How I AI

Grace Clarke is an AI educator and former marketing consultant who taught herself Claude Code earlier this year and built a curriculum out of the process. She now runs her entire service business on tools she’s built with Claude, including a pipeline operator, a proposal maker, and a Gmail replacement she created in under 30 minutes, and teaches individuals and teams to do the same.

What you’ll learn:

1. How to build an hourly pipeline in Claude that moves clients through your process automatically
2. Why Grace ditched traditional proposals for password-protected, interactive HTML documents built in Claude
3. How she uses a “voice guide” skill file so every Claude output sounds like her, not like AI slop
4. The two-step forcing function she teaches non-technical clients to build the muscle of opening Claude
5. Why she started building in Claude Code, then handed the work off to Cowork via a Markdown session file
6. How she replaced Gmail entirely with a custom inbox
7. Why she teaches “intent engineering” instead of prompt engineering, and what that looks like in practice
8. How she uses Claude on her phone, on walks, to track workouts and manage plants alongside client work

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Brought to you by:

Bolt.new—Turn your idea into a real product

Hyperagent—Deploy fleets of agents that handle real work

—

In this episode, we cover:

(00:00) Grace’s background and why she started building with Claude

(04:48) The pipeline operator: what it is and how it runs her business every hour

(08:48) Building the muscle memory to use AI

(12:02) What goes into building a skill file (voice guide, proposal rules, versioning)

(13:50) How she built her proposal maker

(16:15) The voice guide: teaching Claude how she thinks, not just how she writes

(21:22) Live demo of the custom Gmail replacement built in Cowork

(30:44) Workout tracking, plant photos, and tiny daily Claude habits

(34:51) The biggest misconception holding people back from adopting AI

(38:36) What Grace does when Claude is not giving her what she wants

(40:38) Claude builds a proposal for Claire in real time

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Tools referenced:

• Claude: https://claude.ai

• Claude Code: https://claude.ai/code

• Netlify: https://www.netlify.com

• Google Forms: https://forms.google.com

• Google Sheets: https://sheets.google.com

• Google Cloud (for service accounts and custom connectors): https://cloud.google.com

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Other reference:

• Stratechery by Ben Thompson: https://stratechery.com

—

Where to find Grace Clarke:

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

X: https://x.com/graceclarke

—

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].

AI writes most of my code now, and that created a new problem: a PR queue I couldn’t keep up with. In this episode, I walk through how I built Merge Mommy, a Vercel Eve agent that reads every PR after checks pass, scores it across six risk dimensions, auto-approves the low-risk ones, and pings me in Slack for anything that needs a human. I built the whole thing in one Codex session, it’s SOC 2 compatible, and it’s already cleared my backlog.

What you’ll learn:

1. Why AI-generated PRs create a review bottleneck and why the answer isn’t reviewing all of them
2. How Intercom 5x’d PR approval speed and reduced revert rates by putting AI in the review loop
3. Why Vercel Eve is the simplest framework I’ve found for deploying AI agents in Slack and GitHub
4. How I built a full PR review agent in Codex with one prompt and a few steering turns
5. The six components I use to score PR risk (blast radius, reversibility, data security, ops impact, verification gap, and change surface)
6. How I used Chrome browser use to handle Slack bot and GitHub app configuration so I never had to click through setup screens manually
7. Why auto-approved PRs can be SOC 2 compliant as long as the process is auditable, queryable, and in your risk policy
8. How to set up Slack escalation so low-risk PRs become a two-click merge with no manual review

—

Brought to you by:

WorkOS—Make your app Enterprise Ready today

—

In this episode, we cover:

(00:00) The PR review backlog problem nobody’s talking about

(02:35) Why you don’t have to review every AI-generated PR

(05:14) How Intercom built AI-approved PRs (and proved they’re safer)

(06:10) How the Eve framework works (directory, skills, channels, connectors)

(09:16) The Codex prompt I used to build the entire bot

(11:36) What the agent actually does: read, score, approve, or escalate

(13:07) Setting up your Eve agent

(15:47) The six-component risk scoring model

(17:23) Merge Mommy in action: three live PR examples

(21:10) Recap and how to build your own version

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Tools referenced:

• Vercel Eve: https://vercel.com/eve

• Vercel AI SDK: https://sdk.vercel.ai/

• Vercel Chat SDK: https://chat-sdk.dev/

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

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Other references:

• AI is approving our pull requests: Here’s how we made it safe: https://www.intercom.com/blog/ai-is-approving-our-pull-requests-heres-how-we-made-it-safe/

—

Where to find Claire Vo:

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

Website: https://clairevo.com/

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

X: https://x.com/clairevo

—

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

Nick Baumann is on the Developer Experience team at OpenAI, where he spends his days building with, testing, and communicating the capabilities of ChatGPT Codex and ChatGPT Work. In this episode, Nick walks me through several features that have launched or evolved recently: the new voice interface with its screen-reading orb, the Heartbeats automation system in ChatGPT Work on mobile, the live ChatGPT Sites deployment feature, and his personal use case for AI-assisted UGC video editing.

What you’ll learn:

1. How two-person voice chat works
2. How Heartbeats work
3. How to build and deploy a live website with ChatGPT Sites
4. How to delegate a flight search, hotel booking, and expense report to Codex in a single voice conversation without opening a single app manually
5. Why ChatGPT Work on mobile is the most underutilized AI workflow for people already using the ChatGPT app
6. How to use a custom UGC Video plugin to feed 50 raw clips into ChatGPT, let it pull transcripts, pick the best takes, and assemble a finished vertical video overnight

—

Brought to you by:

Bolt.new—Turn your idea into a real product

Hyperagent—Deploy fleets of agents that handle real work

—

In this episode, we cover:

(00:00) Introduction to Nick Baumann

(02:56) What’s new in Codex

(05:40) ChatGPT Work and Heartbeats

(06:40) Live Codex voice demo

(13:25) Latency vs. intelligence

(14:36) Quick recap

(15:04) Voice on mobile and the ChatGPT Sites workflow

(21:24) Live UGC video demo

(32:30) How I AI website results

(34:04) Lightning round and final thoughts

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Tools referenced:

• ChatGPT Codex: https://chatgpt.com/codex

• ChatGPT Sites: https://chatgpt.site

—

Where to find Nick Baumann:

LinkedIn: linkedin.com/in/nick--baumann

—

Where to find Claire Vo:

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

Website: https://clairevo.com/

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

X: https://x.com/clairevo

—

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

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:

1. How Maddie built a thermal receipt printer that accepts messages from anywhere in the world using a Raspberry Pi and Bluetooth
2. How to use Cursor’s agent view to brainstorm a hardware project
3. What belongs in a personal API and why agents, not just humans, will be the ones using it
4. How to read just enough code to do some damage, without needing to understand all of it
5. Why building for fun, not practicality, is the fastest path to actually shipping physical projects

—

Brought to you by:

Firecrawl—Power AI agents with clean web data

Customer.io—Build customer engagement campaigns from a single prompt

—

In this episode, we cover:

(00:00) Intro

(02:00) Maddie’s AI pill moment

(03:53) The thermal receipt printer: live demo and how it works

(11:10) The pager project

(17:23) Why she uses Cursor’s clean agent view instead of terminals and browsers

(19:05) The personal API: coffee order, pets, favorite snacks, and more

(22:57) Lightning round and final thoughts

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Tools referenced:

• Cursor: https://www.cursor.com/

• Lovable: https://lovable.dev/

• Raspberry Pi: https://www.raspberrypi.com/

• Resend: https://resend.com/

• Cloudflare Workers: https://workers.cloudflare.com/

• Supabase (Conduct database referenced): https://supabase.com/

• Twitter/X API: https://developer.x.com/

• Spoke pager network: https://www.spoke.com/

• OpenClaw: https://openclaw.ai/

—

Where to find Maddie Reese:

Website: https://maddiedreese.com

Message her directly: https://maddiedreese.com/message

X: 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 [email protected].

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:

1. Why I think we’ve hit an intelligence overhang and what that means for which model variables actually matter now
2. How Opus 5’s “neurotic” personality showed up in real coding sessions, including a merge conflict it refused to touch
3. What I learned from asking both Opus 5 and GPT‑5.6 Sol “who’s smarter, you or me?”
4. Where Opus 5, GPT‑5.6 Sol, Sonnet 5, and Gemini 3.1 Pro actually landed on the HIA benchmark leaderboard
5. The one use case where Opus 5 earned straight 5s from me
6. My actual plan for using Opus 5 going forward

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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 [email protected].

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:

1. How browser use and computer use work, and why the Codex desktop app plus Chrome extension is the combo I rely on
2. How I use Codex to QA my onboarding flow, including exhaustive mobile testing I would never do manually
3. Why under-prompting frontier models gets better results than detailed step-by-step instructions
4. How my husband EJ Lawless’s persona-impersonation trick surfaces friction points I can’t see as the builder
5. How I use browser use to get through my LinkedIn inbox without touching it myself
6. How I had Codex shop Free People’s sale and add 10 medium items to my cart (breastfeeding-friendly and Hawaii-ready)
7. How computer use can control iPhone mirroring so your Mac can technically operate your phone
8. Three more computer-use shortcuts: filling annoying forms, creating Google Sheets mid-workflow, and managing router

—

Brought to you by:

Runway—The creative AI platform for images, video and more

Hyperagent—Deploy fleets of agents that handle real work

—

In this episode, we cover:

(00:00) Intro

(01:46) What browser use and computer use actually are

(03:08) Why I use Codex specifically and how the desktop app plus Chrome extension works

(04:15) Use case 1: QA testing my onboarding flow

(10:41) Results: 11 issues, one high-severity blocker, one Google Sheet with screenshots

(12:10) Use case 2: persona testing

(18:20) Use case 3: LinkedIn inbox, hands-free

(20:37) Use case 4: AI personal shopper

(23:47) Rapid-fire uses: forms, iPhone mirroring, router access from out of state, Google Docs

(26:50) Wrap-up

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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

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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 [email protected].

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:

1. Why the blank page is the biggest friction point in content creation, and how an AI Oracle eliminates it
2. How to map your current workflow before you add any AI
3. How Alex built a six-step Content Machine in Claude that goes from idea spike to publishable post
4. Why the interview step (not the drafting step) is where AI slop actually comes from
5. How to codify your voice in a Markdown file so an AI drafts in your register, not the internet’s average
6. Why your employees are your most underleveraged marketing channel right now
7. How the Tenex Creator Cup turned content creation into a team sport with a $5,000 prize pool

—

Brought to you by:

Firecrawl—Power AI agents with clean web data

Customer.io—Build customer engagement campaigns from a single prompt

—

In this episode, we cover:

(00:00) Introduction to Alex Lieberman

(02:35) Why Alex built a content machine

(06:56) Alex’s thoughts on AI slop

(09:00) Mapping the workflow from scratch

(13:24) The six-step Content Machine setup

(23:11) Live demo: Oracle, Interview Panel, and Writer’s Council in action

(30:38) Employee advocacy: the Tenex Creator Cup and $5K prize pool

(36:45) Lightning round: great engineers, AI use cases, slop fixes

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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

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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

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Where to find Alex Lieberman:

X: https://x.com/businessbarista

LinkedIn: https://www.linkedin.com/in/alex-lieberman/

Tenex: https://www.tenex.co/

—

Where to find Claire Vo:

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

Website: https://clairevo.com/

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

X: https://x.com/clairevo

—

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

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:

1. How Alex chose between a Mac Studio (512 GB unified memory), DGX Spark, and RTX 5090, and what each is actually good for
2. Why Tailscale is worth installing even on a single machine, and how it lets one agent manage your entire hardware fleet
3. How the build loop and review loop in Claude Code work
4. How to allocate tasks by machine and model
5. Why unlimited local inference changes the use-case math in a way a $20 cloud subscription never can
6. What OpenClaw and Hermes are each best suited for, and why Alex runs five agents total with failover baked in

—

Brought to you by:

Runway—The creative AI platform for images, video, and more

Jira Product Discovery—Prioritize with insights, build with confidence

—

In this episode, we cover:

(00:00) Intro

(02:58) Alex's hardware stack

(03:48) What "ambient AI" means

(04:15) Alex's red-pill moment with OpenClaw

(07:04) Mac Studio vs. DGX Spark vs. RTX 5090

(13:24) How to set up local models with no technical knowledge (Tailscale + OpenClaw/Hermes)

(17:16) Fleet control dashboard: assigning 24/7 tasks across machines

(20:42) Local models as security scanners feeding Claude Code

(22:25) How Alex allocates GLM 5.2, Qwen 3.6, and Ornith 1.0 by task

(24:28) OpenClaw vs. Hermes: the honest comparison

(26:55) The software factory: build loop, review loop, rocket emoji

(31:55) Lightning round: favorite hardware, favorite model, prompting style

(34:46) Where to find Alex

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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/

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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

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Where to find Alex Finn:

LinkedIn: https://www.linkedin.com/in/alex-finn-1848684a

YouTube: https://www.youtube.com/@AlexFinnOfficial

X: 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 [email protected].

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:

1. How I scored five AI models (including GPT 5.6 Sol, Fable 5, and Sonnet 5) using my “Claire Weighted Index” benchmark across PRDs, prototypes, code, and agentic voice
2. The difference between GPT-5.6 Sol (Terra) and Sol for PRD writing
3. How Fable’s precision and pedantry made it harder to collaborate with, and the exact moment Sol broke through where Fable got stuck
4. Why Sonnet 5 is still my go-to for agentic voice in OpenClaw, even after this whole benchmark
5. How I used GPT-5.6 Sol in Codex to build a fully gamified homework tracking app for my kids in one shot
6. The video editing use case that saved me hours clipping a talk I gave at Cursor’s event
7. How to use Codex plus GPT-5.6 and Chrome for browser automation, and why this is my single most-loved use case right now

—

In this episode, I cover:

(00:00) Intro

(01:10) The three GPT-5.6 models: Sol, Terra, Luna

(02:17) Pricing: Sol vs. Fable API costs

(03:24) The How I AI benchmark

(05:03) Claire-weighted Index results

(07:00) Per-task winners: prototypes, PRDs, agentic voice

(11:59) What Claire actually rewards

(13:20) Full-fidelity prototype side-by-sides (Sol vs. Fable)

(17:45) Wireframes

(18:19) Agentic voice

(19:15) Where Sol is better than other models

(23:56) Gamified kids’ homework app, built in one shot

(28:02) Fable’s pedantry problem and how Sol broke through it

(31:49) Two bonus use cases: video editing and browser use

(35:08) Final summary and model recommendations

—

Tools referenced:

• GPT 5.6 (Sol, Terra, Luna): https://help.openai.com/en/articles/20001325-a-preview-of-gpt-56-sol-terra-and-luna

• Codex: https://openai.com/codex

• ChatPRD: https://www.chatprd.ai/

• CapCut: https://www.capcut.com/

• Math Academy: https://www.mathacademy.com/

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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 [email protected].

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:

1. What a harness actually is
2. When to build a harness versus when to stick with a general-purpose tool like Claude Code or Codex
3. How to encode specific permissions into a harness
4. The three components every harness needs
5. How I used GPT-5.5 and Claude Opus to build the harness code itself (and where they both initially resisted)
6. 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 [email protected].