GM readers 👋 More and more, AI feels less like a feature and more like the foundation. Not the shiny thing you bolt on, but the thing you build around. This week’s stories show what that actually looks like: agents buying stuff, bots onboarding teammates, apps getting shipped at light speed. Let’s dive in.
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In this issue:
Standardizing how AI agents spend money 💵
A home server, Claude, and a second brain 🧠
The bot that answers HR questions so humans don’t have to 📱
From rural Brazil to a $3M AI launch 🚀
Shipping a complex product with just one prompt 💪
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News
Google just built the payment rails for AI agents
TL;DR: Google launched the Agent Payments Protocol (AP2), an open standard that lets AI agents securely initiate payments on your behalf, across any platform, currency, or payment method. Backed by over 60 major partners (from Coinbase to Mastercard), AP2 could become the Stripe of agentic commerce.
AP2 solves three critical gaps in agent-driven payments: proving user authorization, verifying merchant authenticity, and assigning accountability for fraud or errors.
The protocol uses cryptographically signed "Mandates" to create a tamper-proof, auditable record of user intent—whether in real-time purchases or autonomous tasks.
It supports credit cards, bank transfers, stablecoins, and crypto via an A2A x402 extension, built with partners like MetaMask and the Ethereum Foundation.
Early use cases include agents tracking inventory, negotiating offers, and coordinating bookings without a human in the loop.
Agent-led commerce is coming fast, but until now there’s been no standard to ensure agents transact safely or legitimately. AP2 lays the groundwork for a new class of AI-native shopping, banking, and business workflows—and gives developers a shared protocol to build on. Think of it like OAuth for payments, but designed for bots.
AI
TL;DR: Noah Brier turned Claude Code into a true “thinking partner” by running it locally on a home server integrated with his Obsidian vault. This lets him organize thoughts, code, and write—entirely from his phone. The setup redefines deep work, blending AI reasoning, version control, and mobile-first flexibility into one seamless system.
Claude doesn’t just generate content. It helped Noah reflect, synthesize, and plan. He builds sub-agents to ask questions, keep logs, and evolve ideas over time.
All of this runs off a home server that syncs to Git and Obsidian, giving him full control, traceability, and portability of his notes and projects.
He often uses voice mode (via Grock in his Tesla) to think, research, and log ideas on the go—setting a new bar for AI-powered mobility.
His daughter built an app using vibe coding. Noah sees this as a glimpse of AI's potential to democratize software creation for anyone, not just developers.
Most people think of AI as a content generator. Noah shows it can be a cognitive partner—one that organizes your brain, not just automates your output. His setup collapses the gap between thinking and shipping, with mobile-first workflows that prove deep work doesn’t require a desk. For founders and builders, it’s a powerful prototype of what AI-native productivity could look like.
Productivity
Why every fast-growing team should build an HR chatbot
TL;DR: Nicole Replogle lays out how to build an onboarding chatbot in Zapier for teams of all sizes. These bots answer common HR questions instantly (from “Where’s the PTO policy?” to “How do I set up 2FA?”), freeing HR teams from repetitive pings while giving new hires a better first week.
HR chatbots act as first-line support: 24/7, consistent, and scalable as your team grows.
Zapier’s builder lets you pull in real docs (PDFs, intranet pages, handbooks) to ensure answers are accurate and company-specific.
You can trigger actions—like creating Asana tasks or alerting HR in Slack—based on bot interactions.
The bot lives where your team already works: Slack, Teams, or email, embedded directly into onboarding flows.
HR is one of the most under-automated functions in fast-growing companies. Chatbots aren’t just time-savers, they’re how you scale culture, clarity, and compliance without overwhelming your people team. If you're hiring fast, you need this yesterday.
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Founder Story
How he made $3M in 48 hours by building the right product fast
TL;DR: Kaio, CEO of Group Kill, scaled an education platform to $3M in two days by combining freemium strategy, SEO, and practical AI. His team built and launched the product in under two weeks (reaching over 3 million students monthly) and did it by focusing obsessively on solving real problems, not chasing tech trends.
Kaio differentiates between coding and product development. Success came when he started building with users, not just for them.
Group Kill’s growth was fueled by transparency: open content indexed by Google drove organic traffic and trust.
The platform’s freemium model prioritized reach first, with premium offerings layered on top.
He predicts websites will vanish within five years, replaced by AI interfaces—think less “homepage,” more “chat with the product.”
Too many founders overbuild and under-validate. Kaio’s story is a blueprint for the opposite: launch small, move fast, focus hard on user value, and let AI do the heavy lifting behind the scenes. For anyone building in AI, education, or B2C SaaS, this is a good watch.
Tutorial/Framework
TL;DR: Using Replit’s Agent 3 and a PRD generated by ChatGPT, Jesse builds a live, two-way synced Figma-integrated project management tool in about 15 minutes. Replit handles everything from database setup to UI and bug fixing, proving that MVPs can now be shipped at the speed of thought.
The process starts with idea validation. ChatGPT confirms feasibility and generates a structured PRD, reducing upfront planning risk and aligning technical scope early.
Replit’s Agent 3 autonomously executes the build from a single prompt: spinning up auth, database schema, UI components, and integrations.
The two-way sync between Figma comments and task management is a standout feature, solving a real pain point in design-to-dev handoffs and showcasing the power of practical AI integrations.
Customization is fully prompt-driven: users can update themes, interaction logic, or visuals on the fly, making it easy to iterate based on real user feedback.
This demo shows what happens when AI shifts from assistant to builder. It collapses product timelines, reduces the need for technical resources, and opens the door for more experimentation at lower cost. For early-stage teams and solo builders, this means faster validation, tighter feedback loops, and the ability to test real products, not just prototypes.
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