The MonkCast

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Oct 2026

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About the show

Join the developer-focused industry analysts at RedMonk as they discuss news and trends in the software space with leaders and practicioners in cloud, AI, IaC, security, DevOps, developer relations, observability, data, and more. Can't get enough of the Monks? Visit the RedMonk YouTube channel or check out our research at RedMonk.com . You can also follow RedMonk on Bluesky , Twitter (X), and LinkedIn . Meet RedMonk's Analysts James Governor , Principal Analyst & Co-founder @monkchips , LinkedIn , Blog Stephen O'Grady , Principal Analyst & Co-founder @sogrady , LinkedIn , Blog Rachel Stephens , Research Director @rstephensme , LinkedIn , Blog Kate Holterhoff , Senior Industry Analyst @KateHolterhoff , LinkedIn , Blog

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October 22, 202635 min

Sanjay Sarathy on Core Principles of Developer Trust

In this episode of the MonkCast, James Governor sits down with Sanjay Sarathy, SVP of Developer Experience at Cloudinary, to discuss developer trust, and why it still matters in the agentic software development era. With more than four million developers signed up, and 40% of new registrations now coming from agents, Cloudinary is rethinking what trust means, but maintaining true to its core values. Sanjay walks through the principles guiding that work: products that work as promised every time, human support that scales, accuracy in AI-assisted answers, transparency, long-term API stability, keeping the developer voice in the product loop, and investing in education for students and early-career developers. The RedMonk video is sponsored by Cloudinary. Show notes: https://redmonk.com/videos/sanjay-sarathy/ Chapters 00:07 Introduction to trust in developer platforms 00:51 Why trust is crucial in a rapidly changing environment 02:05 Cloudinary's longstanding focus on trust 04:50 Principle 1: Consistency in product performance 08:40 Scaling support for millions of developers 12:58 Ensuring AI-assisted accuracy and trust 17:40 Transparency as a trust foundation 21:02 Long-term API commitment and stability 24:42 Listening to the developer voice 28:58 Supporting developer education and growth 34:18 Summary and closing thoughts

October 8, 202646 min

Inside the AWS Agent Toolkit with Liam Greenamyre

Why do some teams see massive speedups from AI coding agents while others barely notice a difference? In this RedMonk New Builders Conversation with AWS, Kate Holterhoff talks with Liam Greenamyre, Principal PM of Developer Platforms at AWS, about the uneven impact of AI on software development and how to restructure work so agents can succeed. Liam explains why making tasks verifiable is the single biggest lever, then walks through the Agent Toolkit for AWS: a remote MCP server, 100+ skills, plugins, and a rules file that work with Claude Code, Codex, Kiro, and more. They dig into how a single RunScript tool covers 15,000+ AWS APIs token-efficiently, how IAM context keys separate human and agent actions, how agents discover new services like Aurora DSQL, and why isolated dev accounts make safe sandboxes for autonomous agents. Liam closes with a bold prediction: the biggest changes ahead are organizational, not technological. This New Builders conversation is sponsored by AWS. Show notes: https://redmonk.com/videos/liam-greenamyre/ Chapters 00:04 Introduction to AI in Software Development 03:07 The Uneven Adoption of AI in Development Teams 06:20 Strategies for Successful AI Integration 10:09 Exploring the AWS Agent Toolkit 17:01 Guardrails and Context for AI Agents 20:10 The Impact of AI on FinOps and Cost Management 24:28 Evolution of Agent Tooling 32:17 Enhancing Discoverability in AWS 35:56 Guardrails and Safety in AWS Operations 41:03 Future of AI in Software Development

September 29, 202620 min

Platform Engineering 2.0: Evolution, Not a Reset with Pankaj Gupta

In this RedMonk conversation, Pankaj Gupta, Senior Director of Product Marketing, VCF Division, at Broadcom, chats Platform Engineering in the AI era with Stephen O'Grady. Gupta explains that more than 90% of organizations run an internal platform, but despite widespread adoption, Gupta thinks most platforms are hitting two ceilings. They were built, in his phrase, "AI blind," and they were built for exactly one user, the developer. AI has already moved the bottleneck from writing code to getting it into production, and the user base is about to change shape. Also in this episode: when golden paths harden into golden cages, why FinOps and security can no longer be bolted on, and Gupta's five pillars for platform engineering 2.0. The RedMonk conversation is sponsored by Broadcom. Show notes: https://redmonk.com/videos/pankaj-gupta/ Chapters 00:04 Introduction to Sponsorships 00:08 Introduction to Platform Engineering 02:52 Current State and Challenges of Platform Engineering 05:47 The Evolution of Platform Engineering 08:33 AI's Impact on Platform Engineering 11:23 The Rise of Agent Personas 14:52 Five Pillars of Modern Platform Engineering 17:47 Next Steps for Platform Engineers

September 24, 202658 min

The BOM Explosion & Why PQC Stopped Being 10 Years Away with David Pollak & Allan Friedman

In this episode, David Pollak, founder and CEO of Spice Labs, and Allan Friedman, Senior Technical Advisor at the Institute for Security and Technology, discuss the importance of software supply chain security, bills of materials, and the emerging field of post-quantum cryptography with Kate Holterhoff. Friedman, "the godfather of SBOMs," gives a state of play for 2026: where the tooling has matured, where it still falls short, and why the Cyber Resilience Act is about to make this everybody's problem. From there the conversation moves to CBOMs and post-quantum cryptography. Pollak argues PQC stopped being a 2035 problem in June, when the White House, the US military and French standards bodies all pulled their deadlines forward by five years. Also covered: whether developers should have to think about security at all, what the IPv6 transition taught us, and the 360,000 Maven packages carrying vulnerable code nobody disclosed. This RedMonk conversation is sponsored by Spice Labs. Show notes: https://redmonk.com/videos/ david-pollak-allan-friedman / Chapters 00:04 Introduction to the Guests and Topics 06:29 The State of SBOMs in 2026 13:46 Understanding Cryptographic Bills of Materials 22:12 The Intersection of Security and Development 31:25 The CBOM Approach and Its Importance 33:23 Post-Quantum Cryptography: A Future Concern? 38:55 Navigating Regulations and Compliance 43:57 Finding vs. Fixing Vulnerabilities in Open Source 49:58 AI's Role in Security and Vulnerability Management 53:31 Lessons from IPv6 and Cryptography Libraries

October 15, 202648 min

Anne Bertucio de Hollembeak on Why Trust & Safety Needs Open Source

In this RedMonk Conversation, Anne Bertucio de Hollembeak, COO of ROOST (Robust Open Online Safety Tools) , chats with Kate Holterhoff about the importance of open source tools in trust and safety, especially in the age of AI. She shares insights on building safety tools, the role of open source, and how these efforts can create a safer online environment for everyone. Show notes: https://redmonk.com/videos/anne-bertucio-de-hollembeak/ Chapters 00:04 Introduction to Roost and Ambertuccio's Background 02:53 Understanding Trust and Safety in Online Spaces 05:44 The Role of Open Source in Trust and Safety 08:43 Child Safety Tools and Their Impact 11:29 Mila's Suicide Prevention Guardrail Project 14:21 AI Contributions and Community Engagement 17:20 Maintainer Burnout and AI Slop Management 26:13 Introduction to Bug Bounty and Security Processes 26:57 Understanding Osprey: The Tool for Safety and Investigation 28:18 The Story Behind Osprey and Its Origins 30:22 Roost: A Nonprofit Approach to Open Source Safety 32:04 Partnerships and Collaborations in the Safety Space 33:46 Blue Sky: A Case Study in Collaboration 35:21 Challenges in Decentralized Safety Solutions 37:14 Addressing the Bot Issue and AI Challenges 43:47 Open Source Policies and Their Implications 45:49 Conclusion and Resources for Further Engagement

October 1, 202628 min

Securing Enterprise Open Source with Coalitions & Clearing Houses: Lightwell from IBM & Red Hat

Most of an enterprise application stack is open source that nobody is paid to maintain. Gunnar Hellekson of Red Hat puts it at 60 to 70 percent, and that gap is what IBM's Lightwell is built for. He walks James Governor through the mechanics: members scan and triage their own dependencies, submit what they find with the source identity stripped out, and get fixes back after an embargo period before those fixes reach a wider network and, where a maintainer still exists, the upstream project. Financial services goes first. Hellekson is candid that plenty of customers cannot use the service yet, because joining would only lengthen a patch backlog already measured in months. Mythos, the two agree, mostly revealed how bad things already were. They also work through token economics, why nobody needs to scan OpenSSL separately, and why a fix should end up as deterministic code rather than something an AI keeps rediscovering. Red Hat is a RedMonk client, but this is free and independent content. Show notes: https://redmonk.com/videos/gunnar-hellekson/ Chapters 00:00 Introduction: IBM's big bet on open source security 03:18 A win-win for customers and the open source community 05:27 The problem: 60–70% of the stack is unsupported open source 07:15 Carry your own fork or hope upstream fixes it 09:17 Eat your vegetables first: hardened images and paid platforms 09:45 How Lightwell works 11:57 Scope: application dependencies 12:57 Network effects, industry clearinghouses, and identity washing 14:34 When to move off orphaned projects 16:04 Mythos showed how bad things already were 19:51 Delivery via Maven, PyPI, and npm 20:33 Attackers vs. defenders at machine speed 21:44 Why fixes should be deterministic code, not endless AI 23:06 Token economics and shared remediation 24:05 Nobody needs to scan OpenSSL separately 25:08 Will every enterprise build its own software? 27:38 Wrap-up

September 17, 202648 min

Good Wigs & Golden Paths: Dev Tools in the AI Era with Victoria Melnikova

Kate Holterhoff talks with Victoria Melnikova, head of new business at Evil Martians, about what product market fit looks like for developer tools in 2026. Drawing on research by Evil Martians CEO Irina Nazarova, Melnikova argues PMF is a spectrum, not a switch. Product signals and revenue signals are separate things, and the gap between them tells you where to focus. Her advice to most early stage founders? Your product is probably fine, go do sales. Their conversation covers why dev tools resist the golden path metrics of generic SaaS (what's the golden path in Photoshop?), why agent experience (AX) matters now that LLMs increasingly pick tools for their users, and why engineers who use AI well are becoming the hires companies fight over. Also: a corrupted video shoot, agencies as pirate ships, and why AI should be like a good wig. Show notes: https://redmonk.com/videos/victoria-melnikova/ Chapters 00:04 Introduction to Dev Tools and Product Market Fit 10:52 Understanding Product Market Fit in DevTools 18:19 The Shifting Landscape of DevTools and AI 25:33 Agent Experience and Its Impact on DevTools 28:54 Navigating Agent Experience and AI Integration 34:17 The Future of Hiring in Tech 38:58 Designing with AI: Balancing Human Touch and Automation 47:09 Security in Open Source and AI

September 10, 202621 min

"Somebody is always paying the token bill": AI and Open Source with Brian Proffitt & Jason Brooks

Generated code is arriving faster than maintainers can read it. RedMonk's Steve O'Grady discusses this new reality with Jason Brooks and Brian Proffitt, both managers in Red Hat's Open Source and AI Program Office. Proffitt's teams are adding what he calls human speed bumps upstream: PR templates that ask whether AI wrote the patch, and if so, what the prompt was. The point is transparency, not blocking the flow. Brooks argues for the importance of human decision makers because writing code may cost close to nothing now, but getting another person to accept it still costs plenty, and someone always pays the token bill. The conversation also touches on maintainer burnout, why saying "no" is still a legitimate answer to a pull request, Fedora Hummingbird and its agentically assembled packages, and the thing Proffitt worries about most: trust. This RedMonk video is sponsored by Red Hat. Show notes: https://redmonk.com/videos/ brian-proffitt-jason-brooks / Chapters 00:04 Introduction to AI and Open Source Governance 00:32 Guest Introductions: Jason Brooks and Brian Proffitt 02:10 AI's Ubiquity and Industry Impact 03:06 Guardrails for AI Code Quality in Projects 04:22 Implementing Human Speed Bumps for AI Contributions 05:17 Encouraging Transparency in AI Contributions 06:46 Automated Review and Guardrails in Open Source 07:50 Verifying AI-Generated Code in Fedora 09:10 Human Oversight in AI Processes 13:39 Developer Burnout and Managing Contribution Floods 16:09 Trust and Transparency in AI-Driven Ecosystems 18:30 Evolving Policies for AI in Fedora 20:16 Future Challenges and Community Trust in AI 21:11 Closing Remarks and Future Outlook

September 3, 202619 min

From Stock Photos to Trillions of Artifacts: Shutterstock's Data Journey with Jefferson Frazer

In this conversation recorded at Fastly Xcelerate, James Governor talks with Jefferson Frazer, Director of AI at Shutterstock, about how a two-decade-old media company reinvented itself for the age of foundation models. Frazer explains how Shutterstock's long history of human-reviewed, meticulously labeled content left it uniquely positioned when hyperscalers began seeking diverse, web-scale datasets for multimodal training. Frazer digs into the technical backbone too: a 100-plus petabyte footprint, cloud-agnostic architecture built on Fastly's private fiber and Wasm-based edge compute, and the discipline of tracking data provenance through standards like C2PA and SynthID. Looking ahead, he makes the case that unified embedding spaces and portable metadata—not any single model provider—will define the next chapter of AI-ready content. This RedMonk video is sponsored by Fastly. Show notes: https://redmonk.com/videos/jefferson-frazer/ Chapters 00:00 Introduction to Shutterstock and AI 04:14 Data Scale and Infrastructure 07:25 Fastly's Role in Data Management 10:28 The Future of AI and Business Models 13:28 Regulation and Provenance in AI 16:35 Excitement for Unified Embedding Spaces

August 27, 202638 min

Why AI Agents Are Blowing Up Your Telemetry Bill with Nikhil Mungel

Most conversations about AI agents stay on what agents can do. Less attention goes to what they leave behind: logs, traces, and tool-call chains at a volume nobody's pipeline was built for. Nikhil Mungel, Head of AI R&D at Cribl, talks with Kate Holterhoff about why that is becoming observability's cost and noise problem. They discuss why token prices keep falling while total spend climbs, how employee spend differs from the tokens a product burns serving customers, credit-based versus outcome-based billing, why users always reach for the priciest model, and how the Open Cybersecurity Schema Framework (OCSF) standardizes what agents emit. Underneath all of it sits the question the industry keeps dodging, which is whether to keep scaling the backend or get stricter about what gets collected at all. This RedMonk conversation is sponsored by Cribl. Show notes: https://redmonk.com/videos/nikhil-mungel/ Chapters 00:25 Leading AI research and development at Cribl 01:46 Token prices fell, token bills grew 03:39 The research agent that thought all night 06:54 More software, more agents, more telemetry 09:37 Employee spend versus cost of goods sold 12:05 Credits, outcomes, and other ways to bill for agents 14:46 Shadow AI and the cloud governance rerun 17:00 Writing policy: cost, access, and blast radius 17:49 Local models and regulated industries 18:40 What Cribl collects, plus Cribl Guard 21:24 Token gateways as the enforcement point 24:56 Model anxiety and automatic routing 27:29 What OCSF is and why it caught on 30:15 Observability as an information compression problem 33:48 Zero data retention versus model safety 35:45 Should agents know what they cost? 36:35 Where to find Nikhil and Cribl's research