Modern Web

This episode of the Modern Web Podcast features Cody De Arkland, Head of Developer Experience at Sentry, in conversation with hosts Rob Ocel and Danny Thompson. They explore how Sentry has embraced a culture of experimentation with AI, from grassroots innovation in Slack channels to leadership setting the tone for rapid adoption. Cody shares insights into Sentry’s new AI monitoring tools, including MCP support and agent tracing, which give developers visibility into token usage, tool calls, and debugging flows. The discussion also touches on how AI is reshaping developer workflows, the balance between writing code and prompting, and why structured thinking is key to getting useful results.Keypoints from this episode:- Sentry fosters a playful, experimental environment where both grassroots initiatives and leadership drive AI adoption.- Sentry has rolled out AI monitoring with MCP support and agent tracing to give visibility into token usage, tool calls, and debugging.- AI is changing how developers approach coding, blending prompting with traditional programming.- Success with AI depends on framing problems clearly, not just relying on raw prompts.Cody De Arkland on Linkedin: https://www.linkedin.com/in/codydearkland/ Rob Ocel on Linkedin: https://www.linkedin.com/in/robocel/Danny Thompson on Linkedin: https://www.linkedin.com/in/dthompsondev/This Dot Labs Twitter: https://x.com/ThisDotLabsThis Dot Media Twitter: https://x.com/ThisDotMediaThis Dot Labs Instagram: https://www.instagram.com/thisdotlabs/This Dot Labs Facebook: https://www.facebook.com/thisdot/This Dot Labs Bluesky: https://bsky.app/profile/thisdotlabs.bsky.socialSponsored by This Dot Labs: https://ai.thisdot.co

In this episode of the Modern Web Podcast, Rob Ocel and Danny Thompson talk with Philipp Krenn, Head of Developer Advocacy at Elastic, about how Elasticsearch has evolved from a search engine into a foundation for observability, security, and AI-powered systems. Philipp explains how Elastic approaches information retrieval beyond just vector search, using tools like LLMs for smarter querying, log parsing, and context-aware data access.They also discuss how Elastic balances innovation with stability through regular releases and a focus on long-term reliability. For teams building with AI, Elastic offers a way to handle search, monitoring, and logging in one platform, making it easier to ship faster without adding complexity.Key points from this episode: Elasticsearch has expanded beyond search to support observability and security by treating all of them as information retrieval problems.Elastic integrates with AI tools like LLMs to improve search relevance, automate log parsing, and enable features like query rewriting and retrieval-augmented generation.Vector search is just one feature in a larger toolkit for finding relevant data, and Elastic supports hybrid and traditional search approaches.Elastic maintains a steady release cadence with a focus on stability, making it a reliable choice for both fast-moving AI projects and long-term production systems.Philipp Krenn on Linkedin: https://www.linkedin.com/in/philippkrenn/Rob Ocel on Linkedin: https://www.linkedin.com/in/robocel/Danny Thompson on Linkedin: https://www.linkedin.com/in/dthompsondev/This Dot Labs Twitter: https://x.com/ThisDotLabsThis Dot Media Twitter: https://x.com/ThisDotMediaThis Dot LabsInstagram: https://www.instagram.com/thisdotlabs/This Dot Labs Facebook: https://www.facebook.com/thisdot/This Dot Labs Bluesky: https://bsky.app/profile/thisdotlabs.bsky.socialSponsored by This Dot Labs: ai.thisdot.co

Sean Roberts, Head of AX Architecture at Netlify, dives into the innovative world of Agentic Experience (AX). He explains how AX transforms digital service design for AI agents, discussing why user flows often falter in agent-driven settings. Sean highlights the importance of effective discoverability through SEO and structured content. He also questions the relevance of traditional CMS platforms amid evolving AI needs, sharing a humorous incident where an AI agent took down Netlify’s homepage, underscoring the growing complexity of web content interaction.

In this episode of Modern Web, Danny Thompson chats with MelkeyDev, a Machine Learning Infrastructure Engineer at Twitch, about AI’s real-world applications, developer productivity, and the future of careers in Go. They cover everything from the rise of tiny AI-driven teams competing with large enterprises to how system prompts may matter more than model choice. Melkey shares his thoughts on cost-effective LLMs, production pitfalls, and the cognitive downsides of over-relying on AI. The conversation also explores backend development with Go, what makes it great for fast-moving teams, and how new developers can get started.Keypoints from this episode:- AI’s real value lies in business use cases. Melkey emphasizes that AI isn’t just a productivity tool; it enables small teams to build faster, cheaper, and more effectively than ever before.- System prompts are underrated. When it comes to LLM performance, prompt engineering often matters more than the model itself, especially for UI generation and agent design.- Cognitive cost of AI reliance. Referencing recent research, Melkey warns that overusing AI tools can reduce your ability to retain knowledge and perform certain tasks independently.- Go remains a strong backend choice. Despite being “boring,” Go continues to power developer velocity and scalable infrastructure, making it a smart language for backend-focused engineers.Follow MelkeyDev on Twitter: https://x.com/MelkeyDevSponsored by This Dot Labs: thisdot.co

In this episode of the Modern Web Podcast, hosts Rob Ocel and Danny Thompson sit down with Andre Landgraf, Senior Developer Advocate at Neon (now part of Databricks), to explore the evolving role of AI agents in developer workflows. They discuss how more Neon databases are being spun up by agents than humans, what that means for developer and agent experience (DX vs AX), and how tools like MCP and step functions are enabling scalable agent orchestration. The conversation also touches on agent security concerns, real-time vs. async UX, and how developers can build resilient, human-in-the-loop AI systems today. Plus, Andre shares practical insights from building his own personal CRM agent and experimenting with tools like Cortex and Ingest.Keypoints from this episode:- Agents now outpace humans in provisioning databases on Neon, thanks to agent-friendly APIs, early MCP support, and seamless integration with platforms like Replit and v0.dev.- Developer experience (DX) principles directly inform agent experience (AX), tools designed for simplicity and clarity often translate well to agent interactions, but agents still need unique guardrails like resumability and fine-grained permissions.- Agent orchestration is the next big frontier, with tools like LangBase, Ingest, and step functions offering patterns for chaining tasks, running agents in parallel, and retrying failed steps—enabling more resilient and scalable AI systems.- Async UX patterns are crucial for agent-powered apps, especially as LLMs become slower and more complex. Real-time feedback, task progress indicators, and human-in-the-loop controls will define effective agent interactions.Chapters00:00 Why apps don’t talk to each other 01:44 Meet Andre Landgraf from Neon 02:39 Agents now outnumber humans on Neon 05:03 DX vs AX: Building for agents 08:58 Security and authorization for agents 13:06 What’s missing for real adoption 17:06 Building a personal CRM with agents 20:04 MCP as the universal app interface 23:32 Agent orchestration and async UX 26:46 Step functions and background tasks 30:04 Are agents ready for real-time UX? 33:19 Human-in-the-loop patterns 35:59 Where to find Andre Follow Andre Landgraf on Social Media:Twitter: https://x.com/AndreLandgraf94Linkedin: https://www.linkedin.com/in/andre-landgraf/Sponsored by This Dot Labs: thisdotlabs.com

On this episode of the Modern Web Podcast, Rob Ocel and Danny Thompson talk with Brian Morrison, Senior Developer Educator at Clerk. They cover the state of authentication today, what makes Clerk stand out for small teams and indie builders, and how thoughtful developer experience design can make or break adoption.Brian shares why bundling tools like auth, billing, and user management is becoming more common, how Clerk handles real-world concerns like bot protection and social login, and why starting with a great developer experience matters more than ever.The conversation also explores the role of AI in software development and content creation, where it helps, where it hurts, and how to use it responsibly without losing quality or trust.Keypoints for this Episode:Modern auth is about experience, not just security. Clerk simplifies user management, social login, bot protection, and subscription billing with developer-friendly APIs and polished default UIs.Bundled platforms are making a comeback. Developers are shifting from handpicking tools to using tightly integrated services that reduce setup time and complexity.Developer education needs more care and creativity. Brian emphasizes the importance of visual storytelling, thoughtful structure, and anticipating confusion to help devs learn faster and retain more.AI is a productivity multiplier, not a replacement. The group discusses how AI can accelerate development and content creation when used with oversight, but warn against using it to blindly build entire apps.Follow Brian Morrison on Social MediaTwitter: https://x.com/brianmmdevLinkedin: https://www.linkedin.com/in/brianmmdev/Sponsored by This Dot: thisdotlabs.com

Tejas Kumar, host of The Contagious Code podcast and Developer Relations Engineer at DataStax, dives into the revolutionary Model Context Protocol (MCP). He explains how MCP enables seamless communication between AI apps and servers, essentially making AI interactions more intuitive. The conversation highlights real-world use cases like AI managing emails or booking flights. They also tackle the challenge of AI hallucinations and discuss the balance between user convenience and data privacy, envisioning a voice-driven future for online transactions.

In this episode of the Modern Web Podcast, Rob Ocel, Danny Thompson, and Adam Rackis sit down with Ahmad Awais, CEO and founder of LangBase, to talk about agents, context, and the future of AI-assisted software development. Ahmad shares the origin story of Chai.new, an agent that builds agents, and why he believes context, not code, is the true value layer in the AI era. The group unpacks how "vibe coding" is reshaping who can build software, why Chai isn’t just another AI assistant, and how agents might evolve into personalized, production-grade tools for everyone, technical or not. Plus: Tailwind analogies, Stanford lectures, sports nutrition agents, and a CLI that went viral in a hospital.Key points from this episode:- Ahmad Awais explains that AI agents aren't magic; they're just a new paradigm for writing software. What makes them powerful is their ability to act autonomously with relevant context, not just generate text.- Chai.new helps developers (and non-developers) create purpose-built agents without needing deep ML expertise. It abstracts complex concepts like memory, retrieval, and orchestration into an approachable interface.- Ahmad emphasizes that the real opportunity lies in agents tailored to individual users and use cases. Personal agents with custom context outperform generic ones, much like small teams beat massive frameworks for specific problems.- Chai and LangBase aim to bring AI development to the millions of engineers who aren't AI researchers. With tools like Chai, you don’t need a PhD to build powerful, production-ready AI agents.Follow Ahmad Awais on Social MediaTwitter: https://x.com/MrAhmadAwaisLinkedin: https://www.linkedin.com/in/mrahmadawais/Sponsored by This Dot: thisdot.co

In this episode of the Modern Web Podcast, Danny Thompson sits down with Reed Harmeyer, CTO of Skylight Social, and Brandon Mathis, React Native engineer at This Dot Labs. They unpack the technical and strategic decisions behind Skylight’s meteoric growth: why they built on the AT Protocol, how they tackled video discovery and scaling challenges, and how a fast-tracked in-app video editor gave them an edge.Keypoints from this episode:Skylight Social was built on the AT Protocol, allowing users to retain followers across platforms like Blue Sky and enabling creators to publish interoperable content in a decentralized social network.The team used React Native with Expo to achieve rapid development and cross-platform performance—launching a high-quality, TikTok-like video experience in just days.An in-app video editor was prioritized to reduce friction for creators, built using a native SDK wrapped with Expo Modules, enabling features like clip rearranging, overlays, voiceovers, and AI-generated captions.User behavior data—specifically watch time—drives content recommendations, not just likes or follows, helping Skylight offer a personalized experience while navigating scaling challenges from hypergrowth.Follow Reed Harmeyer on Social MediaBluesky: https://bsky.app/profile/reedharmeyer.bsky.socialLinkedin: https://www.linkedin.com/in/reed-harmeyer/

In this episode of the Modern Web Podcast, Rob Ocel and Danny Thompson sit down with Julián Duque, Principal Developer Advocate at Heroku, to talk about Heroku’s evolution into an AI Platform-as-a-Service. Julián breaks down Heroku’s new Managed Inference and Agents (MIA) platform, how they’re supporting Claude, Cohere, and Stable Diffusion, and what makes their developer experience stand out.They also get into Model Context Protocols (MCPs)—what they are, why they matter, and how they’re quickly becoming the USB-C for AI. From internal tooling to agentic infrastructure and secure AI deployments, this episode explores how MCPs, trusted environments, and better AI dev tools are reshaping how we build modern software.Key Points from this episode:- Heroku is evolving into an AI Platform-as-a-Service with its new MIA (Managed Inference and Agents) platform, supporting models like Claude, Cohere, and Stable Diffusion while maintaining a strong developer experience.- MCPs (Model Context Protocols) are becoming a key standard for extending AI capabilities—offering a structured, secure way for LLMs to access tools, run code, and interact with resources.- Heroku's AI agents can perform advanced operations like scaling dynos, analyzing logs, and self-healing failed deployments using grounded MCP integrations tied to the Heroku CLI.- Despite rapid adoption, MCPs still have rough edges—developer experience, tooling, and security protocols are actively improving, and a centralized registry for MCPs is seen as a missing piece.Chapters0:00 – What is MCP and why it matters3:00 – Heroku’s pivot to AI Platform-as-a-Service6:45 – Agentic apps, model hosting, and tool execution10:50 – Why REST isn’t ideal for LLMs14:10 – Developer experience challenges with MCP18:00 – Hosting secure MCPs on Heroku23:00 – Real-world use cases: scaling, healing, recommendations30:00 – Common scaling challenges and hallucination risks34:30 – Testing, security, and architecture tips36:00 – Where to start and final advice on using AI tools effectivelyFollow Julián Duque on Social MediaTwitter/X: https://x.com/julian_duqueLinkedin: https://www.linkedin.com/in/juliandavidduque/Sponsored by This Dot: thisdotlabs.com