Modern Web

• Why Prompt Engineering Skills Matter More than Your AI Model with Melkey Dev

  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

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• What is Agent Experience (AX)? + Scalable AI Agent Orchestration

  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

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• The State of Authentication: The Future is BUNDLED!

  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

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• How MCP is Changing AI App Building

  On this episode of the Modern Web Podcast, hosts Rob Ocel, Danny Thompson, and Adam Rackis are joined by Tejas Kumar, host of The Contagious Code podcast, author of Fluent React, and Developer Relations Engineer for Generative AI at DataStax. They unpack the current wave of AI announcements from Google I/O and Microsoft Build, and zoom in on the significance of MCP (Model Context Protocol) as a foundational shift in how AI-powered apps will be built and used.Tejas breaks down what MCP is, why it's catching on across the industry, and how it could become the HTTP of AI apps. The group explores real-world examples, like AI apps managing your inbox or booking flights without ever opening a browser, and discuss how MCP servers enable secure, agent-driven experiences that can act on your behalf. They also touch on hallucinations, the role of fine-tuning vs. tool integration, and the future of checkout flows powered by AI agents.Keypoints from this Episode:- MCP enables structured communication between AI apps and servers, allowing agents to perform real tasks like sending emails or booking flights- Users will increasingly interact with applications through natural language, with agents handling workflows behind the scenes- Connecting models to tools via MCP helps reduce hallucinations by ensuring actions and responses are grounded in real data- Most use cases benefit more from retrieval-augmented generation and strong tool integration than from expensive model fine-tuningFollow Tejas on Social MediaTwitter: https://x.com/TejasKumar_Linkedin: https://www.linkedin.com/in/tejasq/

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• Building AI Agents That Build AI Agents: Inside Chai.new

  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

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