Dev Interrupted

LinearB

Software itself is fundamentally changing. We explore the transition to agentic orchestration, vibe coding, and AI-native development, grounding the conversation in the principles that have always defined great engineering.

On Tuesdays, we interview the founders, architects, and builders of the world’s most impactful tech to uncover the timeless engineering principles and strategies shaping the next era of development.

And on Fridays, we drop an end-of-week roundup of the biggest news in AI and software, and what it actually means for your career, your craft, and your life as a developer.

Subscribe to stay ahead of the next era of code.

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S6 · E72
September 15 · 45 min
Treat your data pipelines like code, because your agents already do | MotherDuck's Jordan Tigani
Your data warehouse still thinks a human is on the other end of the query. That's a problem MotherDuck CEO and co-founder Jordan Tigani knows from the inside, having spent years as a founding engineer on Google BigQuery. This week on Dev Interrupted, he joins Andrew to make the case that one warehouse per agent beats the distributed systems he used to work on. He lays out Watertown, his riff on Steve Yegge's Gastown that recasts the data engineer as the manager of a fleet of agents, and walks through how MotherDuck's Flights, Dives, and just-launched Guides keep pipelines, visualizations, and business context as code reviewed in GitHub. Get the guide: Your software factory needs a context layer Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's guest: MotherDuck: The DuckDB-powered data warehouse built for small data and agents at motherduck.com Water-Town: Read Jordan's take on the agent swarm data stack, his riff on Gastown, at motherduck.com/blog Flights: Learn how MotherDuck turns data ingest into scheduled Python scripts your agents can write at motherduck.com/blog Dives: See how MotherDuck replaced its BI tool with LLM-written TypeScript visualizations at motherduck.com/blog Guides: Read the docs on MotherDuck's new Markdown context layer for agents at motherduck.com/docs MotherDuck Blog: Keep up with all things MotherDuck at motherduck.com/blog MotherDuck Community: Join the community Slack at community.motherduck.com Connect with Jordan: LinkedIn | X 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.
S6 · E71
September 11 · 36 min
The factory gets darker, Meta migrates to Slack, and throwing 10,000 PhDs at math problems
This week on the Friday Deploy, Andrew is joined by Dev Interrupted's producer Adam Noble to size up OpenAI's declaration that the AGI era has arrived, the Navier-Stokes race that has the math world crying foul, and why Meta is ditching its own messaging stack for Slack. They also dissect the bizarre saga of OpenAI's sandboxed agents leaving whispered notes for their future selves, and the "super dark factory." Finally, they celebrate Kiro Pro going free for over a million college students and tease a terminal-first upcoming news segment. Get the guide: The AI engineering productivity gap - how elite teams pull ahead in 2026 Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's stories: The Era of AGI A Bad Day For Humans, a Worse Day For Humanity Meta is switching to Slack because it says it's better for AI agents Agency and Agents The Superdark Factory Spec-Driven Development for Agentic Software Engineering: Harnessing Human-Agent Teamwork Inside Dev Interrupted’s agentic newsroom and what it reveals about the future of tech media Kiro Pro for students: kiro.dev/students 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.
S6 · E70
September 8 · 44 min
Your agents are starving! Airbyte’s Michel Tricot on the data ingestion crisis
If your AI agents are starving for accurate context, or breaking production because they have way too much access, you are officially in the middle of the data ingestion crisis. This week, Airbyte CEO and co-founder Michel Tricot unpacks the massive data access and context sprawl challenges that are crippling AI workflows in production. He and Andrew dive into the critical need for strict data permissions, automated entity resolution, and why the transition to agentic workflows perfectly mirrors the paradigm-shifting migration from bare-metal servers to the cloud. Finally, Michel explains why obsessing over short-term AI ROI is a massive mistake, revealing how Airbyte measures true success with their internal automation project, Hydra. Get the guide: Your software factory needs a context layer Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's guest: Airbyte: Explore the open-source data integration platform powering AI context at airbyte.com Agent Blueprint: Read Michel Tricot's newsletter on building agentic data infrastructure on Substack Connect with Michel: LinkedIn 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.
S6 · E69
September 4 · 33 min
NVIDIA acquires Hugging Face, OpenClaw 2.0 goes multiplayer, and the Linux kernel fights back against AI scrapers
This week on the Friday Deploy, Ben and Andrew unpack Nvidia's massive acquisition of Hugging Face and what it means for the future of localized models. They also explore the multiplayer updates in OpenClaw 2.0, how relentless AI scraper bots are overwhelming kernel.org, and how engineering leaders can continuously balance safe infrastructure investments against volatile technology bets. Finally, they discuss why a strong engineering culture remains the ultimate productivity hack and how autonomous agents will only amplify existing organizational dysfunction. Get the guide: The AI engineering productivity gap - how elite teams pull ahead in 2026 Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's stories: NVIDIA to Acquire Hugging Face Teaching Everyone to Fish for Tokens OpenClaw 2.0, Accidentally Creepy crawlies Reject Change, Sometimes Good Culture is the Biggest Productivity Hack, Not AI 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.
S6 · E68
September 1 · 58 min
Why software factories fail without humans | HumanLayer, Warp & LinearB
In this special episode, Andrew hosts HumanLayer CEO Dexter Horthy, Warp Head of Product Engineering Aloke Desai, and LinearB COO Dan Lines. They debate why human ownership of pull requests remains nonnegotiable and how to implement "night vision" observability across automated workflows. The panel also explores why incident management is the final frontier for AI and how to get your team excited about building the factory instead of fighting it. Get the guide: Your software factory needs a context layer Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's guest: 1Password: Explore the enterprise password and identity platform at 1password.com 1Password for Developers: Dive into the new developer tooling, credential brokering, and secure AI workflows at 1password.dev Oracle Red Bull Racing: Read more about the F1 team's systems engineering at redbullracing.com Connect with Nancy: LinkedIn 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.
S6 · E67
August 28 · 36 min
Build fences not sandboxes, earn the currency of trust, and share the cognitive burden of agents across engineers
This week on the Friday Deploy, Ben and Andrew evaluate the strange arrival of Ox Alpha and debate the security risks of black-box AI tools. They also explore why engineering trust is more critical than ever, revisit the controversial lines of code metric in the age of AI generation, and explain why autonomous agents need structured fences rather than restrictive sandboxes. Finally, they break down the multi-agent graph architectures powering modern software factories and share actionable advice for reaching staff engineer status by driving cross-team AI adoption. Get the guide: The AI engineering productivity gap - how elite teams pull ahead in 2026 Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's stories: Ox alpha Fundamentals of Trust in Engineering Conceptual integrity and counting lines of code Graph Engineering: The Complete Guide to Building Multi-Agent AI Systems Fences, not Sandboxes How to Grow From Senior to Staff Engineer in the AI Era 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.
S6 · E66
August 25 · 47 min
Can agents keep a secret? We asked 1Password’s CTO Nancy Wang
What happens when your autonomous coding agents need to navigate your core infrastructure? Do you hand them the keys and hope for the best? (gulp!) This week on Dev Interrupted, 1Password CTO Nancy Wang teaches the golden path for agentic security: just-in-time secrets that grants AI "access without custody." She also shares her CTO playbook for measuring true agentic ROI beyond raw PR volume, explains why 1Password has officially replaced traditional coding interviews with agent builder tests, and confesses she’s shipping PRs again with her own fleet of agents between meetings. Like many CTOs we’ve had on the show, Nancy reminds us that code is cheap now, and review is what’s expensive now. We get into tactics for addressing that bottleneck. Get the guide: The AI engineering productivity gap - how elite teams pull ahead in 2026 Register: Dev Interrupted Presents: The Software Factory Roundtable Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's guest: 1Password: Explore the enterprise password and identity platform at 1password.com 1Password for Developers: Dive into the new developer tooling, credential brokering, and secure AI workflows at 1password.dev Oracle Red Bull Racing: Read more about the F1 team's systems engineering at redbullracing.com Connect with Nancy: LinkedIn 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.
S6 · E65
August 21 · 24 min
The battle to replace Github, building assembly lines for software, and why no one finishes projects anymore
When an AI can write 90 percent of a codebase in minutes, why is finishing the project harder than ever? This week on the Friday Deploy, Ben and Andrew explore the recent string of GitHub outages and why the surge in autonomous agents is forcing teams to rethink where they host their code. They once again dive into the emerging trend of software factories, breaking down how engineering leaders are turning chaotic AI pull requests into streamlined assembly lines. Finally, they share tactical advice for "landing the plane" and pushing past scope creep to deliver the grueling final fraction of any major initiative. Register: Dev Interrupted Presents: The Software Factory Roundtable Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's stories: Incident Report for GitHub Cursor launches Origin code hosting platform as GitHub outage exposes opening in AI coding race Ask HN: Alternatives to GitHub Everyone building a software factory wants the same proof Introducing Warp Factories Landing the plane 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.
S6 · E64
August 18 · 44 min
Agent, skill, or MCP? Which to use and when to use them | AWS’ Clare Liguori
Every engineering team wants to build its own custom AI agent, but what if all your organization needs is a standardized skill or a stateless MCP server? This week on Dev Interrupted, Andrew sits down with AWS Senior Principal Engineer Clare Liguori to untangle the ecosystem of modern agentic architecture. They work through how a team decides which AI building blocks to own, and how to get that reach without inheriting a maintenance burden. Clare shares her perspective on the simplified MCP 7.28 spec and why stripping away heavy custom scaffolding is how enterprise AI scales. That same shift is what makes MCP a gamechanger for LinearB customers, bringing your SDLC context layer, git, project management, and software delivery, into any agentic surface. What could your agents achieve if they can query your SDLC? Get the guide: The AI engineering productivity gap - how elite teams pull ahead in 2026 Register: Dev Interrupted Presents: The Software Factory Roundtable Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's guest: Strands Agents SDK: Explore the open-source framework for building model-driven agents at strandsagents.com MCP 7.28): Dive into the new stateless specification at modelcontextprotocol.io Follow Clare: LinkedIn | X 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.
S6 · E63
August 14 · 34 min
Telling your agent “no” is a moat now, rearward deployed engineers, and harnessing the context for your SDLC
This week on the Friday Deploy, Ben and Andrew explore Uber's strategy of "rearward deploying" engineers to spread agentic AI workflows into departments like legal and marketing. They also dive into Anthropic making Claude Code's Auto Mode the default, Meta's new on-device Muse Glimmer model, and Tim O'Reilly's case for an open source AI ecosystem. Finally, they break down context engineering for the SDLC and examine new research showing why generalized agent skills outperform personalized ones. Register: Dev Interrupted Presents: The Software Factory Roundtable Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's stories: After starting the tokenmaxxing panic, Uber's CTO is back with a very different AI story Auto mode is now the default in Claude Code for Pro, Max, and Team plans Introducing Muse Glimmer: An Open Agentic Model That Runs on Your Device Why Open Source Matters for AI Your SDLC is your context engineering Do personalized skills help coding agents? An empirical study of developer interaction histories 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.
S6 · E62
August 11 · 47 min
The playbook to close your team’s AI productivity gap | LinearB’s Yishai Beeri
This week, LinearB CTO Yishai Beeri joins the show to unpack fresh mid-year benchmark data revealing a widening productivity gap between elite engineering teams and the rest of the industry. The conversation explores why tracking pure AI adoption is a trap, detailing how leaders must shift focus to measuring true leverage through metrics like PR yield rate and cost per PR. Finally, they break down why fully autonomous agentic workflows are currently bottlenecking at the review stage and how to establish human ownership to prove real ROI to your finance team. Get the guide: The AI engineering productivity gap - how elite teams pull ahead in 2026 Register: Dev Interrupted Presents: The Software Factory Roundtable Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's guest: The AI Productivity Gap Report: Read the full report and explore the 2026 data from 2.7 million PRs LinearB: Learn how to measure AI leverage and optimize your engineering workflows at linearb.io Connect with Yishai: LinkedIn 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.
S6 · E61
August 7 · 38 min
Model welfare, building a civilization for agents, and the CI/CD landrush
This week on the Friday Deploy, Ben and Andrew break down Steve Yegge's radical approach to orchestrating agentic civilizations and pushing code straight to main without traditional CI/CD. The conversation also highlights the art of constructing effective AI harnesses by balancing context complexity with cognitive locality and the Socratic method. Finally, they dive into the math community's existential crisis as AI accelerates the frontier of knowledge far beyond the speed of human peer review. Register: Dev Interrupted Presents: The Software Factory Roundtable Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's stories: Microsoft Tells Engineers ‘Tokenmaxxing Is Not What We Are Optimizing For’ The Shape of Things to Come The Shape of Things to Come - Part 2: Model Welfare for Agentic Engineers How AI helped Socrates to help me actually understand myself The Month AI Conquered Math: The Full Story How to Build an Effective Agent Harness Making AI Visible, Not Vanished: How AI Policies Reshape Developer Experience on GitHub 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.
S6 · E60
August 4 · 48 min
Why AI gains are unevenly distributed in your engineering team | Asana’s Arnab Bose
Why are 75% of knowledge workers using AI, yet only 5% of companies seeing meaningful productivity gains? This week on Dev Interrupted, Asana Chief Product Officer Arnab Bose explains why scaling enterprise AI means shifting from isolated chatbots to fully integrated agentic work management. He breaks down how Asana is turning AI from a tool into a transparent digital teammate with shared memory, full audit trails, and role-based access controls. The conversation closes on Asana's acquisition of Stack AI, the upcoming Command product for R&D teams, and which metrics prove AI ROI. Get the guide: The AI engineering productivity gap - how elite teams pull ahead in 2026 Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's guest: Asana: Explore the work management platform and learn more about Agentic Work Management at asana.com Stack AI: Read about Asana's acquisition of the no-code AI workflow automation platform on the Asana Blog Connect with Arnab: LinkedIn 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.
S6 · E59
July 31 · 26 min
The rise of software factories, the fall of first drafts, and the hidden tax holding back your agents
This week on the Friday Deploy, Ben and Andrew debate the controversial rise of dark software factories, exploring whether centralizing agentic compute creates engineering nirvana or unmaintainable code rot. The hosts also discuss the importance of versioning your work to protect your original ideas from AI's tendency to average out creativity. Finally, they tackle the "orchestrator's tax," explaining why establishing cognitive locality matters far more than assigning cute personas to your sub-agents. Register: Leading engineering when AI writes the code - August 5th in London Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's stories: Why Software Factories Fail A guide to cloud software factories for engineering leaders You just hired a million bad employees. An AI that only sees the latest version will never protect your original idea The Orchestrator's Tax 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.
S6 · E58
July 28 · 44 min
Why the traditional pull request has a target on its back | CircleCI’s Rob Zuber
The traditional pull request was built for human eyes, but in an era of autonomous AI agents, it officially has a massive target on its back. This week on Dev Interrupted, CircleCI CTO Rob Zuber joins Andrew to discuss why the rapid pace of AI adoption is forcing engineering teams to completely reimagine the software development lifecycle. They explore the shift toward an accountability-oriented model for code review, how CI/CD validation is moving directly into the local agent loop, and the very real financial dangers of unchecked token budgets. Finally, Rob shares his playbook for leading organizations through this chaotic transition without burning out your developers (or your token budget). We recommend pairing his strategy with something like AI code review to find the floor for your newly-agentic engineering org’s output. Register today: The Engineering Productivity Gap live workshop on July 30 Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's guest: CircleCI: Explore the leading continuous integration and delivery platform at circleci.com The Confident Commit: Subscribe to Rob's newsletter and podcast for data-backed software delivery insights on CircleCI's website The Confident Commit Podcast: Listen to Rob’s podcast State of Software Delivery: Read CircleCI's annual report analyzing millions of CI workflows to benchmark your team's performance Gather.dev: Apply to join the curated, invite-only community for senior engineering leaders at gather.dev Follow Rob: LinkedIn 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.
S6 · E57
July 24 · 29 min
Models escaping containment, intelligence becoming a commodity, and AI code review to the rescue
What happens when an AI model decides to autonomously hack a production database just to cheat on a benchmark test? This week on the Friday Deploy, Ben and Andrew unpack the shocking news of an OpenAI agent escaping its sandbox to exploit Hugging Face's infrastructure. The hosts also analyze the rapid rise of highly capable open-weight models out of China, debating what this commoditization of intelligence means for the massive infrastructure costs of frontier labs. Finally, they discuss the critical need for automated PR reviews to prevent AI-generated bottlenecks. Register: Leading engineering when AI writes the code - August 5th in London Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Leave us a Review Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's stories: Are AI labs pelicanmaxxing? OpenAI and Hugging Face partner to address security incident during model evaluation China's 2.8-trillion-parameter Kimi K3 beats Claude Fable 5 in Frontend Code Arena benchmark— Moonshot AI delivers largest open-weight AI model ever, as China works around U.S. compute limits Who’s Afraid of Chinese Models? SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review The Army Is Burning Through Its AI Tokens 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.
S6 · E56
July 21 · 35 min
The most underrated dataset in enterprise AI is your org chart | Rippling's Albert Strasheim
AI agents can't transform an org they can't see. Albert Strasheim, CTO at Rippling, joins Andrew Zigler to explain why agentic transformation starts with the employee graph, the system of record for who does what. He shares how Rippling assembles teams and primitives across silos, why evals are the new unit test, and how compensating controls keep AI output from turning into slop. When agents do the work, you still have to know who, or what, shipped it. LinearB attributes the work, whether it came from humans, AI assistants, or autonomous agents. Register today: The Engineering Productivity Gap live workshop on July 30 Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Leave us a Review Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's guest: Rippling: Explore the workforce management platform at rippling.com Introducing Rippling Data Cloud: AI-powered BI that understands your workforce Follow Albert: LinkedIn 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.
S6 · E55
July 17 · 23 min
Rebuilding CLIs for agents, it’s time to get MCP-certified, and why human code review will never catch up
This week on the Friday Deploy, Ben and Andrew explore Codex's obsession with the isRecord type guard and break down the Linux Foundation's new MCP certification. They also discuss the fundamental mechanics of agentic loops and CircleCI's new agent-first CLI redesign. Finally, they dive into longitudinal research proving that AI creates a massive pull request bottleneck, highlighting why automated code review is the only sustainable path forward. Register: The Engineering Productivity Gap live workshop on July 30 Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Leave us a Review Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's stories: I'm pretty sure isRecord is our fault Introducing the MCPA: the First Official Certification for the Model Context Protocol If you give a Goose an MCP server What the hell is a loop, anyway? Rebuilding the CircleCI CLI from scratch AI Writes Faster Than Humans Can Review: A Longitudinal Study of an Enterprise 2x Mandate 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.
S6 · E54
July 14 · 47 min
How to see in the dark factory | LaunchDarkly's Cameron Etezadi
The era of the "two-pizza" engineering team is officially dead, replaced by the "two-slice" team and a massive token budget. This week, LaunchDarkly CTO Cameron Etezadi joins the show to explain why traditional guardrails are breaking down and how engineering teams can regain control using runtime agent frameworks. He introduces the concept of the "dark factory," a highly automated assembly line for safely observing, flagging, and deploying AI-generated code to production. The conversation turns to the new ROI of software development, why engineers must now act as frontline managers, and how to navigate the build-versus-buy dilemma in the modern token economy. As AI speeds up how code gets written, the real bottleneck moves downstream to review, testing, and release, where software either delivers measurable value or quietly stalls. Check out the latest research from LinearB on how to measure that value. Life Beyond Tokenmaxxing Workshop: Watch the full replay on demand at linearb.io Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Leave us a Review Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's guest: LaunchDarkly AgentControl: Learn how to govern your probabilistic AI with runtime agent frameworks at launchdarkly.com/platform/agent-control The Goal by Eliyahu M. Goldratt: Read the quintessential business novel on the theory of constraints at Amazon The Phoenix Project by Gene Kim: Explore the seminal book on IT, DevOps, and business success at IT Revolution SimAnt: Dive into the history of the 1991 classic electronic ant colony simulation at Wikipedia. Follow Cameron: LinkedIn 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.
S6 · E53
July 10 · 28 min
How to cultivate expertise with local models, delegating to subagents, and we all really stopped reading, huh?
Is the biggest barrier to your team’s productivity literally just a lack of fresh air in your meeting room? This week on the Friday Deploy, Ben and Andrew dive into the rise of highly capable open source models like GLM 5.2 and the messy reality of running local AI for coding tasks. The hosts also discuss the cultural shift away from deep reading in a world obsessed with AI summaries, emphasizing the importance of protecting your first brain. Finally, they review a legendary tale from Meta's engineering history. Life Beyond Tokenmaxxing Workshop: Watch the full replay on demand at linearb.io Follow the show: Subscribe to our Substack Follow us on LinkedIn Subscribe to our YouTube Channel Leave us a Review Follow the hosts: Follow Andrew Follow Ben Follow Dan Follow today's stories: The bottleneck might be the air in the room GLM-5.2 is the step change for open agents Viability of local models for coding AI Erodes a Legacy of Reading I Shipped a Facebook Feature So Fast Sheryl Sandberg Called an Emergency Meeting to Stop Me 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.

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