Dev Interrupted

Before anyone reimagines the software lifecycle for agents, someone has to secure the place all that code lands. Entire CEO Thomas Dohmke, formerly CEO of GitHub, and Apiiro co-founder and CEO Idan Plotnik see the same problem from opposite ends of the pipeline: agents now write, review, test and deploy the code, and nobody reads it.
This week on Dev Interrupted, they join Andrew to argue that the fix is context, injected before the prompt through hooks and captured afterward as session logs committed next to the code. Idan puts numbers on the perfect storm of agents shipping more code as Mythos scans surface 10x the vulnerabilities, while Thomas makes the case that code is the what and the session log is the why. They close the conversation on why every pull request needs a business owner, not just a code owner. 
Save your seat: Sustaining engineering health through your AI transformation
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Entire: https://entire.io/
Entire CLI (open source): https://github.com/entireio/cli
Apiiro: https://apiiro.com/
Apiiro Guardian Agent: https://apiiro.com/blog/introducing-apiiro-guardian-agent/
JPMorgan's Patchmageddon report: https://privatebank.jpmorgan.com/nam/en/insights/latest-and-featured/eotm/patchmageddon
Connect with Thomas: LinkedIn | Twitter/X
Connect with Idan: LinkedIn
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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
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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

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Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era.
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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.

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
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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.

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
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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.

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 
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Follow the hosts:
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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.