How I AI

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 Bluetooth
How to use Cursor’s agent view to brainstorm a hardware project
What belongs in a personal API and why agents, not just humans, will be the ones using it
How to read just enough code to do some damage, without needing to understand all of it
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
—
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 jordan@penname.co.

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 now
How Opus 5’s “neurotic” personality showed up in real coding sessions, including a merge conflict it refused to touch
What 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 leaderboard
The one use case where Opus 5 earned straight 5s from me
My 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.

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 on
How I use Codex to QA my onboarding flow, including exhaustive mobile testing I would never do manually
Why under-prompting frontier models gets better results than detailed step-by-step instructions
How my husband EJ Lawless’s persona-impersonation trick surfaces friction points I can’t see as the builder
How I use browser use to get through my LinkedIn inbox without touching it myself
How 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 phone
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
—
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.

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 it
How to map your current workflow before you add any AI
How Alex built a six-step Content Machine in Claude that goes from idea spike to publishable post
Why the interview step (not the drafting step) is where AI slop actually comes from
How to codify your voice in a Markdown file so an AI drafts in your register, not the internet’s average
Why your employees are your most underleveraged marketing channel right now
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
—
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/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 jordan@penname.co.

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 for
Why Tailscale is worth installing even on a single machine, and how it lets one agent manage your entire hardware fleet
How the build loop and review loop in Claude Code work
How to allocate tasks by machine and model
Why unlimited local inference changes the use-case math in a way a $20 cloud subscription never can
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
—
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-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 jordan@penname.co.