Podcasts about confluent

Shriram Sridharan is the Co-founder and CTO of Rox, an AI-first revenue operating system that helps enterprise sales teams research accounts, automate workflows, and act on timely customer insights. He helps lead the company's product and technical strategy, building AI agents that make sales processes more focused, efficient, and repeatable. Before Rox, Shriram held engineering leadership roles at Confluent and Amazon Web Services, where he worked on scalable data and cloud infrastructure. His work focuses on using AI to turn complex revenue operations into practical systems that help teams win. In this episode… Enterprise sales can do more than track leads and manage deals — it can surface the right signals, automate repetitive work, and give teams more time to build customer relationships. But what separates a sales team using AI as another tool from one using AI agents to actually move revenue forward? Shriram Sridharan, a technical leader who has built large-scale systems at AWS and Confluent, says the key is using revenue agents to automate or augment the entire revenue lifecycle. He highlights the importance of connecting customer data, public signals, and internal workflows so AI can handle account research, meeting preparation, outbound personalization, and RFP detection. Instead of forcing account executives to switch between tools or spend hours on manual research, Shriram explains how agents can deliver the work behind the scenes with a human in the loop. The result is a more focused sales organization where teams spend less time on grunt work and more time generating pipeline, preventing churn, and growing revenue. In this episode of the Inspired Insider Podcast, Dr. Jeremy Weisz sits down with Shriram Sridharan, Co-founder and CTO of Rox, to discuss how revenue agents are reshaping enterprise sales. Shriram breaks down AI agent orchestration, sales signals, data warehouses, outbound automation, and RFP detection. He also shares his founder journey and key influences.

Tim Berglund talks to Andrew Schofield (Confluent) about his career in Apache Kafka. Andrew's first job: working on queuing systems in 1991. His challenge: working at Confluent and in the Kafka community to bring queue semantics into Kafka while also helping shape major efforts like diskless Kafka, disaster recovery, and the project's broader evolution.► Queues for Kafka Explained (KIP-932): https://youtu.be/Wb0xyqgaIqwSEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo  

On this episode of Run the Numbers, CJ sits down with Confluent CFO Rohan Sivaram to talk goal setting, prioritization, consumption-based pricing, hybrid zero-based budgeting, and the frameworks finance leaders use to scale companies. Rohan shares why he carries his 12-month goals with him, how he evaluates opportunities through TAM, technology, and team, and why usage-based pricing changes the entire operating model.—SPONSORS:EY has been part of Silicon Valley since it was just a valley, helping the most successful names in tech go from startup to exit to megacap. With teams across strategy, tax, audit, and transactions, EY helps you get your financials right early, long before your investors start asking for it. You build the next big thing, and EY will help you build it right. Learn more at https://www.ey.com/techstartupsSpendHound cuts your SaaS and AI spend by up to 30% using real pricing benchmarks across 10,000 vendors, so you always know what fair pricing looks like before your next renewal. Rated #1 on G2 in SaaS spend management, it's free forever for teams up to 1,000 employees. Sign up by June 12th and get $500 just for getting started. Go to https://www.spendhound.com/cjBrex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cj—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/rohan-sivaram-69007b7/Company: https://www.confluent.io/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:A CFO Explains Marketplaceshttps://youtu.be/LpbH9GpBrSY—TIMESTAMPS:0:00 Preview and Intro2:26 Writing down 12-month goals and carrying them6:33 Rule of 168: 168 hours a week7:36 Delegation and calendar management9:25 Learning to say no: cultural shift11:32 Sponsors — EY | SpendHound | Brex14:29 Joining Confluent: the state of the company16:57 Building blocks of a budgeting process19:46 Execute, learn, adapt21:59 Healthy tension in the planning cycle22:26 Sponsors — Aleph | RightRev | Rillet25:46 What is hybrid zero-based budgeting?30:37 Moving from subscription to consumption pricing32:22 Why this was a one-way door33:56 New metrics required in a consumption business35:28 Evaluating job opportunities: the three T's37:39 Networking and reciprocity39:54 Lightning round40:04 Screwed up: free cash flow sign error42:03 Advice to younger self: take more risks42:38 Finance software stack43:00 AI tools the team has built43:44 Credits

Send us Fan MailMost people building with AI are working from the same playbook. Dr. Sean Falconer isn't.As AI Entrepreneur in Residence at Confluent, Sean sits at the intersection of real-time data streaming and production AI — where the decisions get hard and the stakes are real. In this episode, he and Al get into what it actually looks like to run multiple LLMs simultaneously, what happened when Sean tested autonomous agents in real-world conditions, and why some of the loudest claims in AI deserve a second look.Sean also pulls back the curtain on Confluent's technology, explains why he chose the company, and shares his framework for thinking about where AI is actually headed — not where the headlines say it is.If you want a grounded, experienced perspective on building at the edge of AI innovation — from someone who is doing it — this one is worth a replay.Timestamps04:38  Meet Sean Falconer11:11  Lifelong Learning12:31  AI Entrepreneur in Residence16:28  Multiple LLMs in Action21:07  The Tech Behind Confluent25:51  Why Sean Chose Confluent28:40  Invest or Short?36:58  Testing Agents IRL40:51  The Contrarian AI Take42:27  Looking Ahead: The Future of AI Guest LinksLinkedIn: linkedin.com/in/seanf/Substack: softwarehuddle.substack.com/Medium: seanfalconer.medium.com/Want to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

Send us Fan MailMost people building with AI are working from the same playbook. Dr. Sean Falconer isn't.As AI Entrepreneur in Residence at Confluent, Sean sits at the intersection of real-time data streaming and production AI — where the decisions get hard and the stakes are real. In this episode, he and Al get into what it actually looks like to run multiple LLMs simultaneously, what happened when Sean tested autonomous agents in real-world conditions, and why some of the loudest claims in AI deserve a second look.Sean also pulls back the curtain on Confluent's technology, explains why he chose the company, and shares his framework for thinking about where AI is actually headed — not where the headlines say it is.If you want a grounded, experienced perspective on building at the edge of AI innovation — from someone who is doing it — this one is worth a replay.Timestamps04:38  Meet Sean Falconer11:11  Lifelong Learning12:31  AI Entrepreneur in Residence16:28  Multiple LLMs in Action21:07  The Tech Behind Confluent25:51  Why Sean Chose Confluent28:40  Invest or Short?36:58  Testing Agents IRL40:51  The Contrarian AI Take42:27  Looking Ahead: The Future of AI Guest LinksLinkedIn: linkedin.com/in/seanf/Substack: softwarehuddle.substack.com/Medium: seanfalconer.medium.com/Want to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

Tim Berglund talks to John Miller (Enid Technologies) and Eric Broda (Agentic Mesh Company) about their work on agent mesh and enterprise agents. Their challenge: rethinking agents as enterprise-grade participants in business processes.SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo  

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover Apple‘s uncertain path beyond the iPhone. They also discuss Google‘s agentic pivot at Google I/O, a surge in DuckDuckGo traffic following Google’s default switch to AI mode, and payroll platform Remote surpassing 300 million in ARR with flat headcount. Gregor and Sean also dig into why consumer subscriptions don’t seem to correspond to actual costs, how enterprise is quietly subsidizing the AI economy, why the true moat has shifted from model quality to context management and agentic harness, and what the coming wave of token cost optimization might look like as companies start scrutinizing their AI bills. Finally, they highlight standout threads from Hacker News including Doom running on a travel router touchscreen, a viral post asking whether AI productivity gains should translate to a day off, YouTube‘s move to automatically label AI-generated content, and SimCity 3000 running in 4K. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: Apple's AI Problem, The Real Business Model of AI, and Token Cost Reckoning appeared first on Software Engineering Daily.

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover Apple‘s uncertain path beyond the iPhone. They also discuss Google‘s agentic pivot at Google I/O, a surge in DuckDuckGo traffic following Google’s default switch to AI mode, and payroll platform Remote surpassing 300 million in ARR with flat headcount. Gregor and Sean also dig into why consumer subscriptions don’t seem to correspond to actual costs, how enterprise is quietly subsidizing the AI economy, why the true moat has shifted from model quality to context management and agentic harness, and what the coming wave of token cost optimization might look like as companies start scrutinizing their AI bills. Finally, they highlight standout threads from Hacker News including Doom running on a travel router touchscreen, a viral post asking whether AI productivity gains should translate to a day off, YouTube‘s move to automatically label AI-generated content, and SimCity 3000 running in 4K. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: Apple's AI Problem, The Real Business Model of AI, and Token Cost Reckoning appeared first on Software Engineering Daily.

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover Apple‘s uncertain path beyond the iPhone. They also discuss Google‘s agentic pivot at Google I/O, a surge in DuckDuckGo traffic following Google’s default switch to AI mode, and payroll platform Remote surpassing 300 million in ARR with flat headcount. Gregor and Sean also dig into why consumer subscriptions don’t seem to correspond to actual costs, how enterprise is quietly subsidizing the AI economy, why the true moat has shifted from model quality to context management and agentic harness, and what the coming wave of token cost optimization might look like as companies start scrutinizing their AI bills. Finally, they highlight standout threads from Hacker News including Doom running on a travel router touchscreen, a viral post asking whether AI productivity gains should translate to a day off, YouTube‘s move to automatically label AI-generated content, and SimCity 3000 running in 4K. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: Apple's AI Problem, The Real Business Model of AI, and Token Cost Reckoning appeared first on Software Engineering Daily.

Tim Berglund talks to Matthias J. Sax (Confluent) about 10 whole years of Kafka Streams! Matthias' first job: electrician-in-training on BMW's assembly lines. His challenge: reflecting on 10 years of Kafka Streams growth, major milestones, and what comes next.SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo  

Tim Berglund talks to Gunnar Morling (Confluent) about his career in open source Java and data infrastructure. Gunnar's first job: a student PHP developer in AMD's e-learning group. His challenge: building Hardwood, a fast, multi-threaded Parquet engine for Java with minimal dependencies.► The One Billion Row Challenge blog post: https://www.morling.dev/blog/one-billion-row-challenge/SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo  

If you're a consultant and you're not using AI agents yet, your competitors are. No surprise, but they're delivering faster, cheaper, and better than ever.Chris Tabb, founder of LEIT Data, joins me live at Confluent Current London 2026 to talk honestly about how AI agents are reshaping the consultancy model, from billing structures and team rollouts, to building internal tribal knowledge and outpacing firms that are still staffing up the old way.Timestamps:0:33 — How Chris is Going Agentic1:56 — Token Maxing Leaderboards5:26 — AI Agents: Year-Over-Year7:08 — Tagile: Agentic Development9:00 — AI in Consultancy17:22 — Prompt Management & Context Quality

Gunnar Morling, technologist at Confluent and Java Champion, shares his experiences with building high-performance applications in Java, especially in the data space. He shares insights from experiments with building durable execution engines, bootstrapping, and AI natively developing Apache Hardwood - a minimal dependencies Java parser for Apache Parquet. Read a transcript of this interview: https://bit.ly/49cwnoI Newsletter: Subscribe to the Software Architects' Newsletter for your monthly guide to the essential news and experience from industry peers on emerging patterns and technologies: https://www.infoq.com/software-architects-newsletter InfoQ online certification cohorts: Online cohorts for senior engineers and architects, built around QCon talks. Join a 5-week confidential peer group to validate your approach and apply practitioner frameworks to the technical challenges you face at work. Learn more: https://certification.qconferences.com/ Upcoming Events: QCon AI Boston 2026 (June 1-2, 2026) Learn how real teams are accelerating the entire software lifecycle with AI. https://boston.qcon.ai QCon San Francisco 2026 (November 16-20, 2026) https://qconsf.com/ The InfoQ Podcasts: Weekly inspiration to drive innovation and build great teams from senior software leaders. Listen to all our podcasts and read interview transcripts: - The InfoQ Podcast https://www.infoq.com/podcasts/ - Engineering Culture Podcast by InfoQ https://www.infoq.com/podcasts/#engineering_culture - Generally AI: https://www.infoq.com/generally-ai-podcast/ Follow InfoQ: - Mastodon: https://techhub.social/@infoq - X: https://x.com/InfoQ?from=@ - LinkedIn: https://www.linkedin.com/company/infoq/ - Facebook: https://www.facebook.com/InfoQdotcom# - Instagram: https://www.instagram.com/infoqdotcom/?hl=en - Youtube: https://www.youtube.com/infoq - Bluesky: https://bsky.app/profile/infoq.com Write for InfoQ: Learn and share the changes and innovations in professional software development. - Join a community of practitioners. - Increase your visibility. - Grow your career. https://www.infoq.com/write-for-infoq

Formal methods are a branch of mathematics and computer science focused on proving the correctness of systems, and they have long promised a more rigorous foundation for software. However, their complexity has kept them confined to a small community of specialists. That is now changing as agentic AI systems take on increasingly autonomous roles. The question of how to define, enforce, and verify what those agents are allowed to do has become urgent, and automated reasoning is emerging as a critical part of the answer. Byron Cook is a VP and Distinguished Scientist at AWS, a professor at University College London, and a program manager at DARPA. He founded the Automated Reasoning Group at AWS over a decade ago, where his team built the foundations behind products like IAM Access Analyzer, VPC Reachability Analyzer, and Bedrock Guardrails. In this episode, Byron joins Sean Falconer to discuss how automated reasoning works and why it scales so well with AI, the rise of neurosymbolic approaches that combine formal logic with large language models, what it means to formally specify agent behavior using temporal logic, and why the convergence of agentic AI and formal methods may represent one of the most significant shifts in how software is built and verified. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Formal Methods as Agent Guardrails appeared first on Software Engineering Daily.

Formal methods are a branch of mathematics and computer science focused on proving the correctness of systems, and they have long promised a more rigorous foundation for software. However, their complexity has kept them confined to a small community of specialists. That is now changing as agentic AI systems take on increasingly autonomous roles. The question of how to define, enforce, and verify what those agents are allowed to do has become urgent, and automated reasoning is emerging as a critical part of the answer. Byron Cook is a VP and Distinguished Scientist at AWS, a professor at University College London, and a program manager at DARPA. He founded the Automated Reasoning Group at AWS over a decade ago, where his team built the foundations behind products like IAM Access Analyzer, VPC Reachability Analyzer, and Bedrock Guardrails. In this episode, Byron joins Sean Falconer to discuss how automated reasoning works and why it scales so well with AI, the rise of neurosymbolic approaches that combine formal logic with large language models, what it means to formally specify agent behavior using temporal logic, and why the convergence of agentic AI and formal methods may represent one of the most significant shifts in how software is built and verified. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Formal Methods as Agent Guardrails appeared first on Software Engineering Daily.

Formal methods are a branch of mathematics and computer science focused on proving the correctness of systems, and they have long promised a more rigorous foundation for software. However, their complexity has kept them confined to a small community of specialists. That is now changing as agentic AI systems take on increasingly autonomous roles. The question of how to define, enforce, and verify what those agents are allowed to do has become urgent, and automated reasoning is emerging as a critical part of the answer. Byron Cook is a VP and Distinguished Scientist at AWS, a professor at University College London, and a program manager at DARPA. He founded the Automated Reasoning Group at AWS over a decade ago, where his team built the foundations behind products like IAM Access Analyzer, VPC Reachability Analyzer, and Bedrock Guardrails. In this episode, Byron joins Sean Falconer to discuss how automated reasoning works and why it scales so well with AI, the rise of neurosymbolic approaches that combine formal logic with large language models, what it means to formally specify agent behavior using temporal logic, and why the convergence of agentic AI and formal methods may represent one of the most significant shifts in how software is built and verified. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Formal Methods as Agent Guardrails appeared first on Software Engineering Daily.

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubEkaterina Gorshkova - Apache Kafka Engineer at SOFTEC & Author of "Kafka for Architects"Viktor Gamov - Principal Developer Advocate at Confluent & Co-Author of "Kafka in Action"Check out more here:https://gotopia.tech/episodes/440RESOURCESEkaterinahttps://www.linkedin.com/in/ekaterina-gorshkova-978bb6https://medium.com/@katyagorshkovaViktorhttps://bsky.app/profile/gamussa.devhttps://x.com/gAmUssAhttps://github.com/gamussahttps://www.linkedin.com/in/vikgamovhttps://gamov.ioLinks45% off discount code (expires on 25 May 2026): GOTOKGKafkaAffiliate link: https://hubs.la/Q044HgTvhttps://current.confluent.io/londonDESCRIPTIONApache Kafka has evolved far beyond a simple message broker — it has become a foundational layer for modern enterprise software. In this GOTO Book Club episode, Ekaterina Gorshkova, author of "Kafka for Architects", shares how her decade-long journey with Kafka — starting in a Czech bank's integration team in 2015 — shaped her understanding of what it really takes to design Kafka-based systems at scale. The conversation covers core architectural decisions, real-world patterns for enterprise integration, the role of Kafka Streams, and how to avoid the classic pitfalls of building systems that "only three engineers understand".The episode also looks forward: Ekaterina and host Viktor Gamov explore how Kafka is increasingly becoming the connective tissue for AI-driven systems, acting as an orchestration layer between intelligent agents, real-time data, and business workflows. Her book's central argument is that while AI and tooling change fast, the fundamental knowledge of how to design robust, event-driven systems is durable and career-proof. Kafka for Architects is framed not just as a technical manual, but as a roadmap for architects who want to get Kafka right from day one — requirements, design, testing, and all.RECOMMENDED BOOKSEkaterina Gorshkova • Kafka for Architects • https://amzn.to/42mDarUDylan Scott, Viktor Gamov & Dave Klein • Kafka in Action • https://amzn.to/4vJ3KcjViktor Gamov, Tartakovsky, Rasputnis & Fain • Enterprise Web Development • https://amzn.to/3CezL0RShapira, Palino, Sivaram & Petty • Kafka: The Definitive Guide • https://amzn.to/3RPtdLPBill Bejeck • Kafka Streams in Action • https://amzn.to/3CGJiiMBlueskyInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

durée : 00:58:04 - Avec philosophie - par : Géraldine Muhlmann - Alexandrie s'est imposée comme carrefour intellectuel et culturel de l'Antiquité, devenant un lieu majeur de rencontre entre la culture grecque et les traditions orientales, notamment juives. On y observe un véritable échange d'idées, de langues et de savoirs entre les civilisations. - réalisation : Anna Pheulpin, Carla Michel, Corinne Amar, Nicolas Berger, Nassim El Kabli, Luna Hadjla - invités : Mireille Hadas Lebel Historienne, spécialiste du judaïsme antique et de la langue hébraïque, Nathalie Cohen Agrégée de lettres classiques, enseignante en grec et latin, essayiste Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

Adi Polak talks to Russell Spitzer (Snowflake) about his career in open source data infrastructure. Russell's first job: software engineer in test at DataStax. His challenge: making Apache Iceberg ready for AI and streaming.SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo  

Vector search has risen to become a foundational tool in modern search and retrieval systems, including the RAG pipelines that power many AI applications. However, the demands on retrieval systems are growing more sophisticated, which is revealing the limits of relying on a single vector similarity score. Vespa is a popular open source search and data serving engine. Central to Vespa’s architecture is tensor-based retrieval, which is an approach that represents data as tensors rather than simple vectors. Tensor-based retrieval enables richer mathematical operations and more flexible ranking functions that can surmount the limitations of a single vector similarity score. Radu Gheorghe is a software engineer at Vespa with a background spanning nearly 12 years of consulting and training on Elasticsearch and Solr. In this episode, Radu joins Sean Falconer to discuss why vector similarity alone falls short in production, how tensor-based retrieval generalizes to support richer ranking functions, the trade-offs in chunking and multi-stage re-ranking architectures, and where AI search is headed next. Full Disclosure: This episode is sponsored by Vespa. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Vespa AI and Surpassing the Limits of Vector Search appeared first on Software Engineering Daily.

Vector search has risen to become a foundational tool in modern search and retrieval systems, including the RAG pipelines that power many AI applications. However, the demands on retrieval systems are growing more sophisticated, which is revealing the limits of relying on a single vector similarity score. Vespa is a popular open source search and data serving engine. Central to Vespa’s architecture is tensor-based retrieval, which is an approach that represents data as tensors rather than simple vectors. Tensor-based retrieval enables richer mathematical operations and more flexible ranking functions that can surmount the limitations of a single vector similarity score. Radu Gheorghe is a software engineer at Vespa with a background spanning nearly 12 years of consulting and training on Elasticsearch and Solr. In this episode, Radu joins Sean Falconer to discuss why vector similarity alone falls short in production, how tensor-based retrieval generalizes to support richer ranking functions, the trade-offs in chunking and multi-stage re-ranking architectures, and where AI search is headed next. Full Disclosure: This episode is sponsored by Vespa. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Vespa AI and Surpassing the Limits of Vector Search appeared first on Software Engineering Daily.

Vector search has risen to become a foundational tool in modern search and retrieval systems, including the RAG pipelines that power many AI applications. However, the demands on retrieval systems are growing more sophisticated, which is revealing the limits of relying on a single vector similarity score. Vespa is a popular open source search and data serving engine. Central to Vespa’s architecture is tensor-based retrieval, which is an approach that represents data as tensors rather than simple vectors. Tensor-based retrieval enables richer mathematical operations and more flexible ranking functions that can surmount the limitations of a single vector similarity score. Radu Gheorghe is a software engineer at Vespa with a background spanning nearly 12 years of consulting and training on Elasticsearch and Solr. In this episode, Radu joins Sean Falconer to discuss why vector similarity alone falls short in production, how tensor-based retrieval generalizes to support richer ranking functions, the trade-offs in chunking and multi-stage re-ranking architectures, and where AI search is headed next. Full Disclosure: This episode is sponsored by Vespa. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Vespa AI and Surpassing the Limits of Vector Search appeared first on Software Engineering Daily.

Patrick Moorhead and Daniel Newman dig into the week's biggest moves in enterprise AI: Anthropic and OpenAI launching PE-backed enterprise JVs on the same day, Anthropic filling its compute gap with SpaceX's Colossus, Cerebris filing for a $3.5 billion IPO, NVIDIA going deep on co-packaged optics with Corning, and a full IBM Think and ServiceNow recap. Plus, for The Flip, hosts debate whether Anthropic, at $1.2 trillion, is the most important company in enterprise tech. The handpicked topics for this week are: 1. Anthropic and OpenAI Launch PE-Backed Enterprise JVs on the Same Day — Both companies announced private equity joint ventures, with OpenAI backed by Bain, Brookfield, and Advent, and Anthropic partnering with Blackstone, Goldman Sachs, Apollo, and General Atlantic. Daniel's read is that this is fundamentally a distribution play, using private equity portfolio companies as a deployment channel for AI at scale. Pat sees it as the clearest admission yet that enterprise AI cannot be self-implemented at scale without specialized consulting support, and flags that mid-tier systems integrators (SIs) could get cut out of the middle. (The Decode) 2. Anthropic Signs Massive Compute Deal with SpaceX Colossus — Anthropic urgently needed compute and SpaceX had 300 megawatts and 220,000 GPUs sitting at Colossus One in Memphis without enough business to fill them. Pat's take is blunt: this move is pragmatic. Anthropic needs it, xAI has it. Daniel adds that Dario himself said they planned for 10x growth and got 80x, and this deal is the fast backfill that reality demanded. The side note both hosts flag: Anthropic is running on H100s, H200s, and B200s, which puts the whole "Anthropic only runs on Trainium and TPUs" narrative to rest. (The Decode) 3. Cerebris Files for a $3.5 Billion IPO at $26.6 Billion Valuation — This marks their second attempt at an IPO after pulling the first filing. The architecture is genuinely unique, a complete wafer with massive on-chip SRAM and interconnects built directly onto the wafer rather than copper or photonics. Pat calls it the first credible Western alternative for AI inference. Daniel's framing cuts through: you do not have to beat NVIDIA to sell right now. You just need to have availability. The more interesting headline, both hosts agree, is that Sam Altman and Greg Brockman are angel investors, which adds fuel to the ongoing OpenAI lawsuit. (The Decode) 4. NVIDIA and Corning Announce $500 Million Optical Partnership — Three new US factories, co-packaged optics for Vera Rubin, and a supply chain strategy that mirrors what NVIDIA did with Coherent. Pat's context: this is vertical integration through investment rather than acquisition. Daniel's observation is that the pace of movement toward co-packaged optics is accelerating faster than anyone expected, and his "rule of and" applies here too. Copper is not going away. Optics are being added on top because the data volumes moving across these racks are outrunning what copper alone can handle. US manufacturing in North Carolina and Texas is a strategic bonus. (The Decode) 5. IBM Think 2026: Day Zero, Sovereign Core, and the Quantum Plus AI Bet — Pat moderated on stage with CEO Arvind Krishna and calls this IBM's best showing in five years. Arvind opened with the AI divide, the gap between companies still running POCs and companies already in production, and framed where IBM sits as day zero, not because nothing has happened, but because enterprise AI deployment at scale is still so early. Daniel's biggest takeaways: watsonX Orchestrate updates, Sovereign Core going GA with policy at runtime, and the Confluent acquisition potentially being IBM's most important asset since Red Hat, given that 40% of Fortune 500 companies run on it and real-time streaming data is foundational to agentic systems. Both hosts land on quantum plus AI as IBM's next inflection moment. (The Decode) 6. ServiceNow Knowledge 2026: Enterprise SaaS 2.0 is Emerging — Daniel got there on day three of the event and noted the conference was densely packed. His observation: enterprises have not gotten the memo from Wall Street that SaaS is supposedly dead. His emerging thesis is that middleware could make a comeback for AI, with companies needing a layer that lets agents work across any infrastructure, any app, and within the rules of their specific business. Pat agrees and adds that the growth question is about mix, not survival. (The Decode) 7. The Flip: Is Anthropic at $1.2 Trillion the Most Important Company in Enterprise Tech? — Daniel took the affirmative citing that Claude Code is deeply entrenched in developer workflows. Anthropic went from $9 billion to $45 billion ARR in months. Every major hyperscaler is both a customer and an investor. The PE JVs are turning verticals into Anthropic engines. Dario said they planned for 10x and got 80x. Pat's counter: the enterprise trust gap is real after what Anthropic pulled on pricing and performance. Microsoft has 2 billion users across 365, Azure, and Copilot. NVIDIA is the infrastructure Anthropic runs on. And workforce replacement, which is how Anthropic extracts its terminal value, is not arriving as fast as the valuation suggests. In reality, both hosts admit their notes looked almost identical. (The Flip) 8. AMD — Lisa Su guided AI data center growth up from 60% to 80%. With OpEx growing 83%, net income up 95%, free cash flow ripping, and CPUs growing at nearly 40% without price increases, Pat reads this as unit market share gains coming soon. Daniel's framing: AMD is now a two-headed juggernaut with CPUs and GPUs for the data center. And Helios has not even started shipping yet. Both hosts take a victory lap for previously calling this one. (Bulls and Bears) 9. Palantir — Triple beat on revenue, EPS, and forward guidance. Rule of 40 at 145%. Government revenue up 84%, 47 deals over $10 million, and the largest guidance raise in the company's history. Daniel's take: Palantir is redefining the category entirely. It's not a software company in the Salesforce or ServiceNow sense. It's technology, plus ontology, plus people, deployed at the deepest layers inside governments and enterprises. Pat adds that the four deployed FTE model lets them stand up AIP POCs within a week, which is why they are winning business at this pace. (Bulls and Bears) 10. ARM — AGI processor demand doubled from $1 billion to $2 billion within 45 days. Record revenue, strong pipeline, royalty growth at 21% for the full year. The stock ripped after hours, then sold the next day when management confirmed only enough supply for $1 billion of that $2 billion demand. Pat's read: 50% CPU market share with hyperscalers at the core level is the most underdiscussed signal on the call. Daniel adds that the worry about ARM competing with its own customer base in custom silicon has been quietly swept away by the sheer volume of compute demand. (Bulls and Bears) 11. Supermicro — A board member allegedly used a hairdryer to remove labels from GPU boxes being shipped to China. Approximately 20% of their revenue has reportedly been illegally shipped to China. They beat on EPS and Q4 guide but missed Q3 revenue versus consensus. Stock still ripped 18%. Daniel's take: if you are selling picks and shovels during a gold rush and you are this messed up, he cannot imagine owning it with the overhang that is building. (Bulls and Bears) 12. Lattice Semi and Coherent — Lattice revenue up 42%, back into growth, guiding to 50% year-on-year at midpoint. The AMI acquisition at $1.65 billion doubles their serviceable market from $6 billion to $12 billion and puts them inside every AI server on the planet at the BIOS and platform firmware layer. Pat calls the timing right: core financials crushing it, time to make a move. Coherent printed 21% year-on-year growth, 55% EPS growth, margins expanding, debt coming down, entered the S&P 500, and sits at the center of the co-packaged optics trend that is accelerating. Pat's choke point note: Indium phosphide capacity is the constraint. Six-inch fabs are doubling capacity in 2026, a quarter ahead of plan, and competitors are still ramping their transitions. (Bulls and Bears) Want the full breakdown from IBM Think and ServiceNow Knowledge, and check out our on-the-ground coverage linked in the show notes. Be part of our community. Hit that subscribe button and let us know what you want us to cover next week in the comments. Intro Pat on Stage at IBM Think https://x.com/PatrickMoorhead/status/2051381046537601101?s=20 The Decode OpenAI and Anthropic Both Launch PE-Backed Enterprise Services JVs on the Same Day — The Palantir FDE Model Goes Mainstream https://www.bloomberg.com/news/articles/2026-05-04/openai-finalizes-10-billion-joint-venture-with-pe-firms-to-deploy-ai https://techcrunch.com/2026/05/04/anthropic-and-openai-are-both-launching-joint-ventures-for-enterprise-ai-services/ https://www.semafor.com/article/05/04/2026/openai-anthropic-ramp-up-enterprise-push Anthropic and SpaceX Sign Massive Compute Deal — Full 300MW / 220,000 GPU Colossus 1 Memphis Data Center Plus Exploration of Multi-Gigawatt Orbital AI Compute https://www.cnbc.com/2026/05/06/anthropic-spacex-data-center-capacity.html https://www.bloomberg.com/news/articles/2026-05-06/anthropic-inks-computing-deal-with-spacex-to-meet-ai-demand https://www.tomshardware.com/tech-industry/artificial-intelligence/musks-spacex-has-rented-out-access-to-its-supercomputers-220-000-nvidia-gpus-and-300-megawatts-of-ai-compute-power-to-rival-anthropic Cerebras Files for $3.5B IPO at $26.6B Valuation — The First Major AI Chip IPO of 2026 https://www.cnbc.com/2026/05/04/cerebras-ipo-ai-chipmaker.html https://theaiinsider.tech/2026/05/06/cerebras-systems-eyes-3-5b-in-largest-tech-ipo-of-2026-on-strength-of-ai-chip-demand/ https://www.briefs.co/news/ai-chipmaker-cerebras-just-filed-for-a-3-5-billion-ipo/ NVIDIA and Corning Announce Game-Changing Optical Partnership — $500M Investment, 3 New U.S. Factories, and Co-Packaged Optics for Vera Rubin and Beyond https://www.corning.com/worldwide/en/about-us/news-events/news-releases/2026/05/nvidia-and-corning-announce-long-term-partnership-to-strengthen-us-manufacturing-for-ai-infrastructure.html https://www.cnbc.com/2026/05/06/nvidia-corning-optical-factories-nc-texas-ai.html https://www.wsj.com/tech/nvidia-corning-form-partnership-to-expand-fiber-optic-manufacturing-17f525de https://kfgo.com/2026/05/06/corning-partners-with-nvidia-to-expand-us-fiber-optic-output-for-ai-growth/ IBM Think 2026 Boston — Watsonx Orchestrate Next-Gen, Confluent Real-Time Data, IBM Concert, and Sovereign Core Define IBM's Agentic Operating Model https://newsroom.ibm.com/2026-05-05-think-2026-ibm-delivers-the-blueprint-for-the-ai-operating-model-as-the-ai-divide-widens https://www.ibm.com/new/announcements/ibm-announcements-at-think-2026 https://www.instagram.com/reel/DX42DlrglOs/ ServiceNow Knowledge 2026 Las Vegas https://www.servicenow.com/events/knowledge.html https://newsroom.servicenow.com/press-releases/details/2026/Cohesity-and-ServiceNow-Deliver-Real-Time-Recovery-for-Enterprise-AI-Agents/default.aspx https://www.cnbc.com/2025/09/04/nvidia-backed-cohesity-eyes-2026-ipo-with-valuation-rivaling-17-billion-rubrik.html   The Flip: Anthropic at $1.2T Now the Most Important Company in Enterprise Tech — More Important Than NVIDIA, Microsoft, or OpenAI FOR: Dual-hyperscaler compute anchor (Amazon $33B + Google $40B = $73B) is structural — unmatched https://futurumgroup.com/insights/anthropics-gigawatt-scale-tpu-deal-with-broadcom-creates-a-structural-advantage/ Constitutional AI safety positioning wins regulated industries https://www.anthropic.com/news/anthropic-nec-japan-ai-engineering-workforce $900B valuation surpasses OpenAI ($852B) at faster revenue growth and lower burn rate https://techcrunch.com/2026/04/30/anthropic-potential-900b-valuation-round-could-happen-within-two-weeks/   AGAINST: NVIDIA still controls the substrate — every Anthropic dollar of revenue requires NVIDIA inference at some layer https://www.cnbc.com/2026/04/27/nvidia-just-hit-an-all-time-high-why-some-think-a-rally-is-just-getting-started.html Microsoft has the enterprise distribution — 365 + Azure + Copilot reach >2 billion users https://www.marketbeat.com/originals/microsofts-maia-200-the-profit-engine-ai-needs/ $900B valuation is venture marketing — the IPO will reset the number https://www.semafor.com/article/05/04/2026/openai-anthropic-ramp-up-enterprise-push   Bulls & Bears: AMD Q1 2026 — Revenue $10.3B (+38% YoY), MI300X Data Center GPU Demand Drives Stock +20% on the Print https://ir.amd.com/news-events/press-releases/detail/1284/amd-reports-first-quarter-2026-financial-results https://www.cnbc.com/2026/05/05/amd-q1-2026-earnings-report.html https://finance.yahoo.com/markets/stocks/articles/amd-q1-2026-earnings-revenue-203331768.html Palantir Q1 2026 — Revenue +85% YoY, US Commercial +133%, Rule of 40 Score Hits 145%; Largest Guidance Raise in Company History https://investors.palantir.com/files/Palantir%20-%20Q1%202026%20Business%20Update.pdf https://www.reddit.com/r/PLTR/comments/1t3t0me/palantir_reports_q1_2026_us_revenue_growth_of_104/ https://finance.yahoo.com/markets/stocks/articles/palantir-technologies-inc-q1-2026-002218719.html https://semiconalpha.substack.com/p/palantir-q1-2026-rewriting-the-rule Arm Holdings Q4 FY2026 — Record $1.49B Quarter, Full-Year Revenue Crosses $4.92B, $2B AGI CPU Pipeline; Stock +16% After Hours https://finance.yahoo.com/markets/stocks/articles/arm-q4-earnings-call-highlights-225942093.html https://www.stocktitan.net/sec-filings/ARM/6-k-arm-holdings-plc-uk-current-report-foreign-issuer-7e9ca9ac7dda.html https://semiconalpha.substack.com/p/arm-q4-fy2026-record-quarter-2-billion Super Micro Computer Q3 FY2026 — Revenue $10.2B (+123% YoY), Strong Q4 Guide; Stock +18% AH on First Earnings Call Since Co-Founder Indictment Drama https://www.cnbc.com/2026/05/05/super-micro-smci-q3-earnings-report-2026.html https://www.stocktitan.net/sec-filings/SMCI/8-k-super-micro-computer-inc-reports-material-event-e70b2f8b3cb7.html https://www.instagram.com/reel/DX42DlrglOs/ Lattice Semiconductor Q1 2026 — Beat-and-Raise Quarter ($170.9M, +42% YoY) Paired With $1.65B AMI Acquisition That Doubles Lattice's SAM to $12B https://www.stocktitan.net/sec-filings/LSCC/8-k-lattice-semiconductor-corp-reports-material-event-642a862b2bf9.html https://www.ami.com/resources/ami-announces-agreement-to-be-acquired-by-lattice-semiconductor/ https://www.linkedin.com/posts/patmoorhead_lattice-semiconductor-posts-beat-and-raise-activity-7457411226944425984-xA8T Coherent Q3 2026 Earnings https://www.msn.com/en-us/money/companies/coherent-cohr-tops-revenue-expectations-in-q3-as-ai-demand-accelerates-shares-decline/ar-AA22Bz24?ocid=finance-verthp-feeds  

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover Anthropic's controversial “Mythos” security model and what it means for vulnerability discovery at scale. They also discuss recent layoffs at Snap and Meta, and how AI investment pressures are reshaping hiring, organizational priorities, and the economics of big tech. Gregor and Sean then zoom out to examine the massive wave of AI infrastructure spending—hundreds of billions in capex across Amazon, Google, Microsoft, and Meta, and what it signals about the future of cloud platforms, model providers, and the engineers who build on top of them. They explore the emerging entanglement between model labs and infrastructure providers, the evolving role of engineers in an AI-native world, and the growing gap between rapid AI adoption and security readiness. Finally, they highlight standout threads from Hacker News, including creative uses of AI coding tools to revive abandoned side projects, new approaches to training smaller yet highly capable models, surprising demographic data visualizations, and even the mathematics of “cheating” at Tetris. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: Anthropic's Mythos, Supply Chain Hacks, and the AI Spending Surge appeared first on Software Engineering Daily.

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover Anthropic's controversial “Mythos” security model and what it means for vulnerability discovery at scale. They also discuss recent layoffs at Snap and Meta, and how AI investment pressures are reshaping hiring, organizational priorities, and the economics of big tech. Gregor and Sean then zoom out to examine the massive wave of AI infrastructure spending—hundreds of billions in capex across Amazon, Google, Microsoft, and Meta, and what it signals about the future of cloud platforms, model providers, and the engineers who build on top of them. They explore the emerging entanglement between model labs and infrastructure providers, the evolving role of engineers in an AI-native world, and the growing gap between rapid AI adoption and security readiness. Finally, they highlight standout threads from Hacker News, including creative uses of AI coding tools to revive abandoned side projects, new approaches to training smaller yet highly capable models, surprising demographic data visualizations, and even the mathematics of “cheating” at Tetris. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: Anthropic's Mythos, Supply Chain Hacks, and the AI Spending Surge appeared first on Software Engineering Daily.

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover Anthropic's controversial “Mythos” security model and what it means for vulnerability discovery at scale. They also discuss recent layoffs at Snap and Meta, and how AI investment pressures are reshaping hiring, organizational priorities, and the economics of big tech. Gregor and Sean then zoom out to examine the massive wave of AI infrastructure spending—hundreds of billions in capex across Amazon, Google, Microsoft, and Meta, and what it signals about the future of cloud platforms, model providers, and the engineers who build on top of them. They explore the emerging entanglement between model labs and infrastructure providers, the evolving role of engineers in an AI-native world, and the growing gap between rapid AI adoption and security readiness. Finally, they highlight standout threads from Hacker News, including creative uses of AI coding tools to revive abandoned side projects, new approaches to training smaller yet highly capable models, surprising demographic data visualizations, and even the mathematics of “cheating” at Tetris. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: Anthropic's Mythos, Supply Chain Hacks, and the AI Spending Surge appeared first on Software Engineering Daily.

Tim Berglund talks to Caleb Grillo (Confluent / WarpStream) about his career in data streaming product management. Caleb's first job: washing windows. Their challenge: reshaping Confluent Cloud's billing and pioneering diskless Kafka to trade latency for huge cost savings.SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo  

Adi Polak talks to Mateo Rojas (LittleHorse) about his career working with Kafka Streams. Mateo's first job: building a real-money policy management platform on early Kafka Streams. His challenge: working at LittleHorse with Kafka as a workflow engine and deciding whether it should be the source of truth.SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo  

AI agents are evolving from individual productivity tools into distributed systems components inside enterprises. The next frontier is coming into focus, and it involves large-scale ecosystems of collaborating agents embedded directly into business processes. However, multi-agent architectures introduce serious challenges around orchestration, state management, trust, governance, and observability. Eric Broda is a veteran of the software industry, and he's the co-author of the new O’Reilly book, Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem. In this episode, Eric joins Sean Falconer to discuss the architectural challenges of deploying agents as core infrastructure, how distributed computing principles apply to multi-agent systems, why trust and explainability are foundational, and what enterprises may look like as agents become full participants in business processes. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Agentic Mesh with Eric Broda appeared first on Software Engineering Daily.

AI agents are evolving from individual productivity tools into distributed systems components inside enterprises. The next frontier is coming into focus, and it involves large-scale ecosystems of collaborating agents embedded directly into business processes. However, multi-agent architectures introduce serious challenges around orchestration, state management, trust, governance, and observability. Eric Broda is a veteran of the software industry, and he's the co-author of the new O’Reilly book, Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem. In this episode, Eric joins Sean Falconer to discuss the architectural challenges of deploying agents as core infrastructure, how distributed computing principles apply to multi-agent systems, why trust and explainability are foundational, and what enterprises may look like as agents become full participants in business processes. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Agentic Mesh with Eric Broda appeared first on Software Engineering Daily.

AI agents are evolving from individual productivity tools into distributed systems components inside enterprises. The next frontier is coming into focus, and it involves large-scale ecosystems of collaborating agents embedded directly into business processes. However, multi-agent architectures introduce serious challenges around orchestration, state management, trust, governance, and observability. Eric Broda is a veteran of the software industry, and he's the co-author of the new O’Reilly book, Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem. In this episode, Eric joins Sean Falconer to discuss the architectural challenges of deploying agents as core infrastructure, how distributed computing principles apply to multi-agent systems, why trust and explainability are foundational, and what enterprises may look like as agents become full participants in business processes. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Agentic Mesh with Eric Broda appeared first on Software Engineering Daily.

Tim Berglund talks to Joseph Marais (Confluent) about his career in data streaming. Joseph's first job: SAN administrator. His challenge: AI radically changing how developers build software.The Pragmatic Engineer episode ft. Grady Booch: https://newsletter.pragmaticengineer.com/p/software-architecture-with-grady-boochSEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo  

Tim Berglund talks to Will LaForest (Confluent) about his career in software and data streaming. Will's first job: a high school internship at DARPA. His challenge: turning advanced technology into something people actually care about.SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo  

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the resurgence of ARM and CPUs as serious compute infrastructure for running local AI agents, a supply chain attack on LiteLLM that exposed API credentials across thousands of developer environments, and the arrival of OpenCode as a fully open source alternative to Claude Code and Codex. They also discuss the diverging strategies of Anthropic and OpenAI following the Pentagon contract controversy, and what it signals about where each company is positioning itself in the enterprise and government markets. Gregor and Sean then dive deep into what the AI coding boom actually means for shipping software. Finally, they highlight standout threads from Hacker News, including Doom running entirely over DNS, the psychology of seafoam green in Cold War-era control rooms, a Tesla Model 3 computer assembled from salvaged crash components, and Apple’s quiet discontinuation of the Mac Pro. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: OpenCode, AI Code vs. Shipped Code, and the LiteLLM Breach appeared first on Software Engineering Daily.

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the resurgence of ARM and CPUs as serious compute infrastructure for running local AI agents, a supply chain attack on LiteLLM that exposed API credentials across thousands of developer environments, and the arrival of OpenCode as a fully open source alternative to Claude Code and Codex. They also discuss the diverging strategies of Anthropic and OpenAI following the Pentagon contract controversy, and what it signals about where each company is positioning itself in the enterprise and government markets. Gregor and Sean then dive deep into what the AI coding boom actually means for shipping software. Finally, they highlight standout threads from Hacker News, including Doom running entirely over DNS, the psychology of seafoam green in Cold War-era control rooms, a Tesla Model 3 computer assembled from salvaged crash components, and Apple’s quiet discontinuation of the Mac Pro. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: OpenCode, AI Code vs. Shipped Code, and the LiteLLM Breach appeared first on Software Engineering Daily.

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the resurgence of ARM and CPUs as serious compute infrastructure for running local AI agents, a supply chain attack on LiteLLM that exposed API credentials across thousands of developer environments, and the arrival of OpenCode as a fully open source alternative to Claude Code and Codex. They also discuss the diverging strategies of Anthropic and OpenAI following the Pentagon contract controversy, and what it signals about where each company is positioning itself in the enterprise and government markets. Gregor and Sean then dive deep into what the AI coding boom actually means for shipping software. Finally, they highlight standout threads from Hacker News, including Doom running entirely over DNS, the psychology of seafoam green in Cold War-era control rooms, a Tesla Model 3 computer assembled from salvaged crash components, and Apple’s quiet discontinuation of the Mac Pro. Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SED News: OpenCode, AI Code vs. Shipped Code, and the LiteLLM Breach appeared first on Software Engineering Daily.

Viktor Gamov talks to Baruch Sudakurski (TuxCare) about his career in developer advocacy. Baruch's first job: fixing electric kettles. His challenge: figuring out how to map a non-relational database (MongoDB) into Spring Data's SQL-oriented model.SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo  

Jay Kreps is the co-founder and CEO of Confluent, the company built around Apache Kafka — the open-source data streaming platform he originally built while at LinkedIn. In this conversation, Jay shares his full journey: how Confluent grew from a scrappy group of engineers with no go-to-market experience into a publicly traded enterprise software company. He makes the case that the difference between what a company can do, and what it must do, is one of the most underrated building levers; illustrated through his years spent pushing Confluent towards a cloud product, in the face of widespread opposition. In this episode, we discuss: Why moving from software engineer to CEO requires almost an entirely new skillset The product marketing pyramid Jay built to explain Kafka to the world How Confluent bludgeoned its way to a cloud-first business when the early product was “embarrassing” The critical difference between what a company can do and what it must do What keeps scaling companies from becoming "Chipotle” References: Amazon Web Services: https://aws.amazon.com/ Apache Kafka: https://kafka.apache.org/ Benchmark: https://www.benchmark.com/ Confluent: https://www.confluent.io/ Jun Rao: https://www.linkedin.com/in/junrao LinkedIn: https://www.linkedin.com/ McKinsey & Company: https://www.mckinsey.com/ MySpace: https://www.myspace.com/ Neha Narkhede: https://www.linkedin.com/in/nehanarkhede Oracle: https://www.oracle.com/ Red Hat: https://www.redhat.com/ Snowflake: https://www.snowflake.com/ Where to find Jay: LinkedIn: https://www.linkedin.com/in/jaykreps/ Twitter/X: https://x.com/jaykreps Where to find Brett: 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/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 01:18 Making the leap from engineer to CEO 03:33 The 80% rule: what a CEO actually needs to know 04:54 Scaling different business disciplines 09:31 How Confluent's story began in LinkedIn 12:13 The growing need for scalable data tech 13:37 What the early Kafka product looked like 16:38 Kafka's underwhelming open-source launch 18:38 The blog post that accelerated Kafka's adoption 20:16 Why so many marketing messages fail 28:08 The decision to build Confluent 34:24 Planning to fundraise before building the product 39:19 Confluent's early years: Tough product decisions 47:07 The underrated growth lever question for companies 55:46 Why founder optimism is an overrated trait 1:00:29 What should founders give up as they scale? 1:02:47 Why people become trapped in a failure mindset 1:08:33 The Chipotle problem: Losing excellence at scale

Adi Polak talks to Arvind Suresh (OpenAI) about his career in distributed systems and real-time streaming. Arvind's first job: coding at school. His challenge: turning OpenAI's fragile Kafka setup into a reliable, multi-region streaming backbone.SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo  

Mountains of data. Instant delivery. AI co-pilots ready to process it all in seconds. By all logic, our decision-making should be getting sharper, easier, and infinitely more effective. Yet, the exact opposite is happening. Leaders are more stressed, more disconnected from their teams, and increasingly regretting their choices.The reality is a much more sobering masterclass in data-driven self-deception. This week, I am examining a recent vendor report from Confluent that argues the solution to our modern leadership crisis is simply more and faster data. But if you look closely at the numbers (like 62% of executives using AI for a majority of their decisions, and 70% second-guessing their own judgment) the data actually holds the keys to why our decision-making processes are breaking down, and exactly what we can do to fix them. I'll explain why we must aggressively interrogate the lenses behind both external vendor reports and internal dashboards, how AI is secretly acting as an echo chamber that isolates executives, and why the ultimate leadership skill right now isn't just moving faster, but knowing how and where to inject "strategic friction".My goal is to move you out of "Spectator Mode" to "Strategic Preparation" by highlighting the greatest opportunities to prepare your organization for what's ahead:​Decoding Data Lenses: We love to assume internal dashboards are objective truth. I break down why every metric has a hidden motive, like a talent acquisition leader celebrating a 20% increase in speed-to-hire while completely missing a drop in 90-day retention. You cannot blindly consume data; you must go into your next meeting prepared to ask what context is missing before making a call.​Escaping the Lethal Triad: We casually assume AI is a collaborative partner, but it's often an echo chamber that isolates leaders from their teams. I share why you must actively fight the triad of isolation, overreliance on AI, and willful ignorance. You need to pause major decisions this week and force messy, human collaboration before you become part of the 75% of leaders who regret moving too fast.​Injecting Strategic Friction: We are making sweeping organizational decisions just to appease the intense social pressure to move faster. I explain why using AI to just execute faster is a disaster waiting to happen. You must use AI and data to map out validation plans, like quickly testing assumptions on a massive upskilling push, so you can apply strategic friction and actually move at the right speed.By the end, I hope you see that true leadership isn't about blindly matching the speed of the machines. You cannot simply wait for a dashboard to tell you what to do; you have to define the friction points that will lead your team to the right outcomes.⸻If this conversation helps you think more clearly about the future we're building, make sure to like, share, and subscribe. You can also support the show by buying me a coffee at https://buymeacoffee.com/christopherlindAnd if your organization is wrestling with how to lead responsibly in the AI era, balancing performance, technology, and people, that's the work I do every day through my consulting and coaching. Learn more at https://christopherlind.co⸻Chapters00:00 – Introduction & The Big AI Stat02:00 – Unpacking the Confluent Report04:30 – The Danger of External Lenses10:30 – Action 1: Auditing Your Upcoming Pre-Reads12:00 – The Lethal Triad: Isolation, AI Overreliance & Regret21:00 – Action 2: Forcing Human Collaboration23:30 – The Speed Trap vs. Strategic Friction29:30 – Action 3: Identifying Friction Points in Fast Projects31:00 – Conclusion & How to Work With Me#ArtificialIntelligence #DataStrategy #Leadership #BusinessStrategy #ChristopherLind #FutureFocused #DecisionMaking #TechTrends #FutureOfWork

Plus: Microsoft reorganizes its Copilot teams. And Nvidia and Uber will expand their partnership to launch a global fleet of robotaxis. Julie Chang hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

IBM CEO Arvind Krishna discusses IBM's $11 billion acquisition of Confluent. Krishna also spoke about the impact of AI, his expectation for IBM to pursue more AI deals, the regulatory environment for those deals, and how AI is a tailwind for the company, causing no net decrease in workers. Krishna spoke with Bloomberg's Caroline Hyde.See omnystudio.com/listener for privacy information.

Tim Berglund talks to Gunnar Morling (Confluent) about his career in open source Java and data streaming. Gunnar's first job: a student PHP developer in AMD's e-learning group. His challenge: working at Decodable on the 1 Billion Row Challenge.SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo  

Retrieval-augmented generation, or RAG, has become a foundational approach to building production AI systems. However, deploying RAG in practice can be complex and costly. Developers typically have to manage vector databases, chunking strategies, embedding models, and indexing infrastructure. Designing effective RAG systems is also a moving target, as techniques and best practices evolve in step with rapidly advancing language models. Google DeepMind recently released the File Search Tool, a fully managed RAG system built directly into the Gemini API. File Search abstracts away the retrieval pipeline, allowing developers to upload documents, code, and other text data, automatically generate embeddings, and query their knowledge base. We wanted to understand how the DeepMind team designed a general-purpose RAG system that maintains high retrieval quality. Animesh Chatterji is a Software Engineer at Google DeepMind and Ivan Solovyev is a Product Manager at DeepMind, and they worked on File Search Tool. They joined the podcast with Sean Falconer to discuss the evolution of RAG, why simplicity and pricing transparency matter, how embedding models have improved retrieval quality, the tradeoffs between configurability and ease of use, and what's next for multimodal retrieval across text, images, and beyond. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post DeepMind's RAG System with Animesh Chatterji and Ivan Solovyev appeared first on Software Engineering Daily.

A structural shift is occurring in the managed IT services landscape as AI capabilities are rapidly embedded across enterprise applications, with oversight and risk management functions increasingly separated out and monetized as add-on services. Vendors, including Microsoft and OpenAI, are deploying AI agents in essential tools such as Outlook, Teams, and Excel, then selling governance, security, and compliance capabilities as additional paid layers. The core mechanism is the transfer of operational and liability risk downstream to IT service providers and their clients, while ownership of the control plane and margin on risk mitigation remain with the vendors. The episode highlights consequential findings regarding AI reliability and adoption. A Nature Medicine study found that OpenAI's ChatGPT Health underestimated emergency severity in 51.6% of cases, prompting concerns about overreliance on AI for critical decisions. Additionally, Confluent's UK executive survey indicated that 62% of organizations are already shifting decision-making to AI, but only 7% have a company-wide AI strategy, and fewer than half of executives and employees agree on actual daily AI usage. Most leaders receive little formal AI training yet are second-guessing their own judgment in favor of AI output. Further reinforcing the governance gap, Microsoft is launching Agent 365 and new enterprise security tiers, while OpenAI's acquisition of Promptfoo signals a focus on AI reliability testing and compliance monitoring. Funding for GRC platforms like IntelliGRC demonstrates capital flowing into third-party oversight solutions. The recurring pattern is vendors first pushing broad agent adoption, then introducing and monetizing governance as a discrete add-on, often outside the default package. Operationally, MSPs and IT leaders face increased liability exposure if they rely on vendor-native governance without independent audit or measurement capability. The absence of industry-standard reliability metrics for AI, combined with the perception and usage gaps inside organizations, calls for MSPs to lead in auditing, documenting, and independently measuring AI usage and performance. Failing to proactively manage these controls can result in silent risk absorption and unfavorable positioning as vendors bundle compliance and pass residual risk downstream to service providers. Three things to know today 00:00 AI vs. Judgment                            02:35 Agents vs. Oversight 04:04 AI Reliability Gap 05:15 Why Do We Care?  Supported by:  ScalePad 

Adi Polak talks to Sage Pierce (Indeed) about his career in software engineering and event-driven architectures. Sage's first job: Java Swing development at a Department of Defense–affiliated research lab. His challenge: working at Indeed on event-driven views and IMI to join data across domains in a polyglot microservices world.Sage's Atleon project: https://github.com/atleon SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo

Viktor Gamov talks to Leonid Igolnik (Former CTO at Clari) about his career in B2B SaaS engineering leadership. Leonid's first job: teaching kids Pascal. His challenge: changing buyer behavior and scale complex systems.Books mentioned:► Influence without Authority: https://www.amazon.com/Influence-Without-Authority-Allan-Cohen/dp/0471463302► Drive: https://www.danpink.com/books/drive/► Blink: The Power of Thinking Without Thinking: https://www.amazon.com/Blink-Power-Thinking-Without/dp/0316172324SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo

Tim Berglund talks to Colt McNealy (LittleHorse Enterprises) about his career in distributed systems. Colt's first job: software engineer at a real estate company. His challenge: working in a complex microservices environment and turning that pain into Little Horse.Colt's Current 2024 talk: https://current.confluent.io/2024-sessions/kafka-streams-as-a-data-store-for-a-workflow-engineGunnar Morling's blog: https://www.morling.dev/blog/Jack Vanlightly's blog: https://jack-vanlightly.com/SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo

Adi Polak talks to Daniel Doubrovkine (Shopify) about his career building data‑intensive systems. Daniel's first job: delivering pharmacy medications by bike. His challenge: building Artsy's Art Genome and auctions as simple as possible.SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo

This week on Market Mondays, we kick things off with our Futures Trading Tip of the Week and a breakdown of the biggest investing mistake of the year. We dive into the shocking rebound of Carvana joining the S&P 500 after nearly going bankrupt — debating whether it's a true comeback story or a sign the market is getting reckless again. We also break down the massive bidding war for Warner Bros Discovery, who needs WBD the most to survive the next decade, and what this means for the future of media. Caleb Silver, Editor-in-Chief of Investopedia, joins us with expert data and insight throughout the discussion.We compare Paramount's heavy debt load to Netflix's growth and free cash flow dominance and question whether a Netflix–WBD deal would spark a new era of media consolidation or run into regulatory roadblocks. From there, we shift to AI and corporate strategy, analyzing IBM's $11B acquisition of Confluent and whether M&A is becoming the quiet force powering the next leg of the AI boom. We also cover holiday spending vs weak investor sentiment, whether investors should rotate into safer stocks, and what Wednesday's Fed decision could mean for markets heading into 2026.To wrap up, we go rapid-fire: the most attractive stocks currently dipping for LEAPs and swing trades, which companies are less likely to be corrupt or mismanaged, whether failed AI bets could force bailouts in tech, Bitcoin's next move toward $65K or $70K, Apple's potential talent crisis after losing multiple key executives, and if an oil collapse into the $30s could make the entire energy sector uninvestable. A packed episode with strategy, clarity, and expert perspective. #MarketMondays #EarnYourLeisure #CalebSilver #Investopedia #Investing #StockMarket #Bitcoin #OptionsTrading #AIStocks #MediaMergers #Carvana #Apple #OilPrices #WealthBuilding #FinancePodcast #EYLSupport this podcast at — https://redcircle.com/marketmondays/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy