How I AI

Claire Vo

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.

  1. GPT-5.6 Sol vs. Claude Fable: Why OpenAI’s new model crushes my benchmark

    3 ਘੰਟੇ ਪਹਿਲਾਂ

    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 voice. Sol won by a meaningful margin on my Claire Weighted Index (70% my taste, 30% Terminal Bench 2.1), and I also tested two use cases I can't stop thinking about: building a gamified homework tracking app for my kids in one shot with Codex, and browser automation with Chrome that burned through 500 LinkedIn replies while I did literally nothing. What you’ll learn: How I scored five AI models (including GPT 5.6 Sol, Fable 5, and Sonnet 5) using my “Claire Weighted Index” benchmark across PRDs, prototypes, code, and agentic voiceThe difference between GPT-5.6 Sol (Terra) and Sol for PRD writingHow Fable’s precision and pedantry made it harder to collaborate with, and the exact moment Sol broke through where Fable got stuckWhy Sonnet 5 is still my go-to for agentic voice in OpenClaw, even after this whole benchmarkHow I used GPT-5.6 Sol in Codex to build a fully gamified homework tracking app for my kids in one shotThe video editing use case that saved me hours clipping a talk I gave at Cursor’s eventHow to use Codex plus GPT-5.6 and Chrome for browser automation, and why this is my single most-loved use case right now— In this episode, I cover: (00:00) Intro (01:10) The three GPT-5.6 models: Sol, Terra, Luna (02:17) Pricing: Sol vs. Fable API costs (03:24) The How I AI benchmark (05:03) Claire-weighted Index results (07:00) Per-task winners: prototypes, PRDs, agentic voice (11:59) What Claire actually rewards (13:20) Full-fidelity prototype side-by-sides (Sol vs. Fable) (17:45) Wireframes (18:19) Agentic voice (19:15) Where Sol is better than other models (23:56) Gamified kids’ homework app, built in one shot (28:02) Fable’s pedantry problem and how Sol broke through it (31:49) Two bonus use cases: video editing and browser use (35:08) Final summary and model recommendations — 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/ X: https://x.com/clairevo — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

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  2. What a harness is and how to build one with Claude Agent SDK

    1 ਦਿਨ ਪਹਿਲਾਂ

    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 ChatPRD, using the Claude Agent SDK, a custom terminal UI built with the Ink library, and opinionated adapters for Sentry, Linear, GitHub, and Vercel. The harness handles evidence gathering, root-cause analysis, and follow-up artifact creation, all without me needing to type “dear agent, please fix this bug” ever again. I also walk through the architecture, share the code structure, and give you the exact process I used so you can build your own harness for any repetitive, structured workflow in your business. What you’ll learn: What a harness actually isWhen to build a harness versus when to stick with a general-purpose tool like Claude Code or CodexHow to encode specific permissions into a harnessThe three components every harness needsHow I used GPT-5.5 and Claude Opus to build the harness code itself (and where they both initially resisted)How to structure the artifacts your harness produces so the whole team can use the output— Brought to you by: Bolt.new—Turn your idea into a real product Customer.io—Build customer engagement campaigns from a single prompt — In this episode, we cover: (00:00) What is an AI harness? (03:19) When to build a harness (04:33) Why Claire picked bug triage (06:00) Why not just use Claude Code? (07:48) Demo: The custom harness interface (11:04) Architecture: runs, tasks, tools, and artifacts (13:44) Building it with Codex and Claude (15:08) Code map and file layout (16:51) A look at the code (19:18) The live investigation result (21:01) How to build your own harness — 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/ X: https://x.com/clairevo — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

    25 ਮਿੰਟ
  3. How I run autonomous coding agents from my phone with OpenAI Symphony + Linear | Alessio Fanelli (Kernel Labs)

    3 ਦਿਨ ਪਹਿਲਾਂ

    How I run autonomous coding agents from my phone with OpenAI Symphony + Linear | Alessio Fanelli (Kernel Labs)

    Alessio Fanelli, founder of Kernel Labs and co-host of Latent Space podcast, walks us through two very different AI workflows: (1) a fully autonomous coding setup using OpenAI Symphony + Linear, where Linear acts as a state machine and Symphony manages agents through the whole dev lifecycle with zero babysitting; (2) Codex with browser access searching eBay for underpriced Pokémon cards—autonomously browsing, extracting PSA certificate numbers, and flagging deals on $10K–$20K cards for his San Carlos card shop, Merlin Games. What you’ll learn: Why “agent manager” is a better mental model than “agent prompter”Why local Mac Minis don’t scale, and what a cloud VPS unlocksHow to wire Symphony and Linear together as an agent state machineHow to track token costs per task (and what 221 million tokens buys you)What Glimpse does, and why better agent senses extend autonomous runsWhy your CLAUDE.md probably needs a full purge, not more instructionsHow Codex scouts underpriced $10K Pokémon cards on eBay at scaleThe new category of small business that AI just made possible— Brought to you by: Firecrawl—Power AI agents with clean web data Jira Product Discovery—Prioritize with insights, build with confidence — In this episode, we cover: (00:00) Intro (02:24) Prompter vs. agent manager (04:31) Live demo: Symphony + Linear (09:31) Setting up Symphony (14:15) Purging your skills files (18:06) The benefits of this system (19:10) Demo: Using Codex to hunt for Pokémon cards (24:17) The benefit of AI for small businesses (28:23) Lightning round — Tools referenced: • OpenAI Codex: https://openai.com/codex • OpenAI Symphony (open-source framework): https://github.com/openai/symphony • Linear (project management/agent state machine): https://linear.app • PSA (Professional Sports Authenticator) grading: https://www.psacard.com • TCGplayer (card pricing): https://www.tcgplayer.com • eBay (used for card price scouting): https://www.ebay.com — Other references: • Meta Ray-Ban glasses: https://www.ray-ban.com/usa/ray-ban-meta-smart-glasses • The Monk and the Riddle by Randy Komisar: https://www.amazon.com/Monk-Riddle-Creating-Making-Living/dp/1578516447/ref=sr_1_1 • The Divine Comedy by Dante Alighieri: https://www.amazon.com/dp/0451208633 • AS Roma (football club Alessio and Claire are both fans of): https://www.asroma.com/en — Where to find Alessio Fanelli: X: https://x.com/FanaHOVA Latent Space podcast: https://www.latent.space/ — 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 jordan@penname.co.

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  4. Sonnet 5 review: I ran 64 generations to find out if it's worth it

    30 ਜੂਨ

    Sonnet 5 review: I ran 64 generations to find out if it's worth it

    I’ve been testing every major frontier model release since the start of the year, and when Anthropic dropped Sonnet 5, I wanted more than a vibe check. I got tired of one-off tests I couldn’t repeat or compare over time, so I built something better: the How I AI Bench, a repeatable eval harness I constructed live using Claude Code while recording this episode. I ran Sonnet 5 blind against four other frontier models (Sonnet 4.6, Opus 4.8, GPT-5.5, and Gemini 3 Pro) across PRD quality, prototype generation, agentic task completion, and agent personality. The results were not what I expected. What you’ll learn: What Anthropic claims Sonnet 5 improves over Sonnet 4.6, and where the benchmark data actually backs that upHow I built the How I AI Bench in under 45 minutes using Claude Code, starting from my own stored session historyWhy I combined human vibe scoring (70%) with LLM as judge scoring (30%) instead of trusting either aloneHow to set up a local HTML scoring page so you can rate AI outputs on gut feel and export those scores as JSONWhich model I recommend for PRDs, which for complex prototypes, and which for chatting with an agent daily— Brought to you by: Runway—The creative AI platform for images, video and more Hyperagent—Deploy fleets of agents that handle real work — In this episode, we cover: (00:00) Sonnet 5 is out (01:55) What Anthropic claims (04:02) Why I’m done with one-off vibe checks (05:05) Building the How I AI Bench live with Claude Code (07:42) The scoring system (10:43) Agent voice eval (11:57) Quick recap (13:58) Results: The How I AI index leaderboard (21:21) What I’m improving for the next run (22:16) Generating a Claire-weighted index (23:53) Model-by-task recommendations — Tools referenced: • Claude Sonnet 5: https://www.anthropic.com/news/claude-sonnet-5 • Claude Opus 4.8: https://www.anthropic.com/news/claude-opus-4-8 • GPT-5.5 (OpenAI): https://openai.com/index/introducing-gpt-5-5/ • Gemini 3 Pro (Google DeepMind): https://deepmind.google/models/gemini/pro/ • Cursor: https://www.cursor.com/ — Other references: • SWE-bench Pro (agentic coding benchmark referenced): https://www.swebench.com/ — Where to find Claire Vo: ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

    26 ਮਿੰਟ
  5. No Figma. No Jira. No docs. How Gusto built a new product line with Claude Code | Eddie Kim (CTO)

    29 ਜੂਨ

    No Figma. No Jira. No docs. How Gusto built a new product line with Claude Code | Eddie Kim (CTO)

    Eddie Kim is the co-founder and CTO of the payroll and HR platform Gusto, which just crossed $1 billion in revenue and serves more than 500,000 small businesses. Recently he did something most CTOs don’t: he went back to writing code. With three other engineers and one designer, Eddie built Gusto Cofounder, a net-new AI product, from zero code to a tier-one launch in 10 weeks. He walks through how that team actually worked, why they threw out nearly every process, and how anyone can copy the approach. What you’ll learn: The trash-can method: how to write, review, and delete a full PR as a product decision instead of a planning docThe two-tool agent stack behind Gusto CofounderThe exact “perma-Zoom” setup that replaced standups, retros, and Slack threads for 10 weeksHow a designer with no engineering background hit the 94th percentile for shipping codeThe eval-first workflow Eddie uses to fix real customer bugs with Claude CodeHow a non-technical leader can prototype an idea to win buy-in, then carry it all the way to production-quality code— Brought to you by: Magic Patterns—Prototypes that look like your product Jira Product Discovery—Prioritize with insights, build with confidence — In this episode, we cover: (00:00) Intro: five people, 10 weeks (02:38) The origins of Cofounder (08:32) Inside the 10-week build process (12:50) Building with no PMs (14:38) The “trash can” method (17:15) The stack architecture (19:10) Shipping to production from day one (22:03) How a designer became a top engineer (29:05) Demo: Cofounder over text and Slack (31:45) Demo: running a real payroll (36:26) Live coding with evals in Claude Code (39:39) Recap: prototype, small team, permission (43:17) Lightning round (48:44) Where to find Eddie and Cofounder — Tools referenced: • Gusto Cofounder (early access/waitlist): https://gusto.com/cofounder • Claude Code (Anthropic): https://claude.ai/code • Cloudflare Workers: https://workers.cloudflare.com/ • Vercel AI SDK: https://sdk.vercel.ai/ • DX (engineering analytics): https://getdx.com/ • Wispr Flow (voice-to-text): https://wisprflow.ai • OpenClaw: https://openclaw.ai/ — Other references: • Gusto (the main product, “Gusto Classic”): https://gusto.com • Mindbody (referenced as customer data source): https://www.mindbodyonline.com/ — Where to find Eddie Kim: LinkedIn: https://www.linkedin.com/in/edawerd/ — Where to find Claire Vo: ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

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  6. GLM 5.2: why I’m replacing Opus in Claude Code with this new model

    24 ਜੂਨ

    GLM 5.2: why I’m replacing Opus in Claude Code with this new model

    I put GLM 5.2, the open-weight coding model from Z.AI, through four real tasks inside my actual codebase: a codebase architecture audit, a UI redesign, and a 45-minute autonomous bug-hunting session pulling from Sentry and Vercel logs. Total cost: $3.36 for roughly 6 million tokens, a prioritized bug-fix dashboard I’m actually shipping from, and a landing page redesign that matched Chat PRD’s design system on the first try. What you’ll learn: What “open-weight” actually means and why it matters for cost and vendor independenceHow to connect GLM 5.2 to Cursor and Claude CodeHow it performs on codebase exploration and autonomous architecture summarization in a real production Next.js appWhether GLM 5.2 can match an existing design systemHow the model handles a 45-minute long-running autonomous taskWhere GLM 5.2 stumbled The actual cost breakdown— Brought to you by: Mercury—Radically different banking loved by over 300K entrepreneurs — In this episode, we cover: (00:00) What open-weight models are and why GLM 5.2 is worth testing (01:38) GLM 5.2 model overview (04:02) Capabilities and benchmark results (06:02) How to set up GLM 5.2 in Cursor (08:37) How to set up GLM 5.2 in Claude Code (11:04) Live test 1: codebase exploration and architecture audit on ChatPRD (12:43) Live test 2: generating an HTML architecture and roadmap page (16:37) Live test 3: redesigning the How I AI landing page in Cursor (20:57) Live test 4: 45-minute autonomous task, pulling Sentry errors and Vercel logs (22:35) Where it struggled (23:49) My verdict on the output (25:23) Cost breakdown — Tools referenced: z.ai: https://z.aiGLM 5.2: https://z.ai/blog/glm-5.2OpenRouter: https://openrouter.aiCursor: https://cursor.comClaude Code: https://docs.anthropic.com/en/docs/claude-codeSentry: https://sentry.ioVercel: https://vercel.com— Other references: SWE-Bench Pro leaderboard (coding benchmark scores referenced in episode): https://www.swebench.comFrontier Suite and Post-Train Bench (additional benchmarks cited): https://scale.com/leaderboardUse Claude Code with OpenRouter: https://openrouter.ai/docs/cookbook/coding-agents/claude-code-integration— Where to find Claire Vo: ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

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  7. How Claude Mythos found a 15-year-old bug in Mozilla Firefox | Brian Grinstead

    22 ਜੂਨ

    How Claude Mythos found a 15-year-old bug in Mozilla Firefox | Brian Grinstead

    Brian Grinstead is a distinguished engineer at Mozilla, where he’s worked on Firefox and the web platform since 2013 (he joined to help launch Firefox DevTools). Recently he and his team pointed an agentic bug-finding pipeline at Firefox—a codebase with tens of thousands of files and tens of millions of lines of code—and shipped a record month of security fixes. The viral chart everyone saw gave the credit to Anthropic’s new Mythos model. Brian’s take is that the harness and pipeline did just as much of the work, and he walks through exactly how it runs and how anyone can build a starter version. What you’ll learn: How to build a basic bug-finding harness by running Claude Code or Codex with one prompt and the -p flag, no SDK requiredWhy pointing an agent at a whole codebase fails, and how an LLM judge can score and rank files before you spend any computeHow a verifier subagent kills false positives by catching the agent when it cheatsThe goal-loop pattern: give an agent a tightly scoped problem, a clear pass/fail signal, and let it retry far past the point a human would quitWhy teams that already invested in fuzzing, CI, and dev tooling are so far aheadHow to weigh model versus harness, and why Brian splits the credit close to 50-50How a non-engineer can reuse the same score, verify, and fix the loop for design quality, conversion rate, or tech debtWhy AI-generated patches still can’t ship on their own, and where humans stay in the loop— Brought to you by: WorkOS—Make your app enterprise-ready today Metaview—The agentic recruiting platform for winning teams — In this episode, we cover: (00:00) Introduction to Brian Grinstead (02:43) The viral chart: Firefox Security Bug Fixes by Month (05:32) How the custom harness works (10:22) Goal loops and guardrails (14:45) How they built it (16:55) Real bugs, including a 15-year-old one (23:00) Open-sourcing it (26:26) Why humans still review every fix (32:30) Live demo and prioritizing files (40:18) Mobilizing the team and recap (42:33) Lightning round — Tools referenced: • Claude Code: https://claude.ai/code • Claude Agent SDK: https://code.claude.com/docs/en/agent-sdk/overview • Codex: https://openai.com/index/openai-codex/ • OpenAI Agent SDK: https://developers.openai.com/api/docs/guides/agents • VS Code: https://code.visualstudio.com/ • Docker: https://www.docker.com/ • Firefox: https://www.mozilla.org/firefox/ • Address Sanitizer: https://github.com/google/sanitizers • RLBox: https://rlbox.dev/ — Other references: • Mozilla Bug Bounty Program: https://www.mozilla.org/security/bug-bounty/ • Mozilla GitHub: https://github.com/mozilla — Where to find Brian Grinstead: LinkedIn: https://www.linkedin.com/in/bgrins/ GitHub: https://github.com/bgrins — Where to find Claire Vo: ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

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  8. How to design AI agent loops: schedules, goals, and subagents in Claude Code and Codex

    17 ਜੂਨ

    How to design AI agent loops: schedules, goals, and subagents in Claude Code and Codex

    I break down every loop type from scratch—what a heartbeat, cron, hook, and goal loop actually are, when each one fits, and the five things any effective loop needs before it touches production. Then I build two live loops: a daily aging-PR reviewer in Claude Code that schedules itself at 10:15 a.m. and spins off its own subagents, and a weekly skills-identification loop in Codex that spawns goal-based subagents to validate its own output in real time. What you’ll learn: The plain-English definition of a loop—and why it’s just an automated prompt, not a scary new paradigmThe four loop types (heartbeat, cron, hook, and goal) and when each one actually fits your workflowHow to think about loop design using the “onboarding an employee” mental modelThe five things every effective loop needs: work trees, skills, plugins/connectors, subagents, and state trackingHow to build a scheduled PR-review routine in Claude Code that babysits aging PRs and alerts your teamHow to set up a weekly skills-identification automation in Codex that spawns its own validating subagentsWhy goal-based loops are the hardest to write well—and where most people burn tokens for nothingThe two warning signs that your loop is going to get expensive before it gets useful— Brought to you by: WorkOS—Make your app enterprise-ready today Runway—The creative AI platform for images, video, and more — In this episode, we cover: (00:00) Prompts are out and loops are in (02:30) Defining a loop (03:03) The four ways to automate a prompt: heartbeat, cron, hooks, and goals (06:03) Five things every effective loop needs (09:26) The “onboarding an employee” framework for designing loops (11:58) Live build #1: Daily aging PR loop in Claude Code (17:08) Subagents inside loops (19:00) Live build #2: Weekly skills identification loop in Codex (22:57) Watching subagents spin up in real time (25:28) Warning signals around loops (27:31) What listeners are doing with loops — Tools referenced: • Claude Code: https://claude.ai/code • Codex: https://chatgpt.com/codex • OpenClaw: https://openclaw.ai/ — Other references: • Claire’s article “Why OpenClaw Feels Alive Even Though It’s Not”: https://x.com/clairevo/article/2017741569521271175 • Addy Osmani’s article on loop engineering: https://addyosmani.com/blog/loop-engineering/ • Using Goals in Codex: https://developers.openai.com/cookbook/examples/codex/using_goals_in_codex — Where to find Claire Vo: ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

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ਜਾਣਕਾਰੀ

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.

ਜਾਣਕਾਰੀ

  • ਰਚਨਾਕਾਰ
    Claire Vo
  • ਸਰਗਰਮ ਸਾਲ
    2025 - 2026
  • ਐਪੀਸੋਡ
    92
  • ਰੇਟਿੰਗ
    ਇਤਰਾਜ਼ਯੋਗ
  • ਕਾਪੀਰਾਈਟ
    © Claire Vo
  • ਸ਼ੋਅ ਦੀ ਵੈੱਬਸਾਈਟ
    How I AI

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