The timelessness of vector databases | Pinecone’s Ram Sriharsha

In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan, VP of AI Software at AMD, but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI.LinearB: Your AI productivity journey starts hereFollow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's guest(s):Follow Anush LinkedIn | XAMD ROCm Software: github.com/ROCmAMD Developer CloudLearn more at: amd.comOFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

What is "spec-driven development," and why is this structured approach the key to unlocking complex AI projects? We're joined by Amit Patel, Director of Software Development for Kiro at AWS, to explore this methodology. He explains why "vibe coding" in a chat window fails on multi-day initiatives: the AI (and the developer) loses context. Kiro solves this by turning requirements and design into a persistent, structured spec that acts as the agent's long-term memory, enabling it to maintain context and build sophisticated applications.Amit shares the inside story of how his team at AWS built Kiro from scratch in under a year. He reveals their virtuous feedback loop with internal developers testing nightly builds and providing real-time feedback. This rapid iteration, which included six full revs of the spec experience, was so successful that the Kiro team famously "used the tool to build the tool," turning a multi-week feature into a two-day task. LinearB: Your AI productivity journey starts hereFollow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's guest(s):Learn more and try Kiro: kiro.devJoin the Kiro Community: Kiro Discord Channel OFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

AI is forcing engineering leaders to become part-CFO, part-governance expert, and part-business strategist. Are you ready for the shift? We're joined by Lake Dai, a globally recognized AI expert, professor at Carnegie Mellon, and founder of Sancus Ventures, to explore the new operating strategies required in an AI-first era. She explains why AI has evolved from a simple tool to a core business metric that leaders are held accountable for on earnings calls. This new reality introduces massive new compute costs—sometimes 30-50% of OpEx—forcing leaders to adopt the financial foresight of a CFO to forecast and justify spending.Beyond the balance sheet, Lake identifies AI governance as the biggest blind spot for most leaders today, outlining the urgent need for an AI handbook to manage unit, system, and ethical risks. This strategic shift also reshapes the engineering org itself, from managing hybrid teams of humans and agents to the need for new training environments, almost like "AI flight simulators." This episode is an essential briefing on these new complexities, all centered on Lake's most urgent advice: in a world moving this fast, the best strategy is to slow down and focus on the fundamentals.LinearB: Your AI productivity journey starts hereFollow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's guest(s):Connect with Lake on LinkedInSubscribe to Lake’s Substack: LakeD-AI UnbundledLearn more about: Sancus VenturesOFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

Forget top-down mandates. How do you foster organic AI adoption on a skeptical, high-performing engineering team? Loic Houssier, Head of Engineering at Superhuman, joins us to share how he did just that. He explains his strategy for overcoming cynicism, which involved leveraging a highly respected internal champion, the Chief Architect, to re-evaluate the tools and prove their potential was no longer just buzz.Discover his team's biggest, unexpected productivity gains: dramatically faster ramp-up times on new codebases and the rapid creation of valuable internal tools, rather than just raw code generation speed. Loic details Superhuman's pragmatic, high-trust approach to measurement, blending qualitative feedback with simple signals like PR labels. He explains how they fostered this adoption with a bottom-up "AI Guild," empowering engineers of all seniorities without a heavy-handed mandate. He also reveals a stunning real-world example where AI turned a potentially days-long compliance task into a 90-minute win. This episode is a practical playbook for building genuine, bottom-up AI adoption.Get the guide: AI productivity guide for engineering leadersFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Connect with Loïc HoussierLearn more about Superhuman: superhuman.comSuperhuman was acquired by Grammarly: Read about the acquisitionReferenced in today's show:Amazon brain drain finally caught up with AWSClaude Skills are awesome, maybe a bigger deal than MCP How to Say NoThe Field Guide to AI SlopOFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

Is your company using AI to trim your budget, or to multiply your team's impact? We're joined by Matt Culver, a senior engineering leader at Super.com, to discuss why the common view of AI as a tool for cost-cutting is a misguided "accounting mindset" that ultimately destroys trust. He argues that leaders should instead see efficiency gains from AI as a powerful opportunity to reinvest in their teams. This conversation reframes the AI debate by urging leaders to look beyond the coding loop to improve the entire product development lifecycle—from ideation to delivery.Matt explains that the key to successful AI adoption is aligning new initiatives with the developer's core incentives: removing friction and enabling the creative flow state that makes their job enjoyable. He provides a human-centric approach for channeling AI's power to solve upstream problems in product planning and market research, rather than just generating more code. Learn how to use AI not as a means to an end, but as a way to empower your developers, generate more value, and build a high-trust engineering culture.Bring AI into your code review process with LinearBFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Super: Super.comConnect with Matt: LinkedIn | [email protected] in today's show:A small number of samples can poison LLMs of any sizeRevisiting "Intelligence Drift"AI profiteering is now indistinguishable from trolling The AI water issue is fakeHTML’s Best Kept Secret: The <output> Tag Life in a World of Pervasive Immorality: The Ethics of Being AliveOFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

With massive context windows and new agent frameworks, do vector databases still matter? Ram Sriharsha, CTO at Pinecone, joins the conversation to make the definitive case that they're more critical than ever. He explains that at the core of all AI is search, and externalizing this function is non-negotiable for security, auditability, and control.Ram offers a clear starting path for engineering leaders: begin with simple Retrieval-Augmented Generation (RAG) applications, but immediately implement a robust evaluation framework to manage hallucinations and ensure quality. He shares his perspective on the skills that matter most now, arguing that curiosity and the rise of the generalist engineer are critical in an AI-powered world. This episode is a guide to building the AI stack from the ground up, from using AI as a "good junior engineer" for testing to cultivating the engineering mindset of tomorrow.Bring AI into your code review process with LinearBFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Pinecone: pinecone.ioPinecone Tutorials & Blog: Explore guides on RAG, vector databases, and moreConnect with Ram Sriharsha: LinkedInReferenced in today's show:Your New 500K AI Coworkers Just ArrivedHow I influence tech company politics as a staff software engineerCoinbase says 40% of code written by AI, mostly tests and TypescriptCelebrating 1 Trillion Web Pages ArchivedOFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

Vibe coding is a developer's dream, but in the enterprise, it can be a nightmare of risk and shadow IT. So how do you saddle the 'wild horse' of modern AI development? Dan Fernandez, VP of Product Management, Developer Services at Salesforce, joins the conversation to share the answer: a new category his team is pioneering called Enterprise Vibe Coding. This discussion reveals how to move beyond flashy greenfield AI demos and build for the reality of most enterprises, where the goal is to safely reuse existing systems, not reinvent them from scratch.Dan breaks down the specific guardrails Salesforce has built, from sandboxed environments for safe testing to automated "quality gates" that act as a bouncer for both human and AI-generated code. He shares the powerful lesson that building customer trust through policies like zero data retention is more important than any single feature. He explains why the real work of enterprise AI is more like secure "plumbing"—connecting the hardened systems you already have. This is an essential guide for any leader looking to apply the speed of AI to the complex reality of enterprise software.Get the guide: AI productivity guide for engineering leadersFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Unleash Your Innovation with Agentforce Vibes: Vibe Coding for the EnterpriseLearn more about Salesforce for Developers: developer.salesforce.comSalesforce Extensions for VS Code: VS Code MarketplaceSalesforce Code Builder: Learn more about the zero-install IDEConnect with Dan Fernandez: LinkedInReferenced in today's show:OFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

The Model Context Protocol (MCP) is evolving beyond local developer experiments and into the secure, remote infrastructure that will power the next generation of the internet. Brendan Irvine-Broque, Director of Product at Cloudflare, joins us to share a roadmap for this future. He explains how Cloudflare's "customer zero" philosophy of dogfooding their own tools provides a unique perspective on what it takes to scale MCP for production.Brendan makes the case for observability as the ideal starting point for enterprises and lays out the vision for MCP's ultimate destination: a universal protocol for agent-to-agent communication. The conversation explores how remote servers can create a decentralized layer for security and user memory, and what the exciting development of MCP UI means for the future of chat-based applications. This is an essential look at the next wave of agentic systems and the infrastructure required to build it.Check out:Watch Closing the AI gap: Surpassing executive expectations for AI productivityFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Cloudflare's work with AI: agents.cloudflare.comRead the latest from Cloudflare: The Cloudflare BlogCloudflare's Unique Primitives Mentioned: Durable ObjectsThe MCP UI Project: MCP UI on GitHub (Project by Ido Salomon)Observability Tools Mentioned: Datadog | HoneycombAI Tools Mentioned: Block/Square's "Goose" | CursorConnect with Brendan Irvine-Broque: X @irvinebroque | LinkedInReferenced in today's show:OFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

What do you learn after spending 15 years at Apple and demoing your work directly to Steve Jobs? Ken Kocienda, Co-founder of Infactory AI and author of Creative Selection, joins us to share the answer. As a former Principal Engineer at Apple who helped create the iPhone keyboard and autocorrect, Ken discusses his incredible journey from a history major to a key figure in building technology used by billions. He explains his core philosophy of bridging the gap between the liberal arts and technology to create meaningful products, and why he believes AI is the next frontier for this mission. (BTW – we sat down with his co-founder Brooke, so if you like this episode be sure to check that one out!)The conversation dives into his disciplined, spec-driven approach to coding with AI and the power of "extractive AI" to unlock hidden value in data. Ken reveals the crucial lesson he learned from Steve Jobs—that "everything is provisional"—and how his "evolutionary design" process is perfectly suited for today's AI challenges. This episode is a deep dive into the timeless principles of design and a powerful argument for why the best technology is so intuitive, it makes technical literacy irrelevant.Check out:Register now: AI productivity guide for engineering leadersFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Infactory AI: infactory.aiConnect with Ken on LinkedInKen's Book: Creative Selection: Inside Apple's Design Process During the Golden Age of Steve JobsReferenced in today's show:MCP is probably the first protocol iOFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

What does it take to transform a brilliant AI model from a research paper into a product customers can rely on? We're joined by Elizabeth Lingg, Director of Applied Research at Contextual AI (the team behind RAG), to explore the immense challenge of bridging the gap between the lab and the real world. Drawing on her impressive career at Microsoft, Apple, and in the startup scene, Elizabeth details her journey from academic researcher to an industry leader shipping production AI. Elizabeth shares her expert approach to measuring AI impact, emphasizing the need to correlate "inner loop" metrics like accuracy with "outer loop" metrics like customer satisfaction and the crucial "vibe check." Learn why specialized, grounded AI is essential for the enterprise and how using multiple, diverse metrics is the key to avoiding model bias and sycophancy. She provides a framework for how research and engineering teams can collaborate effectively to turn innovative ideas into robust products. Check out:Register now: Closing the AI gap: Exceeding executive expectations for AI productivityFollow the hosts:Follow BenFollow AndrewFollow today's guest(s):Learn more about Contextual AI: Contextual.ai WebsiteFollow Contextual AI on Social Media: LinkedIn | X (formerly Twitter)Connect with Elizabeth: LinkedInReferenced in today's show:Throwing AI at Developers Won’t Fix Their ProblemsWhy language models hallucinatei ran Claude in a loop for three months, and it created a genz programming language called cursedOFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.