Avsnitt
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Alex Finn is an AI builder, YouTuber, and the creator of Vibe Code Academy, a community for people learning to build with AI tools. He runs one of the most ambitious local AI setups I’ve come across: three Mac Studio 512 GB machines, a DGX Spark, and a custom RTX 5090 build, all coordinated through a fleet dashboard he built himself. He’s spent five months figuring out which local models belong on which machines, how to wire them to Claude Code loops, and how to get a software factory running without babysitting it.
How Alex chose between a Mac Studio (512 GB unified memory), DGX Spark, and RTX 5090, and what each is actually good forWhy Tailscale is worth installing even on a single machine, and how it lets one agent manage your entire hardware fleetHow the build loop and review loop in Claude Code workHow to allocate tasks by machine and modelWhy unlimited local inference changes the use-case math in a way a $20 cloud subscription never canWhat OpenClaw and Hermes are each best suited for, and why Alex runs five agents total with failover baked in
What you’ll learn:—
Brought to you by:
Runway—The creative AI platform for images, video, and more
Jira Product Discovery—Prioritize with insights, build with confidence
—
In this episode, we cover:
(00:00) Intro
(02:58) Alex's hardware stack
(03:48) What "ambient AI" means
(04:15) Alex's red-pill moment with OpenClaw
(07:04) Mac Studio vs. DGX Spark vs. RTX 5090
(13:24) How to set up local models with no technical knowledge (Tailscale + OpenClaw/Hermes)
(17:16) Fleet control dashboard: assigning 24/7 tasks across machines
(20:42) Local models as security scanners feeding Claude Code
(22:25) How Alex allocates GLM 5.2, Qwen 3.6, and Ornith 1.0 by task
(24:28) OpenClaw vs. Hermes: the honest comparison
(26:55) The software factory: build loop, review loop, rocket emoji
(31:55) Lightning round: favorite hardware, favorite model, prompting style
(34:46) Where to find Alex
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Tools referenced:
• Claude Code: https://claude.ai/code
• OpenClaw: https://openclaw.ai/
• Hermes: https://hermes-agent.nousresearch.com/
• Tailscale: https://tailscale.com/
• Codex (OpenAI): https://openai.com/codex
• GLM 5.2 (z.ai): https://huggingface.co/zai-org/GLM-5.2
• Qwen 3.6 (Alibaba): https://huggingface.co/Qwen/Qwen3.6-35B-A3B
• Ornith 1.0: https://github.com/deepreinforce-ai/Ornith-1
• Gemma 4: https://huggingface.co/collections/google/gemma-4
• Playwright (browser testing): https://playwright.dev/
• Vercel (preview deploys): https://vercel.com/
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Other references:
• DGX Spark (Nvidia): https://www.nvidia.com/en-us/products/workstations/dgx-spark/
• Mac Studio (Apple): https://www.apple.com/mac-studio/
• How to design AI agent loops: schedules, goals, and subagents in Claude Code and Codex: https://www.lennysnewsletter.com/p/how-to-design-ai-agent-loops-schedules
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Where to find Alex Finn:
LinkedIn: https://www.linkedin.com/in/alex-finn-1848684a
YouTube: https://www.youtube.com/@AlexFinnOfficial
X: https://x.com/AlexFinn
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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].
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GPT-5.6 Sol is back, and I ran it through my full How I AI vibe benchmark against GPT-5.6 Terra, Luna, Claude Fable 5, and Sonnet 5 across five categories: PRDs, prototypes, wireframes, debugging, and agentic voice. Sol won by a meaningful margin on my Claire Weighted Index (70% my taste, 30% Terminal Bench 2.1), and I also tested two use cases I can't stop thinking about: building a gamified homework tracking app for my kids in one shot with Codex, and browser automation with Chrome that burned through 500 LinkedIn replies while I did literally nothing.
What you’ll learn:
How I scored five AI models (including GPT 5.6 Sol, Fable 5, and Sonnet 5) using my “Claire Weighted Index” benchmark across PRDs, prototypes, code, and agentic voiceThe difference between GPT-5.6 Sol (Terra) and Sol for PRD writingHow Fable’s precision and pedantry made it harder to collaborate with, and the exact moment Sol broke through where Fable got stuckWhy Sonnet 5 is still my go-to for agentic voice in OpenClaw, even after this whole benchmarkHow I used GPT-5.6 Sol in Codex to build a fully gamified homework tracking app for my kids in one shotThe video editing use case that saved me hours clipping a talk I gave at Cursor’s eventHow to use Codex plus GPT-5.6 and Chrome for browser automation, and why this is my single most-loved use case right now—
In this episode, I cover:
(00:00) Intro
(01:10) The three GPT-5.6 models: Sol, Terra, Luna
(02:17) Pricing: Sol vs. Fable API costs
(03:24) The How I AI benchmark
(05:03) Claire-weighted Index results
(07:00) Per-task winners: prototypes, PRDs, agentic voice
(11:59) What Claire actually rewards
(13:20) Full-fidelity prototype side-by-sides (Sol vs. Fable)
(17:45) Wireframes
(18:19) Agentic voice
(19:15) Where Sol is better than other models
(23:56) Gamified kids’ homework app, built in one shot
(28:02) Fable’s pedantry problem and how Sol broke through it
(31:49) Two bonus use cases: video editing and browser use
(35:08) Final summary and model recommendations
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Tools referenced:
• GPT 5.6 (Sol, Terra, Luna): https://help.openai.com/en/articles/20001325-a-preview-of-gpt-56-sol-terra-and-luna
• Codex: https://openai.com/codex
• ChatPRD: https://www.chatprd.ai/
• CapCut: https://www.capcut.com/
• Math Academy: https://www.mathacademy.com/
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Other references:
• Cursor event where Claire spoke on the future of PM: https://www.youtube.com/watch?v=4CAFK-rc26A
• ChatPRD blog (where benchmark outputs will be published): https://www.chatprd.ai/
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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].
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Saknas det avsnitt?
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Everybody is saying, “It’s not the model, it’s the harness,” but almost nobody stops to explain what a harness actually is. So I did. I built one live on the show: a Sentry bug-debugging harness for my company ChatPRD, using the Claude Agent SDK, a custom terminal UI built with the Ink library, and opinionated adapters for Sentry, Linear, GitHub, and Vercel. The harness handles evidence gathering, root-cause analysis, and follow-up artifact creation, all without me needing to type “dear agent, please fix this bug” ever again. I also walk through the architecture, share the code structure, and give you the exact process I used so you can build your own harness for any repetitive, structured workflow in your business.
What you’ll learn:
What a harness actually isWhen to build a harness versus when to stick with a general-purpose tool like Claude Code or CodexHow to encode specific permissions into a harnessThe three components every harness needsHow I used GPT-5.5 and Claude Opus to build the harness code itself (and where they both initially resisted)How to structure the artifacts your harness produces so the whole team can use the output—
Brought to you by:
Bolt.new—Turn your idea into a real product
Customer.io—Build customer engagement campaigns from a single prompt
—
In this episode, we cover:
(00:00) What is an AI harness?
(03:19) When to build a harness
(04:33) Why Claire picked bug triage
(06:00) Why not just use Claude Code?
(07:48) Demo: The custom harness interface
(11:04) Architecture: runs, tasks, tools, and artifacts
(13:44) Building it with Codex and Claude
(15:08) Code map and file layout
(16:51) A look at the code
(19:18) The live investigation result
(21:01) How to build your own harness
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Tools referenced:
• Claude Agent SDK (Anthropic): https://code.claude.com/docs/en/agent-sdk/overview
• Claude Sonnet 4.6 (model used inside the harness): https://www.anthropic.com/news/claude-sonnet-4-6
• Claude Opus (used to build the harness): https://www.anthropic.com/claude/opus
• GPT-5.5 (Codex, used to build the harness): https://openai.com/index/introducing-gpt-5-5/
• Ink (terminal UI library for Node.js): https://github.com/vadimdemedes/ink
• Sentry (error monitoring): https://sentry.io/
• Linear (project management): https://linear.app/
• GitHub: https://github.com/
• Vercel: https://vercel.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].
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Alessio Fanelli, founder of Kernel Labs and co-host of Latent Space podcast, walks us through two very different AI workflows: (1) a fully autonomous coding setup using OpenAI Symphony + Linear, where Linear acts as a state machine and Symphony manages agents through the whole dev lifecycle with zero babysitting; (2) Codex with browser access searching eBay for underpriced Pokémon cards—autonomously browsing, extracting PSA certificate numbers, and flagging deals on $10K–$20K cards for his San Carlos card shop, Merlin Games.
What you’ll learn:
Why “agent manager” is a better mental model than “agent prompter”Why local Mac Minis don’t scale, and what a cloud VPS unlocksHow to wire Symphony and Linear together as an agent state machineHow to track token costs per task (and what 221 million tokens buys you)What Glimpse does, and why better agent senses extend autonomous runsWhy your CLAUDE.md probably needs a full purge, not more instructionsHow Codex scouts underpriced $10K Pokémon cards on eBay at scaleThe new category of small business that AI just made possible—
Brought to you by:
Firecrawl—Power AI agents with clean web data
Jira Product Discovery—Prioritize with insights, build with confidence
—
In this episode, we cover:
(00:00) Intro
(02:24) Prompter vs. agent manager
(04:31) Live demo: Symphony + Linear
(09:31) Setting up Symphony
(14:15) Purging your skills files
(18:06) The benefits of this system
(19:10) Demo: Using Codex to hunt for Pokémon cards
(24:17) The benefit of AI for small businesses
(28:23) Lightning round
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Tools referenced:
• OpenAI Codex: https://openai.com/codex
• OpenAI Symphony (open-source framework): https://github.com/openai/symphony
• Linear (project management/agent state machine): https://linear.app
• PSA (Professional Sports Authenticator) grading: https://www.psacard.com
• TCGplayer (card pricing): https://www.tcgplayer.com
• eBay (used for card price scouting): https://www.ebay.com
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Other references:
• Meta Ray-Ban glasses: https://www.ray-ban.com/usa/ray-ban-meta-smart-glasses
• The Monk and the Riddle by Randy Komisar: https://www.amazon.com/Monk-Riddle-Creating-Making-Living/dp/1578516447/ref=sr_1_1
• The Divine Comedy by Dante Alighieri: https://www.amazon.com/dp/0451208633
• AS Roma (football club Alessio and Claire are both fans of): https://www.asroma.com/en
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Where to find Alessio Fanelli:
X: https://x.com/FanaHOVA
Latent Space podcast: https://www.latent.space/
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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].
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I’ve been testing every major frontier model release since the start of the year, and when Anthropic dropped Sonnet 5, I wanted more than a vibe check. I got tired of one-off tests I couldn’t repeat or compare over time, so I built something better: the How I AI Bench, a repeatable eval harness I constructed live using Claude Code while recording this episode. I ran Sonnet 5 blind against four other frontier models (Sonnet 4.6, Opus 4.8, GPT-5.5, and Gemini 3 Pro) across PRD quality, prototype generation, agentic task completion, and agent personality. The results were not what I expected.
What you’ll learn:
What Anthropic claims Sonnet 5 improves over Sonnet 4.6, and where the benchmark data actually backs that upHow I built the How I AI Bench in under 45 minutes using Claude Code, starting from my own stored session historyWhy I combined human vibe scoring (70%) with LLM as judge scoring (30%) instead of trusting either aloneHow to set up a local HTML scoring page so you can rate AI outputs on gut feel and export those scores as JSONWhich model I recommend for PRDs, which for complex prototypes, and which for chatting with an agent daily—
Brought to you by:
Runway—The creative AI platform for images, video and more
Hyperagent—Deploy fleets of agents that handle real work
—
In this episode, we cover:
(00:00) Sonnet 5 is out
(01:55) What Anthropic claims
(04:02) Why I’m done with one-off vibe checks
(05:05) Building the How I AI Bench live with Claude Code
(07:42) The scoring system
(10:43) Agent voice eval
(11:57) Quick recap
(13:58) Results: The How I AI index leaderboard
(21:21) What I’m improving for the next run
(22:16) Generating a Claire-weighted index
(23:53) Model-by-task recommendations
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Tools referenced:
• Claude Sonnet 5: https://www.anthropic.com/news/claude-sonnet-5
• Claude Opus 4.8: https://www.anthropic.com/news/claude-opus-4-8
• GPT-5.5 (OpenAI): https://openai.com/index/introducing-gpt-5-5/
• Gemini 3 Pro (Google DeepMind): https://deepmind.google/models/gemini/pro/
• Cursor: https://www.cursor.com/
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Other references:
• SWE-bench Pro (agentic coding benchmark referenced): https://www.swebench.com/
—
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].
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Eddie Kim is the co-founder and CTO of the payroll and HR platform Gusto, which just crossed $1 billion in revenue and serves more than 500,000 small businesses. Recently he did something most CTOs don’t: he went back to writing code. With three other engineers and one designer, Eddie built Gusto Cofounder, a net-new AI product, from zero code to a tier-one launch in 10 weeks. He walks through how that team actually worked, why they threw out nearly every process, and how anyone can copy the approach.
What you’ll learn:
The trash-can method: how to write, review, and delete a full PR as a product decision instead of a planning docThe two-tool agent stack behind Gusto CofounderThe exact “perma-Zoom” setup that replaced standups, retros, and Slack threads for 10 weeksHow a designer with no engineering background hit the 94th percentile for shipping codeThe eval-first workflow Eddie uses to fix real customer bugs with Claude CodeHow a non-technical leader can prototype an idea to win buy-in, then carry it all the way to production-quality code—
Brought to you by:
Magic Patterns—Prototypes that look like your product
Jira Product Discovery—Prioritize with insights, build with confidence
—
In this episode, we cover:
(00:00) Intro: five people, 10 weeks
(02:38) The origins of Cofounder
(08:32) Inside the 10-week build process
(12:50) Building with no PMs
(14:38) The “trash can” method
(17:15) The stack architecture
(19:10) Shipping to production from day one
(22:03) How a designer became a top engineer
(29:05) Demo: Cofounder over text and Slack
(31:45) Demo: running a real payroll
(36:26) Live coding with evals in Claude Code
(39:39) Recap: prototype, small team, permission
(43:17) Lightning round
(48:44) Where to find Eddie and Cofounder
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Tools referenced:
• Gusto Cofounder (early access/waitlist): https://gusto.com/cofounder
• Claude Code (Anthropic): https://claude.ai/code
• Cloudflare Workers: https://workers.cloudflare.com/
• Vercel AI SDK: https://sdk.vercel.ai/
• DX (engineering analytics): https://getdx.com/
• Wispr Flow (voice-to-text): https://wisprflow.ai
• OpenClaw: https://openclaw.ai/
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Other references:
• Gusto (the main product, “Gusto Classic”): https://gusto.com
• Mindbody (referenced as customer data source): https://www.mindbodyonline.com/
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Where to find Eddie Kim:
LinkedIn: https://www.linkedin.com/in/edawerd/
—
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].
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I put GLM 5.2, the open-weight coding model from Z.AI, through four real tasks inside my actual codebase: a codebase architecture audit, a UI redesign, and a 45-minute autonomous bug-hunting session pulling from Sentry and Vercel logs. Total cost: $3.36 for roughly 6 million tokens, a prioritized bug-fix dashboard I’m actually shipping from, and a landing page redesign that matched Chat PRD’s design system on the first try.
What you’ll learn:
What “open-weight” actually means and why it matters for cost and vendor independenceHow to connect GLM 5.2 to Cursor and Claude CodeHow it performs on codebase exploration and autonomous architecture summarization in a real production Next.js appWhether GLM 5.2 can match an existing design systemHow the model handles a 45-minute long-running autonomous taskWhere GLM 5.2 stumbled The actual cost breakdown—
Brought to you by:
Mercury—Radically different banking loved by over 300K entrepreneurs
—
In this episode, we cover:
(00:00) What open-weight models are and why GLM 5.2 is worth testing
(01:38) GLM 5.2 model overview
(04:02) Capabilities and benchmark results
(06:02) How to set up GLM 5.2 in Cursor
(08:37) How to set up GLM 5.2 in Claude Code
(11:04) Live test 1: codebase exploration and architecture audit on ChatPRD
(12:43) Live test 2: generating an HTML architecture and roadmap page
(16:37) Live test 3: redesigning the How I AI landing page in Cursor
(20:57) Live test 4: 45-minute autonomous task, pulling Sentry errors and Vercel logs
(22:35) Where it struggled
(23:49) My verdict on the output
(25:23) Cost breakdown
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Tools referenced:
z.ai: https://z.aiGLM 5.2: https://z.ai/blog/glm-5.2OpenRouter: https://openrouter.aiCursor: https://cursor.comClaude Code: https://docs.anthropic.com/en/docs/claude-codeSentry: https://sentry.ioVercel: https://vercel.com—
Other references:
SWE-Bench Pro leaderboard (coding benchmark scores referenced in episode): https://www.swebench.comFrontier Suite and Post-Train Bench (additional benchmarks cited): https://scale.com/leaderboardUse Claude Code with OpenRouter: https://openrouter.ai/docs/cookbook/coding-agents/claude-code-integration—
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].
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Brian Grinstead is a distinguished engineer at Mozilla, where he’s worked on Firefox and the web platform since 2013 (he joined to help launch Firefox DevTools). Recently he and his team pointed an agentic bug-finding pipeline at Firefox—a codebase with tens of thousands of files and tens of millions of lines of code—and shipped a record month of security fixes. The viral chart everyone saw gave the credit to Anthropic’s new Mythos model. Brian’s take is that the harness and pipeline did just as much of the work, and he walks through exactly how it runs and how anyone can build a starter version.
What you’ll learn:
How to build a basic bug-finding harness by running Claude Code or Codex with one prompt and the -p flag, no SDK requiredWhy pointing an agent at a whole codebase fails, and how an LLM judge can score and rank files before you spend any computeHow a verifier subagent kills false positives by catching the agent when it cheatsThe goal-loop pattern: give an agent a tightly scoped problem, a clear pass/fail signal, and let it retry far past the point a human would quitWhy teams that already invested in fuzzing, CI, and dev tooling are so far aheadHow to weigh model versus harness, and why Brian splits the credit close to 50-50How a non-engineer can reuse the same score, verify, and fix the loop for design quality, conversion rate, or tech debtWhy AI-generated patches still can’t ship on their own, and where humans stay in the loop—
Brought to you by:
WorkOS—Make your app enterprise-ready today
Metaview—The agentic recruiting platform for winning teams
—
In this episode, we cover:
(00:00) Introduction to Brian Grinstead
(02:43) The viral chart: Firefox Security Bug Fixes by Month
(05:32) How the custom harness works
(10:22) Goal loops and guardrails
(14:45) How they built it
(16:55) Real bugs, including a 15-year-old one
(23:00) Open-sourcing it
(26:26) Why humans still review every fix
(32:30) Live demo and prioritizing files
(40:18) Mobilizing the team and recap
(42:33) Lightning round
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Tools referenced:
• Claude Code: https://claude.ai/code
• Claude Agent SDK: https://code.claude.com/docs/en/agent-sdk/overview
• Codex: https://openai.com/index/openai-codex/
• OpenAI Agent SDK: https://developers.openai.com/api/docs/guides/agents
• VS Code: https://code.visualstudio.com/
• Docker: https://www.docker.com/
• Firefox: https://www.mozilla.org/firefox/
• Address Sanitizer: https://github.com/google/sanitizers
• RLBox: https://rlbox.dev/
—
Other references:
• Mozilla Bug Bounty Program: https://www.mozilla.org/security/bug-bounty/
• Mozilla GitHub: https://github.com/mozilla
—
Where to find Brian Grinstead:
LinkedIn: https://www.linkedin.com/in/bgrins/
GitHub: https://github.com/bgrins
—
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].
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I break down every loop type from scratch—what a heartbeat, cron, hook, and goal loop actually are, when each one fits, and the five things any effective loop needs before it touches production. Then I build two live loops: a daily aging-PR reviewer in Claude Code that schedules itself at 10:15 a.m. and spins off its own subagents, and a weekly skills-identification loop in Codex that spawns goal-based subagents to validate its own output in real time.
What you’ll learn:
The plain-English definition of a loop—and why it’s just an automated prompt, not a scary new paradigmThe four loop types (heartbeat, cron, hook, and goal) and when each one actually fits your workflowHow to think about loop design using the “onboarding an employee” mental modelThe five things every effective loop needs: work trees, skills, plugins/connectors, subagents, and state trackingHow to build a scheduled PR-review routine in Claude Code that babysits aging PRs and alerts your teamHow to set up a weekly skills-identification automation in Codex that spawns its own validating subagentsWhy goal-based loops are the hardest to write well—and where most people burn tokens for nothingThe two warning signs that your loop is going to get expensive before it gets useful—
Brought to you by:
WorkOS—Make your app enterprise-ready today
Runway—The creative AI platform for images, video, and more
—
In this episode, we cover:
(00:00) Prompts are out and loops are in
(02:30) Defining a loop
(03:03) The four ways to automate a prompt: heartbeat, cron, hooks, and goals
(06:03) Five things every effective loop needs
(09:26) The “onboarding an employee” framework for designing loops
(11:58) Live build #1: Daily aging PR loop in Claude Code
(17:08) Subagents inside loops
(19:00) Live build #2: Weekly skills identification loop in Codex
(22:57) Watching subagents spin up in real time
(25:28) Warning signals around loops
(27:31) What listeners are doing with loops
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Tools referenced:
• Claude Code: https://claude.ai/code
• Codex: https://chatgpt.com/codex
• OpenClaw: https://openclaw.ai/
—
Other references:
• Claire’s article “Why OpenClaw Feels Alive Even Though It’s Not”: https://x.com/clairevo/article/2017741569521271175
• Addy Osmani’s article on loop engineering: https://addyosmani.com/blog/loop-engineering/
• Using Goals in Codex: https://developers.openai.com/cookbook/examples/codex/using_goals_in_codex
—
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].
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In this episode, I sit down with Ankur Goyal, founder and CEO of Braintrust, the AI evals and observability platform used by teams like Notion, Stripe, Vercel, and Zapier. This one is for the senior engineers, staff engineers, VPs of engineering, and CTOs in my audience. We get into how coding agents can take on deeply technical architecture and infrastructure work that no single human engineer could tackle before, and then we demystify evals so you can use them to make your AI products better without touching the implementation.
What you’ll learn:
How Ankur uses Codex to run week-long benchmark experiments across database indexes, column store formats, and execution engines to speed up slow queriesWhy he argues there’s no excuse to skip rigorous benchmarking now that agents can run them tirelesslyThe “agent line” framework: how to decide which decisions, directions, and interactions you can hand off to an agentHow I think about the practical vs. theoretical quality of AI on hard technical problems, and why human attention decays on tedious workWhy evals are the modern version of a PRD, and how to encode “what good looks like” so a model can figure out the “how”How to build a scoring function live and let an agent improve your prompt inside a safe playgroundHow Ankur turned his designer David’s taste into a repeatable eval so quality scales beyond one personWhy fixing your CI is the highest-leverage way to speed up engineering velocity—
Brought to you by:
Guru—The AI layer of truth
Persona—Trusted identity verification for any use case
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In this episode, we cover:
(00:00) Introduction to Ankur Goyal
(03:00) Using AI agents for database optimization
(06:10) Running exhaustive benchmarks with coding agents
(09:03) Why staff engineers are wrong about AI limitations
(11:30) The “agent line” framework for delegation
(14:00) Ankur’s workflow: running 4 to 6 concurrent agents
(17:16) Technical setup: foreground agents, background agents, and cloud environments
(20:32) Spending time with AI tools
(23:06) Demystifying evals
(26:02) Live demo: Building an eval for documentation answers
(30:20) The alternative to evals: vibe checks and whack-a-mole
(32:09) Capturing designer taste in scoring functions
(33:13) Quick recap
(33:44) Managing velocity and throughput
(35:40) Why CI/CD investment is critical for AI-accelerated teams
(37:30) Ankur’s prompting strategy when agents fail
(39:10) Closing thoughts and how to connect
—
Tools referenced:
• Braintrust: https://www.braintrust.dev/
• Codex: https://openai.com/codex/
• GPT 5.4: https://developers.openai.com/api/docs/models/gpt-5.4
• Claude: https://claude.ai/
—
Other references:
• GPT 5.5 just did what no other model could: https://www.lennysnewsletter.com/p/gpt-55-just-did-what-no-other-model
• Paul Graham’s Maker vs. Manager Schedule: http://www.paulgraham.com/makersschedule.html
• tmux: https://github.com/tmux/tmux
• Chris Tate at Vercel: https://www.linkedin.com/in/ctatedev/
—
Where to find Ankur Goyal:
LinkedIn: https://www.linkedin.com/in/ankrgyl/
—
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].
-
Claude Fable 5 is the first Mythos-class intelligence model to be generally available, and I got early access to test it before launch. In this episode, I walk through what Anthropic is promising, what actually stood out when I used it on real work, and where I think it fits in your AI stack.
—
In this episode, we cover:
(00:00) Introduction: Fable 5 is finally here
(00:31) What Anthropic says about the model
(05:14) Token-intensive by design
(06:28) Safety classifiers and the new fallback concept
(07:46) Is this or is this not Mythos?
(08:30) New product launches: Managed Agents and more
(09:20) Crushing benchmarks
(09:55) What it’s actually like to use (the good and the bad)
(11:40) Test 1: product graph spec
(12:56) Test 2: designing a skills registry
(14:04) Conservative on execution
(14:43) Test 3: multi-agent orchestration
(15:39) My takeaways
—
Tools referenced:
• Claude Fable 5: https://www.anthropic.com/news/claude-fable-5-mythos-5
• Claude Managed Agents: https://platform.claude.com/docs/en/managed-agents/overview
—
Other reference:
• SWBench Pro benchmark: https://www.swebench.com/
—
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].
-
Nicole Ruiz is a writer and parent who has built a comprehensive AI-powered shopping system to help her family buy high-quality, long-lasting items while avoiding the noise of drop-shipping brands, paid ads, and poorly made products. She writes an interview series on Substack about how technology is changing the household.
What you’ll learn:
How to build a Claude Project with custom instructions for vetting brands based on heritage, craftsmanship, and return policiesThe shopping criteria that help surface century-old manufacturers over trendy direct-to-consumer brandsHow to use Claude to search through trusted vendor websites that have terrible UXWhy AI actually helps small artisans and heritage brands compete against Amazon’s infrastructureHow to use Claude Cowork to automate returns by finding receipts in your email and drafting refund requestsThe technique for getting Claude to analyze whether a brand is legitimate or just a drop-shipping operationHow to shop within a specific budget or with gift cards using AI assistance—
Brought to you by:
Orkes—The enterprise platform for reliable applications and agentic workflows
Metaview—The agentic recruiting platform for winning teams
—
In this episode, we cover:
(00:00) Introduction to Nicole and AI-powered shopping
(02:29) The problem
(04:55) Building a Claude Project for household purchasing
(07:44) The “anti-to-do list” concept for reducing mental overhead
(10:30) Shopping for a can opener: the system in action
(15:53) How AI helps century-old brands with terrible websites
(18:45) Processing returns with Claude Cowork
(25:06) Using gift cards strategically
(26:33) Vetting brands
(29:40) Recap, lightning round, and final thoughts
—
Tools referenced:
• Claude: https://claude.ai/
• Claude Cowork: https://www.anthropic.com/product/claude-cowork
—
Other references:
• Boston General Store: https://bostongeneralstore.com/
• L.L.Bean: https://www.llbean.com/
• Manufactum: https://www.manufactum.com/
• 5 OpenClaw agents run my home, finances, and code | Jesse Genet: https://www.lennysnewsletter.com/p/5-openclaw-agents-run-my-home-finances
• From a $6.90 newsletter to $3M API: How a non-coder built Memelord | Jason Levin: https://www.lennysnewsletter.com/p/from-a-690-newsletter-to-3m-api-how
—
Where to find Nicole Ruiz:
X: https://x.com/nwilliams030
Substack (The Third Oikos): https://www.thirdoikos.com/
—
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].
-
In this experimental episode, I document my real-time attempt to create an AI avatar of myself using Google Flow and the new Gemini Omni video generation model. I walk through the entire process—from scanning my face with my phone to generating a complete one-minute hype video for the podcast, all in about 15 minutes.
What you’ll learn:
How to create an AI avatar using Google Flow in under five minutesWhy video AI tools unlock creative possibilities for people with zero video production skillsThe step-by-step process of generating a full storyboard using AI as your creative producerHow to use character consistency features to generate multiple video scenes with the same avatarThe uncanny-valley moments you’ll encounter when your AI clone doesn’t quite nail emotions or physicsHow to stitch together AI-generated scenes into a complete video using built-in editing tools—
Brought to you by:
Merge—Connective infrastructure for production AI
Jira Product Discovery—Prioritize with insights, build with confidence
—
In this episode, we cover:
(00:00) Getting started with Google Flow and Gemini Omni
(01:38) The avatar creation process: scanning and photo capture
(02:55) Using Flow to brainstorm a hype video storyboard
(06:59) Generating the first video scene with the avatar
(08:41) Troubleshooting: accidentally generating images instead of videos
(09:32) Generating all seven scenes for the complete video
(11:37) Reviewing the avatar videos
(13:13) Stitching the videos together in the browser-based editor
(14:32) The complete How I AI hype video
(15:32) What worked and what didn’t
(19:04) Final thoughts
—
Tools referenced:
• Google Flow: https://labs.google/fx/tools/flow
• Gemini Omni: https://gemini.google/overview/video-generation/
• Veo 3: https://deepmind.google/technologies/veo/
—
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].
-
Bryce Rattner Keithley has spent her career in talent and recruiting, working with technical leaders but never writing a line of code herself. Yet she managed to build Daily Hundred—a fitness app featuring custom AI-generated videos of anthropomorphic animals demonstrating exercises—and ship it to the App Store before her software engineer friends. Using Replit, Claude, Gemini, and a relentless beginner’s mindset, Bryce proves that in the AI era, execution is no longer the constraint on good ideas.
What you’ll learn:
How to build and ship an iPhone app using Replit without any coding knowledgeThe step-by-step process for creating custom AI-generated workout videos by combining Gemini images with real exercise footageHow to use Claude as your technical architect and Claude Code as your software engineerHow to navigate App Store submission requirements (including fixing rejection feedback)Why being hyper-literal in your prompts unlocks better AI resultsWhy a beginner’s mind is actually an advantage when building with AI tools—
Brought to you by:
WorkOS—Make your app enterprise-ready today
Metaview—The agentic recruiting platform for winning teams
—
In this episode, we cover:
(00:00) Introduction to Bryce and Daily Hundred
(04:48) Building with Replit
(06:16) The beginner’s mindset advantage
(11:17) Creating anthropomorphic animals
(22:55) Moving from static image to video
(27:15) The floating genie and other anthropomorphic animal generations
(30:46) Shifting from web app to App Store submission
(36:24) User feedback
(37:41) Lightning round and final thoughts
—
Tools referenced:
• Replit: https://replit.com/
• Lovable: https://lovable.dev/
• Claude: https://claude.ai/
• Claude Code: https://claude.ai/code
• Gemini: https://gemini.google.com/
• Higgsfield: https://higgsfield.ai/
• Kling: https://kling.ai/
• Railway: https://railway.app/
• TestFlight: https://developer.apple.com/testflight/
—
Other references:
• How a 91-year-old vibe coded a complex event management system using Claude and Replit | John Blackman: https://www.lennysnewsletter.com/p/how-a-91-year-old-vibe-coded-a-complex
• What Got You Here Won’t Get You There: https://www.amazon.com/What-Got-Here-Wont-There/dp/1401301304
• How Women Rise: https://www.amazon.com/How-Women-Rise-Holding-Careers/dp/0316440124
• A Whole New Mind: https://www.amazon.com/Whole-New-Mind-Right-Brainers-Future/dp/1594481717
• How to Win Friends and Influence People: https://www.amazon.com/How-Win-Friends-Influence-People/dp/0671027034
—
Where to find Bryce Rattner Keithley:
LinkedIn: https://www.linkedin.com/in/brycerattner/
GitHub: https://github.com/brk-bot/
Daily Hundred on the App Store: https://apps.apple.com/us/app/daily100-fitness-challenge/id6762108062
—
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].
-
I got a few hours of early-access testing with Anthropic’s newly released model Opus 4.8. I walk through real coding, design, and strategy tasks across Claude Code and Claude Cowork, and give you my unfiltered view on what impressed me and what didn’t.
—
What you’ll learn:
Where Opus 4.8 excels: greenfield prototypes, one-shot features, and fast executionWhere it struggles: the last 10%, edge cases in existing codebases, and hallucinationsHow Opus 4.8 compares to Opus 4.7 on business strategy workWhy I’m still reaching for Opus 4.7 on data-heavy strategy and roadmap workThe new features shipping alongside the model: dynamic workflows with parallel subagents and effort control in Claude.ai and CoworkThe prompting and harness strategy I’d use to get the most out of it—
In this episode, we cover:
(00:00) Introduction to Opus 4.8
(00:44) Benchmark performance and pricing
(01:53) First coding test: Building a prototyping tool
(03:00) Where it failed: The last 10% problem
(03:27) The hallucination problem
(04:23) Testing Opus 4.8 on existing codebases
(05:24) The ambition test: Building games for a 9-year-old
(07:03) Business strategy test: 4.7 vs 4.8
(08:23) The roadmap test
(09:17) Final verdict
—
References:
• System Card: Claude Opus 4.8: https://cdn.sanity.io/files/4zrzovbb/website/c886650a2e96fc0925c805a1a7ca77314ccbf4a6.pdf
• Introducing Claude Opus 4.8 on X: https://x.com/claudeai/status/2060042702150930686?s=20
—
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].
-
In this 30-minute episode, I walk through my favorite feature in Codex: the /goal command. I show how Goals transform AI from a turn-based assistant that needs constant ‘what’s next?’ prompting into an autonomous agent that can work for hours on complex, multi-step tasks. I share three real examples: eliminating thousands of Sentry errors, cleaning 3,900 emails down to 68, and organizing hundreds of Linear tasks.
What you’ll learn:
What Goals are and how they differ from standard promptsHow I used /goal to eliminate hundreds of error logs in my codebase over a five-hour autonomous runThe non-technical use cases that make Goals incredibly powerful: cleaning up 3,900 emails in under four hours and organizing hundreds of project management tasks in LinearHow to write effective /goal prompts with measurable outcomes, verification methods, and constraintsWhen not to use Goals and what makes a strong versus weak GoalWhy Goals represent a fundamental shift in how we work with AI, from babysitting the model to managing it—
Brought to you by:
Mercury—Radically different banking loved by over 300K entrepreneurs
—
In this episode, we cover:
(00:00) Introduction
(01:50) What is /goal and when should you use it?
(02:45) The difference between prompts and Goal-based loops
(04:06) Claire’s first five-hour 45-minute autonomous coding task
(05:05) How to manage a Goal lifecycle: view, pause, resume, and clear
(06:06) How to write strong goals: outcomes vs. outputs
(07:34) The six components of effective Goals
(08:57) Example: Reducing P95 checkout latency with /goal
(09:36) Demo: Using /goal to eliminate Sentry errors in ChatPRD
(13:18) Demo: Burning down Vercel API errors
(17:28) Non-technical use case: Cleaning 3,900 emails with /goal
(21:24) Demo: Using /goal to clean up Linear project tasks
(24:41) When not to use /goal
(26:10) Why /goal changes everything
—
Tools referenced:
• Codex: https://openai.com/codex/
• Sentry: https://sentry.io/
• Vercel: https://vercel.com/
• Linear: https://linear.app/
—
Other reference:
• OpenAI blog post “Using Goals in Codex”: https://developers.openai.com/cookbook/examples/codex/using_goals_in_codex
—
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].
-
Felix Rieseberg is the engineering lead for Claude Cowork and Claude Code Desktop at Anthropic. He previously spent five years at Slack building developer tools. In this episode, Felix demonstrates how he uses Claude to solve real-life problems: analyzing floor plans to build interactive 3D house walkthroughs, automatically tracking promises he makes on Twitter, and building a $20 hardware device that physically approves Claude actions with a button press.
What you’ll learn:
How to use Claude Cowork to turn a 2D floor plan into an interactive 3D walkthrough where you can move furniture aroundThe “go one abstraction layer up” philosophy: why you should never manually enter data Claude can find itselfHow to use your email as an inventory database for furniture, clothing, and personal purchasesWhen to use Opus vs. Sonnet 4.6 (hint: it’s about how well you can scope the problem, not technical complexity)How live artifacts work and why they’re powerful for dashboards that refresh with real-time data from your connectorsThe product philosophy behind making latency delightfulHow to build your own $20 hardware device using Claude Code (no hardware experience required)Why Felix never reads the code Claude writes and judges it purely on output—
Brought to you by:
Magic Patterns—Prototypes that look like your product
Guru—The AI layer of truth
—
In this episode, we cover:
(00:00) Introduction to Felix Rieseberg
(02:40) Felix’s role at Anthropic
(03:25) The multiple tabs in Claude and why they exist
(05:55) Using Claude Cowork to design a new house using floor plans
(09:52) When to use Opus versus Sonnet 4.6
(12:37) Building an interactive 3D furniture planner
(14:30) Using your email as a source of truth for personal inventory
(15:58) The anti-to-do list: going one abstraction layer up
(23:14) Introduction to live artifacts
(26:02) Building a personal dashboard with live data
(28:37) Being polite to Claude (and why it matters for your humanity)
(30:28) Claude interaction tips
(32:33) Looking at the daily dashboard
(33:55) How live artifacts work with connectors
(35:02) Redesigning the dashboard
(37:55) The biggest gap: people don’t know what problems AI can solve
(41:52) The reverse interview
(42:30) Making latency delightful through asynchronous design
(44:05) The redesigned dashboard
(45:28) AI should free up your creative energy
(46:44) Building a $20 hardware Claude buddy
(52:33) Why kids are magical AI users
(54:30) Recap and final thoughts
—
Tools referenced:
• Claude Cowork: https://www.anthropic.com/product/claude-cowork
• Claude Code: https://claude.ai/code
• Claude for Chrome: https://code.claude.com/docs/en/chrome
• Claude Desktop: https://claude.ai/download
• Live Artifacts: https://support.claude.com/en/articles/14729249-use-live-artifacts-in-claude-cowork
• Connectors (Spotify, Gmail, Calendar, Notion): https://claude.ai/settings/connectors
• Slack: https://slack.com/
—
Where to find Felix Rieseberg:
Website: https://felixrieseberg.com/
LinkedIn: https://www.linkedin.com/in/felixrieseberg/
X: https://x.com/felixrieseberg
GitHub: https://github.com/felixrieseberg
—
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].
-
Today is day one of Google I/O 2026, and I walk through every major announcement live—from the new Gemini 3.5 model family to Anti-Gravity 2.0, Google AI Studio, Gemini’s consumer redesign, the Omni video model, Flow, Stitch, and Pomelli. I test them in real time and tell you exactly which ones delivered.
What you’ll learn:
How Gemini 3.5 Flash benchmarks against Claude and GPT models on speed and agentic coding tasksHow Anti-Gravity 2.0’s new features (projects, scheduled tasks, subagents, slash commands) compare to Codex and Claude CodeWhy the /grill-me slash command could be a more aggressive alternative to Claude Code’s clarification flow—and how to use itHow Google AI Studio’s new Workspace integration is designed to own the internal productivity app use caseHow Google’s new creative tools work in practice: Omni (video generation), Flow (cinematic video editing and character consistency), Stitch (streaming UI design with inline edits), and Pomelli (brand identity and asset generation)Why Google’s launch-to-availability gap is still a problem—and what to do when a featured product doesn’t actually work yet—
Brought to you by:
Magic Patterns—Prototypes that look like your product
Thoughtspot—Build AI-powered analytics into your product
—
In this episode, we cover:
(00:00) Google I/O 2026 day 1 overview
(01:47) Gemini 3.5 flash
(04:19) Antigravity updates
(06:32) CLI test and agent features
(07:59) Core agent features released today—May 19th, 2026
(09:43) New slash commands
(11:20) Antigravity test results and takeaways
(12:25) AI Studio updates
(13:52) Access issues
(15:20) Gemini redesign
(17:24) Gemini image gen test
(19:16) Omni (video generation)
(22:56) Flow (cinematic editing)
(24:31) Avatar creation test
(26:45) Pomelli and Stitch
(31:13) Recap and final thoughts
—
Tools referenced:
• Gemini 3.5 Flash: https://deepmind.google/technologies/gemini/
• Antigravity: https://antigravity.google/
• Google AI Studio: https://aistudio.google.com/
• Google Gemini: https://gemini.google.com/
• Omni (video generation): https://gemini.google/overview/video-generation/
• Google Flow: https://flow.google/
• Stitch: https://stitch.withgoogle.com/
• Pomelli (Google brand tool): https://labs.google.com/pomelli/about/
—
Other references:
• Google I/O 2026 announcements: https://blog.google/innovation-and-ai/sundar-pichai-io-2026/
—
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].
-
Thariq Shihipar is an engineer at Anthropic working on the Claude Code team. He’s spent the past several months experimenting with HTML as a replacement for Markdown in planning and implementation workflows, discovering that richer visual formats lead to better human engagement—and, ultimately, better products. In this episode, filmed at Anthropic’s Code with Claude event in San Francisco, Thariq demonstrates how to use HTML artifacts to create interactive plans, build throwaway UIs for specific problems, and maintain living design systems that travel with your codebase.
What you’ll learn:
Why HTML has replaced Markdown as the ideal format for AI agent communication and planningHow to brainstorm in HTML to get visual mockups and interactive demos instead of text listsThe technique for building throwaway micro-UIs to edit specific parts of your planHow to create a living design system in HTML that lives in your repo and travels with every projectWhy “complexity has to earn its keep” and how HTML helps you stay in the loop without over-constraining ClaudeThe prompting technique that gives Claude flexibility while ensuring that you get what you needWhy 99% of your AI-generated tokens should go to planning, interfaces, and communication—not production code—
Brought to you by:
Celigo—Intelligent automation built for AI
Persona—Trusted identity verification for any use case
—
In this episode, we cover:
(00:00) Introduction
(02:39) HTML as the new Markdown
(04:30) The compute allocator mindset
(05:51) How HTML makes specs more engaging
(06:48) Demo: Brainstorming in HTML with Claude Code
(09:24) From brainstorm to full implementation plan
(11:20) Prompting philosophy: Trust Claude but give it constraints
(13:50) The future of PRDs and tech specs
(18:16) Making HTML specs editable
(20:23) The abundance mindset
(24:17) Just-in-time documentation and throwaway software
(25:39) Using plans as artifacts for implementation
(26:39) Demo: Living design systems in HTML
(30:16) Adding comments and annotations to HTML plans
(31:42) Recap: The HTML workflow
(32:21) Lightning round and final thoughts
—
Tools referenced:
• Claude Code: https://claude.ai/code
• Claude Design: https://claude.ai/design
• AWS: https://aws.amazon.com/
• Figma: https://www.figma.com/
• GitHub: https://github.com/
—
Other references:
• Anthropic Code with Claude event: https://claude.com/code-with-claude
• SpaceX partnership announcement: https://www.anthropic.com/news/higher-limits-spacex
• Jevons paradox: https://en.wikipedia.org/wiki/Jevons_paradox
—
Where to find Thariq Shihipar:
Website: https://www.thariq.io/
LinkedIn: https://www.linkedin.com/in/thariqshihipar/
X: https://x.com/trq212
GitHub: https://github.com/ThariqS
—
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].
-
Ryan Nystrom is a software engineer at Notion. He joined in December 2024 after Notion acquired Campsite, the team communication platform he co-founded with Brian Lovin. At Notion, he’s been a core builder of Notion AI and the Custom Agents feature launched in February 2026. He manages a team of six to seven engineers while still writing code himself, currently running Project Afterburner, a push to cut Notion’s CI time to a quarter of its current duration.
What you’ll learn:
How to build a Notion AI custom agent that auto-generates your daily standup pre-read by pulling from Slack, GitHub, Honeycomb metrics, and yesterday’s meeting transcriptHow to configure subagents and MCP integrations within Notion AIHow Notion’s internal “Boxy” system lets engineers @mention Codex from within Notion comments and get a full pull request with screenshots in 20 minutesThe spec-first development workflow: dictate an idea into Whisper, have Codex format it as a proper spec, commit it to the repo, and let the agent implement and verify it autonomouslyWhy fast CI is absolutely critical in the age of AI coding agentsHow to prompt AI coding agents to defend their reasoning under pushbackWhy engineering managers and even senior executives should keep writing code—
Brought to you by:
WorkOS—Make your app enterprise-ready today
Orkes—The enterprise platform for reliable applications and agentic workflows
—
In this episode, we cover:
(00:00) Introduction to Ryan Nystrom
(02:48) How AI has upended 12+ years of the same working routine
(04:30) Project Afterburner: Notion’s push to cut CI time to a quarter
(09:00) Why high-frequency, high-quality meetings beat lower-frequency standups
(11:10) How automated context surfaces every engineer’s work equally
(12:15) Why cutting meeting prep is a burnout protection mechanism
(14:26) The case for engineering managers writing code
(16:13) Inside “Boxy”: Notion’s internal VM-based background agent system
(20:30) Old World vs. New World code review
(24:51) Prompting Codex from Notion comments
(29:20) The emotions around code review
(31:01) Quick recap
(32:00) Spec-first development: writing and checking agent specs into the repo
(35:10) The spec as changelog: version control for how a feature actually works
(37:53) How engineers’ roles are evolving
(39:00) Lightning round
(45:21) Where to find Ryan
—
Tools referenced:
• Notion AI: https://www.notion.com/product/ai
• Notion Custom Agents: https://www.notion.com/blog/introducing-custom-agents
• Codex (OpenAI): https://openai.com/codex
• Claude Code (Anthropic): https://claude.ai/code
• Honeycomb (observability + MCP): https://www.honeycomb.io
• Whisper (OpenAI voice transcription): https://openai.com/research/whisper
• Slack: https://slack.com
• GitHub: https://github.com
—
Other references:
• How Stripe built “minions”—AI coding agents that ship 1,300 PRs weekly from Slack reactions | Steve Kaliski (Stripe): https://www.chatprd.ai/how-i-ai/stripes-ai-minions-ship-1300-prs-weekly-from-a-slack-emoji
• Notion 3.3 Custom Agents launch (February 24, 2026): https://www.notion.com/releases/2026-02-24
—
Where to find Ryan Nystrom:
X: https://x.com/ryannystrom
LinkedIn: https://www.linkedin.com/in/ryannystrom/
GitHub: https://github.com/rnystrom
—
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].
- Visa fler
