How I AI
- Épisodes
- 120
- Parution
- plusieurs fois par semaine
- Dernier épisode
- 21 Oct 2026
À propos du podcast
How I AI, hosted by Claire Vo, is for anyone wondering how to actually use these magical new tools to improve the quality and efficiency of their work. In each episode, guests will share a specific, practical, and impactful way they’ve learned to use AI in their work or life. Expect 30-minute episodes, live screen sharing, and tips/tricks/workflows you can copy immediately. If you want to demystify AI and learn the skills you need to thrive in this new world, this podcast is for you.
Épisodes 120
Claude Duet is beautiful. I still forgot to use it
Anthropic's new personal agent, Claude Duet, lives on your phone, inherits all your Claude plugins and connectors, and is pitched at small business owners and busy professionals. It's the most polished agent …
Notes de l’épisode
Anthropic's new personal agent, Claude Duet, lives on your phone, inherits all your Claude plugins and connectors, and is pitched at small business owners and busy professionals. It's the most polished agent launch I've seen: thoughtful onboarding, task lists, first-class artifacts, and Slack-style channels. But polish isn't enough when you're competing with every other personal agent for attention. I put it to work on my inbox and Slack, my sales pipeline, a Google Docs sales deck, a launch video, and finding Warriors tickets for my kids, and I'll walk you through what it nails, where it stalls, and what Anthropic needs to fix before it can become anyone's number one agent.
What you’ll learn:
- What Claude Duet is and who Anthropic built it for
- The design details that make it far more polished than other personal agent launches
- How task lists, artifacts, and channels work as agent primitives
- Why a “proactive” agent that never nudges you is a growth problem
- How model latency kills your willingness to work with an agent synchronously
- Where it fell short on real tasks, from inbox triage to sales decks to launch videos
- Why missing desktop and multiplayer support keeps it out of my working stack
—
In this episode, we cover:
(00:00) Personal agent fatigue: does Claude Duet make the cut?
(00:40) What Claude Duet is and who it’s for
(01:39) Why the design polish stands out
(03:06) Task lists, artifacts, and channels
(04:53) The icon
(05:27) What I didn’t like: not proactive enough
(07:02) Testing it on email, Slack, and Warriors tickets
(07:41) Why latency matters so much in a personal agent
(10:20) The missing desktop app
(12:12) Sales pipeline and a Google Docs deck
(13:10) Launch videos and the local skills gap
(14:00) Single-player vs. multiplayer agents
(15:05) Final verdict
—
Tools referenced:
• Claude Duet (Anthropic): https://claude.com/resources/articles/claude-duet
• Claude Duet Waitlist: https://claude.com/product/claude-duet
• Claude Cowork: https://claude.com/product/cowork
• Meta Muse: https://muse.ai/
• Codex (OpenAI): https://openai.com/codex
• Grok Bot: https://x.ai/bot
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
“Claude cuts my TikTok”: How this creator is scaling video with AI | Brooke Lacey
Brooke Lacey has been making videos since before Facebook had video, and she built a TikTok audience of more than 290,000 by learning to cut short-form videos herself. With over 25 years in tech, she now builds …
Notes de l’épisode
Brooke Lacey has been making videos since before Facebook had video, and she built a TikTok audience of more than 290,000 by learning to cut short-form videos herself. With over 25 years in tech, she now builds her own apps, including Zosha, a free mobile app for learning AI. In this episode, Brooke walks me through the Claude Code, FFmpeg, and Whisper workflow that edits her TikToks in about 15 seconds, how she mines her comments for new scripts, and how she runs a weekly short-form operation with no editor.
What you’ll learn:
- How Claude Code edits a raw phone video on her computer in about 15 seconds
- How she taught Claude her jump-cut cadence
- Why her TikTok comments became her content brief
- How she keeps growing her audience while posting just once a week—and what’s next
- What over 25 years in tech taught her about this AI moment
—
Brought to you by:
Firecrawl—Give your AI agents web data and beyond
Auros—The human network behind AI
—
In this episode, we cover:
(00:00) Intro and guest welcome
(02:30) Brooke’s background in video
(06:56) Her first session cutting videos with Claude Code
(08:51) Live demo: Claude edits a video in real time
(11:05) Walking through the edit chain
(15:58) How she taught Claude her cadence, her hooks, and what funny sounds like
(25:36) Quick recap
(26:20) Posting frequency, live streaming plans, and TikTok ads
(28:00) Lightning round: Gen X, wisened experience, and YOLO energy
(32:43) When Claude disappoints: the lane OS, status files, and how she prompts it back
(34:50) Where to find Brooke and the Zosha app
—
Tools referenced:
• Claude Code: https://claude.ai/code
• FFmpeg: https://ffmpeg.org
• Whisper (OpenAI): https://openai.com/research/whisper
—
Other reference:
• How I AI episode with John Blackman (91-year-old vibe coder): https://www.lennysnewsletter.com/p/how-a-91-year-old-vibe-coded-a-complex?utm_source=publication-search
—
Where to find Brooke:
TikTok: https://www.tiktok.com/@brookejlacey
Zosha app: http://zosha.app/
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Meta’s new ‘Muse Wonder’ model: good for AI agents, bad for design
I put Meta’s new Muse Wonder (and Wonder Full) frontier models through the How I AI bench, which covers nearly 20 tasks across prototyping, agentic workflows, coding, writing, and creative work. I scored …
Notes de l’épisode
I put Meta’s new Muse Wonder (and Wonder Full) frontier models through the How I AI bench, which covers nearly 20 tasks across prototyping, agentic workflows, coding, writing, and creative work. I scored everything with an LLM-as-a-judge pass and my own voice notes, then pulled together the first-look results you see here.
What you’ll learn:
- Why Meta’s “personal and professional agent” framing mostly holds up, and where it doesn’t
- What active focus mode actually does on long-running tasks, and whether it’s real
- The three agentic tasks where Muse ranked first, and why those categories matter
- What Muse’s design output actually looks like (it’s not good)
- How it compares to Sonnet on voice assistant tasks
- Where Muse lands on SVG and short-form video versus Opus 5.5, Astra, and Fable
- Whether I’m actually putting Muse into any of my agent workflows
—
Brought to you by:
DX—Engineering intelligence for the AI era
Jira Product Discovery—Prioritize with insights, build with confidence
—
In this episode, we cover:
(00:00) Intro and what Muse Wonder is
(01:19) Meta's frontier capability claims
(04:36) Muse code
(06:06) Benchmark overview and how the How I AI bench works
(07:51) Overall results and TL;DR verdict
(11:53) Agentic tasks
(13:36) Email writing and voice assistant performance
(14:35) Where Muse falls short
(18:28) SVG illustration results
(19:33) Short-form video editing
(21:18) Fast on one-shot work, slow on agent tasks
(22:15) Final verdict and where I'll use Muse Wonder next
—
Tools referenced:
- Meta Muse Wonder / Muse Wonder Full: https://research.meta.ai/blog/muse-wonder
- Muse Code: https://developer.meta.com/ai/lp/muse-code
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
How Meta designed Muse: an AI agent people actually want to talk to | Alex Cornell (Design Director)
Alex Cornell is a design director at Meta, where he leads design on Muse, Meta’s consumer AI agent. He came to the role with a long background in software product design, including time as a founder, and has …
Notes de l’épisode
Alex Cornell is a design director at Meta, where he leads design on Muse, Meta’s consumer AI agent. He came to the role with a long background in software product design, including time as a founder, and has spent the past year building one of the most thoughtful agent interfaces in the consumer AI space. Muse ships with a custom avatar, a three-layer status display, a personalized ideas surface, and a generative feed. Alex has been sharing the reasoning behind each of those design decisions publicly on Threads.
What you’ll learn:
- Why Alex built Muse to feel like messaging, not an AI app
- How Muse’s three-layer status system accommodates different levels of technical expertise
- What generative UI actually means in a shipped consumer product
- How Alex turned years of forgotten Instagram saves into a daily shopping catalog
- Why his mom apologized to her Muse, and what it means for AI design
- How he uses Muse to track his golf game, shot by shot, as he works toward a single-digit handicap
- The mindset shift designers need when the output is never pixel-perfect
- When to use the main chat versus a side chat, and where the architecture still falls short
—
Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
—
In this episode, we cover:
(00:00) Welcome and guest intro
(02:50) Designing Muse
(05:33) Choosing which primitives to surface: characters, ideas, feed
(08:50) Speed of model and response times
(11:01) Generative UI and personalization
(14:25) SkyMall demo: Instagram saves turned into a shopping catalog
(17:16) Shopping, monitoring, and price alerts with Muse
(19:07) Instagram and Pinterest connectors for visual data
(21:40) Golf use case: round recaps, shot data, practice breakdowns
(25:28) Main chat versus side chats
(27:56) Old blog turned morning podcast: 20 years of posts via the Wayback Machine
(29:39) Chess dashboard: game analysis with a snarky coach
(30:59) What designers need to learn for AI, and what still works
(34:33) Prompting strategy when Muse is being obstinate
(36:31) Where to find Alex
—
Tools referenced:
• Meta Muse: https://muse.ai
—
Other references:
• The Wayback Machine: https://web.archive.org
• Instagram: https://www.instagram.com
• Pinterest: https://www.pinterest.com
• Chess.com: https://www.chess.com
• SkyMall/Sharper Image aesthetic: https://www.skymall.com
—
Where to find Alex Cornell:
LinkedIn: linkedin.com/in/alex-cornell-5449a99
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
How OpenAI uses ChatGPT Sites (live at DevDay!) | Kath Korevec (Product Lead)
Kath Korevec is a member of the Product staff at OpenAI working on Codex, and she spent over a year building and using ChatGPT Sites internally before its public launch. She’s been on the front lines of …
Notes de l’épisode
Kath Korevec is a member of the Product staff at OpenAI working on Codex, and she spent over a year building and using ChatGPT Sites internally before its public launch. She’s been on the front lines of shipping Plugin Insights, MCP plugin hosting, and the connector ecosystem, which now includes around 60 integrations.
What you’ll learn:
- What Plugin Insights actually does, and why it changes who can use a site
- The incident command site Kath built for her OpenAI team, and how it uses live Slack and Notion connectors
- The one phrase that tells Codex to wire up connectors for you
- The infrastructure layer inside Sites that most people haven’t touched yet
- How Kath fixed her Spotify after her kids wrecked it, using Reddit and computer use
- The skill distribution model behind her community dungeon crawler, and why it’s a new way to think about collaboration
- Why model speed is what actually determines how creative you get
- Where Kath draws a hard line on AI acting in her name
—
Brought to you by:
Merge—Connective infrastructure for production AI
Vanta—Automate compliance and simplify security
—
In this episode, we cover:
(00:00) Welcome and intro
(01:00) Sites: internal testing and use cases
(05:53) Sites infrastructure
(09:00) Kath’s favorite connectors
(11:10) Unique ways to use Sites
(12:28) Curating a custom Spotify playlist
(15:50) Game design and development
(25:42) Bringing inference into Sites: the widget experiment
(30:00) Awesome Sites gallery
(31:57) Kath’s prompting strategy
(34:20) Wrap-up and how to find Kath
—
Tools referenced:
• ChatGPT Sites: https://chatgpt.com/sites
• OpenAI Codex: https://openai.com/codex
—
Other references:
• OpenAI DevDay: https://openai.com/devday
• Awesome Sites: https://awesomesites.ai
—
Where to find Kath Korevec:
LinkedIn: https://www.linkedin.com/in/kathleensimpson/?isSelfProfile=false
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Jev: 8 real use cases for the fastest, cheapest model I’ve ever used | John Lindquist
John Lindquist created egghead.io, a developer education platform used by hundreds of thousands of working engineers. These days he’s building mega.dev, a hands-on program specifically for developers who want …
Notes de l’épisode
John Lindquist created egghead.io, a developer education platform used by hundreds of thousands of working engineers. These days he’s building mega.dev, a hands-on program specifically for developers who want to do real work with AI agents, not just prototype them.
What you’ll learn:
- Why Jev is a decision engine, not a chatbot, and what that distinction actually changes about how you build
- How John built a real-time voice to-do app that classifies and executes commands with no visible pause
- The data deduplication pattern that merges messy records in milliseconds using confidence scores
- Why Jev works best as a router, and how a single text input can navigate users deep into an app
- What a chess match between Jev and a low-reasoning LLM reveals about speed, cost, and when to use which
- The multi-step classification pattern John reaches for when one Jev pass isn’t enough
- Where Jev falls short, and when you should still reach for a full generative model
—
Brought to you by:
Vanta—Automate compliance and simplify security
—
In this episode, we cover:
(00:00) John Lindquist returns for Jev week
(04:32) What Jev actually outputs
(06:15) Demo: real-time voice to-do app
(08:17) How sequential Jev calls chain together
(10:38) Demo: plain English to function name (grocery cart)
(11:50) Demo: data deduplication and record merging
(13:45) Confidence scores and multi-model validation
(15:06) Demo: Jev as a multi-level app router
(18:23) Architecting around Jev
(19:35) Demo: Jev vs. traditional LLM at chess (speed and cost benchmarks)
(24:29) DOM interactions as a decision set, not an infinite canvas
(28:21) Demo: Wikipedia “path to philosophy” route mapper
(30:28) Demo: multi-agent coordination and collision avoidance
(33:36) Demo: real-time presentation coach
(36:56) Quick recap
(39:54) Lightning round and final thoughts
—
Tools referenced:
• Jev (TypeSafe AI decision model): https://typesafe.ai/blog/introducing-system-one-models-and-jev
• Vercel AI Gateway: https://vercel.com/docs/ai-gateway
• OpenRouter: https://openrouter.ai
• Opus 5.5 (mentioned in context of iterative demo building): https://www.anthropic.com/claude-opus-5-5
—
Where to find John Lindquist:
LinkedIn: linkedin.com/in/john-lindquist-84230766
X: https://x.com/johnlindquist
Mega.dev: https://mega.dev/
Egghead.io: https://egghead.io/
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
OpenAI Dev Day 2026: The releases that actually matter
I spent the day at OpenAI’s DevDay in San Francisco, and I have good news and bad news: OpenAI released a lot of stuff.In this episode, I break down the announcements worth paying attention to - and show you …
Notes de l’épisode
I spent the day at OpenAI’s DevDay in San Francisco, and I have good news and bad news: OpenAI released a lot of stuff.
In this episode, I break down the announcements worth paying attention to - and show you what happened when I tested some of them early. We’ll meet my Dot, explore why Spaces and Sites could matter for how teams work, and get into the model and API updates I’m most excited about as a developer.
I use the Decisions API to find podcast thumbnails where nobody looks awkward, build a collaborative sketchpad with Astra ultrafast, and let my kids redesign a 3D world in real time. That last experiment cost about $97. My wallet has thoughts.
These are my early impressions: what’s promising, what still feels rough, and what I think you should try first.
What you’ll learn:
- What OpenAI’s Dots can do, how I’ve been using mine, and why I’m waiting to give a full verdict
- Why Spaces might be one of the most underhyped announcements for collaboration between humans and agents
- How Sites with connectors and plugins could help teams share internal tools with the right data permissions
- Where GPT-6.1 Sol fits in my model stack—and why speed and cost matter
- What vision adds to the Decisions API, including my thumbnail-selection and hot dog demos
- What Astra ultrafast makes possible for interactive AI apps, from collaborative drawing to a changing 3D game
- Where the speed feels magical, where the experience still needs work, and what it costs
—
In this episode, we cover:
(00:00) OpenAI DevDay recap—and pressing the Codex reset button
(00:58) Dots: early impressions and rough edges
(06:57) Spaces: working with humans and agents
(10:37) Sites, connectors, and sharing internal tools
(13:06) Models and platform: GPT-6.1 Sol
(14:36) Decisions API: fast decisions with vision
(15:27) Finding better podcast thumbnails with AI
(16:29) Hot dog or not hot dog?
(17:17) Astra ultrafast: speed, pricing, and possibilities
(18:50) The Other Pencil: drawing alongside AI
(19:45) Little Starship: a 3D world you can change with a prompt
(21:24) The $97 AI game—and what it makes possible
(22:25) Agents API, computer use, plugins, and plan updates
(23:03) What I’d try first
—
Tools referenced:
• ChatGPT: Dots, Spaces, and Sites: https://chatgpt.com/
• Codex: https://openai.com/codex/
• OpenAI API — GPT-6.1 Sol, Decisions API, and Astra ultrafast: https://platform.openai.com/
• Jev: https://typesafe.ai/
—
Other references:
• OpenAI DevDay 2026: https://devday.openai.com/
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Jev for beginners: how to use it and what to build
Jev is TypeSafe AI’s new decision model. It returns type-safe structured values (a choice, a score, a probability) instead of generated text, at 4 cents per million input tokens with no output charge. This week …
Notes de l’épisode
Jev is TypeSafe AI’s new decision model. It returns type-safe structured values (a choice, a score, a probability) instead of generated text, at 4 cents per million input tokens with no output charge. This week I ran it on five real projects: PR categorization, a meta-analysis of my own Claude and Codex sessions, Gmail triage, the ChatPRD product insights graph, and a live audience dashboard built from 4,500 YouTube comments.
What you’ll learn:
- What makes Jev fundamentally different from every other model I’ve used
- How I analyzed 1,700 PRs for 9 cents and what I found out about where my engineering effort actually went
- The personal meta-analysis you can run on your own Claude and Codex sessions right now
- Why I stopped using Jev alone, and what I pair it with now
- How I turned 4,500 YouTube comments into a searchable audience dashboard for almost nothing
- The real-time app I built in an afternoon that shows something surprising about Jev’s speed
- Why Jev’s pricing model is different from any LLM I’ve used, and what it makes practical to build
- The ChatPRD product insights project: 1,100 signals, 200,000 classifications, and what it cost me
—
Brought to you by:
OpenArt—An all-in-one AI creation platform for images, videos, music, audio, and more
—
In this episode, we cover:
(00:00) Jev launch and what makes it different from every other model
(02:49) Type-safe values explained
(05:28) Understanding Jev outputs
(07:39) Use case 1: PR categorization and pairwise clustering
(11:12) Use case 2: analyzing your own local Claude Code and Codex sessions
(13:00) Use case 3: Gmail triage with Jev scoring and LLM follow-up
(14:30) Use case 4: ChatPRD’s product insights graph
(18:17) Demo: How I AI audience signal dashboard
(22:14) Demo: voice-to-color emotion-mapping app
(25:16) Jev week recap and what’s coming in episode 2
—
Tools referenced:
• Jev (TypeSafe AI): https://typesafe.ai
• Vercel: https://vercel.com/ai
• GitHub API: https://docs.github.com/en/rest
• YouTube Data API v3: https://developers.google.com/youtube/v3
• OpenAI Realtime Voice API: https://platform.openai.com/docs/guides/realtime
• Gemini 3.5 Flash-Lite: https://ai.google.dev/gemini-api/docs/models/gemini-3.5-flash-lite
• API Ninjas Quotes API: https://api-ninjas.com/api/quotes
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Opus 5.5 vs. GPT-6 Sol: which model won my blind taste test?
I got up early to record an Opus 5.5 review. Then Anthropic and OpenAI dropped new models on the same morning, and I decided to do something I’d never done before: take the How I AI bench live. I put GPT-6 …
Notes de l’épisode
I got up early to record an Opus 5.5 review. Then Anthropic and OpenAI dropped new models on the same morning, and I decided to do something I’d never done before: take the How I AI bench live. I put GPT-6 Astra, GPT-6 Sol, Claude Opus 5.5, and more through the work I actually care about: emails, PRDs, frontend prototypes, backend work, long-running agents, SVGs, and video editing. I scored the outputs without knowing which model made them, so you get to watch me make predictions, change my mind, and reveal my own very inconsistent taste. Astra won my heart. Opus 5.5 won my week. Sol still has me split. There’s a creative result I got completely wrong, an LLM judge that disagreed with me, and a return to Barbie Bench: the 3D fashion game that keeps reminding me how far we have to go. The hands are tragic. AGI has not arrived.
What you’ll learn:
- How I run the How I AI bench blind, and what gets an output a bad score before I even know which model made it
- Why Astra won my heart while Opus 5.5 might be overall strongest, especially for long-running agents and B2B frontend
- Where Sol still wins me over on clear writing, readable PRDs, and price
- The character SVG results that completely overturned my prediction about Anthropic
- What happened when I asked these models to edit video, and why I think skills explain part of the disappointment
- Why an LLM judge disagreed with my rankings, and what it was rewarding that I wasn’t
—
In this episode, we cover:
(00:00) LIVE setup and new model launches
(01:30) What’s new in Opus 5.5, Sol, and Luna
(04:11) Guardrails, personality, and speed
(09:00) The How I AI bench and blind evaluation process
(11:31) Email and personal-productivity results
(13:50) Frontend prototype vibe checks
(24:10) Backend, agent personality, and long-running tasks
(28:25) SVG illustration test
(29:48) AI video-editing results
(30:43) Predictions before the reveal
(31:20) Barbie Bench: the 3D fashion-game test
(34:17) Results: Astra, Sol, and Opus 5.5
(35:04) Writing clarity and creative surprises
(36:51) Why the LLM judge disagreed with me
(37:24) What each model is actually best for
—
Tools referenced:
• Claude Opus 5.5: https://www.anthropic.com/claude-opus-5-5
• GPT-6 Sol and Luna: https://openai.com/index/introducing-gpt-6-sol-and-luna/
• Codex (OpenAI): https://openai.com/codex
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
I left Claude for months. Opus 5.5 is why I'm back
I’ve been off Claude for months. Not because it got dumb, but because it got annoying. The rambling, the hedging, the preachy little disclaimers on tasks that didn’t need them. I moved most of my daily work to …
Notes de l’épisode
I’ve been off Claude for months. Not because it got dumb, but because it got annoying. The rambling, the hedging, the preachy little disclaimers on tasks that didn’t need them. I moved most of my daily work to Codex and I didn’t miss it. Then Anthropic shipped Opus 5.5: 40% cheaper than Opus 5, faster, and with what they’re calling a fundamentally different alignment approach. I ran it for a week across real work, including four long-running agentic tasks, a full ChatPRD homepage redesign, an SVG benchmark, and one very firm refusal, and I’m ready to give you the honest verdict. There’s a lot to like. There are still two things that drive me a little crazy. And there’s one capability I genuinely wasn’t expecting.
What you’ll learn:
- Why I walked away from Claude entirely, and what it took for me to come back
- The real cost math on Opus 5.5 and why pricing matters more for agentic work than single prompts
- What happened when I ran four long-running agentic tasks, including one that tried to manipulate Claude mid-run
- Why Opus 5.5 is now my go-to for frontend prototyping, and where it still lets me down
- The one capability I genuinely didn’t see coming, and no other model in my stack can match it
- The moment Opus 5.5 told me flat-out no, and what that says about where Anthropic’s safety posture actually lands in practice
- Where Codex still wins, and how I’m splitting my model stack after a full week of testing
—
In this episode:
(00:00) Why I stopped using Claude
(01:02) What Anthropic says Opus 5.5 is
(01:54) Cost, speed, and benchmark overview
(03:20) Safety, alignment, and the cybersecurity limits
(05:02) How I AI bench
(05:39) Voice test: is it actually not annoying?
(07:54) Long-running agentic task results
(10:50) Frontend prototyping
(17:23) Writing voice and email
(19:41) SVG illustrations
(20:46) Video editing
(21:42) My verdict: what it’s good at, what it still isn’t
—
Tools referenced:
• Claude Opus 5.5: https://www.anthropic.com/claude-opus-5-5
• ElevenLabs MCP connector: https://elevenlabs.io/mcp
• Codex (OpenAI): https://openai.com/codex
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
How Warp ships 2,000 PRs a month with AI factories | Zach Lloyd (CEO, Warp)
Zach Lloyd is the co-founder and CEO of Warp, an AI-powered terminal and software factory platform used by tens of thousands of engineers. Before Warp, he spent nearly a decade at Google, including time as a …
Notes de l’épisode
Zach Lloyd is the co-founder and CEO of Warp, an AI-powered terminal and software factory platform used by tens of thousands of engineers. Before Warp, he spent nearly a decade at Google, including time as a principal engineer on Google Sheets. He built Warp from the ground up as a modern, AI-native alternative to legacy terminals, and the team has since expanded into software factories: a full cloud-based system that takes an idea in Slack all the way through to a merged PR.
In this episode:
- Why a software factory is more than a coding agent
- The public Slack → Linear → GitHub → QA workflow
- Human interactions per PR as a signal of automation and throughput
- Why human review is still the bottleneck
- Scoring agent runs, finding failure modes, and self-improving agent workflows
- Replaying real tasks to choose model cost and quality tradeoffs
- CEO workflows with Figma MCP, Granola, and research agents
—
Brought to you by:
DX—Engineering intelligence for the AI era
OpenArt—An all-in-one AI creation platform for images, videos, music, audio, and more
—
In this episode, we cover:
(00:00) Intro
(02:35) Warp’s AI software factory, Wilson
(09:23) Automatic factory triggers
(11:12) The engineering leader dashboard Zach wishes he’d had
(15:18) How code review is changing in an AI factory
(17:08) Tracking cost per PR across model configs
(18:47) Using LLM-as-a-judge to score every agent run
(20:02) Catching redundant tests
(22:19) How the factory self-improves from failed runs
(26:03) Quick recap
(28:33) Building a cost-quality Pareto chart for model selection
(31:35) How Zach uses AI for non-technical CEO work
(32:10) Figma MCP demo
(35:43) Granola MCP demo
(36:41) GOG CLI demo
(38:20) Thinking in parallel tasks instead of sequential ones
(40:42) Zach’s prompting strategy for factory tasks
(44:48) Where to find Zach
—
Tools referenced:
• Warp (AI terminal and software factories): https://warp.dev
• Warp Factories: https://warp.dev/factories
• Linear (project and issue tracking): https://linear.app
• GitHub (version control and PR management): https://github.com
• Slack (team communication and factory input layer): https://slack.com
• Sentry (crash reporting and automated issue triggers): https://sentry.io
• Figma (design, used via Figma MCP): https://figma.com
• Granola (AI meeting notes and MCP integration): https://granola.so
• Grok Bot (fast inference, cost/quality trade-off): https://x.ai/bot/guides/grok-bot-101
—
Where to find Zach:
X: https://x.com/ZachLloydTweets
—
Where to find Claire:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Muse review: The personal AI agent that gets consumer UX right
I spent a few hours putting Meta’s Muse, its new personal AI agent, through a real first-pass test: onboarding, calendar management, goal setting, a one-shot family morning newsletter, browser-based shopping …
Notes de l’épisode
I spent a few hours putting Meta’s Muse, its new personal AI agent, through a real first-pass test: onboarding, calendar management, goal setting, a one-shot family morning newsletter, browser-based shopping, and the animated avatar that honestly surprised me.
What you’ll learn:
- Why Muse is the best-designed personal agent I’ve tested, and what specifically made it feel that way
- The one-shot family PDF Muse produced that Claude and Codex never quite nailed
- How Muse’s permission model works, and why it’s different from every other agent I’ve used
- Why I set up a sleep training goal in Muse, and what it revealed about agent tone
- The activity feed feature I immediately wished Codex and Claude Code had
- Where Muse failed, and what it says about the limits of this category right now
- The animated avatar decision that showed me what top-of-craft AI product design actually looks like
—
Brought to you by:
Optimizely—Your AI agent orchestration platform for marketing and digital teams
OpenArt—An all-in-one AI creation platform for images, videos, music, audio, and more
—
In this episode, we cover:
(00:00) What Muse is and who it’s actually built for
(04:41) Signing in and the onboarding flow
(07:16) The activity feed and its task lineage
(08:24) First real task: managing the family calendar and deleting soccer practice
(09:48) Requesting a morning newsletter PDF
(14:29) The personalized news feed and how I set it up
(16:05) The “Ideas” feature as an out-of-the-box prompt library
(17:10) Setting up personal goals (water, shoes, and sleep training)
(21:40) Library: documents, websites, images, videos, and podcasts
(23:11) Quick recap and what I love
(23:56) Activity feed design deep dive: tool calls and step-by-step lineage
(25:18) How Muse handles permissions
(26:12) The animated avatar: Polly becomes Slime, the teal dragon
(29:34) Browser use test: shopping for New Balance 9060s (not great)
(31:15) Browser use test 2: buying IMAX tickets for The Odyssey (much better)
(33:34) TL;DR and what I’ll actually use Muse for going forward
—
Tools referenced:
• Muse: https://muse.ai/
• Stripe Link (payment method featured in Muse): https://link.com
• 1Password (future Muse integration mentioned): https://1password.com
• OpenClaw (Claire’s previous personal agent setup): https://openclaw.ai/
• Grok Bot (Grok-based agent from prior stack): https://x.ai/news/introducing-grok-bot
• Codex (OpenAI coding agent, comparison point): https://openai.com/codex
• NotebookLM (Google, comparison to Muse’s podcast generation): https://notebooklm.google.com
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
How Grok Bot designers use AI agents to build personal sites and product prototypes | John Bai & Peng Zheng
John Bai and Peng Zheng are designers on the Grok Bot team at SpaceXAI, where they’re building one of the most talked-about AI products right now. John writes publicly about his design process (his piece …
Notes de l’épisode
John Bai and Peng Zheng are designers on the Grok Bot team at SpaceXAI, where they’re building one of the most talked-about AI products right now. John writes publicly about his design process (his piece “Designing Grok Bot with Grok Bot” has already made the rounds) and shares bot templates with the design community. Peng brings a product-design sensibility to personal tools, and his website doubles as a live demo of what he builds.
What you’ll learn:
- How Peng built a self-updating personal website using Grok Bot as the entire backend pipeline, with no CMS and no Figma file
- The exact check-in bot setup that lets Peng send a photo or a place name and have his portfolio update itself automatically
- How John’s Figma Bro bot handles production design tasks while he’s at the gym
- How John uses voice memos to direct Figma work through an MCP connection without opening his laptop
- The “shower thought to prototype” workflow John uses with DevBot to test interaction ideas without first going through a product manager or engineer
- The “trash can method” of software development
- How both designers organize their personal bot ecosystems
- What John and Peng actually think AI means for the future of design as a craft
—
Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
Vanta—Automate compliance and simplify security
—
In this episode, we cover:
(00:00) Introducing John and Peng
(02:53) The Grok Bot hype train
(04:35) Peng’s self-updating personal website built with Grok Bot
(15:12) How AI makes design more accessible
(19:35) Website update result
(20:13) John’s Figma Bro bot
(23:35) Creating marketing materials for the bot marketplace
(26:00) DevBot: from shower thoughts to working prototypes
(28:48) The trash can method of software development
(31:19) Other bots John and Peng are using
(39:05) Practical tips for when bots don’t do what you want
—
Tools referenced:
• Grok Bot (xAI): https://x.ai/bot
• Figma: https://www.figma.com
• Figma MCP server: https://www.figma.com/mcp-catalog/
• Google Places API: https://developers.google.com/maps/documentation/places/web-service
• Notion: https://www.notion.so
• Swarm (Foursquare): https://www.swarmapp.com
—
Other references:
• Designing Grok Bot with Grok Bot: https://x.ai/bot/guides/designing-grok-bot-with-grok-bot
• Figma Bro bot template (shared by John Bai): https://x.ai/bot/marketplace/bots/figma-bro
• From zero coding background to hardware hacker: How Cursor + a Raspberry Pi makes AI fun: https://www.lennysnewsletter.com/p/from-zero-coding-background-to-hardware?utm_source=publication-search
—
Where to find John and Peng:
John Bai on X: https://x.com/johnbai
Peng Zheng on X: https://x.com/pengzheng_
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Build your own company brain: the enterprise AI playbook from Stripe’s engineering team | Sharadh Krishnamurthy
Sharadh Krishnamurthy is an engineering manager at Stripe, where he helped build Kai, the company’s internal AI agent used by more than 10,000 employees every week. He’s worked across several of Stripe’s core …
Notes de l’épisode
Sharadh Krishnamurthy is an engineering manager at Stripe, where he helped build Kai, the company’s internal AI agent used by more than 10,000 employees every week. He’s worked across several of Stripe’s core infrastructure teams, including data and developer experience, which gives him a grounded, systems-level perspective on what it actually takes to make AI work at enterprise scale. He’s currently focused on the governance, skills, and infrastructure layers that let every Stripe employee use AI safely and effectively, regardless of their technical background.
What you’ll learn:
- Why Stripe built Kai from scratch instead of buying, and what tipped the decision
- What Kai knows about you by default and what you actually control
- Why “projects” at Stripe are a governance mechanism, not just a folder
- How Stripe structured its data layer so agents can query safely at scale
- Why the infrastructure Stripe built for human developers turned out to be exactly what agents needed
- How Kai’s skills platform lets any employee package a workflow, and what happens when you have 2,000 of them
- What Sharadh learned the hard way when agents nearly took down production systems
—
Brought to you by:
DX—Engineering intelligence for the AI era
Hyperagent—Deploy fleets of agents that handle real work
—
In this episode, we cover:
(00:00) Introducing Sharadh
(02:46) Why Stripe built an AI agent (Kai) instead of buying tools
(05:18) What Kai knows about you (and what you can turn off)
(06:51) Projects as a governance layer
(10:04) Live demo: Kai builds a dashboard
(12:18) Tools, skills, and the secure sandbox
(17:22) Why Stripe has benefited so much from AI
(19:20) Agentic identity, load shedding, and rogue agents
(20:41) Iterating on the dashboard
(25:01) How they rolled out Kai across the team
(29:07) How projects work
(34:18) Bespoke agents for bespoke use cases
(35:58) The skill builder workflow
(40:40) Skill quality, evals, and telemetry
(43:01) Recap
(45:13) Lightning round
—
Tools referenced:
• Trino: https://trino.io/
• Anthropic: https://www.anthropic.com/
• Gemini: https://gemini.google.com/
• Cursor: https://www.cursor.com/
—
Where to find Sharadh Krishnamurthy:
LinkedIn: https://www.linkedin.com/in/sharadhk
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
GPT-6 Astra is a banger - here’s everything I’ve built
I got early access to GPT-6 Astra: when I say this model broke through tasks I couldn’t crack with 5.6 Sol or Fable, I mean it specifically: the ChatPRD product intelligence feature, building 3D games, the …
Notes de l’épisode
I got early access to GPT-6 Astra: when I say this model broke through tasks I couldn’t crack with 5.6 Sol or Fable, I mean it specifically: the ChatPRD product intelligence feature, building 3D games, the hardware hack, and a handful of one-shot coding projects I’d tried and failed on repeatedly.
What you’ll learn:
- Why Astra’s computer use feels different, and which production tools I’m trusting it with
- The one feature I’d thrown every model at for six months, and what finally got it to 90%
- How I’m using browser use for QA, not building, and what it found that I would’ve missed
- Why I think UI is genuinely back, and what that means for SaaS and MCPs
- The hardware hack I’d been chasing since GPT-5.5, and how Astra finally cracked it
- What Astra built me in Blender in one shot, and why 3D is my new capability benchmark
- The AIM-style Mac app Astra made in one shot, and what it signals about desktop development now
- An honest take on speed, cost, and whether Astra is worth making your daily driver
—
In this episode, we cover:
(00:00) GPT-6 Astra overview
(03:44) Browser/computer use test on my CRM
(09:08) Flora thumbnail generation
(13:00) Browser use for QA
(15:20) Coding: ChatPRD product intelligence feature, finally one-shotted
(18:36) Hardware hack: Divoom MiniToo CLI and live streaming display
(22:23) Building an AIM-style Mac app
(24:24) Blender and 3D assets: Barbie Bench and the kids’ family app
(28:52) Summary: what Astra is great at and what to try first
—
Tools referenced:
• GPT-6 Astra: https://openai.com/index/gpt-6-astra/
• Codex: https://openai.com/codex
• Flora (node-based AI image/video editing): https://flora.ai/
• Figma: https://www.figma.com
• Blender: https://www.blender.org
• GPT Image 2: https://developers.openai.com/api/docs/models/gpt-image-2
• Divoom MiniToo: https://divoom.com/products/minitoo
• cxo.dev: https://www.cxo.dev/
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Grok Bot vs. OpenClaw: How I replaced my entire agent stack
I’m running about 30 active agents at any given moment, and in this episode I break down my full Grok Bot setup: what it is, how it compares to OpenClaw, and the nine bots I’ve built for work and my personal …
Notes de l’épisode
I’m running about 30 active agents at any given moment, and in this episode I break down my full Grok Bot setup: what it is, how it compares to OpenClaw, and the nine bots I’ve built for work and my personal life. We go deep on Chief (my chief-of-staff bot sweeping six inboxes and multiple Slack workspaces), TradBot (the family agent that prints a kitchen-table newspaper for my kids), two engineering bots handling my PR queue and SOC 2 compliance monitoring, Holly Helpdesk, and a handful of personal bots I didn’t expect to actually love. I also walk through how I migrated everything from OpenClaw, including the script I used to export and transplant each agent’s identity and schedule.
What you’ll learn:
- The three primitives Grok Bot is built on, and why one of them changes what agents can actually do
- How Chief, my general-purpose chief of staff, handles a scope I didn’t think a single bot could manage
- The writing quirk I noticed immediately with the Grok model, and what I did about it before letting it near my inbox
- Why I created a family agent, what it produces every morning, and the design principle I used that has nothing to do with a screen
- The two engineering bots doing work I used to do myself, and how one of them handles compliance in a way that surprised me
- How Holly Helpdesk started getting five-star reviews from customers who had no idea they were talking to a bot
- The personal bots I built mostly on a whim, and the one I now look forward to every Monday morning
—
Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
Hyperagent—Deploy fleets of agents that handle real work
—
In this episode, we cover:
(00:00) Why I migrated from OpenClaw to Grok Bot
(02:10) Grok Bot overview: the three core primitives
(07:41) Chief: my chief-of-staff bot
(11:51) OpenClaw vs. Grok Bot
(12:41) TradBot: my family agent
(19:51) LGTM the PR Closer
(22:03) Lockdown: SOC 2 control monitoring bot
(24:00) Holly Helpdesk: customer support
(26:58) Penny Pincher: subscription audit, insurance negotiation, Rolex shopping
(29:37) ShopZilla and Sylvie Style: personal shopping and wardrobe bots
(32:54) How to migrate your OpenClaws
(34:26) Final take
—
Tools referenced:
• Grok Bot (SpaceXAI multi-agent platform): https://x.ai/news/introducing-grok-bot
• OpenClaw (previous agent platform): https://openclaw.ai/
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
How I turned Claude into a self-improving PM assistant | Daniel Blum (PM, Melio)
Daniel Blum is a product manager at Melio, a B2B payments company, and one of the most systematic thinkers I’ve had on the show when it comes to personal AI infrastructure. He’s spent the past year building a …
Notes de l’épisode
Daniel Blum is a product manager at Melio, a B2B payments company, and one of the most systematic thinkers I’ve had on the show when it comes to personal AI infrastructure. He’s spent the past year building a Claude- and Cowork-based productivity system that manages his Notion board, processes his Slack and email, and runs self-improvement loops every week without needing to be prompted. Beyond his own workflow, Daniel built and scaled a “Workstation” onboarding plugin that gets any Melio employee up and running with a personalized Claude setup in about 15 minutes.
What you’ll learn:
- Why Daniel says the two rules that make any AI system powerful aren’t about the tool you pick
- How his weekly prep automation fills an entire Notion board from scratch every Sunday, without his touching it
- The morning brief feature that teaches Claude new internal terms on its own, so company jargon never slows it down
- Why he describes Notion as “read-only” now, and what that says about how PM workflows are changing
- The self-improvement loop that watches Daniel’s edits, spots recurring friction, and suggests new skills to build
- How he uses a skill called “Improve” to filter the endless flood of AI tips without drowning in them
- What he built to scale his personal system to every PM at Melio, and the UX lesson he learned the hard way
- The capability gap that’s still keeping him from running 100% of his work through Claude
—
Brought to you by:
Optimizely—Your AI agent orchestration platform for marketing and digital teams
Jira AI SDLC—Get your tokens’ worth with Jira
—
In this episode, we cover:
(00:00) Daniel’s background and the PM overhead problem he needed to solve
(03:30) His AI stack at Melio
(05:00) The two rules that make any AI system genuinely powerful
(06:00) The Notion board Cowork built for him (and manages on his behalf)
(07:30) How he contextualizes Claude with voice memos, links, and recurring updates
(09:00) His weekly prep automation
(11:00) His morning brief
(15:00) How Claude flags unknown internal terms and saves them to context
(17:30) Running 70% to 80% of his workday through Cowork
(19:00) Chrome connector vs. MCPs for tools without integrations
(20:00) The real ROI question: why the early weeks feel slow, and why you push through anyway
(25:00) Scaling the system to the team with the Workstation plugin
(26:30) The self-improvement loop
(31:00) How the Improve skill separates actually useful AI tips from the hype
(32:00) The Workstation onboarding flow, and the UX lesson from distributing “Spectacular”
(38:00) The 20% Claude still can’t do, and what changes when it can
(41:00) What Daniel spends his reclaimed time on
(42:30) Claude rage
—
Tools referenced:
• Claude: https://claude.ai
• Notion: https://notion.so
—
Other references:
• 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?utm_source=publication-search
• How the founder of Morning Brew built a Claude content machine that never runs out of ideas and never sounds like slop | Alex Lieberman: https://www.lennysnewsletter.com/p/how-the-founder-of-morning-brew-built?utm_source=publication-search
—
Where to find Daniel Blum:
LinkedIn: https://www.linkedin.com/in/blumd/
Website: https://www.imdanielblum.com
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
I spent $20,000 on Devin in a month. Here’s what I learned | Ryan Carson (solo founder)
Ryan Carson is a five-time founder and the current solo founder of Untangle, a B2B SaaS platform for family law firms. Before Untangle, he co-founded Treehouse, an online coding education platform, and has …
Notes de l’épisode
Ryan Carson is a five-time founder and the current solo founder of Untangle, a B2B SaaS platform for family law firms. Before Untangle, he co-founded Treehouse, an online coding education platform, and has spent the better part of two decades building and leading tech companies. He’s active on X, where he shares his solo founder journey in real time, including what he actually spends on AI tools each month.
What you’ll learn:
- Why Ryan manages 15 concurrent Devin agents with a folder system and a piece of paper, not a dashboard
- The Watchdog playbook: what he built to replace a customer success team across every law firm account
- How his LAN PR skill closes the loop on 40 daily PRs without a QA team reviewing a single one
- Why he moved off local agents almost entirely, and the one situation where he still reaches for Codex
- The design workflow we’re both using: Claude Design into a Markdown spec, then Codex to build the real thing
- What he found when he got away from his computer and met a real customer, and why it changed his entire product direction
- How he’s hiring his first engineer without a single phone screen or interview
- Why we both think more AI output is actually the wrong goal, and what to optimize for instead
—
Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
Jira AI SDLC—Get your tokens’ worth with Jira
—
In this episode, we cover:
(00:00) Introduction to Ryan Carson
(02:55) Ryan’s update: Untangle, the divorce PMF pivot, and B2B growth
(07:35) Ryan’s current Devin stack: folders, P0 threads, and the paper list
(16:29) Watchdog playbook: account monitoring across every firm
(18:09) Managing agent decision fatigue: Ryan’s method vs. Claire’s
(20:32) Producing more output does not make a better product
(22:47) Using cloud agents for ops beyond just code
(25:14) When to use Codex vs. Devin vs. Claude Code
(27:15) Merge Mommy recap
(28:15) LAN PR skill: review loops, video walkthrough, and auto-merge
(30:20) Slack vs. Devin threads for async team communication
(35:58) Claude Design plus Codex for building a technical design system
(39:00) EA tools: Polly the OpenClaw vs. Claude Code on a Mac Mini
(42:09) Quick recap and final thoughts
—
Tools referenced:
• Devin (Cognition): https://www.cognition.ai/
• Codex (OpenAI): https://openai.com/codex
• Claude Code/Claude Design (Anthropic): https://www.anthropic.com/claude
• OpenClaw (Claude-based desktop client): https://openclaw.ai
• Cursor: https://www.cursor.com/
• BugBot (Devin’s built-in PR review): https://cursor.com/bugbot
• Sentry (error monitoring referenced in Watchdog): https://sentry.io/
• Ugmonk (analog to-do system): https://ugmonk.com/
—
Other references:
• Devin playbooks/skills documentation: https://docs.cognition.ai/
• Jack Dorsey/Buzz (async-first communication referenced): https://buzz.new/
• Merge Mommy (Claire’s Eve agent for PR risk scoring, deployed on Vercel): https://www.lennysnewsletter.com/p/build-an-ai-code-review-bot-in-30
—
Where to find Ryan Carson:
Untangle: https://untangle.us
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
I tested Grok Bot, Grok 4.6, and Cursor Origin - here’s my honest take
This week I’m doing a solo breakdown of everything xAI and Cursor have shipped recently, including Grok Bot, Cursor Origin, and the Grok 4.6 model. I set up five Grok Bots, ran Grok 4.6 through my Claire …
Notes de l’épisode
This week I’m doing a solo breakdown of everything xAI and Cursor have shipped recently, including Grok Bot, Cursor Origin, and the Grok 4.6 model. I set up five Grok Bots, ran Grok 4.6 through my Claire Weighted Index against GPT-5.6 Sol, Claude Sonnet 5, and Opus 5, and spent time actually using Origin as a GitHub replacement. Here’s what’s worth your attention, what’s overhyped, and where I’m personally putting my time.
What you’ll learn:
- The one Grok Bot feature no other agent platform has shipped yet, and why it made me actually use the product
- What a week of real Grok Bot use revealed, and why I still reach for my OpenClaws
- Whether Cursor Origin is a GitHub replacement or just a pretty redesign
- Where Grok 4.6 landed on the Claire Index, and the one category where it genuinely surprised me
—
Brought to you by:
Bolt.new—Turn your idea into a real product
Jira AI SDLC—Get your tokens’ worth with Jira
—
In this episode, we cover:
(00:00) Why everyone’s quietly switching to Grok
(01:52) Grok Bot overview and setup
(03:22) My 5 Grok Bots
(04:30) The killer feature: multi-account connectors
(06:07) Grok Bot’s virtual machine and how it actually works
(06:41) Experience overview
(07:35) What I don’t love about Grok Bot
(10:08) Grok Bot use cases and my honest verdict
(12:20) Cursor Origin: the agent-native GitHub replacement
(13:47) What Origin actually looks like in practice
(14:59) Why I’m not switching from GitHub yet
(17:42) What would get me to move over
(18:52) Grok 4.6 and the How I AI Vibe bench
(20:41) Claire Index results: where Grok 4.6 ranked
(23:03) Design evals: where Grok surprised me
(25:00) My conclusion and how I’m splitting my time now
—
Tools referenced:
• Grok Bot: https://x.ai/bot
• Cursor: https://cursor.com/home
• Cursor Origin: https://cursor.com/origin
• OpenClaw: https://openclaw.ai/
• GitHub: https://github.com
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
How a solo founder used Codex and ChatGPT to launch a fashion brand without engineers | Yana Welinder
Yana Welinder is the solo founder of Yana Bana, an AI-native fashion brand built with AI as her technical co-founder, starting from hand-drawn sketches and ending with runway photos, CAD files for 3D printing …
Notes de l’épisode
Yana Welinder is the solo founder of Yana Bana, an AI-native fashion brand built with AI as her technical co-founder, starting from hand-drawn sketches and ending with runway photos, CAD files for 3D printing, and a live Stripe-connected pre-order site—no engineers required. A former product leader, she brings an operator’s rigor to her creative process: her “fashion prompt” is a detailed spec covering silhouette, volume, fabric behavior, movement, and sound, and watching her use Codex plus computer use to navigate 3D design software that’s entirely new to her is a clarifying demo of what today’s toolset actually makes possible.
What you’ll learn:
- How Yana uses a custom fashion prompt as a technical spec to get consistent, realistic, on-design outputs
- Why ChatGPT Images 2.0 outperforms other models for fashion design
- How she uses Codex plus computer use to operate CAD and fashion software she’s never personally learned
- The workflow for taking a garment from hand-drawn sketch to product photo, runway photo, and influencer shot in a single session
- How she ran vendor outreach end to end using deep research and browser use
- How she built a full e-commerce site with voting, databases, and Stripe integration
- Why she’s testing human patternmakers and Codex in parallel
—
Brought to you by:
Merge—Connective infrastructure for production AI
Jira AI SDLC—Get your tokens’ worth with Jira
—
In this episode, we cover:
(00:00) Introducing Yana Welinder and Yana Bana
(02:38) Tour of the Yana Bana site
(05:20) The fashion prompt stack
(07:39) Live demo: generating a jacket from a prompt in ChatGPT
(10:01) Why Image Gen 2.0 beats other models
(11:51) The “prompt as spec” principle
(14:02) Iterating the design
(17:12) Using Codex and computer use to build CAD files in 3D software
(20:50) Vendor research, outreach emails, and Superhuman browser use
(23:34) Building the full e-commerce site
(27:40) Quick recap and what’s still hard
(30:05) How Yana prompts when AI pushes back
(31:15) Where to find Yana and how to vote on her garments
—
Tools referenced:
• ChatGPT (Images 2.0): https://chat.openai.com
• Codex (OpenAI): https://openai.com/codex
• CLO 3D (fashion pattern software): https://www.clo3d.com
• Vercel: https://vercel.com
• GitHub: https://github.com
• Stripe: https://stripe.com
• Superhuman: https://superhuman.com
—
Other references:
• Ruth Asawa: https://ruthasawa.com
• SFMOMA (Ruth Asawa): https://www.sfmoma.org/artist/Ruth_Asawa/
—
Where to find Yana Welinder:
LinkedIn: https://www.linkedin.com/in/ywelinder/
Website: https://www.yanabana.com
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Claude Code for normal people: skills, voice mode, and how to collaborate with AI
Grace Clarke is an AI educator and former marketing consultant who taught herself Claude Code earlier this year and built a curriculum out of the process. She now runs her entire service business on tools she’s …
Notes de l’épisode
Grace Clarke is an AI educator and former marketing consultant who taught herself Claude Code earlier this year and built a curriculum out of the process. She now runs her entire service business on tools she’s built with Claude, including a pipeline operator, a proposal maker, and a Gmail replacement she created in under 30 minutes, and teaches individuals and teams to do the same.
What you’ll learn:
- How to build an hourly pipeline in Claude that moves clients through your process automatically
- Why Grace ditched traditional proposals for password-protected, interactive HTML documents built in Claude
- How she uses a “voice guide” skill file so every Claude output sounds like her, not like AI slop
- The two-step forcing function she teaches non-technical clients to build the muscle of opening Claude
- Why she started building in Claude Code, then handed the work off to Cowork via a Markdown session file
- How she replaced Gmail entirely with a custom inbox
- Why she teaches “intent engineering” instead of prompt engineering, and what that looks like in practice
- How she uses Claude on her phone, on walks, to track workouts and manage plants alongside client work
—
Brought to you by:
Bolt.new—Turn your idea into a real product
Hyperagent—Deploy fleets of agents that handle real work
—
In this episode, we cover:
(00:00) Grace’s background and why she started building with Claude
(04:48) The pipeline operator: what it is and how it runs her business every hour
(08:48) Building the muscle memory to use AI
(12:02) What goes into building a skill file (voice guide, proposal rules, versioning)
(13:50) How she built her proposal maker
(16:15) The voice guide: teaching Claude how she thinks, not just how she writes
(21:22) Live demo of the custom Gmail replacement built in Cowork
(30:44) Workout tracking, plant photos, and tiny daily Claude habits
(34:51) The biggest misconception holding people back from adopting AI
(38:36) What Grace does when Claude is not giving her what she wants
(40:38) Claude builds a proposal for Claire in real time
—
Tools referenced:
• Claude: https://claude.ai
• Claude Code: https://claude.ai/code
• Netlify: https://www.netlify.com
• Google Forms: https://forms.google.com
• Google Sheets: https://sheets.google.com
• Google Cloud (for service accounts and custom connectors): https://cloud.google.com
—
Other reference:
• Stratechery by Ben Thompson: https://stratechery.com
—
Where to find Grace Clarke:
LinkedIn: https://www.linkedin.com/in/gracegclarke/
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Build an AI code review bot in 30 minutes with Vercel Eve
AI writes most of my code now, and that created a new problem: a PR queue I couldn’t keep up with. In this episode, I walk through how I built Merge Mommy, a Vercel Eve agent that reads every PR after checks …
Notes de l’épisode
AI writes most of my code now, and that created a new problem: a PR queue I couldn’t keep up with. In this episode, I walk through how I built Merge Mommy, a Vercel Eve agent that reads every PR after checks pass, scores it across six risk dimensions, auto-approves the low-risk ones, and pings me in Slack for anything that needs a human. I built the whole thing in one Codex session, it’s SOC 2 compatible, and it’s already cleared my backlog.
What you’ll learn:
- Why AI-generated PRs create a review bottleneck and why the answer isn’t reviewing all of them
- How Intercom 5x’d PR approval speed and reduced revert rates by putting AI in the review loop
- Why Vercel Eve is the simplest framework I’ve found for deploying AI agents in Slack and GitHub
- How I built a full PR review agent in Codex with one prompt and a few steering turns
- The six components I use to score PR risk (blast radius, reversibility, data security, ops impact, verification gap, and change surface)
- How I used Chrome browser use to handle Slack bot and GitHub app configuration so I never had to click through setup screens manually
- Why auto-approved PRs can be SOC 2 compliant as long as the process is auditable, queryable, and in your risk policy
- How to set up Slack escalation so low-risk PRs become a two-click merge with no manual review
—
Brought to you by:
WorkOS—Make your app Enterprise Ready today
—
In this episode, we cover:
(00:00) The PR review backlog problem nobody’s talking about
(02:35) Why you don’t have to review every AI-generated PR
(05:14) How Intercom built AI-approved PRs (and proved they’re safer)
(06:10) How the Eve framework works (directory, skills, channels, connectors)
(09:16) The Codex prompt I used to build the entire bot
(11:36) What the agent actually does: read, score, approve, or escalate
(13:07) Setting up your Eve agent
(15:47) The six-component risk scoring model
(17:23) Merge Mommy in action: three live PR examples
(21:10) Recap and how to build your own version
—
Tools referenced:
• Vercel Eve: https://vercel.com/eve
• Vercel AI SDK: https://sdk.vercel.ai/
• Vercel Chat SDK: https://chat-sdk.dev/
• Codex (OpenAI): https://openai.com/codex
—
Other references:
• AI is approving our pull requests: Here’s how we made it safe: https://www.intercom.com/blog/ai-is-approving-our-pull-requests-heres-how-we-made-it-safe/
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
ChatGPT Codex Voice + browser + Sites: an expert’s AI workflow | Nick Baumann (OpenAI)
Nick Baumann is on the Developer Experience team at OpenAI, where he spends his days building with, testing, and communicating the capabilities of ChatGPT Codex and ChatGPT Work. In this episode, Nick walks me …
Notes de l’épisode
Nick Baumann is on the Developer Experience team at OpenAI, where he spends his days building with, testing, and communicating the capabilities of ChatGPT Codex and ChatGPT Work. In this episode, Nick walks me through several features that have launched or evolved recently: the new voice interface with its screen-reading orb, the Heartbeats automation system in ChatGPT Work on mobile, the live ChatGPT Sites deployment feature, and his personal use case for AI-assisted UGC video editing.
What you’ll learn:
- How two-person voice chat works
- How Heartbeats work
- How to build and deploy a live website with ChatGPT Sites
- How to delegate a flight search, hotel booking, and expense report to Codex in a single voice conversation without opening a single app manually
- Why ChatGPT Work on mobile is the most underutilized AI workflow for people already using the ChatGPT app
- How to use a custom UGC Video plugin to feed 50 raw clips into ChatGPT, let it pull transcripts, pick the best takes, and assemble a finished vertical video overnight
—
Brought to you by:
Bolt.new—Turn your idea into a real product
Hyperagent—Deploy fleets of agents that handle real work
—
In this episode, we cover:
(00:00) Introduction to Nick Baumann
(02:56) What’s new in Codex
(05:40) ChatGPT Work and Heartbeats
(06:40) Live Codex voice demo
(13:25) Latency vs. intelligence
(14:36) Quick recap
(15:04) Voice on mobile and the ChatGPT Sites workflow
(21:24) Live UGC video demo
(32:30) How I AI website results
(34:04) Lightning round and final thoughts
—
Tools referenced:
• ChatGPT Codex: https://chatgpt.com/codex
• ChatGPT Sites: https://chatgpt.site
—
Where to find Nick Baumann:
LinkedIn: linkedin.com/in/nick--baumann
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
From zero coding background to hardware hacker: How Cursor + a Raspberry Pi makes AI fun
Maddie Reese is a vibe coder, hardware tinkerer, and builder. She builds things at the intersection of software and hardware, including a thermal receipt printer that people around the world can message …
Notes de l’épisode
Maddie Reese is a vibe coder, hardware tinkerer, and builder. She builds things at the intersection of software and hardware, including a thermal receipt printer that people around the world can message directly, a fully functional Twitter pager running on a Raspberry Pi, and a personal API that tells you her coffee order so you don’t have to ask. Maddie approaches hardware the same way she approaches software: dump the idea into Cursor, let it interview her, get a shopping list, triple-check the parts before buying, and build. She got her start after her dad introduced her to Lovable, and she locked herself in her room and didn’t come up for air.
What you’ll learn:
- How Maddie built a thermal receipt printer that accepts messages from anywhere in the world using a Raspberry Pi and Bluetooth
- How to use Cursor’s agent view to brainstorm a hardware project
- What belongs in a personal API and why agents, not just humans, will be the ones using it
- How to read just enough code to do some damage, without needing to understand all of it
- Why building for fun, not practicality, is the fastest path to actually shipping physical projects
—
Brought to you by:
Firecrawl—Power AI agents with clean web data
Customer.io—Build customer engagement campaigns from a single prompt
—
In this episode, we cover:
(00:00) Intro
(02:00) Maddie’s AI pill moment
(03:53) The thermal receipt printer: live demo and how it works
(11:10) The pager project
(17:23) Why she uses Cursor’s clean agent view instead of terminals and browsers
(19:05) The personal API: coffee order, pets, favorite snacks, and more
(22:57) Lightning round and final thoughts
—
Tools referenced:
• Cursor: https://www.cursor.com/
• Lovable: https://lovable.dev/
• Raspberry Pi: https://www.raspberrypi.com/
• Resend: https://resend.com/
• Cloudflare Workers: https://workers.cloudflare.com/
• Supabase (Conduct database referenced): https://supabase.com/
• Twitter/X API: https://developer.x.com/
• Spoke pager network: https://www.spoke.com/
• OpenClaw: https://openclaw.ai/
—
Where to find Maddie Reese:
Website: https://maddiedreese.com
Message her directly: https://maddiedreese.com/message
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Claude Opus 5 review: this model is brilliant (but annoying)
I’m tired of new models. Every week there’s a new benchmark, a new frontier intelligence claim, a new thing to test. But here we are, because Opus 5 just dropped and I’ve had real hands-on time with it, so …
Notes de l’épisode
I’m tired of new models. Every week there’s a new benchmark, a new frontier intelligence claim, a new thing to test. But here we are, because Opus 5 just dropped and I’ve had real hands-on time with it, so you’re getting the honest version.
This is my full Opus 5 review: personality analysis, live benchmark results from my 7-model How I AI eval, and an actual verdict on whether I’m swapping it in. Spoiler: the answer surprised me.
What you’ll learn:- Why I think we’ve hit an intelligence overhang and what that means for which model variables actually matter now
- How Opus 5’s “neurotic” personality showed up in real coding sessions, including a merge conflict it refused to touch
- What I learned from asking both Opus 5 and GPT‑5.6 Sol “who’s smarter, you or me?”
- Where Opus 5, GPT‑5.6 Sol, Sonnet 5, and Gemini 3.1 Pro actually landed on the HIA benchmark leaderboard
- The one use case where Opus 5 earned straight 5s from me
- My actual plan for using Opus 5 going forward
—
In this episode, I cover:
(00:00) Opus 5 is here
(03:15) First impressions
(06:12) Opus 5 vs. GPT‑5.6 Sol personality comparison
(14:39) Claude Slop: the verbosity problem and why it makes my blood boil
(16:55) How the How I AI benchmark works (7 models, 6 tasks, blind scoring)
(18:30) Live benchmark results: the leaderboard reveal
(23:25) My verdict and how I’ll actually use Opus 5
—
Tools referenced:
• Claude Opus 5:
• Anthropic blog: https://www.anthropic.com/news
• GPT‑5.6 Sol: https://openai.com/index/previewing-gpt-5-6-sol/
• Sonnet 5: https://www.anthropic.com/news/claude-sonnet-5
• Gemini 3.1 Pro: https://deepmind.google/models/gemini/pro/
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Computer & browser use in Codex (5 real examples)
Today I’m walking you through one of my absolute favorite AI features right now: browser and computer use via Codex (the ChatGPT desktop app). I use this every single day, personally and professionally, and I …
Notes de l’épisode
Today I’m walking you through one of my absolute favorite AI features right now: browser and computer use via Codex (the ChatGPT desktop app). I use this every single day, personally and professionally, and I wanted to share the specific workflows I’ve built, the moments that surprised me, and the mental model that makes it actually click.
What you’ll learn:
- How browser use and computer use work, and why the Codex desktop app plus Chrome extension is the combo I rely on
- How I use Codex to QA my onboarding flow, including exhaustive mobile testing I would never do manually
- Why under-prompting frontier models gets better results than detailed step-by-step instructions
- How my husband EJ Lawless’s persona-impersonation trick surfaces friction points I can’t see as the builder
- How I use browser use to get through my LinkedIn inbox without touching it myself
- How I had Codex shop Free People’s sale and add 10 medium items to my cart (breastfeeding-friendly and Hawaii-ready)
- How computer use can control iPhone mirroring so your Mac can technically operate your phone
- Three more computer-use shortcuts: filling annoying forms, creating Google Sheets mid-workflow, and managing router
—
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) Intro
(01:46) What browser use and computer use actually are
(03:08) Why I use Codex specifically and how the desktop app plus Chrome extension works
(04:15) Use case 1: QA testing my onboarding flow
(10:41) Results: 11 issues, one high-severity blocker, one Google Sheet with screenshots
(12:10) Use case 2: persona testing
(18:20) Use case 3: LinkedIn inbox, hands-free
(20:37) Use case 4: AI personal shopper
(23:47) Rapid-fire uses: forms, iPhone mirroring, router access from out of state, Google Docs
(26:50) Wrap-up
—
Tools referenced:
• Codex (ChatGPT desktop app): https://openai.com/codex
• Claude desktop app: https://claude.ai/download
• Monologue (voice dictation for AI): https://monologue.app
• iPhone mirroring (Apple): https://support.apple.com/en-us/111775
• Google Sheets: https://sheets.google.com
—
Other references:
• Jesse Genet episode (How I AI): https://www.lennysnewsletter.com/p/5-openclaw-agents-run-my-home-finances?utm_source=publication-search
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
How the founder of Morning Brew built a Claude content machine that never runs out of ideas and never sounds like slop | Alex Lieberman
Alex Lieberman co-founded Morning Brew in college and grew it into one of the most-read business newsletters in the world before selling it to Business Insider. Now he’s the co-founder and co-managing partner …
Notes de l’épisode
Alex Lieberman co-founded Morning Brew in college and grew it into one of the most-read business newsletters in the world before selling it to Business Insider. Now he’s the co-founder and co-managing partner of Tenex. In this episode, Alex explains why distribution is becoming a durable moat, why founders and teams need to “climb Cringe Mountain,” and how he rebuilt his content process around AI without letting it produce generic slop. He walks us through every step of his Content Machine live: an Oracle that scans internal systems and the internet for content spikes, an interview panel that pulls out his real ideas, voice and style files that keep drafts sounding like him, an editorial council that scores and revises posts, and a lessons loop that learns from his feedback.
What you’ll learn:
- Why the blank page is the biggest friction point in content creation, and how an AI Oracle eliminates it
- How to map your current workflow before you add any AI
- How Alex built a six-step Content Machine in Claude that goes from idea spike to publishable post
- Why the interview step (not the drafting step) is where AI slop actually comes from
- How to codify your voice in a Markdown file so an AI drafts in your register, not the internet’s average
- Why your employees are your most underleveraged marketing channel right now
- How the Tenex Creator Cup turned content creation into a team sport with a $5,000 prize pool
—
Brought to you by:
Firecrawl—Power AI agents with clean web data
Customer.io—Build customer engagement campaigns from a single prompt
—
In this episode, we cover:
(00:00) Introduction to Alex Lieberman
(02:35) Why Alex built a content machine
(06:56) Alex’s thoughts on AI slop
(09:00) Mapping the workflow from scratch
(13:24) The six-step Content Machine setup
(23:11) Live demo: Oracle, Interview Panel, and Writer’s Council in action
(30:38) Employee advocacy: the Tenex Creator Cup and $5K prize pool
(36:45) Lightning round: great engineers, AI use cases, slop fixes
—
Tools referenced:
• Claude / Claude Code (Anthropic): https://claude.ai
• Wispr Flow (voice-to-text transcription): https://wisprflow.ai
• Notion: https://notion.so
• Linear: https://linear.app
• Slack: https://slack.com
—
Other references:
• Morgan Housel: https://www.morganhousel.com
• David Perell: https://perell.com
• Shaan Puri / My First Million podcast: https://www.mfmpod.com
• Gary Vaynerchuk: https://garyvaynerchuk.com
—
Where to find Alex Lieberman:
X: https://x.com/businessbarista
LinkedIn: https://www.linkedin.com/in/alex-lieberman/
Tenex: https://www.tenex.co/
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
This solo builder runs 24/7 local AI on his own hardware | Alex Finn
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 …
Notes de l’épisode
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.
What you’ll learn:- How Alex chose between a Mac Studio (512 GB unified memory), DGX Spark, and RTX 5090, and what each is actually good for
- Why Tailscale is worth installing even on a single machine, and how it lets one agent manage your entire hardware fleet
- How the build loop and review loop in Claude Code work
- How to allocate tasks by machine and model
- Why unlimited local inference changes the use-case math in a way a $20 cloud subscription never can
- What OpenClaw and Hermes are each best suited for, and why Alex runs five agents total with failover baked in
—
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
—
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/
—
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
—
Where to find Alex Finn:
LinkedIn: https://www.linkedin.com/in/alex-finn-1848684a
YouTube: https://www.youtube.com/@AlexFinnOfficial
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
GPT-5.6 Sol vs. Claude Fable: Why OpenAI’s new model crushes my benchmark
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 …
Notes de l’épisode
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 voice
- The difference between GPT-5.6 Sol (Terra) and Sol for PRD writing
- How Fable’s precision and pedantry made it harder to collaborate with, and the exact moment Sol broke through where Fable got stuck
- Why Sonnet 5 is still my go-to for agentic voice in OpenClaw, even after this whole benchmark
- How I used GPT-5.6 Sol in Codex to build a fully gamified homework tracking app for my kids in one shot
- The video editing use case that saved me hours clipping a talk I gave at Cursor’s event
- How 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
—
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/
—
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/
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
What a harness is and how to build one with Claude Agent SDK
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 …
Notes de l’épisode
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 is
- When to build a harness versus when to stick with a general-purpose tool like Claude Code or Codex
- How to encode specific permissions into a harness
- The three components every harness needs
- How 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
—
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/
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Voici les 100 derniers épisodes sur 120. Les épisodes plus anciens se trouvent dans le flux du podcast.
