How I AI

I have personal-agent fatigue, so I ran the first How I AI personal-agent benchmark: Grok Bot, Dot, Muse, and Claude Duet across nine real tasks, from a Slack reply and a Sentry bug to a Figma thumbnail, inbox triage, a family newspaper, physics homework for a fourth grader, shoe shopping, and a ChatPRD launch video. I used computer use to operate them: Claude ran Dot, Codex ran the rest and scored the results live. Here’s the scoreboard—and which agent I’d actually trust for work vs. life.—Brought to you by:WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and moreFirecrawl—Give your AI agents web data and beyond—In this episode, we cover:(00:00) Introduction(02:13) The personal-agent benchmark setup(05:00) The nine tasks and the prompts I used(07:20) Scoreboard overview(10:06) Task 1: Draft a Slack reply as me(11:39) Task 2: Cut a short-form video from raw Dropbox footage(14:56) Task 3: Fix a bug from Sentry(17:30) Task 4: Build a How I AI thumbnail in Figma(19:25) Task 5: Triage my inbox for to-dos(21:06) Task 6: Build a family newspaper(23:30) Task 7: Explain a physics homework question to a fourth grader(24:54) Task 8: Shop for shoes(26:46) Task 9: Make a ChatPRD Product Intelligence launch video(30:01) Final scores(32:38) My takeaways—Tools referenced:• Grok Bot: https://x.ai/bot• Dot: https://chatgpt.com/features/dots/• Meta Muse: https://ai.meta.com/muse/• Claude Duet: https://claude.com/product/claude-duet• Codex: https://openai.com/codex• ElevenLabs: https://elevenlabs.io/• Figma: https://www.figma.com/• Sentry: https://sentry.io/• Linear: https://linear.app/• Dropbox: https://www.dropbox.com/—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevo—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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 forThe design details that make it far more polished than other personal agent launchesHow task lists, artifacts, and channels work as agent primitivesWhy a “proactive” agent that never nudges you is a growth problemHow model latency kills your willingness to work with an agent synchronouslyWhere it fell short on real tasks, from inbox triage to sales decks to launch videosWhy 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/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:​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 eraJira 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/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:What Plugin Insights actually does, and why it changes who can use a siteThe incident command site Kath built for her OpenAI team, and how it uses live Slack and Notion connectorsThe one phrase that tells Codex to wire up connectors for youThe infrastructure layer inside Sites that most people haven’t touched yetHow Kath fixed her Spotify after her kids wrecked it, using Reddit and computer useThe skill distribution model behind her community dungeon crawler, and why it’s a new way to think about collaborationWhy model speed is what actually determines how creative you getWhere Kath draws a hard line on AI acting in her name—Brought to you by:Merge—Connective infrastructure for production AIVanta—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=falseX: https://x.com/simpsoka—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:What OpenAI’s Dots can do, how I’ve been using mine, and why I’m waiting to give a full verdictWhy Spaces might be one of the most underhyped announcements for collaboration between humans and agentsHow Sites with connectors and plugins could help teams share internal tools with the right data permissionsWhere GPT-6.1 Sol fits in my model stack—and why speed and cost matterWhat vision adds to the Decisions API, including my thumbnail-selection and hot dog demosWhat Astra ultrafast makes possible for interactive AI apps, from collaborative drawing to a changing 3D gameWhere 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/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:How I run the How I AI bench blind, and what gets an output a bad score before I even know which model made itWhy Astra won my heart while Opus 5.5 might be overall strongest, especially for long-running agents and B2B frontendWhere Sol still wins me over on clear writing, readable PRDs, and priceThe character SVG results that completely overturned my prediction about AnthropicWhat happened when I asked these models to edit video, and why I think skills explain part of the disappointmentWhy 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/X: https://x.com/clairevo—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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 agentThe public Slack → Linear → GitHub → QA workflowHuman interactions per PR as a signal of automation and throughputWhy human review is still the bottleneckScoring agent runs, finding failure modes, and self-improving agent workflowsReplaying real tasks to choose model cost and quality tradeoffsCEO workflows with Figma MCP, Granola, and research agents—Brought to you by:DX—Engineering intelligence for the AI eraOpenArt—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/X: https://x.com/clairevo—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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 fileThe exact check-in bot setup that lets Peng send a photo or a place name and have his portfolio update itself automaticallyHow John’s Figma Bro bot handles production design tasks while he’s at the gymHow John uses voice memos to direct Figma work through an MCP connection without opening his laptopThe “shower thought to prototype” workflow John uses with DevBot to test interaction ideas without first going through a product manager or engineerThe “trash can method” of software developmentHow both designers organize their personal bot ecosystemsWhat 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 moreVanta—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/johnbaiPeng 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/X: https://x.com/clairevo—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].