How I AI

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:

1. What Claude Duet is and who Anthropic built it for
2. The design details that make it far more polished than other personal agent launches
3. How task lists, artifacts, and channels work as agent primitives
4. Why a “proactive” agent that never nudges you is a growth problem
5. How model latency kills your willingness to work with an agent synchronously
6. Where it fell short on real tasks, from inbox triage to sales decks to launch videos
7. Why missing desktop and multiplayer support keeps it out of my working stack

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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

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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

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Where to find Claire Vo:

ChatPRD: https://www.chatprd.ai/

Website: https://clairevo.com/

LinkedIn: https://www.linkedin.com/in/clairevo/

X: https://x.com/clairevo

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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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:

1. How Claude Code edits a raw phone video on her computer in about 15 seconds
2. How she taught Claude her jump-cut cadence
3. Why her TikTok comments became her content brief
4. How she keeps growing her audience while posting just once a week—and what’s next
5. What over 25 years in tech taught her about this AI moment

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Brought to you by:

Firecrawl—Give your AI agents web data and beyond

Auros—The human network behind AI

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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

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Tools referenced:

• Claude Code: https://claude.ai/code

• FFmpeg: https://ffmpeg.org

• Whisper (OpenAI): https://openai.com/research/whisper

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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

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Where to find Brooke:

TikTok: https://www.tiktok.com/@brookejlacey

X: https://x.com/brookejlacey

Zosha app: http://zosha.app/

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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].

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:

1. ​Why Meta’s “personal and professional agent” framing mostly holds up, and where it doesn’t
2. ​What active focus mode actually does on long-running tasks, and whether it’s real
3. ​The three agentic tasks where Muse ranked first, and why those categories matter
4. ​What Muse’s design output actually looks like (it’s not good)
5. ​How it compares to Sonnet on voice assistant tasks
6. ​Where Muse lands on SVG and short-form video versus Opus 5.5, Astra, and Fable
7. ​Whether I’m actually putting Muse into any of my agent workflows

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Brought to you by:

DX—Engineering intelligence for the AI era

Jira Product Discovery—Prioritize with insights, build with confidence

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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

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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/

X: https://x.com/clairevo

—

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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:

1. Why Alex built Muse to feel like messaging, not an AI app
2. How Muse’s three-layer status system accommodates different levels of technical expertise
3. What generative UI actually means in a shipped consumer product
4. How Alex turned years of forgotten Instagram saves into a daily shopping catalog
5. Why his mom apologized to her Muse, and what it means for AI design
6. How he uses Muse to track his golf game, shot by shot, as he works toward a single-digit handicap
7. The mindset shift designers need when the output is never pixel-perfect
8. When to use the main chat versus a side chat, and where the architecture still falls short

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Brought to you by:

WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more

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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

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Tools referenced:

• Meta Muse: https://muse.ai

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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

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Where to find Alex Cornell:

LinkedIn: linkedin.com/in/alex-cornell-5449a99

X: https://x.com/alexcornell

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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].

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:

1. What Plugin Insights actually does, and why it changes who can use a site
2. The incident command site Kath built for her OpenAI team, and how it uses live Slack and Notion connectors
3. The one phrase that tells Codex to wire up connectors for you
4. The infrastructure layer inside Sites that most people haven’t touched yet
5. How Kath fixed her Spotify after her kids wrecked it, using Reddit and computer use
6. The skill distribution model behind her community dungeon crawler, and why it’s a new way to think about collaboration
7. Why model speed is what actually determines how creative you get
8. Where Kath draws a hard line on AI acting in her name

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Brought to you by:

Merge—Connective infrastructure for production AI

Vanta—Automate compliance and simplify security

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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

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Tools referenced:

• ChatGPT Sites: https://chatgpt.com/sites

• OpenAI Codex: https://openai.com/codex

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Other references:

• OpenAI DevDay: https://openai.com/devday

• Awesome Sites: https://awesomesites.ai

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Where to find Kath Korevec:

LinkedIn: https://www.linkedin.com/in/kathleensimpson/?isSelfProfile=false

X: https://x.com/simpsoka

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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].

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:

1. Why Jev is a decision engine, not a chatbot, and what that distinction actually changes about how you build
2. How John built a real-time voice to-do app that classifies and executes commands with no visible pause
3. The data deduplication pattern that merges messy records in milliseconds using confidence scores
4. Why Jev works best as a router, and how a single text input can navigate users deep into an app
5. What a chess match between Jev and a low-reasoning LLM reveals about speed, cost, and when to use which
6. The multi-step classification pattern John reaches for when one Jev pass isn’t enough
7. Where Jev falls short, and when you should still reach for a full generative model

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Brought to you by:

Vanta—Automate compliance and simplify security

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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

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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

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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/

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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].

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:

1. What OpenAI’s Dots can do, how I’ve been using mine, and why I’m waiting to give a full verdict
2. Why Spaces might be one of the most underhyped announcements for collaboration between humans and agents
3. How Sites with connectors and plugins could help teams share internal tools with the right data permissions
4. Where GPT-6.1 Sol fits in my model stack—and why speed and cost matter
5. What vision adds to the Decisions API, including my thumbnail-selection and hot dog demos
6. What Astra ultrafast makes possible for interactive AI apps, from collaborative drawing to a changing 3D game
7. Where the speed feels magical, where the experience still needs work, and what it costs

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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

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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/

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Other references:

• OpenAI DevDay 2026: https://devday.openai.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].

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:

1. What makes Jev fundamentally different from every other model I’ve used
2. How I analyzed 1,700 PRs for 9 cents and what I found out about where my engineering effort actually went
3. The personal meta-analysis you can run on your own Claude and Codex sessions right now
4. Why I stopped using Jev alone, and what I pair it with now
5. How I turned 4,500 YouTube comments into a searchable audience dashboard for almost nothing
6. The real-time app I built in an afternoon that shows something surprising about Jev’s speed
7. Why Jev’s pricing model is different from any LLM I’ve used, and what it makes practical to build
8. The ChatPRD product insights project: 1,100 signals, 200,000 classifications, and what it cost me

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Brought to you by:

OpenArt—An all-in-one AI creation platform for images, videos, music, audio, and more

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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

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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

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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].

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:

1. 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
2. Why Astra won my heart while Opus 5.5 might be overall strongest, especially for long-running agents and B2B frontend
3. Where Sol still wins me over on clear writing, readable PRDs, and price
4. The character SVG results that completely overturned my prediction about Anthropic
5. What happened when I asked these models to edit video, and why I think skills explain part of the disappointment
6. Why an LLM judge disagreed with my rankings, and what it was rewarding that I wasn’t

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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

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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

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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].

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:

1. Why I walked away from Claude entirely, and what it took for me to come back
2. The real cost math on Opus 5.5 and why pricing matters more for agentic work than single prompts
3. What happened when I ran four long-running agentic tasks, including one that tried to manipulate Claude mid-run
4. Why Opus 5.5 is now my go-to for frontend prototyping, and where it still lets me down
5. The one capability I genuinely didn’t see coming, and no other model in my stack can match it
6. The moment Opus 5.5 told me flat-out no, and what that says about where Anthropic’s safety posture actually lands in practice
7. Where Codex still wins, and how I’m splitting my model stack after a full week of testing

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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

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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

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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].