder of Artisanal Ventures and Artisanal Talent, one of Silicon Valley's top search firms. He's helped build leadership teams at companies like Databricks, Snowflake, Confluent, Abnormal Security, AcuityMD, and many more.In this episode, he shares…- How founders can differentiate in the talent war today- Maximizing the success rate of executive hires- Why interviews are a waste of time- The best ways to do references- How to choose the right search firm& more (00:00) Intro(02:02) Andy Price's Background and Career Journey(03:20) The Role of Founders in Hiring(04:32) Challenges in Early Stage Hiring(10:08) Importance of Venture Capital Brand(12:14) Effective Search Processes and Candidate Evaluation(23:27) Backchannel References and Networking(29:10) Identifying Key Players in Sales Growth(29:44) The Importance of Minimal Disruption(30:40) Effective Founder-Executive Relationships(30:57) The Role of Soak Time in Differentiation(31:52) Hiring Strategies for Rapid Growth(33:42) Common Failure Modes in Hiring(34:32) Aligning Founder and Executive Expectations(38:26) Building a Strong Talent Acquisition Team(40:51) The Talent Wars and Hiring Choke Points(44:24) Balancing Skill Sets and Company Culture(47:29) Evaluating and Upleveling Team Members(49:59) The Importance of Forecasting and Planning(51:34) Handling Executive Transitions Smoothly(59:09) The Art of Firing: Best Practices(59:32) Handling Employee Terminations with Dignity(01:02:19) Negotiating with Candidates: Tips and Tricks(01:06:31) Understanding Compensation Trends(01:08:18) Avoiding Common Founder Mistakes(01:11:28) Scaling Operations in Hypergrowth(01:15:00) Navigating the Current VC and Talent Ecosystem(01:23:34) The Importance of Specialized Search Firms(01:28:03) Adapting to the New Market Realities(01:30:46) Final Thoughts and Reflections Executive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

Alissa Coram and Ken Shreve analyze Thursday's market action and discuss key stocks to watch on Stock Market Today.

If you're in SF, join us tomorrow for a fun meetup at CodeGen Night!If you're in NYC, join us for AI Engineer Summit! The Agent Engineering track is now sold out, but 25 tickets remain for AI Leadership and 5 tickets for the workshops. You can see the full schedule of speakers and workshops at https://ai.engineer!It's exceedingly hard to introduce someone like Bret Taylor. We could recite his Wikipedia page, or his extensive work history through Silicon Valley's greatest companies, but everyone else already does that.As a podcast by AI engineers for AI engineers, we had the opportunity to do something a little different. We wanted to dig into what Bret sees from his vantage point at the top of our industry for the last 2 decades, and how that explains the rise of the AI Architect at Sierra, the leading conversational AI/CX platform.“Across our customer base, we are seeing a new role emerge - the role of the AI architect. These leaders are responsible for helping define, manage and evolve their company's AI agent over time. They come from a variety of both technical and business backgrounds, and we think that every company will have one or many AI architects managing their AI agent and related experience.”In our conversation, Bret Taylor confirms the Paul Buchheit legend that he rewrote Google Maps in a weekend, armed with only the help of a then-nascent Google Closure Compiler and no other modern tooling. But what we find remarkable is that he was the PM of Maps, not an engineer, though of course he still identifies as one. We find this theme recurring throughout Bret's career and worldview. We think it is plain as day that AI leadership will have to be hands-on and technical, especially when the ground is shifting as quickly as it is today:“There's a lot of power in combining product and engineering into as few people as possible… few great things have been created by committee.”“If engineering is an order taking organization for product you can sometimes make meaningful things, but rarely will you create extremely well crafted breakthrough products. Those tend to be small teams who deeply understand the customer need that they're solving, who have a maniacal focus on outcomes.”“And I think the reason why is if you look at like software as a service five years ago, maybe you can have a separation of product and engineering because most software as a service created five years ago. I wouldn't say there's like a lot of technological breakthroughs required for most business applications. And if you're making expense reporting software or whatever, it's useful… You kind of know how databases work, how to build auto scaling with your AWS cluster, whatever, you know, it's just, you're just applying best practices to yet another problem. "When you have areas like the early days of mobile development or the early days of interactive web applications, which I think Google Maps and Gmail represent, or now AI agents, you're in this constant conversation with what the requirements of your customers and stakeholders are and all the different people interacting with it and the capabilities of the technology. And it's almost impossible to specify the requirements of a product when you're not sure of the limitations of the technology itself.”This is the first time the difference between technical leadership for “normal” software and for “AI” software was articulated this clearly for us, and we'll be thinking a lot about this going forward. We left a lot of nuggets in the conversation, so we hope you'll just dive in with us (and thank Bret for joining the pod!)Timestamps* 00:00:02 Introductions and Bret Taylor's background* 00:01:23 Bret's experience at Stanford and the dot-com era* 00:04:04 The story of rewriting Google Maps backend* 00:11:06 Early days of interactive web applications at Google* 00:15:26 Discussion on product management and engineering roles* 00:21:00 AI and the future of software development* 00:26:42 Bret's approach to identifying customer needs and building AI companies* 00:32:09 The evolution of business models in the AI era* 00:41:00 The future of programming languages and software development* 00:49:38 Challenges in precisely communicating human intent to machines* 00:56:44 Discussion on Artificial General Intelligence (AGI) and its impact* 01:08:51 The future of agent-to-agent communication* 01:14:03 Bret's involvement in the OpenAI leadership crisis* 01:22:11 OpenAI's relationship with Microsoft* 01:23:23 OpenAI's mission and priorities* 01:27:40 Bret's guiding principles for career choices* 01:29:12 Brief discussion on pasta-making* 01:30:47 How Bret keeps up with AI developments* 01:32:15 Exciting research directions in AI* 01:35:19 Closing remarks and hiring at Sierra Transcript[00:02:05] Introduction and Guest Welcome[00:02:05] Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co host swyx, founder of smol.ai.[00:02:17] swyx: Hey, and today we're super excited to have Bret Taylor join us. Welcome. Thanks for having me. It's a little unreal to have you in the studio.[00:02:25] swyx: I've read about you so much over the years, like even before. Open AI effectively. I mean, I use Google Maps to get here. So like, thank you for everything that you've done. Like, like your story history, like, you know, I think people can find out what your greatest hits have been.[00:02:40] Bret Taylor's Early Career and Education[00:02:40] swyx: How do you usually like to introduce yourself when, you know, you talk about, you summarize your career, like, how do you look at yourself?[00:02:47] Bret: Yeah, it's a great question. You know, we, before we went on the mics here, we're talking about the audience for this podcast being more engineering. And I do think depending on the audience, I'll introduce myself differently because I've had a lot of [00:03:00] corporate and board roles. I probably self identify as an engineer more than anything else though.[00:03:04] Bret: So even when I was. Salesforce, I was coding on the weekends. So I think of myself as an engineer and then all the roles that I do in my career sort of start with that just because I do feel like engineering is sort of a mindset and how I approach most of my life. So I'm an engineer first and that's how I describe myself.[00:03:24] Bret: You majored in computer[00:03:25] swyx: science, like 1998. And, and I was high[00:03:28] Bret: school, actually my, my college degree was Oh, two undergrad. Oh, three masters. Right. That old.[00:03:33] swyx: Yeah. I mean, no, I was going, I was going like 1998 to 2003, but like engineering wasn't as, wasn't a thing back then. Like we didn't have the title of senior engineer, you know, kind of like, it was just.[00:03:44] swyx: You were a programmer, you were a developer, maybe. What was it like in Stanford? Like, what was that feeling like? You know, was it, were you feeling like on the cusp of a great computer revolution? Or was it just like a niche, you know, interest at the time?[00:03:57] Stanford and the Dot-Com Bubble[00:03:57] Bret: Well, I was at Stanford, as you said, from 1998 to [00:04:00] 2002.[00:04:02] Bret: 1998 was near the peak of the dot com bubble. So. This is back in the day where most people that they're coding in the computer lab, just because there was these sun microsystems, Unix boxes there that most of us had to do our assignments on. And every single day there was a. com like buying pizza for everybody.[00:04:20] Bret: I didn't have to like, I got. Free food, like my first two years of university and then the dot com bubble burst in the middle of my college career. And so by the end there was like tumbleweed going to the job fair, you know, it was like, cause it was hard to describe unless you were there at the time, the like level of hype and being a computer science major at Stanford was like, A thousand opportunities.[00:04:45] Bret: And then, and then when I left, it was like Microsoft, IBM.[00:04:49] Joining Google and Early Projects[00:04:49] Bret: And then the two startups that I applied to were VMware and Google. And I ended up going to Google in large part because a woman named Marissa Meyer, who had been a teaching [00:05:00] assistant when I was, what was called a section leader, which was like a junior teaching assistant kind of for one of the big interest.[00:05:05] Bret: Yes. Classes. She had gone there. And she was recruiting me and I knew her and it was sort of felt safe, you know, like, I don't know. I thought about it much, but it turned out to be a real blessing. I realized like, you know, you always want to think you'd pick Google if given the option, but no one knew at the time.[00:05:20] Bret: And I wonder if I'd graduated in like 1999 where I've been like, mom, I just got a job at pets. com. It's good. But you know, at the end I just didn't have any options. So I was like, do I want to go like make kernel software at VMware? Do I want to go build search at Google? And I chose Google. 50, 50 ball.[00:05:36] Bret: I'm not really a 50, 50 ball. So I feel very fortunate in retrospect that the economy collapsed because in some ways it forced me into like one of the greatest companies of all time, but I kind of lucked into it, I think.[00:05:47] The Google Maps Rewrite Story[00:05:47] Alessio: So the famous story about Google is that you rewrote the Google maps back in, in one week after the map quest quest maps acquisition, what was the story there?[00:05:57] Alessio: Is it. Actually true. Is it [00:06:00] being glorified? Like how, how did that come to be? And is there any detail that maybe Paul hasn't shared before?[00:06:06] Bret: It's largely true, but I'll give the color commentary. So it was actually the front end, not the back end, but it turns out for Google maps, the front end was sort of the hard part just because Google maps was.[00:06:17] Bret: Largely the first ish kind of really interactive web application, say first ish. I think Gmail certainly was though Gmail, probably a lot of people then who weren't engineers probably didn't appreciate its level of interactivity. It was just fast, but. Google maps, because you could drag the map and it was sort of graphical.[00:06:38] Bret: My, it really in the mainstream, I think, was it a map[00:06:41] swyx: quest back then that was, you had the arrows up and down, it[00:06:44] Bret: was up and down arrows. Each map was a single image and you just click left and then wait for a few seconds to the new map to let it was really small too, because generating a big image was kind of expensive on computers that day.[00:06:57] Bret: So Google maps was truly innovative in that [00:07:00] regard. The story on it. There was a small company called where two technologies started by two Danish brothers, Lars and Jens Rasmussen, who are two of my closest friends now. They had made a windows app called expedition, which had beautiful maps. Even in 2000.[00:07:18] Bret: For whenever we acquired or sort of acquired their company, Windows software was not particularly fashionable, but they were really passionate about mapping and we had made a local search product that was kind of middling in terms of popularity, sort of like a yellow page of search product. So we wanted to really go into mapping.[00:07:36] Bret: We'd started working on it. Their small team seemed passionate about it. So we're like, come join us. We can build this together.[00:07:42] Technical Challenges and Innovations[00:07:42] Bret: It turned out to be a great blessing that they had built a windows app because you're less technically constrained when you're doing native code than you are building a web browser, particularly back then when there weren't really interactive web apps and it ended up.[00:07:56] Bret: Changing the level of quality that we [00:08:00] wanted to hit with the app because we were shooting for something that felt like a native windows application. So it was a really good fortune that we sort of, you know, their unusual technical choices turned out to be the greatest blessing. So we spent a lot of time basically saying, how can you make a interactive draggable map in a web browser?[00:08:18] Bret: How do you progressively load, you know, new map tiles, you know, as you're dragging even things like down in the weeds of the browser at the time, most browsers like Internet Explorer, which was dominant at the time would only load two images at a time from the same domain. So we ended up making our map tile servers have like.[00:08:37] Bret: Forty different subdomains so we could load maps and parallels like lots of hacks. I'm happy to go into as much as like[00:08:44] swyx: HTTP connections and stuff.[00:08:46] Bret: They just like, there was just maximum parallelism of two. And so if you had a map, set of map tiles, like eight of them, so So we just, we were down in the weeds of the browser anyway.[00:08:56] Bret: So it was lots of plumbing. I can, I know a lot more about browsers than [00:09:00] most people, but then by the end of it, it was fairly, it was a lot of duct tape on that code. If you've ever done an engineering project where you're not really sure the path from point A to point B, it's almost like. Building a house by building one room at a time.[00:09:14] Bret: The, there's not a lot of architectural cohesion at the end. And then we acquired a company called Keyhole, which became Google earth, which was like that three, it was a native windows app as well, separate app, great app, but with that, we got licenses to all this satellite imagery. And so in August of 2005, we added.[00:09:33] Bret: Satellite imagery to Google Maps, which added even more complexity in the code base. And then we decided we wanted to support Safari. There was no mobile phones yet. So Safari was this like nascent browser on, on the Mac. And it turns out there's like a lot of decisions behind the scenes, sort of inspired by this windows app, like heavy use of XML and XSLT and all these like.[00:09:54] Bret: Technologies that were like briefly fashionable in the early two thousands and everyone hates now for good [00:10:00] reason. And it turns out that all of the XML functionality and Internet Explorer wasn't supporting Safari. So people are like re implementing like XML parsers. And it was just like this like pile of s**t.[00:10:11] Bret: And I had to say a s**t on your part. Yeah, of[00:10:12] Alessio: course.[00:10:13] Bret: So. It went from this like beautifully elegant application that everyone was proud of to something that probably had hundreds of K of JavaScript, which sounds like nothing. Now we're talking like people have modems, you know, not all modems, but it was a big deal.[00:10:29] Bret: So it was like slow. It took a while to load and just, it wasn't like a great code base. Like everything was fragile. So I just got. Super frustrated by it. And then one weekend I did rewrite all of it. And at the time the word JSON hadn't been coined yet too, just to give you a sense. So it's all XML.[00:10:47] swyx: Yeah.[00:10:47] Bret: So we used what is now you would call JSON, but I just said like, let's use eval so that we can parse the data fast. And, and again, that's, it would literally as JSON, but at the time there was no name for it. So we [00:11:00] just said, let's. Pass on JavaScript from the server and eval it. And then somebody just refactored the whole thing.[00:11:05] Bret: And, and it wasn't like I was some genius. It was just like, you know, if you knew everything you wished you had known at the beginning and I knew all the functionality, cause I was the primary, one of the primary authors of the JavaScript. And I just like, I just drank a lot of coffee and just stayed up all weekend.[00:11:22] Bret: And then I, I guess I developed a bit of reputation and no one knew about this for a long time. And then Paul who created Gmail and I ended up starting a company with him too, after all of this told this on a podcast and now it's large, but it's largely true. I did rewrite it and it, my proudest thing.[00:11:38] Bret: And I think JavaScript people appreciate this. Like the un G zipped bundle size for all of Google maps. When I rewrote, it was 20 K G zipped. It was like much smaller for the entire application. It went down by like 10 X. So. What happened on Google? Google is a pretty mainstream company. And so like our usage is shot up because it turns out like it's faster.[00:11:57] Bret: Just being faster is worth a lot of [00:12:00] percentage points of growth at a scale of Google. So how[00:12:03] swyx: much modern tooling did you have? Like test suites no compilers.[00:12:07] Bret: Actually, that's not true. We did it one thing. So I actually think Google, I, you can. Download it. There's a, Google has a closure compiler, a closure compiler.[00:12:15] Bret: I don't know if anyone still uses it. It's gone. Yeah. Yeah. It's sort of gone out of favor. Yeah. Well, even until recently it was better than most JavaScript minifiers because it was more like it did a lot more renaming of variables and things. Most people use ES build now just cause it's fast and closure compilers built on Java and super slow and stuff like that.[00:12:37] Bret: But, so we did have that, that was it. Okay.[00:12:39] The Evolution of Web Applications[00:12:39] Bret: So and that was treated internally, you know, it was a really interesting time at Google at the time because there's a lot of teams working on fairly advanced JavaScript when no one was. So Google suggest, which Kevin Gibbs was the tech lead for, was the first kind of type ahead, autocomplete, I believe in a web browser, and now it's just pervasive in search boxes that you sort of [00:13:00] see a type ahead there.[00:13:01] Bret: I mean, chat, dbt[00:13:01] swyx: just added it. It's kind of like a round trip.[00:13:03] Bret: Totally. No, it's now pervasive as a UI affordance, but that was like Kevin's 20 percent project. And then Gmail, Paul you know, he tells the story better than anyone, but he's like, you know, basically was scratching his own itch, but what was really neat about it is email, because it's such a productivity tool, just needed to be faster.[00:13:21] Bret: So, you know, he was scratching his own itch of just making more stuff work on the client side. And then we, because of Lars and Yen sort of like setting the bar of this windows app or like we need our maps to be draggable. So we ended up. Not only innovate in terms of having a big sync, what would be called a single page application today, but also all the graphical stuff you know, we were crashing Firefox, like it was going out of style because, you know, when you make a document object model with the idea that it's a document and then you layer on some JavaScript and then we're essentially abusing all of this, it just was running into code paths that were not.[00:13:56] Bret: Well, it's rotten, you know, at this time. And so it was [00:14:00] super fun. And, and, you know, in the building you had, so you had compilers, people helping minify JavaScript just practically, but there is a great engineering team. So they were like, that's why Closure Compiler is so good. It was like a. Person who actually knew about programming languages doing it, not just, you know, writing regular expressions.[00:14:17] Bret: And then the team that is now the Chrome team believe, and I, I don't know this for a fact, but I'm pretty sure Google is the main contributor to Firefox for a long time in terms of code. And a lot of browser people were there. So every time we would crash Firefox, we'd like walk up two floors and say like, what the hell is going on here?[00:14:35] Bret: And they would load their browser, like in a debugger. And we could like figure out exactly what was breaking. And you can't change the code, right? Cause it's the browser. It's like slow, right? I mean, slow to update. So, but we could figure out exactly where the bug was and then work around it in our JavaScript.[00:14:52] Bret: So it was just like new territory. Like so super, super fun time, just like a lot of, a lot of great engineers figuring out [00:15:00] new things. And And now, you know, the word, this term is no longer in fashion, but the word Ajax, which was asynchronous JavaScript and XML cause I'm telling you XML, but see the word XML there, to be fair, the way you made HTTP requests from a client to server was this.[00:15:18] Bret: Object called XML HTTP request because Microsoft and making Outlook web access back in the day made this and it turns out to have nothing to do with XML. It's just a way of making HTTP requests because XML was like the fashionable thing. It was like that was the way you, you know, you did it. But the JSON came out of that, you know, and then a lot of the best practices around building JavaScript applications is pre React.[00:15:44] Bret: I think React was probably the big conceptual step forward that we needed. Even my first social network after Google, we used a lot of like HTML injection and. Making real time updates was still very hand coded and it's really neat when you [00:16:00] see conceptual breakthroughs like react because it's, I just love those things where it's like obvious once you see it, but it's so not obvious until you do.[00:16:07] Bret: And actually, well, I'm sure we'll get into AI, but I, I sort of feel like we'll go through that evolution with AI agents as well that I feel like we're missing a lot of the core abstractions that I think in 10 years we'll be like, gosh, how'd you make agents? Before that, you know, but it was kind of that early days of web applications.[00:16:22] swyx: There's a lot of contenders for the reactive jobs of of AI, but no clear winner yet. I would say one thing I was there for, I mean, there's so much we can go into there. You just covered so much.[00:16:32] Product Management and Engineering Synergy[00:16:32] swyx: One thing I just, I just observe is that I think the early Google days had this interesting mix of PM and engineer, which I think you are, you didn't, you didn't wait for PM to tell you these are my, this is my PRD.[00:16:42] swyx: This is my requirements.[00:16:44] mix: Oh,[00:16:44] Bret: okay.[00:16:45] swyx: I wasn't technically a software engineer. I mean,[00:16:48] Bret: by title, obviously. Right, right, right.[00:16:51] swyx: It's like a blend. And I feel like these days, product is its own discipline and its own lore and own industry and engineering is its own thing. And there's this process [00:17:00] that happens and they're kind of separated, but you don't produce as good of a product as if they were the same person.[00:17:06] swyx: And I'm curious, you know, if, if that, if that sort of resonates in, in, in terms of like comparing early Google versus modern startups that you see out there,[00:17:16] Bret: I certainly like wear a lot of hats. So, you know, sort of biased in this, but I really agree that there's a lot of power and combining product design engineering into as few people as possible because, you know few great things have been created by committee, you know, and so.[00:17:33] Bret: If engineering is an order taking organization for product you can sometimes make meaningful things, but rarely will you create extremely well crafted breakthrough products. Those tend to be small teams who deeply understand the customer need that they're solving, who have a. Maniacal focus on outcomes.[00:17:53] Bret: And I think the reason why it's, I think for some areas, if you look at like software as a service five years ago, maybe you can have a [00:18:00] separation of product and engineering because most software as a service created five years ago. I wouldn't say there's like a lot of like. Technological breakthroughs required for most, you know, business applications.[00:18:11] Bret: And if you're making expense reporting software or whatever, it's useful. I don't mean to be dismissive of expense reporting software, but you probably just want to understand like, what are the requirements of the finance department? What are the requirements of an individual file expense report? Okay.[00:18:25] Bret: Go implement that. And you kind of know how web applications are implemented. You kind of know how to. How databases work, how to build auto scaling with your AWS cluster, whatever, you know, it's just, you're just applying best practices to yet another problem when you have areas like the early days of mobile development or the early days of interactive web applications, which I think Google Maps and Gmail represent, or now AI agents, you're in this constant conversation with what the requirements of your customers and stakeholders are and all the different people interacting with it.[00:18:58] Bret: And the capabilities of the [00:19:00] technology. And it's almost impossible to specify the requirements of a product when you're not sure of the limitations of the technology itself. And that's why I use the word conversation. It's not literal. That's sort of funny to use that word in the age of conversational AI.[00:19:15] Bret: You're constantly sort of saying, like, ideally, you could sprinkle some magic AI pixie dust and solve all the world's problems, but it's not the way it works. And it turns out that actually, I'll just give an interesting example.[00:19:26] AI Agents and Modern Tooling[00:19:26] Bret: I think most people listening probably use co pilots to code like Cursor or Devon or Microsoft Copilot or whatever.[00:19:34] Bret: Most of those tools are, they're remarkable. I'm, I couldn't, you know, imagine development without them now, but they're not autonomous yet. Like I wouldn't let it just write most code without my interactively inspecting it. We just are somewhere between it's an amazing co pilot and it's an autonomous software engineer.[00:19:53] Bret: As a product manager, like your aspirations for what the product is are like kind of meaningful. But [00:20:00] if you're a product person, yeah, of course you'd say it should be autonomous. You should click a button and program should come out the other side. The requirements meaningless. Like what matters is like, what is based on the like very nuanced limitations of the technology.[00:20:14] Bret: What is it capable of? And then how do you maximize the leverage? It gives a software engineering team, given those very nuanced trade offs. Coupled with the fact that those nuanced trade offs are changing more rapidly than any technology in my memory, meaning every few months you'll have new models with new capabilities.[00:20:34] Bret: So how do you construct a product that can absorb those new capabilities as rapidly as possible as well? That requires such a combination of technical depth and understanding the customer that you really need more integration. Of product design and engineering. And so I think it's why with these big technology waves, I think startups have a bit of a leg up relative to incumbents because they [00:21:00] tend to be sort of more self actualized in terms of just like bringing those disciplines closer together.[00:21:06] Bret: And in particular, I think entrepreneurs, the proverbial full stack engineers, you know, have a leg up as well because. I think most breakthroughs happen when you have someone who can understand those extremely nuanced technical trade offs, have a vision for a product. And then in the process of building it, have that, as I said, like metaphorical conversation with the technology, right?[00:21:30] Bret: Gosh, I ran into a technical limit that I didn't expect. It's not just like changing that feature. You might need to refactor the whole product based on that. And I think that's, that it's particularly important right now. So I don't, you know, if you, if you're building a big ERP system, probably there's a great reason to have product and engineering.[00:21:51] Bret: I think in general, the disciplines are there for a reason. I think when you're dealing with something as nuanced as the like technologies, like large language models today, there's a ton of [00:22:00] advantage of having. Individuals or organizations that integrate the disciplines more formally.[00:22:05] Alessio: That makes a lot of sense.[00:22:06] Alessio: I've run a lot of engineering teams in the past, and I think the product versus engineering tension has always been more about effort than like whether or not the feature is buildable. But I think, yeah, today you see a lot more of like. Models actually cannot do that. And I think the most interesting thing is on the startup side, people don't yet know where a lot of the AI value is going to accrue.[00:22:26] Alessio: So you have this rush of people building frameworks, building infrastructure, layered things, but we don't really know the shape of the compute. I'm curious that Sierra, like how you thought about building an house, a lot of the tooling for evals or like just, you know, building the agents and all of that.[00:22:41] Alessio: Versus how you see some of the startup opportunities that is maybe still out there.[00:22:46] Bret: We build most of our tooling in house at Sierra, not all. It's, we don't, it's not like not invented here syndrome necessarily, though, maybe slightly guilty of that in some ways, but because we're trying to build a platform [00:23:00] that's in Dorian, you know, we really want to have control over our own destiny.[00:23:03] Bret: And you had made a comment earlier that like. We're still trying to figure out who like the reactive agents are and the jury is still out. I would argue it hasn't been created yet. I don't think the jury is still out to go use that metaphor. We're sort of in the jQuery era of agents, not the react era.[00:23:19] Bret: And, and that's like a throwback for people listening,[00:23:22] swyx: we shouldn't rush it. You know?[00:23:23] Bret: No, yeah, that's my point is. And so. Because we're trying to create an enduring company at Sierra that outlives us, you know, I'm not sure we want to like attach our cart to some like to a horse where it's not clear that like we've figured out and I actually want as a company, we're trying to enable just at a high level and I'll, I'll quickly go back to tech at Sierra, we help consumer brands build customer facing AI agents.[00:23:48] Bret: So. Everyone from Sonos to ADT home security to Sirius XM, you know, if you call them on the phone and AI will pick up with you, you know, chat with them on the Sirius XM homepage. It's an AI agent called Harmony [00:24:00] that they've built on our platform. We're what are the contours of what it means for someone to build an end to end complete customer experience with AI with conversational AI.[00:24:09] Bret: You know, we really want to dive into the deep end of, of all the trade offs to do it. You know, where do you use fine tuning? Where do you string models together? You know, where do you use reasoning? Where do you use generation? How do you use reasoning? How do you express the guardrails of an agentic process?[00:24:25] Bret: How do you impose determinism on a fundamentally non deterministic technology? There's just a lot of really like as an important design space. And I could sit here and tell you, we have the best approach. Every entrepreneur will, you know. But I hope that in two years, we look back at our platform and laugh at how naive we were, because that's the pace of change broadly.[00:24:45] Bret: If you talk about like the startup opportunities, I'm not wholly skeptical of tools companies, but I'm fairly skeptical. There's always an exception for every role, but I believe that certainly there's a big market for [00:25:00] frontier models, but largely for companies with huge CapEx budgets. So. Open AI and Microsoft's Anthropic and Amazon Web Services, Google Cloud XAI, which is very well capitalized now, but I think the, the idea that a company can make money sort of pre training a foundation model is probably not true.[00:25:20] Bret: It's hard to, you're competing with just, you know, unreasonably large CapEx budgets. And I just like the cloud infrastructure market, I think will be largely there. I also really believe in the applications of AI. And I define that not as like building agents or things like that. I define it much more as like, you're actually solving a problem for a business.[00:25:40] Bret: So it's what Harvey is doing in legal profession or what cursor is doing for software engineering or what we're doing for customer experience and customer service. The reason I believe in that is I do think that in the age of AI, what's really interesting about software is it can actually complete a task.[00:25:56] Bret: It can actually do a job, which is very different than the value proposition of [00:26:00] software was to ancient history two years ago. And as a consequence, I think the way you build a solution and For a domain is very different than you would have before, which means that it's not obvious, like the incumbent incumbents have like a leg up, you know, necessarily, they certainly have some advantages, but there's just such a different form factor, you know, for providing a solution and it's just really valuable.[00:26:23] Bret: You know, it's. Like just think of how much money cursor is saving software engineering teams or the alternative, how much revenue it can produce tool making is really challenging. If you look at the cloud market, just as a analog, there are a lot of like interesting tools, companies, you know, Confluent, Monetized Kafka, Snowflake, Hortonworks, you know, there's a, there's a bunch of them.[00:26:48] Bret: A lot of them, you know, have that mix of sort of like like confluence or have the open source or open core or whatever you call it. I, I, I'm not an expert in this area. You know, I do think [00:27:00] that developers are fickle. I think that in the tool space, I probably like. Default towards open source being like the area that will win.[00:27:09] Bret: It's hard to build a company around this and then you end up with companies sort of built around open source to that can work. Don't get me wrong, but I just think that it's nowadays the tools are changing so rapidly that I'm like, not totally skeptical of tool makers, but I just think that open source will broadly win, but I think that the CapEx required for building frontier models is such that it will go to a handful of big companies.[00:27:33] Bret: And then I really believe in agents for specific domains which I think will, it's sort of the analog to software as a service in this new era. You know, it's like, if you just think of the cloud. You can lease a server. It's just a low level primitive, or you can buy an app like you know, Shopify or whatever.[00:27:51] Bret: And most people building a storefront would prefer Shopify over hand rolling their e commerce storefront. I think the same thing will be true of AI. So [00:28:00] I've. I tend to like, if I have a, like an entrepreneur asked me for advice, I'm like, you know, move up the stack as far as you can towards a customer need.[00:28:09] Bret: Broadly, but I, but it doesn't reduce my excitement about what is the reactive building agents kind of thing, just because it is, it is the right question to ask, but I think we'll probably play out probably an open source space more than anything else.[00:28:21] swyx: Yeah, and it's not a priority for you. There's a lot in there.[00:28:24] swyx: I'm kind of curious about your idea maze towards, there are many customer needs. You happen to identify customer experience as yours, but it could equally have been coding assistance or whatever. I think for some, I'm just kind of curious at the top down, how do you look at the world in terms of the potential problem space?[00:28:44] swyx: Because there are many people out there who are very smart and pick the wrong problem.[00:28:47] Bret: Yeah, that's a great question.[00:28:48] Future of Software Development[00:28:48] Bret: By the way, I would love to talk about the future of software, too, because despite the fact it didn't pick coding, I have a lot of that, but I can talk to I can answer your question, though, you know I think when a technology is as [00:29:00] cool as large language models.[00:29:02] Bret: You just see a lot of people starting from the technology and searching for a problem to solve. And I think it's why you see a lot of tools companies, because as a software engineer, you start building an app or a demo and you, you encounter some pain points. You're like,[00:29:17] swyx: a lot of[00:29:17] Bret: people are experiencing the same pain point.[00:29:19] Bret: What if I make it? That it's just very incremental. And you know, I always like to use the metaphor, like you can sell coffee beans, roasted coffee beans. You can add some value. You took coffee beans and you roasted them and roasted coffee beans largely, you know, are priced relative to the cost of the beans.[00:29:39] Bret: Or you can sell a latte and a latte. Is rarely priced directly like as a percentage of coffee bean prices. In fact, if you buy a latte at the airport, it's a captive audience. So it's a really expensive latte. And there's just a lot that goes into like. How much does a latte cost? And I bring it up because there's a supply chain from growing [00:30:00] coffee beans to roasting coffee beans to like, you know, you could make one at home or you could be in the airport and buy one and the margins of the company selling lattes in the airport is a lot higher than the, you know, people roasting the coffee beans and it's because you've actually solved a much more acute human problem in the airport.[00:30:19] Bret: And, and it's just worth a lot more to that person in that moment. It's kind of the way I think about technology too. It sounds funny to liken it to coffee beans, but you're selling tools on top of a large language model yet in some ways your market is big, but you're probably going to like be price compressed just because you're sort of a piece of infrastructure and then you have open source and all these other things competing with you naturally.[00:30:43] Bret: If you go and solve a really big business problem for somebody, that's actually like a meaningful business problem that AI facilitates, they will value it according to the value of that business problem. And so I actually feel like people should just stop. You're like, no, that's, that's [00:31:00] unfair. If you're searching for an idea of people, I, I love people trying things, even if, I mean, most of the, a lot of the greatest ideas have been things no one believed in.[00:31:07] Bret: So I like, if you're passionate about something, go do it. Like who am I to say, yeah, a hundred percent. Or Gmail, like Paul as far, I mean I, some of it's Laura at this point, but like Gmail is Paul's own email for a long time. , and then I amusingly and Paul can't correct me, I'm pretty sure he sent her in a link and like the first comment was like, this is really neat.[00:31:26] Bret: It would be great. It was not your email, but my own . I don't know if it's a true story. I'm pretty sure it's, yeah, I've read that before. So scratch your own niche. Fine. Like it depends on what your goal is. If you wanna do like a venture backed company, if its a. Passion project, f*****g passion, do it like don't listen to anybody.[00:31:41] Bret: In fact, but if you're trying to start, you know an enduring company, solve an important business problem. And I, and I do think that in the world of agents, the software industries has shifted where you're not just helping people more. People be more productive, but you're actually accomplishing tasks autonomously.[00:31:58] Bret: And as a consequence, I think the [00:32:00] addressable market has just greatly expanded just because software can actually do things now and actually accomplish tasks and how much is coding autocomplete worth. A fair amount. How much is the eventual, I'm certain we'll have it, the software agent that actually writes the code and delivers it to you, that's worth a lot.[00:32:20] Bret: And so, you know, I would just maybe look up from the large language models and start thinking about the economy and, you know, think from first principles. I don't wanna get too far afield, but just think about which parts of the economy. We'll benefit most from this intelligence and which parts can absorb it most easily.[00:32:38] Bret: And what would an agent in this space look like? Who's the customer of it is the technology feasible. And I would just start with these business problems more. And I think, you know, the best companies tend to have great engineers who happen to have great insight into a market. And it's that last part that I think some people.[00:32:56] Bret: Whether or not they have, it's like people start so much in the technology, they [00:33:00] lose the forest for the trees a little bit.[00:33:02] Alessio: How do you think about the model of still selling some sort of software versus selling more package labor? I feel like when people are selling the package labor, it's almost more stateless, you know, like it's easier to swap out if you're just putting an input and getting an output.[00:33:16] Alessio: If you think about coding, if there's no ID, you're just putting a prompt and getting back an app. It doesn't really matter. Who generates the app, you know, you have less of a buy in versus the platform you're building, I'm sure on the backend customers have to like put on their documentation and they have, you know, different workflows that they can tie in what's kind of like the line to draw there versus like going full where you're managed customer support team as a service outsource versus.[00:33:40] Alessio: This is the Sierra platform that you can build on. What was that decision? I'll sort of[00:33:44] Bret: like decouple the question in some ways, which is when you have something that's an agent, who is the person using it and what do they want to do with it? So let's just take your coding agent for a second. I will talk about Sierra as well.[00:33:59] Bret: Who's the [00:34:00] customer of a, an agent that actually produces software? Is it a software engineering manager? Is it a software engineer? And it's there, you know, intern so to speak. I don't know. I mean, we'll figure this out over the next few years. Like what is that? And is it generating code that you then review?[00:34:16] Bret: Is it generating code with a set of unit tests that pass, what is the actual. For lack of a better word contract, like, how do you know that it did what you wanted it to do? And then I would say like the product and the pricing, the packaging model sort of emerged from that. And I don't think the world's figured out.[00:34:33] Bret: I think it'll be different for every agent. You know, in our customer base, we do what's called outcome based pricing. So essentially every time the AI agent. Solves the problem or saves a customer or whatever it might be. There's a pre negotiated rate for that. We do that. Cause it's, we think that that's sort of the correct way agents, you know, should be packaged.[00:34:53] Bret: I look back at the history of like cloud software and notably the introduction of the browser, which led to [00:35:00] software being delivered in a browser, like Salesforce to. Famously invented sort of software as a service, which is both a technical delivery model through the browser, but also a business model, which is you subscribe to it rather than pay for a perpetual license.[00:35:13] Bret: Those two things are somewhat orthogonal, but not really. If you think about the idea of software running in a browser, that's hosted. Data center that you don't own, you sort of needed to change the business model because you don't, you can't really buy a perpetual license or something otherwise like, how do you afford making changes to it?[00:35:31] Bret: So it only worked when you were buying like a new version every year or whatever. So to some degree, but then the business model shift actually changed business as we know it, because now like. Things like Adobe Photoshop. Now you subscribe to rather than purchase. So it ended up where you had a technical shift and a business model shift that were very logically intertwined that actually the business model shift was turned out to be as significant as the technical as the shift.[00:35:59] Bret: And I think with [00:36:00] agents, because they actually accomplish a job, I do think that it doesn't make sense to me that you'd pay for the privilege of like. Using the software like that coding agent, like if it writes really bad code, like fire it, you know, I don't know what the right metaphor is like you should pay for a job.[00:36:17] Bret: Well done in my opinion. I mean, that's how you pay your software engineers, right? And[00:36:20] swyx: and well, not really. We paid to put them on salary and give them options and they vest over time. That's fair.[00:36:26] Bret: But my point is that you don't pay them for how many characters they write, which is sort of the token based, you know, whatever, like, There's a, that famous Apple story where we're like asking for a report of how many lines of code you wrote.[00:36:40] Bret: And one of the engineers showed up with like a negative number cause he had just like done a big refactoring. There was like a big F you to management who didn't understand how software is written. You know, my sense is like the traditional usage based or seat based thing. It's just going to look really antiquated.[00:36:55] Bret: Cause it's like asking your software engineer, how many lines of code did you write today? Like who cares? Like, cause [00:37:00] absolutely no correlation. So my old view is I don't think it's be different in every category, but I do think that that is the, if an agent is doing a job, you should, I think it properly incentivizes the maker of that agent and the customer of, of your pain for the job well done.[00:37:16] Bret: It's not always perfect to measure. It's hard to measure engineering productivity, but you can, you should do something other than how many keys you typed, you know Talk about perverse incentives for AI, right? Like I can write really long functions to do the same thing, right? So broadly speaking, you know, I do think that we're going to see a change in business models of software towards outcomes.[00:37:36] Bret: And I think you'll see a change in delivery models too. And, and, you know, in our customer base you know, we empower our customers to really have their hands on the steering wheel of what the agent does they, they want and need that. But the role is different. You know, at a lot of our customers, the customer experience operations folks have renamed themselves the AI architects, which I think is really cool.[00:37:55] Bret: And, you know, it's like in the early days of the Internet, there's the role of the webmaster. [00:38:00] And I don't know whether your webmaster is not a fashionable, you know, Term, nor is it a job anymore? I just, I don't know. Will they, our tech stand the test of time? Maybe, maybe not. But I do think that again, I like, you know, because everyone listening right now is a software engineer.[00:38:14] Bret: Like what is the form factor of a coding agent? And actually I'll, I'll take a breath. Cause actually I have a bunch of pins on them. Like I wrote a blog post right before Christmas, just on the future of software development. And one of the things that's interesting is like, if you look at the way I use cursor today, as an example, it's inside of.[00:38:31] Bret: A repackaged visual studio code environment. I sometimes use the sort of agentic parts of it, but it's largely, you know, I've sort of gotten a good routine of making it auto complete code in the way I want through tuning it properly when it actually can write. I do wonder what like the future of development environments will look like.[00:38:55] Bret: And to your point on what is a software product, I think it's going to change a lot in [00:39:00] ways that will surprise us. But I always use, I use the metaphor in my blog post of, have you all driven around in a way, Mo around here? Yeah, everyone has. And there are these Jaguars, the really nice cars, but it's funny because it still has a steering wheel, even though there's no one sitting there and the steering wheels like turning and stuff clearly in the future.[00:39:16] Bret: If once we get to that, be more ubiquitous, like why have the steering wheel and also why have all the seats facing forward? Maybe just for car sickness. I don't know, but you could totally rearrange the car. I mean, so much of the car is oriented around the driver, so. It stands to reason to me that like, well, autonomous agents for software engineering run through visual studio code.[00:39:37] Bret: That seems a little bit silly because having a single source code file open one at a time is kind of a goofy form factor for when like the code isn't being written primarily by you, but it begs the question of what's your relationship with that agent. And I think the same is true in our industry of customer experience, which is like.[00:39:55] Bret: Who are the people managing this agent? What are the tools do they need? And they definitely need [00:40:00] tools, but it's probably pretty different than the tools we had before. It's certainly different than training a contact center team. And as software engineers, I think that I would like to see particularly like on the passion project side or research side.[00:40:14] Bret: More innovation in programming languages. I think that we're bringing the cost of writing code down to zero. So the fact that we're still writing Python with AI cracks me up just cause it's like literally was designed to be ergonomic to write, not safe to run or fast to run. I would love to see more innovation and how we verify program correctness.[00:40:37] Bret: I studied for formal verification in college a little bit and. It's not very fashionable because it's really like tedious and slow and doesn't work very well. If a lot of code is being written by a machine, you know, one of the primary values we can provide is verifying that it actually does what we intend that it does.[00:40:56] Bret: I think there should be lots of interesting things in the software development life cycle, like how [00:41:00] we think of testing and everything else, because. If you think about if we have to manually read every line of code that's coming out as machines, it will just rate limit how much the machines can do. The alternative is totally unsafe.[00:41:13] Bret: So I wouldn't want to put code in production that didn't go through proper code review and inspection. So my whole view is like, I actually think there's like an AI native I don't think the coding agents don't work well enough to do this yet, but once they do, what is sort of an AI native software development life cycle and how do you actually.[00:41:31] Bret: Enable the creators of software to produce the highest quality, most robust, fastest software and know that it's correct. And I think that's an incredible opportunity. I mean, how much C code can we rewrite and rust and make it safe so that there's fewer security vulnerabilities. Can we like have more efficient, safer code than ever before?[00:41:53] Bret: And can you have someone who's like that guy in the matrix, you know, like staring at the little green things, like where could you have an operator [00:42:00] of a code generating machine be like superhuman? I think that's a cool vision. And I think too many people are focused on like. Autocomplete, you know, right now, I'm not, I'm not even, I'm guilty as charged.[00:42:10] Bret: I guess in some ways, but I just like, I'd like to see some bolder ideas. And that's why when you were joking, you know, talking about what's the react of whatever, I think we're clearly in a local maximum, you know, metaphor, like sort of conceptual local maximum, obviously it's moving really fast. I think we're moving out of it.[00:42:26] Alessio: Yeah. At the end of 23, I've read this blog post from syntax to semantics. Like if you think about Python. It's taking C and making it more semantic and LLMs are like the ultimate semantic program, right? You can just talk to them and they can generate any type of syntax from your language. But again, the languages that they have to use were made for us, not for them.[00:42:46] Alessio: But the problem is like, as long as you will ever need a human to intervene, you cannot change the language under it. You know what I mean? So I'm curious at what point of automation we'll need to get, we're going to be okay making changes. To the underlying languages, [00:43:00] like the programming languages versus just saying, Hey, you just got to write Python because I understand Python and I'm more important at the end of the day than the model.[00:43:08] Alessio: But I think that will change, but I don't know if it's like two years or five years. I think it's more nuanced actually.[00:43:13] Bret: So I think there's a, some of the more interesting programming languages bring semantics into syntax. So let me, that's a little reductive, but like Rust as an example, Rust is memory safe.[00:43:25] Bret: Statically, and that was a really interesting conceptual, but it's why it's hard to write rust. It's why most people write python instead of rust. I think rust programs are safer and faster than python, probably slower to compile. But like broadly speaking, like given the option, if you didn't have to care about the labor that went into it.[00:43:45] Bret: You should prefer a program written in Rust over a program written in Python, just because it will run more efficiently. It's almost certainly safer, et cetera, et cetera, depending on how you define safe, but most people don't write Rust because it's kind of a pain in the ass. And [00:44:00] the audience of people who can is smaller, but it's sort of better in most, most ways.[00:44:05] Bret: And again, let's say you're making a web service and you didn't have to care about how hard it was to write. If you just got the output of the web service, the rest one would be cheaper to operate. It's certainly cheaper and probably more correct just because there's so much in the static analysis implied by the rest programming language that it probably will have fewer runtime errors and things like that as well.[00:44:25] Bret: So I just give that as an example, because so rust, at least my understanding that came out of the Mozilla team, because. There's lots of security vulnerabilities in the browser and it needs to be really fast. They said, okay, we want to put more of a burden at the authorship time to have fewer issues at runtime.[00:44:43] Bret: And we need the constraint that it has to be done statically because browsers need to be really fast. My sense is if you just think about like the, the needs of a programming language today, where the role of a software engineer is [00:45:00] to use an AI to generate functionality and audit that it does in fact work as intended, maybe functionally, maybe from like a correctness standpoint, some combination thereof, how would you create a programming system that facilitated that?[00:45:15] Bret: And, you know, I bring up Rust is because I think it's a good example of like, I think given a choice of writing in C or Rust, you should choose Rust today. I think most people would say that, even C aficionados, just because. C is largely less safe for very similar, you know, trade offs, you know, for the, the system and now with AI, it's like, okay, well, that just changes the game on writing these things.[00:45:36] Bret: And so like, I just wonder if a combination of programming languages that are more structurally oriented towards the values that we need from an AI generated program, verifiable correctness and all of that. If it's tedious to produce for a person, that maybe doesn't matter. But one thing, like if I asked you, is this rest program memory safe?[00:45:58] Bret: You wouldn't have to read it, you just have [00:46:00] to compile it. So that's interesting. I mean, that's like an, that's one example of a very modest form of formal verification. So I bring that up because I do think you have AI inspect AI, you can have AI reviewed. Do AI code reviews. It would disappoint me if the best we could get was AI reviewing Python and having scaled a few very large.[00:46:21] Bret: Websites that were written on Python. It's just like, you know, expensive and it's like every, trust me, every team who's written a big web service in Python has experimented with like Pi Pi and all these things just to make it slightly more efficient than it naturally is. You don't really have true multi threading anyway.[00:46:36] Bret: It's just like clearly that you do it just because it's convenient to write. And I just feel like we're, I don't want to say it's insane. I just mean. I do think we're at a local maximum. And I would hope that we create a programming system, a combination of programming languages, formal verification, testing, automated code reviews, where you can use AI to generate software in a high scale way and trust it.[00:46:59] Bret: And you're [00:47:00] not limited by your ability to read it necessarily. I don't know exactly what form that would take, but I feel like that would be a pretty cool world to live in.[00:47:08] Alessio: Yeah. We had Chris Lanner on the podcast. He's doing great work with modular. I mean, I love. LVM. Yeah. Basically merging rust in and Python.[00:47:15] Alessio: That's kind of the idea. Should be, but I'm curious is like, for them a big use case was like making it compatible with Python, same APIs so that Python developers could use it. Yeah. And so I, I wonder at what point, well, yeah.[00:47:26] Bret: At least my understanding is they're targeting the data science Yeah. Machine learning crowd, which is all written in Python, so still feels like a local maximum.[00:47:34] Bret: Yeah.[00:47:34] swyx: Yeah, exactly. I'll force you to make a prediction. You know, Python's roughly 30 years old. In 30 years from now, is Rust going to be bigger than Python?[00:47:42] Bret: I don't know this, but just, I don't even know this is a prediction. I just am sort of like saying stuff I hope is true. I would like to see an AI native programming language and programming system, and I use language because I'm not sure language is even the right thing, but I hope in 30 years, there's an AI native way we make [00:48:00] software that is wholly uncorrelated with the current set of programming languages.[00:48:04] Bret: or not uncorrelated, but I think most programming languages today were designed to be efficiently authored by people and some have different trade offs.[00:48:15] Evolution of Programming Languages[00:48:15] Bret: You know, you have Haskell and others that were designed for abstractions for parallelism and things like that. You have programming languages like Python, which are designed to be very easily written, sort of like Perl and Python lineage, which is why data scientists use it.[00:48:31] Bret: It's it can, it has a. Interactive mode, things like that. And I love, I'm a huge Python fan. So despite all my Python trash talk, a huge Python fan wrote at least two of my three companies were exclusively written in Python and then C came out of the birth of Unix and it wasn't the first, but certainly the most prominent first step after assembly language, right?[00:48:54] Bret: Where you had higher level abstractions rather than and going beyond go to, to like abstractions, [00:49:00] like the for loop and the while loop.[00:49:01] The Future of Software Engineering[00:49:01] Bret: So I just think that if the act of writing code is no longer a meaningful human exercise, maybe it will be, I don't know. I'm just saying it sort of feels like maybe it's one of those parts of history that just will sort of like go away, but there's still the role of this offer engineer, like the person actually building the system.[00:49:20] Bret: Right. And. What does a programming system for that form factor look like?[00:49:25] React and Front-End Development[00:49:25] Bret: And I, I just have a, I hope to be just like I mentioned, I remember I was at Facebook in the very early days when, when, what is now react was being created. And I remember when the, it was like released open source I had left by that time and I was just like, this is so f*****g cool.[00:49:42] Bret: Like, you know, to basically model your app independent of the data flowing through it, just made everything easier. And then now. You know, I can create, like there's a lot of the front end software gym play is like a little chaotic for me, to be honest with you. It is like, it's sort of like [00:50:00] abstraction soup right now for me, but like some of those core ideas felt really ergonomic.[00:50:04] Bret: I just wanna, I'm just looking forward to the day when someone comes up with a programming system that feels both really like an aha moment, but completely foreign to me at the same time. Because they created it with sort of like from first principles recognizing that like. Authoring code in an editor is maybe not like the primary like reason why a programming system exists anymore.[00:50:26] Bret: And I think that's like, that would be a very exciting day for me.[00:50:28] The Role of AI in Programming[00:50:28] swyx: Yeah, I would say like the various versions of this discussion have happened at the end of the day, you still need to precisely communicate what you want. As a manager of people, as someone who has done many, many legal contracts, you know how hard that is.[00:50:42] swyx: And then now we have to talk to machines doing that and AIs interpreting what we mean and reading our minds effectively. I don't know how to get across that barrier of translating human intent to instructions. And yes, it can be more declarative, but I don't know if it'll ever Crossover from being [00:51:00] a programming language to something more than that.[00:51:02] Bret: I agree with you. And I actually do think if you look at like a legal contract, you know, the imprecision of the English language, it's like a flaw in the system. How many[00:51:12] swyx: holes there are.[00:51:13] Bret: And I do think that when you're making a mission critical software system, I don't think it should be English language prompts.[00:51:19] Bret: I think that is silly because you want the precision of a a programming language. My point was less about that and more about if the actual act of authoring it, like if you.[00:51:32] Formal Verification in Software[00:51:32] Bret: I'll think of some embedded systems do use formal verification. I know it's very common in like security protocols now so that you can, because the importance of correctness is so great.[00:51:41] Bret: My intellectual exercise is like, why not do that for all software? I mean, probably that's silly just literally to do what we literally do for. These low level security protocols, but the only reason we don't is because it's hard and tedious and hard and tedious are no longer factors. So, like, if I could, I mean, [00:52:00] just think of, like, the silliest app on your phone right now, the idea that that app should be, like, formally verified for its correctness feels laughable right now because, like, God, why would you spend the time on it?[00:52:10] Bret: But if it's zero costs, like, yeah, I guess so. I mean, it never crashed. That's probably good. You know, why not? I just want to, like, set our bars really high. Like. We should make, software has been amazing. Like there's a Mark Andreessen blog post, software is eating the world. And you know, our whole life is, is mediated digitally.[00:52:26] Bret: And that's just increasing with AI. And now we'll have our personal agents talking to the agents on the CRO platform and it's agents all the way down, you know, our core infrastructure is running on these digital systems. We now have like, and we've had a shortage of software developers for my entire life.[00:52:45] Bret: And as a consequence, you know if you look, remember like health care, got healthcare. gov that fiasco security vulnerabilities leading to state actors getting access to critical infrastructure. I'm like. We now have like created this like amazing system that can [00:53:00] like, we can fix this, you know, and I, I just want to, I'm both excited about the productivity gains in the economy, but I just think as software engineers, we should be bolder.[00:53:08] Bret: Like we should have aspirations to fix these systems so that like in general, as you said, as precise as we want to be in the specification of the system. We can make it work correctly now, and I'm being a little bit hand wavy, and I think we need some systems. I think that's where we should set the bar, especially when so much of our life depends on this critical digital infrastructure.[00:53:28] Bret: So I'm I'm just like super optimistic about it. But actually, let's go to w

Shaun Clowes is the chief product officer at Confluent and former CPO at Salesforce's MuleSoft and at Metromile. He was also the first head of growth at Atlassian, where he led product for Jira Agile and built the first-ever B2B growth team. In our conversation, we discuss:• Why most PMs are bad, and how to fix this• Why great AI products are all about the data• Why he changed his mind about being data-driven• How to build your B2B growth team• How to choose your next career stop• Much more—Brought to you by:• Enterpret—Transform customer feedback into product growth• BuildBetter—AI for product teams• Wix Studio—The web creation platform built for agencies—Find the transcript at: https://www.lennysnewsletter.com/p/why-great-ai-products-are-all-about-the-data-shaun-clowes—Where to find Shaun Clowes:• X: https://x.com/ShaunMClowes• LinkedIn: https://www.linkedin.com/in/shaun-clowes-80795014/• Website: https://shaunclowes.com/about-shaun• Reforge: https://www.reforge.com/profiles/shaun-clowes—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Shaun's background(05:08) The state of product management(09:33) Becoming a 10x product manager(13:23) Specific ways to leverage AI in product management(17:15) Feedback rivers(19:20) AI's impact on data management(24:35) The future of enterprise businesses with AI(35:41) Data-driven decision-making(45:50) Building effective growth teams(50:18) The evolution of product-led growth(56:16) Career insights and decision-making(01:07:45) Failure corner(01:12:32) Final thoughts and lightning round—Referenced:• Steve Blank's website: https://steveblank.com/• Getting Out of the Building. 2 Minutes to See Why: https://www.youtube.com/watch?v=TbMgWr1YVfs• OpenAI: https://openai.com/• Claude: https://claude.ai/• Sachin Rekhi on LinkedIn: https://www.linkedin.com/in/sachinrekhi/• Video: Building Your Product Intuition with Feedback Rivers: https://www.sachinrekhi.com/video-building-your-product-intuition-with-feedback-rivers• Confluent: https://www.confluent.io• Workday: https://www.workday.com/• Lenny and Friends Summit: https://lennyssummit.com/• A conversation with OpenAI's CPO Kevin Weil, Anthropic's CPO Mike Krieger, and Sarah Guo: https://www.youtube.com/watch?v=IxkvVZua28k• Anthropic: https://www.anthropic.com/• Salesforce: https://www.salesforce.com/• Atlassian: https://www.atlassian.com/• Jira: https://www.atlassian.com/software/jira• Ashby: https://www.ashbyhq.com/• Occam's razor: https://en.wikipedia.org/wiki/Occam%27s_razor• Breaking the rules of growth: Why Shopify bans KPIs, optimizes for churn, prioritizes intuition, and builds toward a 100-year vision | Archie Abrams (VP Product, Head of Growth at Shopify): https://www.lennysnewsletter.com/p/shopifys-growth-archie-abrams• Charlie Munger quote: https://www.goodreads.com/quotes/11903426-show-me-the-incentive-and-i-ll-show-you-the-outcome• Elena Verna on how B2B growth is changing, product-led growth, product-led sales, why you should go freemium not trial, what features to make free, and much more: https://www.lennysnewsletter.com/p/elena-verna-on-why-every-company• The ultimate guide to product-led sales | Elena Verna: https://www.lennysnewsletter.com/p/the-ultimate-guide-to-product-led• Metromile: https://www.metromile.com/• Tom Kennedy on LinkedIn: https://www.linkedin.com/in/tom-kennedy-37356b2b/• Building Wiz: the fastest-growing startup in history | Raaz Herzberg (CMO and VP Product Strategy): https://www.lennysnewsletter.com/p/building-wiz-raaz-herzberg• Wiz: https://www.wiz.io• Colin Powell's 40-70 rule: https://www.42courses.com/blog/home/2019/12/10/colin-powells-40-70-rule• Detroiters on Netflix: https://www.netflix.com/title/80165019• Glean: https://www.glean.com/• Radical Candor: Be a Kick-Ass Boss Without Losing Your Humanity: https://www.amazon.com/Radical-Candor-Kick-Ass-Without-Humanity/dp/1250103509• Listen: Five Simple Tools to Meet Your Everyday Parenting Challenges: https://www.amazon.com/Listen-Simple-Everyday-Parenting-Challenges/dp/0997459301• Empress Falls Canyon and abseiling: https://bmac.com.au/blue-mountains-canyoning/empress-falls-canyon-and-abseiling—Recommended books:• The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses: https://www.amazon.com/Lean-Startup-Entrepreneurs-Continuous-Innovation/dp/0307887898• Inspired: How to Create Products Customers Love: https://www.amazon.com/Inspired-Create-Products-Customers-Love/dp/0981690408—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Alissa Coram and Ken Shreve analyze Thursday's market action and discuss key stocks to watch on Stock Market Today.

Welcome to The GeekNarrator podcast! In this episode, host Kaivalya Apte goes deeper into the practical applications of formal methods with Jack Vanlightly, a principal technologist at Confluent. With years of experience in distributed systems, Jack discusses his journey and how formal methods have been instrumental in system design verification and bug detection. The conversation covers Jack's background, his process of using formal methods, the significance of modelling, verification, documentation, and systems learning, as well as the future evolution of tooling and its applications. Tune in to understand the intricacies of how formal methods can transform your approach to distributed systems! Chapters: 00:00 Introduction to the episode 00:37 Meet Jack VanLightly: Principal Technologist at Confluent 02:17 Jack's Journey into Distributed Systems 04:29 Discovering the Power of Formal Methods 08:11 Modeling and Simulation in Formal Methods 13:43 Verification and Safety Properties 19:02 Documentation and Communication Challenges 20:43 Formal Methods as a Systems Learning Tool 24:26 Practical Applications and Case Studies 56:38 Future of Formal Verification and Closing Thoughts Jack's Blog: https://jack-vanlightly.com/ Become a member of The GeekNarrator to get access to member only videos, notes and monthly 1:1 with me. Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription. https://app.codecrafters.io/join?via=geeknarrator If you like this episode, please hit the like button and share it with your network. Also please subscribe if you haven't yet. Database internals series: https://youtu.be/yV_Zp0Mi3xs Popular playlists: Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA- Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17 Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_d Modern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsN Stay Curios! Keep Learning!

What does it take to go from leading Kafka development at Confluent to becoming a key figure in the PostgreSQL world? Join us as we talk with Gwen Shapira, co-founder and chief product officer at Nile, about her transition from cloud-native technologies to the vibrant PostgreSQL community. Gwen shares her journey, including the shift from conferences like O'Reilly Strata to PostgresConf and JavaScript events, and how the Postgres community is evolving with tools like Discord that keep it both grounded and dynamic.We dive into the latest developments in PostgreSQL, like hypothetical indexes that enable performance tuning without affecting live environments, and the growing importance of SSL for secure database connections in cloud settings. Plus, we explore the potential of integrating PostgreSQL with Apache Arrow and Parquet, signaling new possibilities for data processing and storage.At the intersection of AI and PostgreSQL, we examine how companies are using vector embeddings in Postgres to meet modern AI demands, balancing specialized vector stores with integrated solutions. Gwen also shares insights from her work at Nile, highlighting how PostgreSQL's flexibility supports SaaS applications across diverse customer needs, making it a top choice for enterprises of all sizes.Follow Gwen on:Nile BlogX (Twitter)LinkedInNile DiscordWhat's New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What's New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories.

Seasoned executive recruiter and venture capitalist Andy Price joins Nolan Church and Kelli Dragovich for an inside-the-room look at the intricate dance of hiring, retaining, and evolving top-tier talent. Andy has assembled leadership teams for companies like Snowflake, DocuSign, Confluent, Amplitude, and ServiceNow.From the critical role of empathy in leadership transitions to the long-term value of investing in talent development, Price's insights challenge listeners preconcieved notions about hiring and maintaining world-class execs. This episode is a compelling exploration of how the right people, in the right roles, with the right support, can transform good companies into great ones. *Email us your questions or topics for Kelli & Nolan: hrheretics@turpentine.coHR Heretics is a podcast from Turpentine.

Send Everyday AI and Jordan a text messageAre you missing out on the power of streaming data? Want to know how to make your AI faster, smarter, and more relevant? Join us for a deep dive into how data streaming can transform AI from a predictive tool into a real-time decision-maker with Will LaForest, Global Field CTO of Confluent.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Will questions on AI and data streaming.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:1. Internal Use of AI2. Data Integration3. Risks and Importance of Data Streaming4. Data Governance and Traceability5. Future of Data and AITimestamps:01:30 Daily AI news05:15 About Will and Confluent09:15 Real-time data is crucial for current accuracy.12:16 Data streaming enables AI use for midsize businesses.15:11 Data streaming enables real-time customer data updates.19:36 Data streaming's stability is crucial for finance.20:27 Data governance ensures safe, accurate data delivery.25:16 Services simplify data streaming integration for companies.Keywords:AI internal use, Data integration, Risks of data streaming, Data governance, Provenance and traceability, Ethical AI use, Future of data and AI, Industry outlook, Data streaming services, Notion AI, Industry-specific integration, Quality data for AI, Real-time data importance, Confluent, Data streaming, Generative AI, Mid-market companies and data streaming, Data streaming use cases, Confluent's mission, Customer testimonial, NVIDIA NVLM, OpenAI competition, Google AI development, OpenAI funding, Will LaForest, Uber and data streaming, Generative AI context, AI in business, Enhanced productivity, Data privacy Get more out of ChatGPT by learning our PPP method in this live, interactive and free training! Sign up now: https://youreverydayai.com/ppp-registration/

This week, we discuss Dell's growth in AI servers, GEICO's transition from VMware to OpenStack, and the concept of Kingmaking. Plus, plenty of thoughts on USB hubs. Watch the YouTube Live Recording of Episode (https://www.youtube.com/watch?v=n8Y7WI3BU0c) 484 (https://www.youtube.com/watch?v=n8Y7WI3BU0c) Runner-up Titles You just put it in a different USB port I am just going to repeat thing you say A Decade of Aftershow Happy Anniversary Whatever :43 it is for you Cascading hubs not supported anywhere. Mac Dongle Pro Getting DevOps to work on Intel Enterprise Gaming PCs. Brian Cantrell as Elon Musk Computers are awesome Matt and JJ can always go to GIECO. I'll take the more. Really good surfers Rundown Dell Dell's AI Server Business Now Bigger Than VMware Used To Be (https://www.nextplatform.com/2024/08/30/dells-ai-server-business-now-bigger-than-vmware-used-to-be/) There's a lot of private cloud out there (https://newsletter.cote.io/p/theres-a-lot-of-private-cloud-out) OpenStack US insurer GEICO drops VMware for OpenStack (https://www.theregister.com/2024/08/28/geico_vmware_openstack_migration/) VMware Migration to OpenStack (https://www.openstack.org/vmware-migration-to-openstack) King Making (https://cloudedjudgement.substack.com/p/clouded-judgement-9624-king-making?utm_source=post-email-title&publication_id=56878&post_id=148500331&utm_campaign=email-post-title&isFreemail=true&r=2l9&triedRedirect=true&utm_medium=email) Relevant to your Interests Nvidia CEO Jensen Huang Surprised by Skeptics (https://www.bloomberg.com/news/newsletters/2024-09-02/nvidia-ceo-jensen-huang-surprised-by-skeptics) Nvidia Has Held Discussions About Joining OpenAI's Funding Round (https://www.bloomberg.com/news/articles/2024-08-29/nvidia-has-held-discussions-about-joining-openai-s-funding-round) Anthropic launches Claude Enterprise plan to compete with OpenAI (https://techcrunch.com/2024/09/04/anthropic-launches-claude-enterprise-plan-to-compete-with-openai/) What's Behind Elastic's Unexpected Return to Open Source? (https://thenewstack.io/whats-behind-elastics-unexpected-return-to-open-source/) The startup teaching your computer how to smell (https://thehustle.co/news/the-startup-teaching-your-computer-how-to-smell) Automatically summarize Word documents with Copilot (https://techcommunity.microsoft.com/t5/microsoft-365-insider-blog/automatically-summarize-word-documents-with-copilot/ba-p/4231202) Intel's Lunar Lake Looks Like a Home Run (https://finance.yahoo.com/news/intels-lunar-lake-looks-home-112000963.html) AT&T sues Broadcom for refusing to renew perpetual license support (https://arstechnica.com/information-technology/2024/09/att-sues-broadcom-for-refusing-to-renew-perpetual-license-support/) Red Hat Enterprise Linux AI (https://www.redhat.com/en/technologies/linux-platforms/enterprise-linux/ai) The Self-Destruction of Open Source Software (https://lunduke.substack.com/p/the-self-destruction-of-open-source) Confluent acquires streaming data startup WarpStream (https://techcrunch.com/2024/09/09/confluent-acquires-streaming-data-startup-warpstream/) There are almost no IPOs. (https://x.com/chrisfralic/status/1833651875046105302?s=46&t=zgzybiDdIcGuQ_7WuoOX0A) Amazon congratulates itself for AI code that mostly works (https://www.theregister.com/2024/09/05/amazon_q_developer_gartner/) Charles Schwab Adopts PostgreSQL (With VMware Tanzu) (https://thenewstack.io/charles-schwab-adopts-postgresql-with-vmware-tanzu/) Sponsor Nasuni: Head to nasuni.com/software (https://bit.ly/3MvMDoY) and see how it can revolutionize your data infrastructure today! Nonsense Delta, Other Airline Loyalty Programs Are Being Probed by US (https://www.bloomberg.com/news/articles/2024-09-05/delta-dal-american-aal-airline-loyalty-programs-under-investigation) The 35-Year-Old CEO Plotting Red Lobster's Comeback (https://www.wsj.com/business/hospitality/the-35-year-old-ceo-plotting-red-lobsters-comeback-3c79d1a3) New Starbucks CEO Brian Niccol outlines priorities to end coffee chain's slump (https://www.cnbc.com/2024/09/10/new-starbucks-ceo-brian-niccol-outlines-plans-for-business.html) Historic Newspaper Uses Janky AI Newscasters Instead of Human Journalists (https://www.404media.co/historic-newspaper-uses-janky-ai-newscasters-instead-of-human-journalists/) Conferences SREday London 2024 (https://sreday.com/2024-london/), Sept 19–20, 2024. Coté speaking, 20% off with code SRE20DAY. Cloud Foundry Day EU (https://events.linuxfoundation.org/cloud-foundry-day-europe/), Karlsruhe, GER, Oct 9, 2024, 20% off with code CFEU24VMW. VMware Explore Barcelona (https://www.vmware.com/explore/eu), Nov 4-7, 2024. Coté speaking. SREday Amsterdam (https://sreday.com/2024-amsterdam/), Nov 21, 2024. Coté speaking (https://sreday.com/2024-amsterdam/Michael_Cote_VMwarePivotal_We_Fear_Change), 20% off with code SRE20DAY. SDT News & Community Join our Slack community (https://softwaredefinedtalk.slack.com/join/shared_invite/zt-1hn55iv5d-UTfN7mVX1D9D5ExRt3ZJYQ#/shared-invite/email) Email the show: questions@softwaredefinedtalk.com (mailto:questions@softwaredefinedtalk.com) Free stickers: Email your address to stickers@softwaredefinedtalk.com (mailto:stickers@softwaredefinedtalk.com) Follow us on social media: Twitter (https://twitter.com/softwaredeftalk), Threads (https://www.threads.net/@softwaredefinedtalk), Mastodon (https://hachyderm.io/@softwaredefinedtalk), LinkedIn (https://www.linkedin.com/company/software-defined-talk/), BlueSky (https://bsky.app/profile/softwaredefinedtalk.com) Watch us on: Twitch (https://www.twitch.tv/sdtpodcast), YouTube (https://www.youtube.com/channel/UCi3OJPV6h9tp-hbsGBLGsDQ/featured), Instagram (https://www.instagram.com/softwaredefinedtalk/), TikTok (https://www.tiktok.com/@softwaredefinedtalk) Book offer: Use code SDT for $20 off "Digital WTF" by Coté (https://leanpub.com/digitalwtf/c/sdt) Sponsor the show (https://www.softwaredefinedtalk.com/ads): ads@softwaredefinedtalk.com (mailto:ads@softwaredefinedtalk.com) Recommendations Brandon: Rebel Ridge (https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://www.netflix.com/title/81157729&ved=2ahUKEwjCjfjMoLmIAxUuMdAFHW1AGn0QFnoECEMQAQ&usg=AOvVaw2-SZ5c5q-XeQL3kiJU9brc) Cloud News of the Month - August 2024 (https://www.thecloudcast.net/2024/09/cloud-news-of-month-august-2024.html) Matt: Interstellar (https://www.imdb.com/title/tt0816692/) Coté: Reino Slippers (https://finlaysonshop.com/products/reino-slippers). Photo Credits Header (https://unsplash.com/photos/people-surfing-on-waves-5__FobjBei8) Artwork (https://unsplash.com/photos/a-close-up-of-a-board-with-wires-attached-to-it-vtg8tAdoWVQ)

Shiva Bhattacharjee is the Co-founder and CTO of TrueLaw, where we are building bespoke models for law firms for a wide variety of tasks. Alignment is Real // MLOps Podcast #260 with Shiva Bhattacharjee, CTO of TrueLaw Inc. // Abstract If the off-the-shelf model can understand and solve a domain-specific task well enough, either your task isn't that nuanced or you have achieved AGI. We discuss when is fine-tuning necessary over prompting and how we have created a loop of sampling - collecting feedback - fine-tuning to create models that seem to perform exceedingly well in domain-specific tasks. // Bio 20 years of experience in distributed and data-intensive systems spanning work at Apple, Arista Networks, Databricks, and Confluent. Currently CTO at TrueLaw where we provide a framework to fold in user feedback, such as lawyer critiques of a given task, and fold them into proprietary LLM models through fine-tuning mechanics, resulting in 7-10x improvements over the base model. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: www.truelaw.ai --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Shiva on LinkedIn: https://www.linkedin.com/in/shivabhattacharjee/ Timestamps: [00:00] Shiva's preferred coffee [00:58] Takeaways [01:17] DSPy Implementation [04:57] Evaluating DSPy risks [08:13] Community-driven DSPy tool [12:19] RAG implementation strategies [17:02] Cost-effective embedding fine-tuning [18:51] AI infrastructure decision-making [24:13] Prompt data flow evolution [26:32] Buy vs build decision [30:45] Tech stack insights [38:20] Wrap up

Ahmed Abdelazim joins us to discuss why he focuses his analysis on small and mid-cap stocks and what metrics he uses (1:15). Heavy debt and why Peloton is a stock to avoid (4:05). Bullish on Confluent (7:00). Clover Health - a solid company with extremely good margins (10:30). When to sell a stock, when to buy more (15:25). Semiconductor space good for small and mid-cap stocks (16:20). Bullish on space sector and companies like AST SpaceMobile (18:20).Episode transcriptsShow Notes:Peloton: A Fragile Resurgence Built On Unsustainable FactorsConfluent: New Revenue Model & Flink Platform Are Game Changers For GrowthClover Health: A Profitable Future Ahead Fueled By AI And Star Rating SuccessAffirm: More Room To Grow In A Favorable BNPL LandscapeFor full access to analyst ratings, stock quant scores and dividend grades, subscribe to Seeking Alpha Premium at seekingalpha.com/subscriptions

In the New Stack Makers episode, Adi Polak, Director, Advocacy and Developer Experience Engineering at Confluent discusses the operational and analytical estates in data infrastructure. The operational estate focuses on fast, low-latency event-driven applications, while the analytical estate handles long-running data crunching tasks. Challenges arise due to the "schema evolution" from upstream operational changes impacting downstream analytics, creating complexity for developers. Apache Iceberg and Flink help mitigate these issues. Iceberg, a table format developed by Netflix, optimizes querying by managing file relationships within a data lake, reducing processing time and errors. It has been widely adopted by major companies like Airbnb and LinkedIn. Apache Flink, a versatile data processing framework, is driving two key trends: shifting some batch processing tasks into stream processing and transitioning microservices into Flink streaming applications. This approach enhances system reliability, lowers latency, and meets customer demands for real-time data, like instant flight status updates. Together, Iceberg and Flink streamline data infrastructure, addressing developer pain points and improving efficiency. Learn more from The New Stack about Apache Iceberg and Flink:Unfreeze Apache Iceberg to Thaw Your Data LakehouseApache Flink: 2023 Retrospective and Glimpse into the Future 4 Reasons Why Developers Should Use Apache Flink Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Speaker Resources:Eastridge Analytics: https://www.eastridge-analytics.com/Graph Data Science with Python and Neo4j book: https://a.co/d/hkfkxPrLinkedIn profile: https://www.linkedin.com/in/timeastridge/NODES 2024 (look for more info on Tim's talk soon!): https://dev.neo4j.com/nodes24Neo4j GraphAcademy: https://graphacademy.neo4j.com/Graph Algorithms for Data Science (Tomaž Bratanic): https://a.co/d/7WhibUkTools of the Month:Jennifer: VS Code https://code.visualstudio.com/Jason: Cursor AI https://www.cursor.com/Tim: Neo4j LLM Graph Builder https://neo4j.com/labs/genai-ecosystem/llm-graph-builder/Announcements / News:Articles:Graph Databases Offer a Deeper Understanding of Organizational Risk https://neo4j.com/developer-blog/graph-database-organizational-risk/Using Embeddings to Represent String Edit Distance in Neo4j https://neo4j.com/developer-blog/embeddings-string-edit-distance/Build a Knowledge Graph-based Agent with Llama 3.1, NVIDIA NIM, and LangChain https://neo4j.com/developer-blog/knowledge-graph-llama-nvidia-langchain/Entity Linking and Relationship Extraction With Relik in LlamaIndex https://neo4j.com/developer-blog/entity-linking-relationship-extraction-relik-llamaindex/Integrating Microsoft GraphRAG into Neo4j https://neo4j.com/developer-blog/microsoft-graphrag-neo4j/Ingesting Documents Simultaneously to Neo4j & Milvus https://neo4j.com/developer-blog/ingest-documents-neo4j-milvus/Enriching Vector Search With Graph Traversal Using the Neo4j GenAI Package https://neo4j.com/developer-blog/graph-traversal-neo4j-genai-package/Create a Neo4j GraphRAG Workflow Using LangChain and LangGraph https://neo4j.com/developer-blog/neo4j-graphrag-workflow-langchain-langgraph/Introducing Concurrent Writes to Cypher Subqueries https://neo4j.com/developer-blog/concurrent-writes-cypher-subqueries/Running Neo4j on a Commodore 64 https://neo4j.com/developer-blog/neo4j-commodore-64/Change Data Capture and Neo4j Connector for Confluent and Apache Kafka Go GA https://neo4j.com/developer-blog/change-data-capture-cdc-ga/Videos:NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEventsAll Neo4j events: https://neo4j.com/events/(Sep 9) Conference (San Francisco, CA, USA): Pre-AI Conference Hack Day:  https://lu.ma/bsype6t6?tk=1dgMCa(Sep 9-11) Conference (San Francisco, CA, USA): AI Conference: https://aiconference.com/(Sep 10) Meetup (San Francisco, CA, USA): AI Tools HackNight: https://lu.ma/ozt7jtq5(Sep 12) Meetup (San Jose, CA, USA): AI & Tech Talks:  https://lu.ma/jjgnoqik?tk=sMOLyE(Sep 24-26) Conference (Dallas, TX, USA): JConf https://2024.jconf.dev/(Sep 30-Oct 3) Conference (Denver, CO, USA): dev2next https://www.dev2next.com/(Oct - TBD) Meetup (Charlotte, NC, USA): Data Science Meetup https://www.meetup.com/Data-Science-Charlotte/

Kristi Henderson is a healthcare leader, digital health pioneer, and clinician who advanced from a practicing nurse practitioner to CEO and is recognized as an industry thought leader for technology-forward healthcare companies. She has a proven track record of optimizing and executing profitable growth strategies, driving strong business operations, and leading differentiated clinical products and services. She is a cultivator of high-performing teams with a keen understanding of drivers of growth, profitability, and equity value creation, who also fosters a civic-minded culture resulting in a high level of collaboration and commitment. She is experienced in leading and scaling high-growth companies and owning & driving P&L results in geographically dispersed, multi-state, multi-site businesses. She boasts health leadership experience in academic and national nonprofit health systems as well as big tech and public pay/provider organizations, including two Fortune 5 companies.  She is the CEO of Confluent Health, a national value-based musculoskeletal health company, where she is responsible for expanding their nationwide services through the growth of community-based physical therapy clinics, workplace services, and virtual/digital solutions using the highest skilled therapists and innovative clinical pathways that optimize the latest in personalization and technology. ​ She was most recently the CEO of MedExpress and Optum Virtual Medical Group at United Health Group with former leadership roles at Amazon, Ascension Health, and the University of Mississippi Medical Center. She has a proven track record of delivering successful programs at scale that improve health and save money. She is known for her ability to execute and sustain these models in advance of them becoming an industry standard as evidenced by her first launch of a telehealth program in 2003 which is recognized as one of only two of HRSA's Centers of Excellence. She is the immediate past chair for the American Telemedicine Association. She is a Fellow in the American Academy of Nursing and remains active in healthcare education providing guest presentations and serving as an adjunct faculty member. She is an honorary Dean at the University of Washington School of Nursing and an adjunct faculty member in Population Health at the Dell Medical School at the University of Texas-Austin.She has testified before multiple U.S. Senate committees and given numerous presentations across the country, including a TEDx talk, to advance telehealth policy and share innovative new models of care. A few of her other leadership roles include service as an executive board member for the Association of American Medical Colleges Telehealth Committee, advisor for the National Quality Forum's telehealth committee and co-chair of the Telehealth Committee for the American Nurses Association. Henderson received her Doctor of Nursing Practice degree from the University of Alabama at Birmingham where she was recognized as the 2019 Distinguished Alumna from the School of Nursing. She maintains national certification as a family and acute care nurse practitioner.

On this episode of the HealthLeaders podcast, Finance Editor Marie DeFreitas is joined by Chief Executive Officer of Confluent Health Dr. Kristi Henderson to discuss the benefits of mergers, as well as how to develop healthy partnerships, select the right partners, and ensure employee and clinician satisfaction throughout the process

Sinkholes forming Memes again - Huge Moves Economy starting to stall Face Ripper moves into the close PLUS we are now on Spotify and Amazon Music/Podcasts! Click HERE for Show Notes and Links DHUnplugged is now streaming live - with listener chat. Click on link on the right sidebar. Love the Show? Then how about a Donation? Follow John C. Dvorak on Twitter Follow Andrew Horowitz on Twitter DONATE - Show 700 Campaign Warm Up - Sinkholes forming - Memes again - Huge Moves - Economy starting to stall - Announcing a NEW CTP Market Update - Face Ripping - Last Friday - Software Stocks hammered - May finished in the green nicely - Oil Drooping - Roaring Kitty is back Face Rip - Stocks ramped on the last day of the month - 300 points in 10 minutes to finish the day on Friday - markets had a good month - -- Large Cap Growth up 6% for the month - US real estate up 5% - Only area LataAm down for May Sinkhole Forming Again - MEGA Caps pulling market up - Primarily NVDA - Monday S&P 500 Flat - S&P Equal Weight  -0.53% Best Buy Earnings - "Sluggish Demand" -  Earnings per share: $1.20 vs. $1.08 expected   Revenue: $8.85 billion vs. $8.96 billion expected - Best Buy has noticed a pullback in purchases of discretionary items as consumers manage higher costs because of inflation. - Shares soar Software stocks Soft - Salesforce plunges 20% after it posted weaker than expected revenue - The WisdomTree Cloud Computing Fund, an exchange-traded fund that tracks cloud stocks, slid 5% this week, the sharpest decline since January. Paycom, GitLab, Confluent, Snowflake and ServiceNow all lost at least 10% of their value in the downdraft. - Dell PLUNGED - 20% too this week Economics - April Construction Spending -0.1% vs 0.2% Briefing.com Consensus; prior -0.2% - May ISM Manufacturing Index 48.7 vs 49.6 Briefing.com consensus; prior 49.2 - May S&P Global US Manufacturing PMI - Final 51.3 vs 50.9 prelim; prior 50.0 Focus on Manufacturing (Briefing.com) - The May ISM Manufacturing Index checked in at 48.7% (consensus 49.6%), down from 49.2% in April. The dividing line between expansion and contraction is 50.0%, so the May reading suggests there was a faster pace of contraction in the manufacturing sector last month. - The key takeaway from the report is that it showed a faster pace of contraction in manufacturing activity that will stir worries about the economy missing its mark with a soft landing. - The New Orders Index slumped to 45.4% from 49.1%, hitting its lowest level since May 2023. - The Prices Index dropped to 57.0% from 60.9%. - The Employment Index increased to 51.1% from 48.6%. - The Backlog of Orders Index fell to 42.4% from 45.4%. - The Supplier Deliveries Index held steady at 48.9%. - The Production Index decreased to 50.2% from 51.3%. - The New Export Orders Index rose to 50.6% from 48.7%. Fed Cuts - Not Happening - According to the latest issues of Barrons - "The Federal Reserve isn't likely to lower interest rates in 2024.  Elevated inflation, a resilient economy, and a still-strong, if softening labor market argue against the need for easing monetary policy, especially as these conditions are expected to persist through year end." WAIT A MINUTE - Rate cut bets for September are back a 66% probability after a slightly weaker than expected JOLTS report - 10YR Yields @ 4.35% (Down from 4.6% last week) GDPNow - Atlanta Fed GDPNow model estimate for Q2 real GDP growth is 1.8%, down from 2.7% on May 31 - Latest downward revision follows today's release of the ISM Manufacturing Index for May and Construction Spending Report for April. As Usual - Backwards - OPEC+ prolongs cuts for one year - OPEC+ agreed on Sunday to extend most of its deep oil output cuts well into 2025 as the group seeks to shore up the market amid tepid demand growth, high interest rates and rising rival U.S. production.

From writing the first lines of Kafka over a Christmas break as a LinkedIn engineer to running a public company as the CEO of Confluent, Jay joins the show to chat about how he and his co-founders convinced investors to take a chance on their vision, what many engineers get wrong about communication, and why engineers can make great CEOs - even when coding is not in the job description. And much more. Segments: (00:01:16) The Shaved Head Bet (00:04:07) Fundraising (00:12:16) The Role of Technical Background in VCs (00:15:48) The power of believing in the possibility of important changes (00:18:29) The Journey to starting Confluent (00:27:11) Kafka's Controversial Beginnings (00:34:30) Effective Communication in Engineering (00:44:20) The Early Days of Kafka (00:48:31) The Power of Storytelling (00:57:19) Early days of Confluent (01:03:06) Do Engineers Make Good CEOs? (01:07:59) A Typical Day in the Life of a CEO (01:12:24) The Evolution of Data Streaming Show Notes: - “The log” blog post that solidified Jay and his co-founders' conviction to found Confluent: https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying - Jay on twitter: https://x.com/jaykreps Stay in touch:

In der heutigen Folge von “Alles auf Aktien” sprechen die Finanzjournalisten Anja Ettel und Holger Zschäpitz über ein Comeback amerikanischer Mode-Einzelhändler, beunruhigende Zahlen für Tesla und was sonst noch wichtig ist in dieser Woche. Außerdem geht es um Walmart, Abercrombie&Fitch, Gap, Target, Kohl's, Footlocker, American Eagle, Franklin FTSE India 30/18 Capped (A2PB5W), Xtrackers MSCI India (DBX0G0), Adani Group, Larsen & Toubro Ltd., NTPC Ltd., NHPC Ltd., Indian Bank, Hindustan Aeronautics, Salesforce, Nutanix, UiPath, Workday, Gitlab, Confluent, Samsara, ServiceNow, Autodesk, Adobe, Microsoft, Alphabet, Braze und Amazon. Eure Sprachnachrichten für die 1000. Folge schickt ihr bitte an die Nummer: 0170/3753558. Wir freuen uns an Feedback über aaa@welt.de. Ab sofort gibt es noch mehr "Alles auf Aktien" bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts und AAA-Newsletter. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. Außerdem bei WELT: Im werktäglichen Podcast „Das bringt der Tag“ geben wir Ihnen im Gespräch mit WELT-Experten die wichtigsten Hintergrundinformationen zu einem politischen Top-Thema des Tages. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

Join us in this amazing episode of the MongoDB Podcast at .local NYC, as we sit down with Lucas Melo from Confluent and Maruti C. from Google Cloud to talk about the fascinating possibilities of Generative AI.

Chris Johnson is Co-founder & Managing Partner of Artisanal Talent Group, a highly specialized boutique executive search firm known for building some of tech's most talented executive teams. Chris leads the Product and Engineering Leaders Practice, and is one of the most highly sought-after search consultants in the field for these critical roles at tier-1, high profile, VC backed and public companies. Over the past few years, Chris has completed Product and Engineering searches for Rippling, Notion, Rubrik, Airtable, ServiceNow, Databricks, Figma, UiPath, Confluent, Mulesoft, Starburst, GitLab, Splunk, Harness, New Relic, Elastic, dbt Labs, MessageBird, Postman, Coalition, Loom, Pendo, Procore, etc. In this episode, Chris shares deep insights on how executive recruiters work and how to work with them. He also shares advice on how to be deliberate and thoughtful about mapping your career journey. Highlights: * Case Study of a CPO Search* How do accomplished executive candidates stand out* Hiring trends - FAANG candidates, Domain relevance* Are you a wartime or peacetime leader* Does a company really need a CPO* How recruiters assess candidates for "fit"* How can candidates craft a strong narrative about themselves* How to balance the use of "I" vs "We" in interviews* How to talk about weak spots on the resume* How to build relationships with exec recruiters* Are you playing Career Chess or Checkers* Diversity, Equity and Inclusion in exec searches* How recruiters think about exceptions to the job spec

Mike Volpi is a longtime venture capitalist who joined Index Ventures in 2009 to establish the firm's San Francisco office and North American operations. Prior, he was Chief Strategy Officer at Cisco, overseeing a run of acquisitions still studied today as a model for technology merger strategy. Mike invests primarily in artificial intelligence, infrastructure, and open-source companies, and currently serves on the boards of multiple companies including Aurora, ClickHouse, Cockroach Labs, Cohere, Confluent, Covariant.ai, Kong, Scale, Sonos, and Wealthfront.     In this episode, Kevin and Mike discuss Mike's early childhood, how he got interested in the study of engineering, and his career experiences—including what led to Mike's long career at Cisco and his current Partner position at Index—including his board experiences with multiple companies.     Mike Volpi | Index Ventures  Kevin Scott    Behind the Tech with Kevin Scott    Discover and follow other Microsoft podcasts.    

Larry Shurtz is the Chief Sales Officer at Genesys where he oversees the company's global go-to-market strategies, including commercial activities, field sales and partner ecosystem operations. Larry has nearly three decades of experience in the software industry, from leading Confluent to delivering more than 60% revenue growth and doubling customer count as Chief Revenue Officer, to scaling a 1,300-person team at Salesforce to $2.1 billion in revenue. In Today's Episode with Larry Shurtz We Discuss: From Robotics Student to $2.1BN Sales Leader at Salesforce How did Larry lead 1300 people to $2.1 billion revenue at Salesforce? What were his takeaways? What did Larry learn about building vertical sales playbooks at Salesforce? Which framework did Larry learn at Salesforce that he still uses at Genesys? Mastering Sales Leadership What are the biggest mistakes sales leaders make on prioritization today? What are Larry's “3 Rs” to master prioritization? What does Larry think are the most common reasons fast-scaling teams break in sales? Has Larry ever caused bad culture in a sales team? What did he learn from the experience? Does Larry think sales is more art or science? How does Larry blend the two? Building the Best Sales Team How does Larry structure the hiring process for a new sales hire? How big should your recruitment team be?  What are Larry's most commonly asked questions when interviewing? What were Larry's biggest hiring mistakes? What did he learn from them? How does Larry structure the comp? How does he get it right? What do most new hires care about today? The Onboarding: The Dos & Don'ts How does Larry structure the onboarding process? Why does Larry onboard new hires with big customers? What is the buddy system? How does Larry tell if a new hire is bad? What are the biggest red flags to look out for? What does Larry mean when he says “You can make all the physical errors, you cannot make mental errors?” Does Larry agree with Max Levchin @ Affirm that “When there's doubt, there's no doubt?”

Alyssa Henry is the former CEO of Square, a financial services company providing products and services used by over 4 million merchants. Formerly at Amazon, Alyssa led the development and growth of Simple Storage Service (S3) at AWS. Alyssa now serves as an Independent Director at Intel and Confluent. —  In today's episode, we discuss: Lessons from Amazon, Microsoft, and Square “Minimum Remarkable Products” versus Minimum Viable Products Navigating different work cultures in big tech Insider reactions to the disruptive launch of AWS “Pioneer” versus “fast-follower” companies —  Referenced: Amazon: https://www.amazon.com Amazon Web Services: https://aws.amazon.com Bill Gates: https://www.linkedin.com/in/williamhgates Block, Inc: https://block.xyz Cash App: https://cash.app Fast Company - Back To Square One: https://www.fastcompany.com/3033412/back-to-square-one Gokul Rajaram: https://www.linkedin.com/in/gokulrajaram1 Jack Dorsey: https://twitter.com/Jack James Hamilton: https://www.linkedin.com/in/jameshamilton4 Jeff Bezos: https://twitter.com/jeffbezos Microsoft: https://www.microsoft.com Oracle Corporation: https://www.oracle.com Sarah Friar: https://www.linkedin.com/in/sarah-friar Square: https://squareup.com Tom Szkutak: https://www.linkedin.com/in/tom-szkutak-4b59817 WSJ - Mobile-Payments Startup Square Discusses Possible Sale: https://www.wsj.com/articles/SB10001424052702303825604579513882989476424 —  Where to find Alyssa Henry: LinkedIn: https://linkedin.com/in/alyssa-henry-0905692 Twitter/X: https://twitter.com/alyssahhenry —  Where to find Brett Berson: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson —  Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast —  Timestamps: (00:00) Introduction (02:20) Lessons from Microsoft and Amazon (08:29) Noticeable consistencies in the human condition (10:50) Differences in culture at Amazon, Microsoft and Square (13:27) Why “customers come first,” even above employees and community (14:01) Why fast-followers can be less customer-focused (15:50) The challenge of commercializing research projects (18:58) Joining Square and “building a picture” of the org (24:55) Knowing what to replicate from past companies (27:45) Questioning norms in new companies (28:41) The importance of effective communication systems (31:31) How to operationalize company values (33:38) Why shared beliefs are crucial for good company culture (37:05) Building Minimal Remarkable Products at Square (38:13) How to scale an aesthetic (42:46) Org design lessons from Square (50:06) How to align different teams behind business priorities (52:57) Lessons learned from fierce competition (57:39) The “fast follower” vs “pioneer” playbook (61:05) The original thinking behind AWS (66:08) The unlikely origin of Amazon CloudFront and other products (73:47) How Jeff Bezos influenced Alyssa

Follow: https://stree.ai/podcast | Sub: https://stree.ai/sub | New episodes every Monday! This week, Tim Berglund chats with Curtis Galione, a Flink aficionado from the Advanced Technology Group at Confluent, about the interplay between Flink and Kafka and how they revolutionize data infrastructure. This conversation explores the complexities and innovations of stream processing, promising to enhance your understanding of current data processing challenges. Remember to use the 30% discount for the Real-Time Analytics Summit: https://stree.ai/rtapod30 (Code: RTAPOD30)► xkcd that Curtis referenced: https://xkcd.com/1838/► One SQL to Rule Them All: https://arxiv.org/abs/1905.12133

Highlights from this week's conversation include:Apruva's background in streaming technology (0:48)Developer experience and Kafka streams (2:47)Motivation to bootstrap a startup (4:09)Meeting the Confluent founders and early work at Confluent (6:59)Projects at Confluent and transition to engineering management (10:34)Overview of Responsive and event-driven applications (12:55)Defining event-driven applications (15:33)Importance of latency and state in event-driven applications (18:54)Low Latency and Stateful Processing (21:52)In-Memory Storage and Evolution of Kafka (25:02)Motivation for KSQL and Kafka Streams (29:46)Category Creation and Database-like Interface (34:33)Developer Experience with Kafka and Kafka Streams (38:50)Kafka Streams Functionality and Operational Challenges (41:44)Metrics and Tuning Configurations (43:33)Architecture and Decoupling in Kafka Streams (45:39)State Storage and Transition from RocksDB (47:48)Future of Event-Driven Architectures (56:30)Final thoughts and takeaways (57:36)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Shaun Clowes Riffs on Waymo, Legos, and Process Advancement This week, Fareed welcomed co-host Joff Redfern, Principle at Menlo VC and former CPO at Atlassian and guest Shaun Clowes, CPO at Confluent and former CPO of Mulesoft at Salesforce, CPO @ Metromile, and Head of Growth @ Atlassian. This week's Deep Dive drops Wednesday and we're focused on Reddit & Cognition Lab's Devin, but our riff this week is simply a classic happy hour conversation. Fareed, Joff and Shaun cover a wide range of topics dropping some brilliant nuggets along the way. Enjoy! For a summary of their takeaways, join us at UnsolicitedFeedback.co.

Follow: https://stree.ai/podcast | Sub: https://stree.ai/sub | Today, Tim dives into the world of Kafka Streams with Matthias Sax, Software Engineer at Confluent and core contributor to Apache Kafka. Matthias updates us on the latest in Interactive Queries, their enhancements in recent releases, insights on stream processing and how Kafka Streams stands out in the real-time analytics landscape. Remember to use the 30% discount Tim mentioned for the Real-Time Analytics Summit: https://stree.ai/rtapod30 (Code: RTAPOD30)

Today's guest is Andrew Sellers, Head of Technology Strategy at Confluent. Previously, he served as Chief Technology Officer at QOMPLX, a high-growth startup in cyber-risk analytics, and as a Senior Cyberspace Operations Officer, CTO, and Assistant Professor of Computer Science in the United States Air Force. Andrew returns to the platform alongside Emerj Senior Editor Matthew DeMello to delve into the upcoming democratization of data and what it means for AI adoption initiatives at financial institutions. Andrew shares strategic insights for business leaders seeking executive buy-in to capitalize on the burgeoning wealth of information. If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

SaaStr 724: CRO Confidential: Bringing Product-Led and Sales-Led Growth Together For Go-To-Market Success with Giancarlo Lionetti, CRO of Zapier. Hosted by Sam Blond, Partner at Founders Fund In this latest episode of CRO Confidential, Sam Blond, Partner at Founders Fund and former CRO at Brex, sits down with the CRO of Zapier, Giancarlo Lionetti (GC), to chat about Product-Led Growth (PLG) and Go-To-Market (GTM). Everything from hiring on the GTM side to layering in a sales-led motion into PLG.  Before joining Zapier, Giancarlo was at Atlassian as a technical sales lead before moving to DropBox as a Senior Director of Growth and Monetization, and was CMO at Confluent – so he has a wealth of knowledge from Zapier's Go-To-Market history as well as these successful SaaS companies to pull from.  From a Go-To-Market perspective, Zapier uses a hybrid model that involves a combination of freemium offerings, subscription plans, and partnerships. Let's dive into what's making that hybrid model successful.  -------------------------------------------------------------------------------------------- SaaStr hosts the largest SaaS community events on the planet. Join us in 2024 at: SaaStr Annual: Sept. 10-12 in the SF Bay Area. Join 12,500 SaaS professionals, CEOs, revenue leaders and investors for the world's LARGEST SaaS community event of the year. Podcast listeners can grab a discount on tickets here: https://www.saastrannual2024.com/buy-tickets?promo=fave20 SaaStr Europa: June 5-6 in London. We'll be hosting the 5th SaaStr Europa in London for two days of content and networking. Join 3,000 SaaS and Cloud leaders. Podcast listeners can grab a discount on Europa tickets here: https://www.saastreuropa2024.com/buy-tickets?promo=fave200 -------------- This episode is sponsored by: Northwest Registered Agent When starting your business, it's important to use a service that will actually help you. Northwest Registered Agent is that service. They'll form your company fast, give you the documents you need to open a business bank account, and even provide you with mail scanning and a business address to keep your personal privacy intact. Visit https://www.northwestregisteredagent.com/saastr to get a 60 percent discount on your next LLC.

Today's guest is Andrew Sellers, Head of Technology Strategy at Confluent. Previously, he served as Chief Technology Officer at QOMPLX, a high-growth startup in cyber-risk analytics, and as a Senior Cyberspace Operations Officer, CTO, and Assistant Professor of Computer Science in the United States Air Force. Andrew joins Emerj Senior Editor Matthew DeMello on today's podcast to talk about the high costs and tight budgets of IT. Throughout his appearance, Andrew offers advice on balancing ROI goals with the typical pitfalls of false start pilot initiatives. To access Emerj's frameworks for AI readiness, ROI, and strategy, visit Emerj Plus at emerj.com/p1.

Oliver Jay is a sales and expansion specialist. Oliver was Chief Revenue Officer at Asana and led the company's global expansion. He grew the team from 20 to 450 people and increased international income to 40% of Asana's total revenue. Prior to this, Oliver built the first business sales team at Dropbox, and led the company's expansion into the Asia-Pacific region while tripling ARR. Oliver is now an advisor and leadership coach focused on assisting founders and executives in scaling their businesses. — In today's episode, we discuss: Common mistakes PLG companies make The “PLG trap” and how to avoid it The playbook for transitioning into enterprise How and when to build an enterprise sales team How PLG companies can break $10 billion market cap Why it's difficult to emulate Atlassian, Slack or Salesforce — Referenced: Airtable: https://www.airtable.com/ Asana: https://asana.com/ Atlassian: https://www.atlassian.com/ Bitbucket: https://bitbucket.org/product/ Confluent: https://www.confluent.io/ Daniel Shapero: https://www.linkedin.com/in/dshapero/ Datadog: https://www.datadoghq.com/ Dennis Woodside: https://www.linkedin.com/in/dennis-woodside-341302/ Dropbox: https://www.dropbox.com/ Dustin Moskovitz: https://www.linkedin.com/in/dmoskov/ Jay Simons: https://www.linkedin.com/in/jaysimons/ Jira: https://www.atlassian.com/software/jira Justin Rosenstein: https://www.linkedin.com/in/justinrosenstein/ Kim Scott: https://www.linkedin.com/in/kimm4/ Salesforce: https://www.salesforce.com/ Slack: https://slack.com/ The PLG Trap: https://www.linkedin.com/pulse/plg-trap-oliver-jay/ The seed, land, and expand framework: https://www.endgame.io/blog/seed-land-expand-framework Zendesk: https://www.zendesk.com/ — Where to find Oliver Jay: LinkedIn: https://www.linkedin.com/in/oliverjayleadership/ Website: https://www.oliverjayleadership.com/ — Where to find Brett Berson: Twitter/X: https://twitter.com/brettberson LinkedIn: https://www.linkedin.com/in/brett-berson-9986094 — Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast — Timestamps: (00:00) Introduction (02:23) Differences between PLG and enterprise companies (05:56) Avoiding the “PLG trap” (07:39) Transitioning to enterprise feels like building two companies (10:57) Thinking about user value versus company value (13:58) The relationship between OKRs and executive champions (14:59) Dropbox had almost no company value (15:33) The strategy PLG companies should avoid (18:30) Why Dropbox is worth $10b, not $50b (19:41) The story of Asana's expansion (21:16) Asana's unique customer success team (23:27) How product strategy relates to finding champions (25:03) How Asana structured its GTM org (27:11) What Oliver would have done differently with Asana's GTM (29:45) Getting executive-level buy-in (31:49) Asana's concept of “selling clarity” (33:18) An inside look at Asana's transition into enterprise (37:59) The champion tree framework (40:43) Structuring Asana's early enterprise sales team (44:27) The impact of company size on GTM (47:20) Common sales mistake (48:29) The seed, land, and expand framework (51:43) Oliver's advice to founders (54:13) Why building horizontally may be a mistake (55:32) Common challenges faced by PLG companies (58:30) How PLG companies can break the $10b market cap (60:17) Why emulating Atlassian's playbook is difficult (63:21) People who had an outsized impact on Oliver

Ben Miller is the co-founder and CEO of Fundrise, the largest direct-to-consumer alternative asset manager in the US. Under his leadership, Fundrise has grown to over 2 million active users and over $3 billion in real estate private equity, private credit, and growth-stage venture capital. Ben also serves as Chairman of the Board of three publicly-registered investment companies and, during his 25-year career, has acquired more than $8 billion of real estate assets, including 37,000 residential units and 5 million square feet of industrial and commercial space. (00:26) - Fundrise's journey to revolutionizing private market investing(8:13) - Benefits of end-to-end platform experience(12:49) - Growth-stage Venture Capital fund (Part I)(15:43) - Real Estate investment strategy(19:20) - Feature: Housing Trust SV (site) - Housing finance & public-private partnerships to create more equitable & affordable communities(20:31) - AI impact on Commercial Real Estate(28:14) - Macro trends impacting Real Estate(29:46) - Economy outlook for 2024(35:24) - Growth-stage Venture Capital fund (Part II)(46:25) - Long-term vs. short-term lessons(48:59) - Collaboration Superpower: Alexander Hamilton

Summary Kafka has become a ubiquitous technology, offering a simple method for coordinating events and data across different systems. Operating it at scale, however, is notoriously challenging. Elad Eldor has experienced these challenges first-hand, leading to his work writing the book "Kafka: : Troubleshooting in Production". In this episode he highlights the sources of complexity that contribute to Kafka's operational difficulties, and some of the main ways to identify and mitigate potential sources of trouble. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It's the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it's real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Elad Eldor about operating Kafka in production and how to keep your clusters stable and performant Interview Introduction How did you get involved in the area of data management? Can you describe your experiences with Kafka? What are the operational challenges that you have had to overcome while working with Kafka? What motivated to write a book about how to manage Kafka in production? There are many options now for persistent data queues. What are the factors to consider when determining whether Kafka is the right choice? In the case where Kafka is the appropriate tool, there are many ways to run it now. What are the considerations that teams need to work through when determining whether/where/how to operate a cluster? When provisioning a Kafka cluster, what are the requirements that need to be considered when determining the sizing? What are the axes along which size/scale need to be determined? The core promise of Kafka is that it is a durable store for continuous data. What are the mechanisms that are available for preventing data loss? Under what circumstances can data be lost? What are the different failure conditions that cluster operators need to be aware of? What are the monitoring strategies that are most helpful for identifying (proactively or reactively) those errors? In the event of these different cluster errors, what are the strategies for mitigating and recovering from those failures? When a cluster's usage expands beyond the original designed capacity, what are the options/procedures for expanding that capacity? When a cluster is underutilized, how can it be scaled down to reduce cost? What are the most interesting, innovative, or unexpected ways that you have seen Kafka used? What are the most interesting, unexpected, or challenging lessons that you have learned while working with Kafka? When is Kafka the wrong choice? What are the changes that you would like to see in Kafka to make it easier to operate? Contact Info LinkedIn (https://www.linkedin.com/in/elad-eldor/?originalSubdomain=il) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Kafka: Troubleshooting in Production (https://amzn.to/3NFzPgL) book (affiliate link) IronSource (https://www.is.com/) Druid (https://druid.apache.org/) Trino (https://trino.io/) Kafka (https://kafka.apache.org/) Spark (https://spark.apache.org/) SRE == Site Reliability Engineer (https://en.wikipedia.org/wiki/Site_reliability_engineering) Presto (https://prestodb.io/) System Performance (https://amzn.to/3tkQAag) by Brendan Gregg (affiliate link) HortonWorks (https://en.wikipedia.org/wiki/Hortonworks) RAID == Redundant Array of Inexpensive Disks (https://en.wikipedia.org/wiki/RAID) JBOD == Just a Bunch Of Disks (https://en.wikipedia.org/wiki/Non-RAID_drive_architectures#JBOD) AWS MSK (https://aws.amazon.com/msk/) Confluent (https://www.confluent.io/) Aiven (https://aiven.io/) JStat (https://docs.oracle.com/javase/8/docs/technotes/tools/windows/jstat.html) Kafka Tiered Storage (https://cwiki.apache.org/confluence/display/KAFKA/KIP-405%3A+Kafka+Tiered+Storage) Brendan Gregg iostat utilization explanation (https://www.brendangregg.com/blog/2021-05-09/poor-disk-performance.html) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

(0:00) Intro(1:26) How being a writer benefited Jay as a CEO(3:05) Building a management team(13:28) The Role of Titles in a Company(17:54) Only Going To One Year of High School(23:02) The Decision to Pursue Computer Science(31:05) The Birth of Project Kafka(34:32) Reflections on the Success of Kafka(36:47) Launching an Open Source Project(37:43) The Power of Product Marketing(39:35) Should You Be A Founder?(42:00) The Transition from Individual Contributor to CEO(47:51) Navigating the Public Markets(1:09:46) What's Wrong With Hybrid Work(1:14:27) Navigating Politics in the Workplace(1:17:36) Why Fairness Matters(1:26:51) The Evolution of Open Source(1:35:22) The Future of Artificial Intelligence(1:43:41) The Shift from Using Software to Becoming Software Produced: Rashad Assir & Leah ClapperMixed and edited: Justin HrabovskyExecutive Producer: Josh Machiz 

AI tools are helping small business owners manage their businesses, so they can stay focused on the aspects of their business they love to do. This week on No Priors, Sarah and Elad are joined by Alyssa Henry, an executive at some of the most impactful companies from Microsoft to Amazon. Most recently she was the CEO of Square. She led Square's team as they were very early adopters of a consumer-facing product that used GPT-2 and have continued to incorporate AI into their offerings. On today's episode, they talk about the whitespace within e-commerce for AI and lessons from the prior generation of infrastructure. Alyssa recently retired from being longtime CEO of Square, within Block. Before that she was a vice president of AWS running, amongst other things, the storage products, or the digital storage bucket for the world. And before AWS, she ran order management software at Amazon Retail and started her tech career at Microsoft. She remains on the boards of Intel, Confluent and was previously on the board of Unity.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @alyssahhenry Show Notes:  (0:00) Alyssa's experience and career trajectory (2:30) Transition from engineer to manager (4:09) AI implementation at Square (7:46) Small business AI applications  (12:14) Latent demand for content generation (15:04) The origin story of Square's GPT-2 products (16:54) Consolidating ecommerce workflows (18:46) How will AI change cloud services (23:07) Hyperscaler foundation models and the AI land grab (25:16) Enterprise demand for open source models (28:08) Startups in the AI semiconductor space (31:02) Scale up architectures vs scaling out (34:32) What's next for Alyssa (36:08) What Elad and Sarah are excited about in 2024

- Earnings Recaps and Future Looking:  Global-E ($GLBE) and Palo Alto Networks $PANW - Shay Stock Watch: Confluent ($CLFT)- Mini Monsters:  Evolv Technologies ($EVLV) making it faster to be safe at large events, schools, concerts, games etc. CHECK OUT SPECIAL PROMOTIONS FOR OUR LISTENERS: Exclusive UNDERDOG FANTASY SPORTS Get up to Bonus Match instantly (Up to $500) and win up to $3M in their BestBall Mania + $15M In total Prizes.  Promo Code: PTT Signup Link: https://play.underdogfantasy.com/p-pounding-the-table Interested in Automated Trading Bots?  Peakbot is our favorite automated trading bots; great for non experts to leverage covered calls.PeakBot (www.UsePeakBot.com)Promo code: PTTWant to Contact Us?  Email us at Hosts@PoundingTheTablePodcast.comLegal Disclaimer:The thoughts and opinions expressed on this podcast, are solely for entertainment purposes and should not be construed as investment advice. The content provided is based on personal experiences, analysis, and general knowledge about stocks and the financial market.The information shared on this podcast is not intended to be a substitute for professional financial advice. Listeners should always consult with a qualified financial advisor or professional before making any investment decisions. Investing in stocks and other financial instruments carries inherent risks, and individuals should carefully consider their own financial situation, risk tolerance, and investment goals before engaging in any investment activities.The hosts and guests on this podcast are not licensed financial advisors or professionals. They are sharing their personal opinions and experiences, which may not be suitable for everyone. The accuracy, completeness, and timeliness of the information presented cannot be guaranteed, as the stock market and investment landscape are subject to constant changes.Any actions taken based on the content of this podcast are done at the listener's own risk. The podcast hosts, guests, and producers assume no responsibility or liability for any investment decisions, losses, or damages incurred as a result of the information provided on the podcast.

In this episode of the Tech Talks Daily Podcast, I speak with Peter Pugh-Jones from Confluent, a company at the forefront of data infrastructure focusing on data in motion. This episode is a deep dive into the alarming rise of fraud in the UK, the complexities of data analysis for fraud detection, and the unique challenges faced by financial services organizations. The conversation is timely, given that over £600 million was stolen in the UK in the first half of 2022 alone due to fraud. The discussion begins with the risks that financial services organizations face, particularly the reputational damage that can result from data breaches. Peter and Neil explore the proactive steps that can be taken to mitigate these risks. They discuss the simplification of architectures, the implementation of event-driven systems, and the creation of data products designed for better management and security. The conversation then shifts to the concept of fusion centers. These specialized units amalgamate different types of data to gain actionable insights to combat fraud. Peter elaborates on how the evolution of technology in the banking industry is not just about enhancing security but also about enriching customer experiences through more accurate data analysis. As the dialogue unfolds, Peter shares strategies for businesses to protect themselves better. He emphasizes the importance of early detection and discusses how adopting data streaming platforms like Confluent can be a game-changer in fraud prevention. Peter also delves into his methods for staying updated and educated, which include reading books, listening to podcasts, and drawing insights from various industries.  

Sebastian Duesterhoeft of Lightspeed Venture Partners joins Nate to discuss Are Software Businesses Defensible? What is the S curve and How to Time it, and Lessons from Investing in Snowflake, Databricks, and Confluent. In this episode we cover: Investing in a Challenging Market with a Growth Investor AI Technology, Its Impact & Potential to Disrupt Industries and Create New Opportunities Investment Strategies and Price Dynamics in the Startup Space Investing in Enterprise Software Companies, Including S-Curve Analysis Market Size and Potential for a New Endpoint Security Player Determining the Size of the Queue for Cloud Security and Software Development Market Sizing, Vertical SAAS, and Software Margins Investing, Brand, and Distribution in the Tech Industry Guest Links: Twitter LinkedIn Lightspeed Venture Partners The hosts of The Full Ratchet are Nick Moran and Nate Pierotti of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter. Are you a founder looking for your next investor? Visit our free tool VC-Rank and we'll send a list of potential investors right to your inbox!

The Dow fell, snapping a 6-day win streak, while the S&P and Nasdaq rose, and Jim Cramer is breaking down all the action - first, he's taking a closer look at how today's pause from the Federal Reserve shifted the market and how investors should approach it. Then, Cramer dove into Confluent last week and wanted more on the story - CEO Jay Kreps sits down with Cramer to share more about what the data company does and its applications. Plus, Cramer's exclusive with Bowlero CEO Tom Shannon. Mad Money Disclaimer