How I AI

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
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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
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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
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

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 voice
The difference between GPT-5.6 Sol (Terra) and Sol for PRD writing
How Fable’s precision and pedantry made it harder to collaborate with, and the exact moment Sol broke through where Fable got stuck
Why Sonnet 5 is still my go-to for agentic voice in OpenClaw, even after this whole benchmark
How I used GPT-5.6 Sol in Codex to build a fully gamified homework tracking app for my kids in one shot
The video editing use case that saved me hours clipping a talk I gave at Cursor’s event
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
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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
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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/
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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.

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 is
When to build a harness versus when to stick with a general-purpose tool like Claude Code or Codex
How to encode specific permissions into a harness
The three components every harness needs
How I used GPT-5.5 and Claude Opus to build the harness code itself (and where they both initially resisted)
How to structure the artifacts your harness produces so the whole team can use the output
—
Brought to you by:
Bolt.new—Turn your idea into a real product
Customer.io—Build customer engagement campaigns from a single prompt
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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
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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/
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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.

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 unlocks
How to wire Symphony and Linear together as an agent state machine
How to track token costs per task (and what 221 million tokens buys you)
What Glimpse does, and why better agent senses extend autonomous runs
Why your CLAUDE.md probably needs a full purge, not more instructions
How Codex scouts underpriced $10K Pokémon cards on eBay at scale
The new category of small business that AI just made possible
—
Brought to you by:
Firecrawl—Power AI agents with clean web data
Jira Product Discovery—Prioritize with insights, build with confidence
—
In this episode, we cover:
(00:00) Intro
(02:24) Prompter vs. agent manager
(04:31) Live demo: Symphony + Linear
(09:31) Setting up Symphony
(14:15) Purging your skills files
(18:06) The benefits of this system
(19:10) Demo: Using Codex to hunt for Pokémon cards
(24:17) The benefit of AI for small businesses
(28:23) Lightning round
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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
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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
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Where to find Alessio Fanelli:
X: https://x.com/FanaHOVA
Latent Space podcast: https://www.latent.space/
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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’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 up
How I built the How I AI Bench in under 45 minutes using Claude Code, starting from my own stored session history
Why I combined human vibe scoring (70%) with LLM as judge scoring (30%) instead of trusting either alone
How to set up a local HTML scoring page so you can rate AI outputs on gut feel and export those scores as JSON
Which model I recommend for PRDs, which for complex prototypes, and which for chatting with an agent daily
—
Brought to you by:
Runway—The creative AI platform for images, video and more
Hyperagent—Deploy fleets of agents that handle real work
—
In this episode, we cover:
(00:00) Sonnet 5 is out
(01:55) What Anthropic claims
(04:02) Why I’m done with one-off vibe checks
(05:05) Building the How I AI Bench live with Claude Code
(07:42) The scoring system
(10:43) Agent voice eval
(11:57) Quick recap
(13:58) Results: The How I AI index leaderboard
(21:21) What I’m improving for the next run
(22:16) Generating a Claire-weighted index
(23:53) Model-by-task recommendations
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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/
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Other references:
• SWE-bench Pro (agentic coding benchmark referenced): https://www.swebench.com/
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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.