How I AI

Епизоде

• 
  From zero coding background to hardware hacker: How Cursor + a Raspberry Pi makes AI fun 27.07.2026 28мин

  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 BluetoothHow to use Cursor’s agent view to brainstorm a hardware projectWhat belongs in a personal API and why agents, not just humans, will be the ones using itHow to read just enough code to do some damage, without needing to understand all of itWhy 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 dataCustomer.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.comMessage her directly: https://maddiedreese.com/messageX: https://x.com/maddiedreese—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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  Claude Opus 5 review: this model is brilliant (but annoying) 24.07.2026 24мин

  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 nowHow Opus 5’s “neurotic” personality showed up in real coding sessions, including a merge conflict it refused to touchWhat 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 leaderboardThe one use case where Opus 5 earned straight 5s from meMy 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/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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  Computer & browser use in Codex (5 real examples) 22.07.2026 27мин

  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 onHow I use Codex to QA my onboarding flow, including exhaustive mobile testing I would never do manuallyWhy under-prompting frontier models gets better results than detailed step-by-step instructionsHow my husband EJ Lawless’s persona-impersonation trick surfaces friction points I can’t see as the builderHow I use browser use to get through my LinkedIn inbox without touching it myselfHow 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 phoneThree 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 moreHyperagent—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/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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  How the founder of Morning Brew built a Claude content machine that never runs out of ideas and never sounds like slop | Alex Lieberman 20.07.2026 42мин

  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 itHow to map your current workflow before you add any AIHow Alex built a six-step Content Machine in Claude that goes from idea spike to publishable postWhy the interview step (not the drafting step) is where AI slop actually comes fromHow to codify your voice in a Markdown file so an AI drafts in your register, not the internet’s averageWhy your employees are your most underleveraged marketing channel right nowHow 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 dataCustomer.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/businessbaristaLinkedIn: 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/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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  This solo builder runs 24/7 local AI on his own hardware | Alex Finn 13.07.2026 35мин

  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 forWhy Tailscale is worth installing even on a single machine, and how it lets one agent manage your entire hardware fleetHow the build loop and review loop in Claude Code workHow to allocate tasks by machine and modelWhy unlimited local inference changes the use-case math in a way a $20 cloud subscription never canWhat OpenClaw and Hermes are each best suited for, and why Alex runs five agents total with failover baked in—Brought to you by:Runway—The creative AI platform for images, video, and moreJira 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-1848684aYouTube: https://www.youtube.com/@AlexFinnOfficialX: https://x.com/AlexFinn—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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  GPT-5.6 Sol vs. Claude Fable: Why OpenAI’s new model crushes my benchmark 09.07.2026 36мин

  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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  What a harness is and how to build one with Claude Agent SDK 08.07.2026 24мин

  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 productCustomer.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.
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  How I run autonomous coding agents from my phone with OpenAI Symphony + Linear | Alessio Fanelli (Kernel Labs) 06.07.2026 35мин

  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 dataJira 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/FanaHOVALatent 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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  Sonnet 5 review: I ran 64 generations to find out if it's worth it 30.06.2026 25мин

  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 moreHyperagent—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.
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  No Figma. No Jira. No docs. How Gusto built a new product line with Claude Code | Eddie Kim (CTO) 29.06.2026 51мин

  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 productJira 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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  GLM 5.2: why I’m replacing Opus in Claude Code with this new model 24.06.2026 27мин

  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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  How Claude Mythos found a 15-year-old bug in Mozilla Firefox | Brian Grinstead 22.06.2026 48мин

  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 todayMetaview—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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  How to design AI agent loops: schedules, goals, and subagents in Claude Code and Codex 17.06.2026 29мин

  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 todayRunway—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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  How Braintrust uses AI agents, evals, and CI to ship better software | Ankur Goyal 15.06.2026 40мин

  In this episode, I sit down with Ankur Goyal, founder and CEO of Braintrust, the AI evals and observability platform used by teams like Notion, Stripe, Vercel, and Zapier. This one is for the senior engineers, staff engineers, VPs of engineering, and CTOs in my audience. We get into how coding agents can take on deeply technical architecture and infrastructure work that no single human engineer could tackle before, and then we demystify evals so you can use them to make your AI products better without touching the implementation.What you’ll learn:How Ankur uses Codex to run week-long benchmark experiments across database indexes, column store formats, and execution engines to speed up slow queriesWhy he argues there’s no excuse to skip rigorous benchmarking now that agents can run them tirelesslyThe “agent line” framework: how to decide which decisions, directions, and interactions you can hand off to an agentHow I think about the practical vs. theoretical quality of AI on hard technical problems, and why human attention decays on tedious workWhy evals are the modern version of a PRD, and how to encode “what good looks like” so a model can figure out the “how”How to build a scoring function live and let an agent improve your prompt inside a safe playgroundHow Ankur turned his designer David’s taste into a repeatable eval so quality scales beyond one personWhy fixing your CI is the highest-leverage way to speed up engineering velocity—Brought to you by:Guru—The AI layer of truthPersona—Trusted identity verification for any use case—In this episode, we cover:(00:00) Introduction to Ankur Goyal(03:00) Using AI agents for database optimization(06:10) Running exhaustive benchmarks with coding agents(09:03) Why staff engineers are wrong about AI limitations(11:30) The “agent line” framework for delegation(14:00) Ankur’s workflow: running 4 to 6 concurrent agents(17:16) Technical setup: foreground agents, background agents, and cloud environments(20:32) Spending time with AI tools(23:06) Demystifying evals(26:02) Live demo: Building an eval for documentation answers(30:20) The alternative to evals: vibe checks and whack-a-mole(32:09) Capturing designer taste in scoring functions(33:13) Quick recap(33:44) Managing velocity and throughput(35:40) Why CI/CD investment is critical for AI-accelerated teams(37:30) Ankur’s prompting strategy when agents fail(39:10) Closing thoughts and how to connect—Tools referenced:• Braintrust: https://www.braintrust.dev/• Codex: https://openai.com/codex/• GPT 5.4: https://developers.openai.com/api/docs/models/gpt-5.4• Claude: https://claude.ai/—Other references:• GPT 5.5 just did what no other model could: https://www.lennysnewsletter.com/p/gpt-55-just-did-what-no-other-model• Paul Graham’s Maker vs. Manager Schedule: http://www.paulgraham.com/makersschedule.html• tmux: https://github.com/tmux/tmux• Chris Tate at Vercel: https://www.linkedin.com/in/ctatedev/—Where to find Ankur Goyal:LinkedIn: https://www.linkedin.com/in/ankrgyl/—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevo—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.
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  Claude Fable 5 review: what the new Mythos model gets right (and very wrong) 09.06.2026 17мин

  Claude Fable 5 is the first Mythos-class intelligence model to be generally available, and I got early access to test it before launch. In this episode, I walk through what Anthropic is promising, what actually stood out when I used it on real work, and where I think it fits in your AI stack.—In this episode, we cover:(00:00) Introduction: Fable 5 is finally here(00:31) What Anthropic says about the model(05:14) Token-intensive by design(06:28) Safety classifiers and the new fallback concept(07:46) Is this or is this not Mythos?(08:30) New product launches: Managed Agents and more(09:20) Crushing benchmarks(09:55) What it’s actually like to use (the good and the bad)(11:40) Test 1: product graph spec(12:56) Test 2: designing a skills registry(14:04) Conservative on execution(14:43) Test 3: multi-agent orchestration(15:39) My takeaways—Tools referenced:• Claude Fable 5: https://www.anthropic.com/news/claude-fable-5-mythos-5• Claude Managed Agents: https://platform.claude.com/docs/en/managed-agents/overview—Other reference:• SWBench Pro benchmark: https://www.swebench.com/—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevo—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.
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  Shopping with Claude: How to find quality brands, automate returns, and buy things that last 100 years | Nicole Ruiz 08.06.2026 36мин

  Nicole Ruiz is a writer and parent who has built a comprehensive AI-powered shopping system to help her family buy high-quality, long-lasting items while avoiding the noise of drop-shipping brands, paid ads, and poorly made products. She writes an interview series on Substack about how technology is changing the household.What you’ll learn:How to build a Claude Project with custom instructions for vetting brands based on heritage, craftsmanship, and return policiesThe shopping criteria that help surface century-old manufacturers over trendy direct-to-consumer brandsHow to use Claude to search through trusted vendor websites that have terrible UXWhy AI actually helps small artisans and heritage brands compete against Amazon’s infrastructureHow to use Claude Cowork to automate returns by finding receipts in your email and drafting refund requestsThe technique for getting Claude to analyze whether a brand is legitimate or just a drop-shipping operationHow to shop within a specific budget or with gift cards using AI assistance—Brought to you by:Orkes—The enterprise platform for reliable applications and agentic workflowsMetaview—The agentic recruiting platform for winning teams—In this episode, we cover:(00:00) Introduction to Nicole and AI-powered shopping(02:29) The problem(04:55) Building a Claude Project for household purchasing(07:44) The “anti-to-do list” concept for reducing mental overhead(10:30) Shopping for a can opener: the system in action(15:53) How AI helps century-old brands with terrible websites(18:45) Processing returns with Claude Cowork(25:06) Using gift cards strategically(26:33) Vetting brands(29:40) Recap, lightning round, and final thoughts—Tools referenced:• Claude: https://claude.ai/• Claude Cowork: https://www.anthropic.com/product/claude-cowork—Other references:• Boston General Store: https://bostongeneralstore.com/• L.L.Bean: https://www.llbean.com/• Manufactum: https://www.manufactum.com/• 5 OpenClaw agents run my home, finances, and code | Jesse Genet: https://www.lennysnewsletter.com/p/5-openclaw-agents-run-my-home-finances• From a $6.90 newsletter to $3M API: How a non-coder built Memelord | Jason Levin: https://www.lennysnewsletter.com/p/from-a-690-newsletter-to-3m-api-how—Where to find Nicole Ruiz:X: https://x.com/nwilliams030Substack (The Third Oikos): https://www.thirdoikos.com/—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevo—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.
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  Gemini Omni: Clone yourself with AI in under 15 minutes 03.06.2026 20мин

  In this experimental episode, I document my real-time attempt to create an AI avatar of myself using Google Flow and the new Gemini Omni video generation model. I walk through the entire process—from scanning my face with my phone to generating a complete one-minute hype video for the podcast, all in about 15 minutes.What you’ll learn:How to create an AI avatar using Google Flow in under five minutesWhy video AI tools unlock creative possibilities for people with zero video production skillsThe step-by-step process of generating a full storyboard using AI as your creative producerHow to use character consistency features to generate multiple video scenes with the same avatarThe uncanny-valley moments you’ll encounter when your AI clone doesn’t quite nail emotions or physicsHow to stitch together AI-generated scenes into a complete video using built-in editing tools—Brought to you by:Merge—Connective infrastructure for production AIJira Product Discovery—Prioritize with insights, build with confidence—In this episode, we cover:(00:00) Getting started with Google Flow and Gemini Omni(01:38) The avatar creation process: scanning and photo capture(02:55) Using Flow to brainstorm a hype video storyboard(06:59) Generating the first video scene with the avatar(08:41) Troubleshooting: accidentally generating images instead of videos(09:32) Generating all seven scenes for the complete video(11:37) Reviewing the avatar videos(13:13) Stitching the videos together in the browser-based editor(14:32) The complete How I AI hype video(15:32) What worked and what didn’t(19:04) Final thoughts—Tools referenced:• Google Flow: https://labs.google/fx/tools/flow• Gemini Omni: https://gemini.google/overview/video-generation/• Veo 3: https://deepmind.google/technologies/veo/—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevo—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.
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  Building an iPhone app with zero technical skills | Bryce Rattner Keithley 01.06.2026 46мин

  Bryce Rattner Keithley has spent her career in talent and recruiting, working with technical leaders but never writing a line of code herself. Yet she managed to build Daily Hundred—a fitness app featuring custom AI-generated videos of anthropomorphic animals demonstrating exercises—and ship it to the App Store before her software engineer friends. Using Replit, Claude, Gemini, and a relentless beginner’s mindset, Bryce proves that in the AI era, execution is no longer the constraint on good ideas.What you’ll learn:How to build and ship an iPhone app using Replit without any coding knowledgeThe step-by-step process for creating custom AI-generated workout videos by combining Gemini images with real exercise footageHow to use Claude as your technical architect and Claude Code as your software engineerHow to navigate App Store submission requirements (including fixing rejection feedback)Why being hyper-literal in your prompts unlocks better AI resultsWhy a beginner’s mind is actually an advantage when building with AI tools—Brought to you by:WorkOS—Make your app enterprise-ready todayMetaview—The agentic recruiting platform for winning teams—In this episode, we cover:(00:00) Introduction to Bryce and Daily Hundred(04:48) Building with Replit(06:16) The beginner’s mindset advantage(11:17) Creating anthropomorphic animals(22:55) Moving from static image to video(27:15) The floating genie and other anthropomorphic animal generations(30:46) Shifting from web app to App Store submission(36:24) User feedback(37:41) Lightning round and final thoughts—Tools referenced:• Replit: https://replit.com/• Lovable: https://lovable.dev/• Claude: https://claude.ai/• Claude Code: https://claude.ai/code• Gemini: https://gemini.google.com/• Higgsfield: https://higgsfield.ai/• Kling: https://kling.ai/• Railway: https://railway.app/• TestFlight: https://developer.apple.com/testflight/—Other references:• How a 91-year-old vibe coded a complex event management system using Claude and Replit | John Blackman: https://www.lennysnewsletter.com/p/how-a-91-year-old-vibe-coded-a-complex• What Got You Here Won’t Get You There: https://www.amazon.com/What-Got-Here-Wont-There/dp/1401301304• How Women Rise: https://www.amazon.com/How-Women-Rise-Holding-Careers/dp/0316440124• A Whole New Mind: https://www.amazon.com/Whole-New-Mind-Right-Brainers-Future/dp/1594481717• How to Win Friends and Influence People: https://www.amazon.com/How-Win-Friends-Influence-People/dp/0671027034—Where to find Bryce Rattner Keithley:LinkedIn: https://www.linkedin.com/in/brycerattner/GitHub: https://github.com/brk-bot/Daily Hundred on the App Store: https://apps.apple.com/us/app/daily100-fitness-challenge/id6762108062—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevo—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.
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  Claude Opus 4.8 is here. Is it as good as they say? 28.05.2026 13мин

  I got a few hours of early-access testing with Anthropic’s newly released model Opus 4.8. I walk through real coding, design, and strategy tasks across Claude Code and Claude Cowork, and give you my unfiltered view on what impressed me and what didn’t.—What you’ll learn:Where Opus 4.8 excels: greenfield prototypes, one-shot features, and fast executionWhere it struggles: the last 10%, edge cases in existing codebases, and hallucinationsHow Opus 4.8 compares to Opus 4.7 on business strategy workWhy I’m still reaching for Opus 4.7 on data-heavy strategy and roadmap workThe new features shipping alongside the model: dynamic workflows with parallel subagents and effort control in Claude.ai and CoworkThe prompting and harness strategy I’d use to get the most out of it—In this episode, we cover:(00:00) Introduction to Opus 4.8 (00:44) Benchmark performance and pricing(01:53) First coding test: Building a prototyping tool(03:00) Where it failed: The last 10% problem(03:27) The hallucination problem(04:23) Testing Opus 4.8 on existing codebases(05:24) The ambition test: Building games for a 9-year-old(07:03) Business strategy test: 4.7 vs 4.8(08:23) The roadmap test(09:17) Final verdict—References:• System Card: Claude Opus 4.8: https://cdn.sanity.io/files/4zrzovbb/website/c886650a2e96fc0925c805a1a7ca77314ccbf4a6.pdf• Introducing Claude Opus 4.8 on X: https://x.com/claudeai/status/2060042702150930686?s=20—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevo—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.
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  The Codex feature that works while you sleep 27.05.2026 30мин

  In this 30-minute episode, I walk through my favorite feature in Codex: the /goal command. I show how Goals transform AI from a turn-based assistant that needs constant ‘what’s next?’ prompting into an autonomous agent that can work for hours on complex, multi-step tasks. I share three real examples: eliminating thousands of Sentry errors, cleaning 3,900 emails down to 68, and organizing hundreds of Linear tasks.What you’ll learn:What Goals are and how they differ from standard promptsHow I used /goal to eliminate hundreds of error logs in my codebase over a five-hour autonomous runThe non-technical use cases that make Goals incredibly powerful: cleaning up 3,900 emails in under four hours and organizing hundreds of project management tasks in LinearHow to write effective /goal prompts with measurable outcomes, verification methods, and constraintsWhen not to use Goals and what makes a strong versus weak GoalWhy Goals represent a fundamental shift in how we work with AI, from babysitting the model to managing it—Brought to you by:Mercury—Radically different banking loved by over 300K entrepreneurs—In this episode, we cover:(00:00) Introduction(01:50) What is /goal and when should you use it?(02:45) The difference between prompts and Goal-based loops(04:06) Claire’s first five-hour 45-minute autonomous coding task(05:05) How to manage a Goal lifecycle: view, pause, resume, and clear(06:06) How to write strong goals: outcomes vs. outputs(07:34) The six components of effective Goals(08:57) Example: Reducing P95 checkout latency with /goal(09:36) Demo: Using /goal to eliminate Sentry errors in ChatPRD(13:18) Demo: Burning down Vercel API errors(17:28) Non-technical use case: Cleaning 3,900 emails with /goal(21:24) Demo: Using /goal to clean up Linear project tasks(24:41) When not to use /goal(26:10) Why /goal changes everything—Tools referenced:• Codex: https://openai.com/codex/• Sentry: https://sentry.io/• Vercel: https://vercel.com/• Linear: https://linear.app/—Other reference:• OpenAI blog post “Using Goals in Codex”: https://developers.openai.com/cookbook/examples/codex/using_goals_in_codex—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevo—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.