Modern Web

Rizel Scarlett, principal developer advocate at Entire and former Goose maintainer, joins Rob Ocel to explore the missing context layer in AI-assisted software development. They unpack why session history, prompts, tool calls, and decision-making need to stay connected to code; how Entire captures that context through Git-based checkpoints; and why AI should be used as a learning partner, not just a code generator. The conversation also covers code-review overload, agent-friendly workflows, and a more sustainable path to adopting AI. 5 Key Takeaways - AI-generated code makes context and intent more valuable: teams need a clear record of the decisions and experiments that led to a change. - Entire uses agent hooks and Git-based checkpoints to capture prompts, tool activity, and session context without cluttering the primary codebase. - AI can be a powerful learning tool: it gives developers a judgment-free way to ask questions, understand unfamiliar code, and build deeper technical intuition. - Traditional PR descriptions and diff-only reviews struggle with agent-produced changes; reviews need to connect customer pain, intent, design, and implementation. - Sustainable AI adoption beats chasing every trend: focused, incremental improvements can deliver real value without the cost and stress of full agent autonomy. Chapters 00:00 — Why AI code needs durable context 04:12 — The documentation and code-review problem 10:11 — AI as a learning tool 13:42 — How Entire captures agent sessions through Git 26:14 — Session handoffs, reusable skills, and better reviews 32:39 — The future of code forges and sustainable AI adoption 41:37 — Where to find Rizel and Entire Rob Ocel on Linkedin: https://www.linkedin.com/in/robocel/ Rizel Scarlett on Linkedin: https://www.linkedin.com/in/rizel-bobb-semple/ This Dot Labs Twitter: https://x.com/ThisDotLabs This Dot Media Twitter: https://x.com/ThisDotMedia This Dot Labs Instagram: https://www.instagram.com/thisdotlabs/ This Dot Labs Facebook: https://www.facebook.com/thisdot/ Sponsored by This Dot: https://www.thisdot.co/

AI models are becoming more capable, but reliability remains one of the biggest challenges. In this episode of the Modern Web Podcast, Rob Ocel and Brandon Mathis are joined by Andrew Baker, Founder & CEO of Balon.ai, and Arif Hosein, COO of Balon.ai, to discuss why AI hallucinations persist and what it will take to reduce them. The conversation explores Balon.ai's Deep Fusion architecture, which combines multiple AI models through a consensus-driven approach to improve accuracy without sacrificing performance. They also discuss why larger models alone won't solve reliability, the tradeoffs between cost and quality, and what engineering teams should consider as AI moves deeper into production systems. Chapters 00:00 Introduction & Meet the Guests 01:20 The AI Hallucination Problem 04:32 What Is an AI Hallucination? 08:22 Sponsor Message 08:47 Are Hallucinations Getting Better? 12:53 Balancing Accuracy, Agency & Emergent Behavior 14:07 Deep Fusion: Balin AI's Multi-Model Architecture 17:20 How Consensus Works Across Multiple Models 20:20 Simultaneous Peer Review for AI 22:34 Why Multiple Imperfect Models Can Produce Better Results 27:58 Can Deep Fusion Fix Bad Prompts? 30:41 The Cost of Multi-Model AI 34:21 AI ROI, Vibe Coding & Enterprise Reality 37:56 How to Try Balin AI 39:37 Final Thoughts & Outro Rob Ocel on Linkedin: https://www.linkedin.com/in/robocel/ Brandon Mathis on Linkedin: https://www.linkedin.com/in/mathisbrandon/ Andrew Baker on Linkedin: https://www.linkedin.com/in/andrewbakeratl/ Arif Hosein on Linkedin: https://www.linkedin.com/in/arif-hosein/ This Dot Labs Twitter: https://x.com/ThisDotLabs This Dot Media Twitter: https://x.com/ThisDotMedia This Dot Labs Instagram: https://www.instagram.com/thisdotlabs/ This Dot Labs Facebook: https://www.facebook.com/thisdot/ Sponsored by This Dot Labs: https://www.thisdot.co/

Rob Ocel sits down with Minko Gechev to unpack one of the fastest-evolving areas in AI-assisted development: skills, agent workflows, and the growing complexity of AI harnesses.Explore AI skills, how they differ from rules and MCPs, and why developers are increasingly treating skills as structured, executable instructions rather than traditional documentation. Minko explains how modern agents operate under the hood, how skills are lazy-loaded into context windows, and why poorly designed skills can create context rot, unnecessary token usage, and degraded performance over time. Rob and Minko also dig into one of the most important but misunderstood topics in AI engineering today: evals. They break down how teams can evaluate AI workflows, measure pass rates, test deterministic versus non-deterministic outputs, and optimize skills for reliability, token efficiency, and execution quality across evolving models and harnesses. Also learn about prompt structure, workflow orchestration, context compression, state-machine agents, React loops, and why many teams are accumulating rules and skills faster than they are pruning them. What you will learn: • Learn how engineering teams are moving AI beyond isolated experiments into repeatable workflows • Explore strategies for improving reliability, testing, and validation in AI-assisted development • Hear practical approaches to scaling AI adoption across engineering organizations • Understand how leaders are thinking about automation, CI/CD, and operational maturity in the age of AI • Connect with technology leaders discussing the challenges and opportunities shaping modern software delivery Rob Ocel on Linkedin: https://www.linkedin.com/in/robocel/ Minko Gechev on Linkedin: https://www.linkedin.com/in/mgechev/ This Dot Labs Twitter: https://x.com/ThisDotLabs This Dot Media Twitter: https://x.com/ThisDotMedia This Dot Labs Instagram: https://www.instagram.com/thisdotlabs/ This Dot Labs Facebook: https://www.facebook.com/thisdot/ Sponsored by This Dot Labs: https://www.thisdot.co/

In this episode of the Modern Web Podcast, Rob Ocel talks with PlanetScale CEO Sam Lambert about what “database scale” actually looks like in 2026. Sam shares migration stories from companies that moved at the perfect time and others that waited until they were already in trouble, plus why sharding and reliability are never just “magic” if your queries and data model are a mess. They also cover PlanetScale’s evolution beyond its MySQL and Vitess roots into Postgres, Metal, and what’s coming next for sharding in the Postgres world. Along the way, they connect the dots to AI workloads, which are increasingly write heavy and put new pressure on performance, uptime, and security. What You'll Learn: - How to spot the “right time” to migrate databases before you’re on fire (and what happens when you wait too long) - What PlanetScale actually gives you “for free” at scale, and what it can’t fix (bad schema, missing indexes, terrible SQL) - Why “auto” database magic is usually a tradeoff, and what to ask for when you want to peek behind the curtain - What PlanetScale is becoming beyond MySQL/Vitess, including Postgres, Metal, and Nikky (sharding for Postgres) - How AI workloads are changing database patterns, especially the shift toward write heavy systems and why that pressures reliability and security Sam Lambert on Linkedin: https://www.linkedin.com/in/isamlambert/ Rob Ocel on Linkedin: https://www.linkedin.com/in/robocel/ This Dot Labs Twitter: https://x.com/ThisDotLabs This Dot Media Twitter: https://x.com/ThisDotMedia This Dot Labs Instagram: https://www.instagram.com/thisdotlabs/ This Dot Labs Facebook: https://www.facebook.com/thisdot/ Sponsored by This Dot Labs: https://www.thisdot.co/

Environment variables and secrets are usually a mess: out of sync .env files, scattered API keys, painful onboarding, and brittle CI configs. In this episode of the Modern Web Podcast, Rob Ocel talks with Varlock co-creators Phil Miller and Theo Ephraim about how Varlock turns .env files into a real schema with types, validation, and documentation, pulls secrets from tools like 1Password and other backends, and centralizes configuration across environments and services. They also dig into protecting secrets in an AI-heavy world by redacting them from logs and responses, preventing accidental leaks from agents, and pushing toward an open env-spec standard so configuration becomes predictable, portable, and actually pleasant to work with. What you will learn: - Why traditional .env files and copy paste workflows break down as teams, services, and environments grow. - How Varlock turns environment variables into a schema with types, validation, documentation, and generated TypeScript.- How to pull secrets from tools like 1Password and other backends without leaving them in plain text or scattering them across dashboards. - How to manage multiple environments such as development, staging, and production from a single, declarative configuration source. - How Varlock helps protect secrets in AI and MCP workflows by redacting them from logs and responses and blocking accidental leaks. - What the env spec standard is and how a common schema format can make configuration more portable across tools, templates, and platforms. Theo Ephraim on Linkedin: https://www.linkedin.com/in/theo-ephraim/ Phil Miller on Linkedin: https://www.linkedin.com/in/themillman/ Rob Ocel on Linkedin: https://www.linkedin.com/in/robocel/ This Dot Labs Twitter: https://x.com/ThisDotLabs This Dot Media Twitter: https://x.com/ThisDotMedia This 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.social Sponsored by This Dot Labs: https://ai.thisdot.co/