How I AI

Brian Scanlan is a senior principal engineer at Intercom, where he’s led the company’s transformation to AI-first engineering. In just nine months, Intercom doubled their R&D throughput while maintaining code quality, with 100% of engineers—plus designers, PMs, and TPMs—now shipping code via Claude Code.

What you’ll learn:
How Intercom doubled their merged PRs per R&D employee in just nine months using Claude Code
The telemetry infrastructure they built to measure AI adoption and quality across hundreds of engineers
Why they built a skills repository with hooks that enforce engineering standards automatically
How they’re preparing their product for an agent-first world with CLIs, MCPs, and ephemeral APIs
The permission and accountability framework that enabled rapid AI adoption
Why backlog zero is now achievable and what that means for engineering culture
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Brought to you by:
Celigo—Intelligent automation built for AI
Cursor—The best way to code with AI
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In this episode, we cover:
(00:00) Introduction to Brian Scanlan
(02:40) Why Intercom went all-in on AI for both product and engineering
(05:01) The breakthrough moment with Opus 4.6 and Christmas break 2025
(07:02) Demo: Intercom’s merged PRs per R&D head
(12:50) Agent-first work as a fundamental reimagining of technical workflows
(14:27) The cost tradeoff: treating AI spend as an investment
(16:47) Measuring quality
(21:22) Demo: Shipping a redirect in the Rails monolith with Claude Code
(24:03) Creating a custom PR skill
(26:33) Building a software factory with predictable quality standards
(30:15) Telemetry infrastructure: Honeycomb for skill usage tracking
(32:10) Session data collection and personalized usage insights
(36:08) Quick overview
(39:20) Walking through Intercom’s skills repository
(42:16) Deep dive: The flaky spec skill and how it reached 100x capability
(46:44) The “and then” workflow for building comprehensive skills
(52:31) The live website and overview of workflows
(53:32) How internal AI experience informs customer product decisions
(56:18) Making SaaS products agent-friendly with CLIs and helpful hints
(01:03:49) Why conversion drop-off is invisible in agent-driven workflows
(01:05:28) Lightning round and final thoughts
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Detailed workflow walkthroughs from this episode:
• How Intercom Doubled Engineering Output: Brian Scanlan's 4 AI Workflows for Claude Code: https://www.chatprd.ai/how-i-ai/how-intercom-doubled-engineering-output-brian-scanlan-ai-workflows-for-claude-code
• Design an Agent-Friendly CLI to Automate SaaS Product Onboarding: https://www.chatprd.ai/how-i-ai/workflows/design-an-agent-friendly-cli-to-automate-saas-product-onboarding
• Build a Self-Improving AI Agent to Automatically Fix Flaky Tests: https://www.chatprd.ai/how-i-ai/workflows/build-a-self-improving-ai-agent-to-automatically-fix-flaky-tests
• Automate High-Quality Pull Request Descriptions with a Custom AI Skill: https://www.chatprd.ai/how-i-ai/workflows/automate-high-quality-pull-request-descriptions-with-a-custom-ai-skill
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Tools referenced:
• Claude Code: https://claude.ai/code
• Cursor: https://cursor.com/
• Honeycomb: https://www.honeycomb.io/
• Snowflake: https://www.snowflake.com/
• Fin AI: https://www.intercom.com/fin
• Vercel: https://vercel.com/
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Other references:
• Intercom GitHub Repo: https://github.com/intercom
• Google API Go Client Repo: https://github.com/googleapis/google-api-go-client
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Where to find Brian Scanlan:
X: https://x.com/brian_scanlan
LinkedIn: https://www.linkedin.com/in/scanlanb/
Company: https://www.intercom.com
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Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
X: https://x.com/clairevo
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

JJ Englert leads community enablement at Tenex. In this episode, JJ provides a complete zero-to-one tutorial on Claude Cowork, Anthropic’s desktop tool that sits between simple chat and full terminal-based coding.

What you’ll learn:
How to create your first Claude Cowork project by connecting a folder on your computer and building context over time
The “brain” file strategy: how to create a preferences document that Claude reads every time to understand who you are and how you work
Why one-click connectors to Gmail, Slack, Notion, and Google Calendar unlock AI that actually does work instead of just suggesting it
How to analyze your sent emails to build a writing skill that perfectly matches your tone and style
The sub-advisory-board technique: spinning up three AI agents with different personas to review your work from multiple perspectives
How to set permissions for each connector so Claude only drafts (never sends) or always asks before taking action
The scheduled-task workflow that creates a morning debrief by reading your email, Slack, and calendar every day at 7:30 a.m.
Why projects with shared memory beat individual chat threads for consistent, high-quality AI outputs
—
Brought to you by:
Tines—Start building intelligent workflows today
Cursor—The best way to code with AI
—
In this episode, we cover:
(00:00) Introduction to JJ Englert
(02:48) What Cowork is and who it’s for
(05:49) Getting started: Opening the Cowork tab in Claude Desktop
(07:04) Understanding projects as folders on your computer
(07:54) Creating your “brain” file, with working preferences and context
(10:24) Demo: Building a daily operating system project from scratch
(12:18) How to prompt Cowork when starting a new project
(14:54) Understanding the project interface and shared memory
(18:37) Setting up connectors to Gmail, Slack, Google Calendar, and other tools
(21:00) Using connectors to analyze your emails and build personalized writing skills
(24:21) Creating a thinking-partner skill for decision support
(26:18) Cowork vs. OpenClaw
(27:18) Building a sub-advisory skill with multiple AI personas for feedback
(34:03) Advanced skill example: Multi-step newsletter creation with research and evaluation
(36:08) Setting up scheduled tasks for morning debriefs
(37:57) Going beyond one-off tasks with AI
(41:00) Progressive trust and the tradeoff of information for productivity
(44:08) Different use cases beyond work productivity
(46:08) Lightning round
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Tools referenced:
• Claude Code: https://claude.ai/code
• Wispr Flow: https://whisperflow.ai/
• Monologue: https://www.monologue.to/
• Domo: https://www.domo.com/
• Pencil.dev: https://pencil.dev/
• Remotion: https://www.remotion.dev/
• Obsidian: https://obsidian.md/
• OpenClaw: https://openclaw.com/
• Notion: https://notion.so/
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Other references:
• Get Started with Claude Cowork: https://support.claude.com/en/articles/13345190-get-started-with-cowork
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Where to find JJ Englert:
YouTube: https://www.youtube.com/channel/UCv2ovDhYVtlJw4QMidLFP8Q
X: https://twitter.com/jjenglert
LinkedIn: https://www.linkedin.com/in/jj-englert-a08836a6/
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Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
X: https://x.com/clairevo
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

Yash Tekriwal is the head of education at Clay. A self-described hyper-optimizer, Yash has built multiple custom productivity applications using Perplexity Computer and OpenClaw to manage his overwhelming daily workflow—including a Slack digest system that categorizes over 150 daily notifications into actionable priorities, and a consolidated news/email/Slack dashboard that serves as his personal command center.

What you’ll learn:
How Yash built a custom Slack digest that categorizes 150+ daily notifications into action-required, need-to-read, and FYI buckets
Why Perplexity Computer beats Claude Code and Codex for building personal productivity apps
His “anti-to-do list” framework: spending an hour daily automating tasks you never want to do again
How to use AI for deterministic tasks (APIs, structured data) vs. subjective tasks (categorization, summarization)
Why the SaaS apocalypse narrative is wrong—and why we’re about to see an explosion of micro-software
How his team uses Perplexity Computer to prototype design systems and communicate with cross-functional partners
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Brought to you by:
Guru—The AI layer of truth
ThoughtSpot—Build AI-powered analytics into your product
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In this episode, we cover:
(00:00) Introduction to Yash
(02:38) The burden of 150 daily Slack notifications
(05:45) When to use AI for tasks vs. building deterministic code
(06:38) Building the Slack digest with OpenClaw
(11:33) Introducing Perplexity Computer and the visual dashboard
(14:28) Three reasons Perplexity Computer beats Claude Code
(16:14) Using connectors to automate meeting follow-ups across Notion and Asana
(18:21) The Kanban-style Slack dashboard
(20:15) The long tail of customer requests and the future of micro-software
(24:09) The anti-to-do list framework
(26:21) Building a consolidated news, email, and Slack digest
(29:48) How Perplexity Computer handles authentication and deployment
(31:46) Team use case: Prototyping persona-based learning journeys for Clay University
(35:49) Lightning round and final thoughts
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Tools referenced:
• Perplexity Computer: https://www.perplexity.ai/computer/new
• OpenClaw: https://openclaw.ai/
• Discord: https://discord.com/
• Claude Code: https://claude.ai/code
• Codex: https://openai.com/codex/
• Asana: https://asana.com/
• Airtable: https://airtable.com/
• Figma: https://www.figma.com/
• Vercel: https://vercel.com/
• ChatGPT: https://chat.openai.com/
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Other references:
• Slack: https://slack.com/
• Notion: https://www.notion.so/
• Superhuman: https://superhuman.com/
• Clay University: https://www.clay.com/university
• Kanban boards: https://en.wikipedia.org/wiki/Kanban_board
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Where to find Yash Tekriwal:
LinkedIn: https://www.linkedin.com/in/yashtekriwal/
X: https://x.com/yash_tek
Company: https://www.clay.com/
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Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
X: https://x.com/clairevo
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

Al Chen is a field engineer at Galileo, an observability platform for AI applications, where he works on the front lines with enterprise customers asking highly technical questions. Despite never having held an engineering role, Al has built a system using Claude Code to query Galileo’s 15 separate repositories, combine that with Confluence documentation and customer-specific quirks, and deliver hyper-personalized answers that would otherwise require constant engineering support.

What you’ll learn:
How to use Claude Code to query multiple repositories simultaneously for customer support
Why code is often a better source of truth than documentation
How to combine repository context with Confluence and Slack using MCPs
The “customer quirks” system that creates hyper-personalized deployment guides
How to build virtuous loops that turn single customer questions into scalable knowledge
Why information organization matters less in the AI era
A simple 16-line script (written by Claude Code) that pulls the latest main branch across all your repositories to keep your context current
How to reduce engineering interruptions to near-zero by empowering customer-facing teams to query the codebase directly
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Brought to you by:
Orkes—The enterprise platform for reliable applications and agentic workflows
Tines—Start building intelligent workflows today
—
In this episode, we cover:
(00:00) Introduction to Al Chen
(02:50) The problem: documentation wasn’t enough
(04:23) Pulling 15 repos into VS Code
(06:03) How Claude Code queries the entire codebase
(08:00) Why current code beats documentation
(08:31) The pull script that keeps everything updated
(09:54) Opening projects at the multi-repo level
(11:40) Live demo: answering deployment questions
(13:25) The customer quirks system
(15:00) Living in chaos: why organization matters less now
(17:03) Competing on customer experience, not just product
(18:20) Should customers be able to query the code directly?
(20:05) Where humans still add value
(25:46) Using AI for reactive Slack support
(29:16) The “and then” workflow discovery
(32:07) Scaling processes across the team
(34:07) Lightning round and final thoughts
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Tools referenced:
• Claude Code: https://claude.ai/code
• VS Code: https://code.visualstudio.com/
• Pylon: https://usepylon.com/
• Confluence: https://www.atlassian.com/software/confluence
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Other references:
• Slack: https://slack.com/
• Kubernetes: https://kubernetes.io/
• Stack Overflow: https://stackoverflow.com/
• Intercom: https://www.intercom.com/
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Where to find Al Chen:
LinkedIn: https://www.linkedin.com/in/thealchen/
Company: https://www.rungalileo.io
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Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
X: https://x.com/clairevo
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

Hilary Gridley is an entrepreneur, former product leader, and new mom who previously appeared on the podcast discussing AI for managers. She returns to share how she's transformed her approach to personal productivity using Claude Code as her primary tool for managing both professional work and life admin. Hilary demonstrates her "anti-system system"—a philosophy that prioritizes simplicity over complex setup, allowing AI to learn preferences through observation rather than upfront configuration.

What you’ll learn:
How to capture to-dos instantly using a simple iPhone back-tap shortcut that requires zero app switching
The “10x impact framework” for deciding what tasks to automate versus where to invest your human effort
How to use Claude Code’s observation capabilities to build a preference file that improves over time without manual setup
Why the “yappers API” (talking about what you’re doing while working) eliminates the need for complex OAuth integrations
A workflow for breaking down overwhelming tasks into 10-minute first steps that actually get completed
How to create Claude Skills by simply describing problems rather than writing code or following tutorials
Techniques for using “recording mode” to demo workflows without exposing personal information
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Brought to you by:
WorkOS—Make your app Enterprise Ready today
Lovable—Build apps by simply chatting with AI
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In this episode, we cover:
(00:00) Introduction to Hilary Gridley
(02:43) The opportunity cost of time as a new mom and entrepreneur
(07:11) Philosophy of the anti-system system
(08:05) Demo: Planning your day with Claude Code
(10:00) Setting up simple iPhone shortcuts for task capture
(11:48) How Claude organizes reminders and learns preferences automatically
(16:19) Breaking down overwhelming tasks into manageable first steps
(23:40) The yappers API: talking to Claude instead of building integrations
(25:28) Daily logging and observation patterns
(27:45) Quick summary
(30:50) The power of screenshots
(32:55) 10x impact framework for automation decisions
(37:51) Applying the framework to different career stages
(39:29) Building a “recording on” skill for anonymizing demos
(44:11) Building a returns tracking skill from scratch
(48:31) Building the muscle memory to reach for AI tools
(50:18) Where to find Hilary
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Tools referenced:
• Claude Code: https://claude.ai/code
• Obsidian: https://obsidian.md/
• iPhone Shortcuts: https://support.apple.com/guide/shortcuts/welcome/ios
• Cursor: https://cursor.sh/
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Other references:
• Figma file Hilary demo’ed: https://www.writerbuilder.com/howiai
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Where to find Hilary Gridley:
Substack: https://hills.substack.com/
Website: https://writerbuilder.com
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Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
X: https://x.com/clairevo
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].