Built This Week

A Bot Emailed a Sold Out Hotel for Me. 15 Minutes Later I Got 33% Off.This week there was no guest. Sam and Jordan spent the episode on bots: what they are, why they are different from chatbots and coding assistants, and how we are using them every day.The big change is that bots like Grok Bot, Muse, and OpenAI Dots run on their own browser and their own computer. They stay logged in, run workflows on a schedule, and keep working when your laptop is closed. Jordan told the story of a hotel that showed as sold out. His bot found the hotel's email, wrote to them, and 15 minutes later the hotel called back with 33% off, about $200 saved for one night.We also covered how we use agents to update our websites, from edits in Webflow to a custom JavaScript tracker with heat maps and AI generated ads, plus the latest Nvidia headline out of China.We cover:What bots are and how they differ from chatbots, sidebar assistants, and coding agentsWhy a bot with its own browser and computer is a step changeConnecting email and LinkedIn so a bot can send, reply, and make connections for youThe hotel story: 33% off and $200 savedWhy bots could disrupt aggregators like Booking.com and ExpediaUse cases: travel, big purchases like a used boat or car, social media, market research, and websitesWhy a bot's own virtual machine matters for long running workflowsHow we use agents to update our websites and build adsThe Nvidia news out of ChinaIf you are curious about bots and agents, or wondering how they will reach the enterprise, this episode is for you.New episodes every Friday at BuiltThisWeek.comTIMESTAMPS[00:00] Intro[01:16] What bots are and how they differ from chatbots and coding assistants[02:16] The step change: bots with their own browser and computer[02:52] Connecting email and LinkedIn to your bot[03:51] New workflows for sales and the enterprise[05:03] Sam on three weeks of using Grok Bot[05:45] The hotel story: 33% off and $200 saved[06:38] Why bots could disrupt aggregators like Booking.com and Expedia[07:16] More use cases: big purchases, social media, research, websites[08:08] Why a bot's own virtual machine changes everything[09:27] Using agents to update our websites[10:41] News: Nvidia and China[11:19] Wrap up#AI #AIAgents #Bots #GrokBot #MetaMuse #OpenAI #Nvidia #BuildWithAI #BuiltThisWeek

This week we sat down with Ivan Lee, co-founder and CEO of Datasaur, to dig into why the most regulated companies on earth are running AI on their own servers. Ivan worked on AI products at Yahoo and then on Siri at Apple before founding Datasaur eight years ago. The FBI, Google, Netflix, Zoom, Deloitte, and AmLaw 100 law firms are all clients. Greg Brockman, co-founder of OpenAI, is an investor.We covered the real token economics of enterprise AI, why the open weights model performance gap has almost closed, how Datasaur's proprietary model factory works, and Ivan's personal MCP build connecting Claude to his Whoop bracelet for a daily training plan.We also covered Meta Muse crossing 10 million users in 6 weeks, Anthropic launching Claude Duet, and why everyone is underestimating Google's personal agent.We cover:What Datasaur does — private on-premise AI for the most regulated enterprises on earthWhy companies are switching from public AI after Anthropic launched its own legal and design productsHow the performance gap between proprietary and open weights models has almost closedMeta's 100 billion parameter model running on two Mac Studios — and what that means for private AI costsOne AI agent task uses 3 million tokens vs 2,000 for a ChatGPT queryAI agents crossing the chasm — what happens to token demand when they doDatasaur's model factory — white glove consulting on the outside, productized AI factory on the insideIvan building an MCP server for his Whoop bracelet and giving Claude control of his training planWhy enterprise agents are a better fit than consumer agents right nowMeta Muse crossing 10 million users in 6 weeks with shopping as the top activityAnthropic launching Claude Duet — and why Ivan, who worked on Siri, is watching closelyThe three-way race between OpenAI Dots, Meta Muse, and Anthropic DuetWhy Google's personal agent is being underestimated by everyoneIf you are building with AI, managing enterprise data, or want to understand the real economics of the agent wave, this episode is for you.Find Ivan and Datasaur:Website: datasaur.aiLinkedIn: linkedin.com/in/iyliTwitter: x.com/datasauraiNew episodes every Friday at BuiltThisWeek.comTIMESTAMPS[00:00] Intro[00:42] Meet Ivan — co-founder and CEO of Datasaur[01:00] Yahoo, Apple Siri, and the path to founding Datasaur[01:24] What Datasaur builds — private AI inside your own infrastructure[02:37] Healthcare, legal, finance, and public sector — who uses it[03:01] Why regulated industries are moving faster on AI than any technology in history[03:54] Domain expertise as the moat — Anthropic enters clients' markets[04:53] Open weights models catching up — the performance gap is closing[05:53] Live benchmark — every open model now clears Claude 4.5[07:24] Hardware costs plummeting — 100B parameter model on two Mac Studios[08:57] The benchmark slide — where agents started actually working[10:43] One AI task uses 3 million tokens — the macro token shift[11:39] Crossing the chasm — chatbots vs agents on the adoption curve[13:41] Agents collapsing software spend — the CPU to token shift[14:23] How Datasaur works with clients — the model factory[16:36] News — Meta Muse crosses 10 million users[17:26] Ivan worked on Siri at Apple — personal agents have always been his passion[18:09] The consumer agent gap — why it took so long[19:14] Jordan on Grok Bot and underestimating Google[20:15] Enterprise vs consumer agents — where the market fit is now[21:08] Ivan's favorite AI build — MCP server for his Whoop bracelet[22:14] Where to find Ivan and Datasaur[22:42] Wrap up#AI #PrivateAI #Datasaur #Enterprise #AppleSiri #OpenAI #BuildWithAI #BuiltThisWeek #Anthropic #MetaMuse #ClaudeDuet #AIAgents

This week we sat down with Jim Olsen, co-founder and CTO of ModelOp, to explore what it takes to get AI into production inside Fortune 500 companies. Building a model is only part of the job. Knowing where it runs, what it costs, who owns it, and whether it meets company policies is where things get complicated.Jim walks us through a live demo of ModelOp, showing how it discovers unregistered enterprise AI, brings it into a central inventory, and uses agents to fill out documentation and flag compliance gaps. His team is turning work that can take months into a process that takes minutes, with humans still in the loop.We also covered why AI spending needs to be measured by business use case, whether an AI-powered pet feeder solves a real problem, and how Jim runs engineering from an off-grid home at 10,200 feet in the Colorado mountains.We cover:• What ModelOp does and why all its clients are Fortune 500 companies• Why enterprise AI projects get stuck between prototypes and production• Tracking token usage and costs by business use case• Shadow AI beyond employees using chatbots• A live demo of discovering and registering enterprise AI• Using AI agents to complete forms and identify compliance gaps• Why model inventories matter when providers change or models retire• The real-world problems that led Jim to co-found ModelOp• Petlibro’s AI-powered feeder and whether everything needs AI• Running an engineering team with solar power and Starlink from an off-grid homeIf you are building with AI, managing enterprise AI deployments, or trying to understand how governance can accelerate delivery, this episode is for you.Find Jim and ModelOp:Website: https://www.modelop.comNew episodes every Friday at BuiltThisWeek.comTIMESTAMPS[00:00] Intro[00:41] Meet Jim Olsen, co-founder and CTO of ModelOp[01:49] What ModelOp does: enterprise AI governance explained[03:15] Tracking AI costs by business use case[04:50] Demo: token usage and cost dashboards[05:41] Automating governance and compliance workflows[06:08] Discovering shadow AI across enterprise platforms[08:49] Bringing a healthcare chatbot into the inventory[09:56] AI agents automate documentation and forms[11:20] Checking policies and flagging compliance gaps[12:05] From months of work to minutes[12:27] The real-world problems behind ModelOp[14:04] Tracking model dependencies and managing changes[15:23] Where to find ModelOp[15:43] News: Petlibro’s AI-powered pet feeder[16:11] Does everything really need AI?[17:20] Automatic feeders, robots, and everyday chores[18:23] Jim’s off-grid setup in the Colorado mountains[19:09] Solar panels, batteries, and working remotely[20:12] Wrap-up#AI #ModelOp #AIGovernance #EnterpriseAI #ShadowAI #AIAgents #Fortune500 #BuildWithAI #BuiltThisWeek

This week we sat down with Rob Williams, co-founder and CTO of Read AI, to dig into what 5 million monthly active users actually looks like at the product and infrastructure level. Most people know Read AI as a meeting note taker. Rob winces every time he hears that. What it really does is capture every conversation across your organization and turn it into intelligence — daily briefings, smart routing, and organizational context that moves itself to where it needs to be.We also covered AI companion apps and the unit economics behind them, the AI adoption divide inside enterprise companies, and Opus 5.5 dropping this week.We cover:What Read AI actually is beyond the meeting note taker labelThe daily briefing feature — hot and hydrated context before every meetingHow smart routing sends meeting data automatically to the right teamWhy some companies set data retention to 7 days and why that makes senseNavigating the AI adoption divide inside enterprise organizationsRob's vision for information moving itself in three yearsWhy meetings are the biggest investment most companies make and nobody measuresHot Girl Hotline — an AI relationship advice app and why the unit economics workAI as a sounding board vs a solver and why both have valueOpus 5.5 dropping and what cheaper intelligence means for buildersToken economics of running 5 million monthly active usersIf you are building with AI, managing a team, or want to understand where enterprise AI is actually heading, this episode is for you.Find Rob and Read AI:Website: readai.comEmail: [email protected]: [LINKEDIN LINK — to be added]New episodes every Friday at BuiltThisWeek.comTIMESTAMPS[00:00] Intro[01:06] Meet Rob — co-founder and CTO of Read AI[01:42] What Read AI actually is beyond meeting notes[02:12] Meetings as the biggest investment companies make[02:33] The daily briefing feature — live demo[03:47] Smart routing — data flows where it needs to go[04:57] Unusual use cases and the AI adoption divide inside companies[06:19] 7-day data retention and what it says about fast-moving companies[06:40] 5 million monthly active users and how they got there[07:00] Token economics at scale[10:00] The future — information that moves itself[15:34] What supersedes old context — the data freshness problem[15:55] News — Hot Girl Hotline, AI relationship advice[17:09] Is AI as a companion dangerous or valuable[18:14] AI memory and the sounding board use case[19:37] Unit economics of AI companion products[20:13] Sam uses AI for big personal decisions[21:32] Where to find Rob[21:50] Wrap up#AI #ReadAI #Meetings #Enterprise #FutureOfWork #BuildWithAI #BuiltThisWeek #Productivity #Anthropic #AICompanion

He Ran a Rocket Launch Company. Now He Is Building Autocomplete for Hardware Design.This week we sat down with Sohrab Haghighat, CEO and co-founder of Hestus, to demo the AI layer that sits on top of your existing CAD software and autocompletes your next design move. Rob spent years as an aerospace engineer, ran a rocket launch company, and built Hestus to eliminate the manual clicks that slow hardware engineers down and introduce errors into designs.We also covered Anthropic 5.5, GPT-6, and a new Grok model all dropping in the same week — and why intelligence getting cheaper is good news for everyone building right now.We cover:What Hestus is and how AI autocomplete works inside CAD softwareLive demo — AI suggesting the next design step before you clickWhy Hestus plugs into your existing CAD tool instead of replacing itHow Hestus learns your preferences within a design sessionWho the target customer is — mechanical, aerospace, and robotics engineersThe gap between a flashy 3D model and one that is actually ready for manufacturingRob's long term vision — doing for hardware what Claude Code did for softwareAnthropic 5.5, GPT-6, and Grok all dropping the same week — intelligence is getting cheaperWhy LLM improvements do not directly translate to improvements in hardware AIIf you are building hardware, working in engineering, or want to understand where AI is going beyond software, this episode is for you.Find Rob and Hestus:Website: hestus.coLinkedIn: linkedin.com/in/sohrabhaghighatTwitter: x.com/s_haghighNew episodes every Friday at BuiltThisWeek.comTIMESTAMPS[00:00] Intro[01:12] Meet Rob — CEO and co-founder of Hestus[01:33] Aerospace engineer, rocket launch company, and the pain that led to Hestus[02:29] Live demo — AI autocomplete inside CAD software[03:50] How Hestus sits on top of your existing CAD tool[04:16] Meeting engineers where they are — no new workflow[05:02] How Hestus learns your preferences within a session[06:32] Available on Autodesk Fusion now, SolidWorks coming soon[07:20] Who is using Hestus — mechanical, aerospace, robotics engineers[08:30] Bottom-up approach — sketch to 3D to manufacturing readiness[10:25] Long term vision — Claude Code for hardware design[11:15] The gap between a flashy model and one ready for manufacturing[12:00] News — Anthropic 5.5, GPT-6, and Grok released same week[13:18] Jordan tests the new Opus — cheaper and competitive with Fable[14:41] Intelligence is getting cheaper — what that means for builders[15:06] Why LLM improvements do not directly improve Hestus[16:08] Where to find Rob and Hestus[16:31] Wrap up#AI #CAD #HardwareDesign #Engineering #Hestus #BuildWithAI #BuiltThisWeek #Anthropic #OpenAI #Aerospace