How I AI - Hallgatás Online

Anthropic's new personal agent, Claude Duet, lives on your phone, inherits all your Claude plugins and connectors, and is pitched at small business owners and busy professionals. It's the most polished agent launch I've seen: thoughtful onboarding, task lists, first-class artifacts, and Slack-style channels. But polish isn't enough when you're competing with every other personal agent for attention. I put it to work on my inbox and Slack, my sales pipeline, a Google Docs sales deck, a launch video, and finding Warriors tickets for my kids, and I'll walk you through what it nails, where it stalls, and what Anthropic needs to fix before it can become anyone's number one agent. What you’ll learn: What Claude Duet is and who Anthropic built it for The design details that make it far more polished than other personal agent launches How task lists, artifacts, and channels work as agent primitives Why a “proactive” agent that never nudges you is a growth problem How model latency kills your willingness to work with…

Brooke Lacey has been making videos since before Facebook had video, and she built a TikTok audience of more than 290,000 by learning to cut short-form videos herself. With over 25 years in tech, she now builds her own apps, including Zosha, a free mobile app for learning AI. In this episode, Brooke walks me through the Claude Code, FFmpeg, and Whisper workflow that edits her TikToks in about 15 seconds, how she mines her comments for new scripts, and how she runs a weekly short-form operation with no editor. What you’ll learn: How Claude Code edits a raw phone video on her computer in about 15 seconds How she taught Claude her jump-cut cadence Why her TikTok comments became her content brief How she keeps growing her audience while posting just once a week—and what’s next What over 25 years in tech taught her about this AI moment — Brought to you by: Firecrawl—Give your AI agents web data and beyond Auros—The human network behind AI — In this episode, we cover: (00:00) Intro and guest welcome (02:30)…

I put Meta’s new Muse Wonder (and Wonder Full) frontier models through the How I AI bench, which covers nearly 20 tasks across prototyping, agentic workflows, coding, writing, and creative work. I scored everything with an LLM-as-a-judge pass and my own voice notes, then pulled together the first-look results you see here. What you’ll learn: ​Why Meta’s “personal and professional agent” framing mostly holds up, and where it doesn’t ​What active focus mode actually does on long-running tasks, and whether it’s real ​The three agentic tasks where Muse ranked first, and why those categories matter ​What Muse’s design output actually looks like (it’s not good) ​How it compares to Sonnet on voice assistant tasks ​Where Muse lands on SVG and short-form video versus Opus 5.5, Astra, and Fable ​Whether I’m actually putting Muse into any of my agent workflows — Brought to you by: DX—Engineering intelligence for the AI era Jira Product Discovery—Prioritize with insights, build with confidence — In this episode, we…

Alex Cornell is a design director at Meta, where he leads design on Muse, Meta’s consumer AI agent. He came to the role with a long background in software product design, including time as a founder, and has spent the past year building one of the most thoughtful agent interfaces in the consumer AI space. Muse ships with a custom avatar, a three-layer status display, a personalized ideas surface, and a generative feed. Alex has been sharing the reasoning behind each of those design decisions publicly on Threads. What you’ll learn: Why Alex built Muse to feel like messaging, not an AI app How Muse’s three-layer status system accommodates different levels of technical expertise What generative UI actually means in a shipped consumer product How Alex turned years of forgotten Instagram saves into a daily shopping catalog Why his mom apologized to her Muse, and what it means for AI design How he uses Muse to track his golf game, shot by shot, as he works toward a single-digit handicap The mindset shift designers…

Kath Korevec is a member of the Product staff at OpenAI working on Codex, and she spent over a year building and using ChatGPT Sites internally before its public launch. She’s been on the front lines of shipping Plugin Insights, MCP plugin hosting, and the connector ecosystem, which now includes around 60 integrations. What you’ll learn: What Plugin Insights actually does, and why it changes who can use a site The incident command site Kath built for her OpenAI team, and how it uses live Slack and Notion connectors The one phrase that tells Codex to wire up connectors for you The infrastructure layer inside Sites that most people haven’t touched yet How Kath fixed her Spotify after her kids wrecked it, using Reddit and computer use The skill distribution model behind her community dungeon crawler, and why it’s a new way to think about collaboration Why model speed is what actually determines how creative you get Where Kath draws a hard line on AI acting in her name — Brought to you by: Merge—Connective…

John Lindquist created egghead.io, a developer education platform used by hundreds of thousands of working engineers. These days he’s building mega.dev, a hands-on program specifically for developers who want to do real work with AI agents, not just prototype them. What you’ll learn: Why Jev is a decision engine, not a chatbot, and what that distinction actually changes about how you build How John built a real-time voice to-do app that classifies and executes commands with no visible pause The data deduplication pattern that merges messy records in milliseconds using confidence scores Why Jev works best as a router, and how a single text input can navigate users deep into an app What a chess match between Jev and a low-reasoning LLM reveals about speed, cost, and when to use which The multi-step classification pattern John reaches for when one Jev pass isn’t enough Where Jev falls short, and when you should still reach for a full generative model — Brought to you by: Vanta—Automate compliance and simplify…

I spent the day at OpenAI’s DevDay in San Francisco, and I have good news and bad news: OpenAI released a lot of stuff. In this episode, I break down the announcements worth paying attention to - and show you what happened when I tested some of them early. We’ll meet my Dot, explore why Spaces and Sites could matter for how teams work, and get into the model and API updates I’m most excited about as a developer. I use the Decisions API to find podcast thumbnails where nobody looks awkward, build a collaborative sketchpad with Astra ultrafast, and let my kids redesign a 3D world in real time. That last experiment cost about $97. My wallet has thoughts. These are my early impressions: what’s promising, what still feels rough, and what I think you should try first. What you’ll learn: What OpenAI’s Dots can do, how I’ve been using mine, and why I’m waiting to give a full verdict Why Spaces might be one of the most underhyped announcements for collaboration between humans and agents How Sites with connectors and…

Jev is TypeSafe AI’s new decision model. It returns type-safe structured values (a choice, a score, a probability) instead of generated text, at 4 cents per million input tokens with no output charge. This week I ran it on five real projects: PR categorization, a meta-analysis of my own Claude and Codex sessions, Gmail triage, the ChatPRD product insights graph, and a live audience dashboard built from 4,500 YouTube comments. What you’ll learn: What makes Jev fundamentally different from every other model I’ve used How I analyzed 1,700 PRs for 9 cents and what I found out about where my engineering effort actually went The personal meta-analysis you can run on your own Claude and Codex sessions right now Why I stopped using Jev alone, and what I pair it with now How I turned 4,500 YouTube comments into a searchable audience dashboard for almost nothing The real-time app I built in an afternoon that shows something surprising about Jev’s speed Why Jev’s pricing model is different from any LLM I’ve used, and…

I got up early to record an Opus 5.5 review. Then Anthropic and OpenAI dropped new models on the same morning, and I decided to do something I’d never done before: take the How I AI bench live. I put GPT-6 Astra, GPT-6 Sol, Claude Opus 5.5, and more through the work I actually care about: emails, PRDs, frontend prototypes, backend work, long-running agents, SVGs, and video editing. I scored the outputs without knowing which model made them, so you get to watch me make predictions, change my mind, and reveal my own very inconsistent taste. Astra won my heart. Opus 5.5 won my week. Sol still has me split. There’s a creative result I got completely wrong, an LLM judge that disagreed with me, and a return to Barbie Bench: the 3D fashion game that keeps reminding me how far we have to go. The hands are tragic. AGI has not arrived. What you’ll learn: How I run the How I AI bench blind, and what gets an output a bad score before I even know which model made it Why Astra won my heart while Opus 5.5 might be overall…

I’ve been off Claude for months. Not because it got dumb, but because it got annoying. The rambling, the hedging, the preachy little disclaimers on tasks that didn’t need them. I moved most of my daily work to Codex and I didn’t miss it. Then Anthropic shipped Opus 5.5: 40% cheaper than Opus 5, faster, and with what they’re calling a fundamentally different alignment approach. I ran it for a week across real work, including four long-running agentic tasks, a full ChatPRD homepage redesign, an SVG benchmark, and one very firm refusal, and I’m ready to give you the honest verdict. There’s a lot to like. There are still two things that drive me a little crazy. And there’s one capability I genuinely wasn’t expecting. What you’ll learn: Why I walked away from Claude entirely, and what it took for me to come back The real cost math on Opus 5.5 and why pricing matters more for agentic work than single prompts What happened when I ran four long-running agentic tasks, including one that tried to manipulate Claude…

Zach Lloyd is the co-founder and CEO of Warp, an AI-powered terminal and software factory platform used by tens of thousands of engineers. Before Warp, he spent nearly a decade at Google, including time as a principal engineer on Google Sheets. He built Warp from the ground up as a modern, AI-native alternative to legacy terminals, and the team has since expanded into software factories: a full cloud-based system that takes an idea in Slack all the way through to a merged PR. In this episode: Why a software factory is more than a coding agent The public Slack → Linear → GitHub → QA workflow Human interactions per PR as a signal of automation and throughput Why human review is still the bottleneck Scoring agent runs, finding failure modes, and self-improving agent workflows Replaying real tasks to choose model cost and quality tradeoffs CEO workflows with Figma MCP, Granola, and research agents — Brought to you by: DX—Engineering intelligence for the AI era OpenArt—An all-in-one AI creation platform for…

I spent a few hours putting Meta’s Muse, its new personal AI agent, through a real first-pass test: onboarding, calendar management, goal setting, a one-shot family morning newsletter, browser-based shopping, and the animated avatar that honestly surprised me. What you’ll learn: Why Muse is the best-designed personal agent I’ve tested, and what specifically made it feel that way The one-shot family PDF Muse produced that Claude and Codex never quite nailed How Muse’s permission model works, and why it’s different from every other agent I’ve used Why I set up a sleep training goal in Muse, and what it revealed about agent tone The activity feed feature I immediately wished Codex and Claude Code had Where Muse failed, and what it says about the limits of this category right now The animated avatar decision that showed me what top-of-craft AI product design actually looks like — Brought to you by: Optimizely—Your AI agent orchestration platform for marketing and digital teams OpenArt—An all-in-one AI creation…

John Bai and Peng Zheng are designers on the Grok Bot team at SpaceXAI, where they’re building one of the most talked-about AI products right now. John writes publicly about his design process (his piece “Designing Grok Bot with Grok Bot” has already made the rounds) and shares bot templates with the design community. Peng brings a product-design sensibility to personal tools, and his website doubles as a live demo of what he builds. What you’ll learn: How Peng built a self-updating personal website using Grok Bot as the entire backend pipeline, with no CMS and no Figma file The exact check-in bot setup that lets Peng send a photo or a place name and have his portfolio update itself automatically How John’s Figma Bro bot handles production design tasks while he’s at the gym How John uses voice memos to direct Figma work through an MCP connection without opening his laptop The “shower thought to prototype” workflow John uses with DevBot to test interaction ideas without first going through a product manager…

Sharadh Krishnamurthy is an engineering manager at Stripe, where he helped build Kai, the company’s internal AI agent used by more than 10,000 employees every week. He’s worked across several of Stripe’s core infrastructure teams, including data and developer experience, which gives him a grounded, systems-level perspective on what it actually takes to make AI work at enterprise scale. He’s currently focused on the governance, skills, and infrastructure layers that let every Stripe employee use AI safely and effectively, regardless of their technical background. What you’ll learn: Why Stripe built Kai from scratch instead of buying, and what tipped the decision What Kai knows about you by default and what you actually control Why “projects” at Stripe are a governance mechanism, not just a folder How Stripe structured its data layer so agents can query safely at scale Why the infrastructure Stripe built for human developers turned out to be exactly what agents needed How Kai’s skills platform lets any…

I got early access to GPT-6 Astra: when I say this model broke through tasks I couldn’t crack with 5.6 Sol or Fable, I mean it specifically: the ChatPRD product intelligence feature, building 3D games, the hardware hack, and a handful of one-shot coding projects I’d tried and failed on repeatedly. What you’ll learn: Why Astra’s computer use feels different, and which production tools I’m trusting it with The one feature I’d thrown every model at for six months, and what finally got it to 90% How I’m using browser use for QA, not building, and what it found that I would’ve missed Why I think UI is genuinely back, and what that means for SaaS and MCPs The hardware hack I’d been chasing since GPT-5.5, and how Astra finally cracked it What Astra built me in Blender in one shot, and why 3D is my new capability benchmark The AIM-style Mac app Astra made in one shot, and what it signals about desktop development now An honest take on speed, cost, and whether Astra is worth making your daily driver — In this…

I’m running about 30 active agents at any given moment, and in this episode I break down my full Grok Bot setup: what it is, how it compares to OpenClaw, and the nine bots I’ve built for work and my personal life. We go deep on Chief (my chief-of-staff bot sweeping six inboxes and multiple Slack workspaces), TradBot (the family agent that prints a kitchen-table newspaper for my kids), two engineering bots handling my PR queue and SOC 2 compliance monitoring, Holly Helpdesk, and a handful of personal bots I didn’t expect to actually love. I also walk through how I migrated everything from OpenClaw, including the script I used to export and transplant each agent’s identity and schedule. What you’ll learn: The three primitives Grok Bot is built on, and why one of them changes what agents can actually do How Chief, my general-purpose chief of staff, handles a scope I didn’t think a single bot could manage The writing quirk I noticed immediately with the Grok model, and what I did about it before letting it near…

Daniel Blum is a product manager at Melio, a B2B payments company, and one of the most systematic thinkers I’ve had on the show when it comes to personal AI infrastructure. He’s spent the past year building a Claude- and Cowork-based productivity system that manages his Notion board, processes his Slack and email, and runs self-improvement loops every week without needing to be prompted. Beyond his own workflow, Daniel built and scaled a “Workstation” onboarding plugin that gets any Melio employee up and running with a personalized Claude setup in about 15 minutes. What you’ll learn: Why Daniel says the two rules that make any AI system powerful aren’t about the tool you pick How his weekly prep automation fills an entire Notion board from scratch every Sunday, without his touching it The morning brief feature that teaches Claude new internal terms on its own, so company jargon never slows it down Why he describes Notion as “read-only” now, and what that says about how PM workflows are changing The…

Ryan Carson is a five-time founder and the current solo founder of Untangle, a B2B SaaS platform for family law firms. Before Untangle, he co-founded Treehouse, an online coding education platform, and has spent the better part of two decades building and leading tech companies. He’s active on X, where he shares his solo founder journey in real time, including what he actually spends on AI tools each month. What you’ll learn: Why Ryan manages 15 concurrent Devin agents with a folder system and a piece of paper, not a dashboard The Watchdog playbook: what he built to replace a customer success team across every law firm account How his LAN PR skill closes the loop on 40 daily PRs without a QA team reviewing a single one Why he moved off local agents almost entirely, and the one situation where he still reaches for Codex The design workflow we’re both using: Claude Design into a Markdown spec, then Codex to build the real thing What he found when he got away from his computer and met a real customer, and why…

This week I’m doing a solo breakdown of everything xAI and Cursor have shipped recently, including Grok Bot, Cursor Origin, and the Grok 4.6 model. I set up five Grok Bots, ran Grok 4.6 through my Claire Weighted Index against GPT-5.6 Sol, Claude Sonnet 5, and Opus 5, and spent time actually using Origin as a GitHub replacement. Here’s what’s worth your attention, what’s overhyped, and where I’m personally putting my time. What you’ll learn: The one Grok Bot feature no other agent platform has shipped yet, and why it made me actually use the product What a week of real Grok Bot use revealed, and why I still reach for my OpenClaws Whether Cursor Origin is a GitHub replacement or just a pretty redesign Where Grok 4.6 landed on the Claire Index, and the one category where it genuinely surprised me — Brought to you by: Bolt.new—Turn your idea into a real product Jira AI SDLC—Get your tokens’ worth with Jira — In this episode, we cover: (00:00) Why everyone’s quietly switching to Grok (01:52) Grok Bot overview…

Yana Welinder is the solo founder of Yana Bana, an AI-native fashion brand built with AI as her technical co-founder, starting from hand-drawn sketches and ending with runway photos, CAD files for 3D printing, and a live Stripe-connected pre-order site—no engineers required. A former product leader, she brings an operator’s rigor to her creative process: her “fashion prompt” is a detailed spec covering silhouette, volume, fabric behavior, movement, and sound, and watching her use Codex plus computer use to navigate 3D design software that’s entirely new to her is a clarifying demo of what today’s toolset actually makes possible. What you’ll learn: How Yana uses a custom fashion prompt as a technical spec to get consistent, realistic, on-design outputs Why ChatGPT Images 2.0 outperforms other models for fashion design How she uses Codex plus computer use to operate CAD and fashion software she’s never personally learned The workflow for taking a garment from hand-drawn sketch to product photo, runway photo,…

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: How to build an hourly pipeline in Claude that moves clients through your process automatically Why Grace ditched traditional proposals for password-protected, interactive HTML documents built in Claude How she uses a “voice guide” skill file so every Claude output sounds like her, not like AI slop The two-step forcing function she teaches non-technical clients to build the muscle of opening Claude Why she started building in Claude Code, then handed the work off to Cowork via a Markdown session file How she replaced Gmail entirely with a custom inbox Why she teaches “intent engineering” instead of prompt…

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: Why AI-generated PRs create a review bottleneck and why the answer isn’t reviewing all of them How Intercom 5x’d PR approval speed and reduced revert rates by putting AI in the review loop Why Vercel Eve is the simplest framework I’ve found for deploying AI agents in Slack and GitHub How I built a full PR review agent in Codex with one prompt and a few steering turns The six components I use to score PR risk (blast radius, reversibility, data security, ops impact, verification gap, and change surface) How I used Chrome browser use to handle Slack bot…

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: How two-person voice chat works How Heartbeats work How to build and deploy a live website with ChatGPT Sites How to delegate a flight search, hotel booking, and expense report to Codex in a single voice conversation without opening a single app manually Why ChatGPT Work on mobile is the most underutilized AI workflow for people already using the ChatGPT app 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…

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…

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…

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…

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…

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…

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…

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…

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…

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…

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 doc The two-tool agent stack behind Gusto Cofounder The exact “perma-Zoom” setup that replaced standups, retros, and Slack threads for 10 weeks How a designer with no engineering background hit the 94th percentile for shipping code The eval-first workflow Eddie uses to fix real customer bugs with Claude Code How a non-technical leader can prototype an idea to win buy-in, then carry it…

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 independence How to connect GLM 5.2 to Cursor and Claude Code How it performs on codebase exploration and autonomous architecture summarization in a real production Next.js app Whether GLM 5.2 can match an existing design system How the model handles a 45-minute long-running autonomous task Where 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…

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 required Why pointing an agent at a whole codebase fails, and how an LLM judge can score and rank files before you spend any compute How a verifier subagent kills false positives by catching the agent when it cheats The goal-loop pattern: give an agent a tightly scoped problem, a clear…

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 paradigm The four loop types (heartbeat, cron, hook, and goal) and when each one actually fits your workflow How to think about loop design using the “onboarding an employee” mental model The five things every effective loop needs: work trees, skills, plugins/connectors, subagents, and state tracking How to build a scheduled PR-review routine in Claude Code that babysits aging PRs and alerts your team How to set up a weekly skills-identification…

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 queries Why he argues there’s no excuse to skip rigorous benchmarking now that agents can run them tirelessly The “agent line” framework: how to decide which decisions, directions, and interactions you can hand off to an agent How I think about the practical vs. theoretical quality of AI on hard technical problems, and…

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:…

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 policies The shopping criteria that help surface century-old manufacturers over trendy direct-to-consumer brands How to use Claude to search through trusted vendor websites that have terrible UX Why AI actually helps small artisans and heritage brands compete against Amazon’s infrastructure How to use Claude Cowork to automate returns by finding receipts in your email and drafting refund requests The technique for getting Claude to analyze whether a brand is legitimate or just a drop-shipping operation How to shop within a specific budget or with gift…

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 minutes Why video AI tools unlock creative possibilities for people with zero video production skills The step-by-step process of generating a full storyboard using AI as your creative producer How to use character consistency features to generate multiple video scenes with the same avatar The uncanny-valley moments you’ll encounter when your AI clone doesn’t quite nail emotions or physics How to stitch together AI-generated scenes into a complete video using built-in editing tools — Brought to you by: Merge—Connective infrastructure for production AI Jira Product Discovery—Prioritize with insights, build with…

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 knowledge The step-by-step process for creating custom AI-generated workout videos by combining Gemini images with real exercise footage How to use Claude as your technical architect and Claude Code as your software engineer How to navigate App Store submission requirements (including fixing rejection feedback) Why being hyper-literal in your prompts unlocks better AI results Why a beginner’s mind is actually an advantage when…

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 execution Where it struggles: the last 10%, edge cases in existing codebases, and hallucinations How Opus 4.8 compares to Opus 4.7 on business strategy work Why I’m still reaching for Opus 4.7 on data-heavy strategy and roadmap work The new features shipping alongside the model: dynamic workflows with parallel subagents and effort control in Claude.ai and Cowork The 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…

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 prompts How I used /goal to eliminate hundreds of error logs in my codebase over a five-hour autonomous run The 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 Linear How to write effective /goal prompts with measurable outcomes, verification methods, and constraints When not to use Goals and what makes a strong versus weak Goal Why Goals represent a fundamental shift in how we work with AI, from babysitting the model to…

Felix Rieseberg is the engineering lead for Claude Cowork and Claude Code Desktop at Anthropic. He previously spent five years at Slack building developer tools. In this episode, Felix demonstrates how he uses Claude to solve real-life problems: analyzing floor plans to build interactive 3D house walkthroughs, automatically tracking promises he makes on Twitter, and building a $20 hardware device that physically approves Claude actions with a button press. What you’ll learn: How to use Claude Cowork to turn a 2D floor plan into an interactive 3D walkthrough where you can move furniture around The “go one abstraction layer up” philosophy: why you should never manually enter data Claude can find itself How to use your email as an inventory database for furniture, clothing, and personal purchases When to use Opus vs. Sonnet 4.6 (hint: it’s about how well you can scope the problem, not technical complexity) How live artifacts work and why they’re powerful for dashboards that refresh with real-time data from your…

Today is day one of Google I/O 2026, and I walk through every major announcement live—from the new Gemini 3.5 model family to Anti-Gravity 2.0, Google AI Studio, Gemini’s consumer redesign, the Omni video model, Flow, Stitch, and Pomelli. I test them in real time and tell you exactly which ones delivered. What you’ll learn: How Gemini 3.5 Flash benchmarks against Claude and GPT models on speed and agentic coding tasks How Anti-Gravity 2.0’s new features (projects, scheduled tasks, subagents, slash commands) compare to Codex and Claude Code Why the /grill-me slash command could be a more aggressive alternative to Claude Code’s clarification flow—and how to use it How Google AI Studio’s new Workspace integration is designed to own the internal productivity app use case How Google’s new creative tools work in practice: Omni (video generation), Flow (cinematic video editing and character consistency), Stitch (streaming UI design with inline edits), and Pomelli (brand identity and asset generation) Why Google’s…

Thariq Shihipar is an engineer at Anthropic working on the Claude Code team. He’s spent the past several months experimenting with HTML as a replacement for Markdown in planning and implementation workflows, discovering that richer visual formats lead to better human engagement—and, ultimately, better products. In this episode, filmed at Anthropic’s Code with Claude event in San Francisco, Thariq demonstrates how to use HTML artifacts to create interactive plans, build throwaway UIs for specific problems, and maintain living design systems that travel with your codebase. What you’ll learn: Why HTML has replaced Markdown as the ideal format for AI agent communication and planning How to brainstorm in HTML to get visual mockups and interactive demos instead of text lists The technique for building throwaway micro-UIs to edit specific parts of your plan How to create a living design system in HTML that lives in your repo and travels with every project Why “complexity has to earn its keep” and how HTML helps you…

Ryan Nystrom is a software engineer at Notion. He joined in December 2024 after Notion acquired Campsite, the team communication platform he co-founded with Brian Lovin. At Notion, he’s been a core builder of Notion AI and the Custom Agents feature launched in February 2026. He manages a team of six to seven engineers while still writing code himself, currently running Project Afterburner, a push to cut Notion’s CI time to a quarter of its current duration. What you’ll learn: How to build a Notion AI custom agent that auto-generates your daily standup pre-read by pulling from Slack, GitHub, Honeycomb metrics, and yesterday’s meeting transcript How to configure subagents and MCP integrations within Notion AI How Notion’s internal “Boxy” system lets engineers @mention Codex from within Notion comments and get a full pull request with screenshots in 20 minutes The spec-first development workflow: dictate an idea into Whisper, have Codex format it as a proper spec, commit it to the repo, and let the agent…

Claire breaks down the biggest announcements from Anthropic’s “Code with Claude” event and what they actually mean for builders shipping AI products today. From scheduled AI routines to outcome-based agents, multi-agent orchestration, and new memory systems, Claire walks through the features she’s most excited to use immediately—and how they could reshape the future of agentic software. What you’ll learn: How Claude Code routines let you automate recurring workflows on schedules or webhooks What “Outcomes” are and how rubric-based agent grading works How multi-agent orchestration enables specialized AI teams with different roles and tools Why Anthropic’s new “Dreams” memory system matters for long-term agent behavior Why increased Claude Code usage limits are a bigger deal than they sound How Claire thinks about building practical agentic products today — Resources: • Code with Claude: https://claude.com/code-with-claude • Claude Code Routines Docs: https://code.claude.com/docs/en/routines • Define Outcomes…

John Kim is the co-founder and CEO of Delight.ai, a customer experience platform that’s transforming how companies deploy AI. But what makes John’s story fascinating isn’t just his product; it’s how he’s turned his entire company into an AI-native organization. His marketing team built a fully functional e-commerce swag store with Stripe integration in days. His sales team built their own CRM tools. His recruiting team automated their entire workflow. And it’s all tracked, measured, and celebrated through an internal platform called Automators. What you’ll learn: How Sendbird’s marketing team built a fully functional swag store with Stripe integration in a day (with no engineering support) How the Automators platform works—an internal marketplace where anyone can request AI tools and engineers (or AI agents) can build them How to create secure, compliant templates so non-technical teams can ship to production safely How Sendbird built a token usage dashboard with five tiers (beginner through AI God) and why…

Owen Williams is a design manager at Stripe who built Protodash, an internal AI-powered prototyping platform that lets designers and PMs create high-quality Stripe dashboard prototypes without writing code. What started as a bundle of Cursor rules and React components evolved into a full web-based prototyping studio that runs in dev boxes, complete with design review modes, variant testing, and AI-powered iteration. Surprisingly, PMs now use Protodash just as much as designers, fundamentally changing how Stripe approaches prototyping, design reviews, and engineering handoffs. What you’ll learn: How Stripe built an internal AI prototyping tool using Cursor rules, MCPs, and their design system Why “blurple slop” happens when designers use generic AI tools—and how to fix it The architecture behind Protodash: React router, design system components, and MCP integrations How Stripe prototypes in dev boxes so designers never have to worry about local setup Why “demos, not memos” transformed Stripe’s design review…