RECENT EPISODES
Wed 4 Nov8 AI-Proof Businesses I'd build in 2027The episode explores businesses the speaker sees as more resilient to AI, especially physical services protected by licensing or regulation and trusted niche media built on attention and loyalty. It also examines paid communities and marketplaces, where belonging, liquidity, and audience can create durable value, and data-rich businesses that curate hard-to-access information for AI labs and other customers.Mon 2 NovThe $1M+ Solo Grok Bot Business (Full Course)Nick explains how he built Bops, a business-operations agent platform, in a 36-hour weekend using Opus 5.5, and how dedicated computers, phone numbers, email, and software connectors power its workflows. He lays out a business model based on industry-specific automation, proposing $25,000 implementations and optional $3,000 monthly maintenance, while Greg questions the value case. They also discuss testing niches, landing an initial client, and growing through referrals.Thu 29 OctSaaS is dead?Is SaaS dead? The speaker argues AI is changing its economics, not eliminating it: as software becomes easier to build, free tools may attract users while companies charge for proprietary data, AI intelligence, or human services. Pricing could shift from per-seat access toward tokens, outputs, or outcomes, though large enterprises may continue paying per seat, potentially at lower prices.Wed 28 OctStop building AI wrappers. Start building agent harnesses.Gamma’s John Narona explains why useful AI products need agent harnesses, not model wrappers: tools, context, and feedback let agents create and inspect finished work. Gamma’s presentation workflow can make nearly 100 calls across 10 to 15 models, while vision-based slide checks remain a specialized challenge. Narona also discusses testing models against user behavior and creative quality, and why founders should start with existing models before building specialized systems.Wed 28 Oct$100M ARR Founder: Build an Agent HarnessGamma founder John Narona explains why an agent harness—the tools, context, and feedback loops around a model—is core product work, not a thin wrapper. He describes Gamma’s multi-model process for turning notes into polished presentations, including vision-model checks and user experiments to judge subjective quality. Narona also discusses when to use frontier models versus fine-tuning, and why a startup’s moat may lie in its workflow, data, or domain-specific taste.Mon 26 OctHow to sell AI to Main Street (Full Playbook)Dickerson argues that AI entrepreneurs may find a stronger opportunity helping established plumbing and HVAC companies than chasing AI-focused customers. The practical pitch is more calls and booked jobs: use AI to find prospects and audit their ads, landing pages, reviews, and booking flows, then build tailored funnels and follow-up. He also discusses positioning as a trusted local advisor and charging for ongoing testing and optimization, not just a one-time build.Wed 21 OctThe 9 Stages of AI Agent Adoption, ExplainedGreg Eisenberg explains AI agent adoption as a trust ladder from researching products to preparing a purchase and making payment, where authorization and fraud concerns sharpen. Businesses are blocking agents, welcoming them to win sales, or charging for data access, as agents threaten ad and upsell revenue. The sequence may lead to trusted identity and payment infrastructure, but stages will overlap and companies will move at different speeds.Mon 19 OctMaking $$$ with Meta Muse AIThis episode explores using Meta Muse to build city-specific service-matching agencies, from finding customers and providers in public online communities to organizing leads by price and trust signals. The host outlines a possible path toward a curated database, niche audience, and automated outreach or booking, while noting private-group limits, uncertain conversion results, and Muse’s subscription costs.Thu 15 OctGemini 4 Argon: AI Marketing, 3D Printing and SaaSGreg Eisenberg discusses using Argon to build distinctive websites and voice-matched marketing, with human taste and creative references shaping the results. He explores “vibe manufacturing,” where AI helps design personalized products for 3D printing, and Shopify customizers as a business opportunity. He also argues SaaS can thrive by solving recurring niche problems, while a second speaker shares lessons from building an AI-assisted app, including the need for testing and iteration.Thu 8 Oct8 GitHub Repos To Go Viral and Make MoneyThe episode tours eight GitHub projects with potential paths to attention and revenue: Bragg can turn a website into a launch video that showcases a project, while Agent Reach helps research customer pain points around software people already pay for. It also explores practical business tools, such as a packaging-cost calculator, and using Impeccable to improve client websites, while emphasizing customer validation, human review, and the judgment these services require.Tue 6 OctHow Will People Make Money in the AI Age?The host argues that people need not start a company to benefit from the AI age; owning assets may also help. He highlights AI-native service firms, where agents could handle about 90% of the work while people review exceptions, and opportunities in proprietary data and industry-specific tools that make general AI models useful in practice. He also sees demand for security and infrastructure as agents take on sensitive tasks.Thu 1 OctBecome a $1M/yr FDE (Full Course)The episode lays out how FDEs create value by redesigning and deploying AI-enabled workflows, not simply adding agents to existing processes. It recommends mapping real work, measuring a baseline, and deciding which steps to remove, automate, or keep human-led, then tracking results after launch. Compensation can be tied to delivered value: one illustrative arrangement offers 10%, so $10 million in value could mean a $1 million payment. FDEs also need production engineering, model evaluation, governance, and executive communication skills.Tue 29 SeptOpenAI DevDay: Dots, Agents & $100B OpportunitiesGreg Eisenberg explores OpenAI’s Dots and its plugin-based vision for personal agents that route requests and complete transactions. He sees a major business opportunity in agent-driven commerce and analytics that measures whether AI platforms discover products and successfully complete tasks. OpenAI’s Agents API could also enable specialized tools for workflows from Shopify catalog cleanup to scientific research, though sensitive data and reliable execution remain challenges.Mon 28 Sept$5T opportunity: AI Roll UpsGreg Eisenberg’s AI roll-up thesis targets a $5 trillion opportunity as about 1 million small businesses are expected to sell by 2035. The playbook is to acquire firms at current earnings, then use human-reviewed AI agents for routine work. Thrive Holdings has acquired close to 50 accounting firms and committed another $1 billion; at one firm, AI helped process 7,000 tax returns while accountants saved 31% of their time on average. Potential margin gains remain unproven.Thu 24 SeptMuse AI Connectors: The Next App Store Moment?Meta’s decision to accept Muse connectors could create an “app store moment,” but scale depends on whether users repeatedly need what they offer and businesses will pay. Connectors can retrieve information and, with approval, complete bookings, while businesses remain responsible for accurate availability and service. The episode explores use cases from restaurant leads to home repairs and recommends testing one task, location, and paying customer before relying on Meta’s directory for growth.Mon 21 SeptThe Right Way To Write With AIIn this episode, Nicholas Cole explores the evolving landscape of writing in the age of AI, emphasizing the need for engaging, original content that resonates with audiences. He argues that while AI can automate basic writing tasks, it cannot replicate personal voice, making unique storytelling essential for standing out.Fri 18 SeptJev is HERE. How to use itIn this episode, we explore Jev, a groundbreaking AI technology that excels in classifying emails with remarkable speed and affordability. Unlike traditional large language models, Jev categorizes emails into segments like shopping and finance, offering a spam score and urgency assessment. With processing times around 200 milliseconds and costs as low as 18 cents for 1,700 emails, Jev presents a cost-effective solution for businesses, though users are advised to employ it cautiously in advisory roles rather than for critical decisions.Tue 15 SeptInstinct AI: The AI Assistant for normal peopleInstinct AI aims to make personal agents usable without technical setup, handling everyday tasks such as restaurant bookings and travel paperwork through iMessage. The speakers found its conversational interface and ability to manage back-and-forth unusually convenient, but questioned how much access users should grant after reports of email indexing and concerns about retained data. They also discuss payment safeguards and the trust required for consequential tasks like immigration paperwork.Mon 14 SeptBuilding a Software Factory that actually works (Full Course)In this episode, listeners will learn how to build an effective software factory that enhances software development efficiency through AI agents, akin to an assembly line. The speaker emphasizes the importance of understanding workflow processes and domain knowledge to optimize model capabilities while maintaining quality. Key topics include isolating new features to prevent conflicts, the significance of evidence-driven testing, and the use of code review agents to ensure high standards before merging code into the main application.Thu 10 SeptYou're using GPT-6 Astra WRONGIn this episode, Ross Mike reveals how users are underutilizing GPT-6 Astra, particularly in launching hardware startups and automating tasks. He emphasizes the importance of performance updates and security audits for applications using the tool, while also advocating for the use of reference images in UI design.Tue 8 SeptLocal AI Clearly ExplainedIn this episode, the hosts delve into the transformative potential of Local AI, which operates on user-controlled hardware, offering enhanced privacy and offline functionality. They explore key components of the local AI landscape, including model warehouses like Huggingface and user-friendly interfaces such as LM Studio. The discussion highlights quantization techniques for model compression and the practical applications of Local AI in niche businesses managing sensitive data, emphasizing the importance of starting with simple tasks to maximize efficiency.Wed 2 SeptThese 5 Github Repos are a goldmineIn this episode, listeners discover five invaluable GitHub repositories that can significantly boost product development and efficiency in the evolving tech landscape. The discussion highlights Peter Yang's no AI slop skill, which enhances AI-generated writing, and a CRM by Tri-Comp AI that optimizes relationship management for businesses. Additionally, the episode covers tools for video editing and security scanning of AI skills, emphasizing the importance of exploring GitHub to stay ahead in innovation and productivity.Mon 31 AugMaking $$$ as a Marketing EngineerIn this episode, we explore the emerging role of marketing engineers, projected to earn between $250,000 and $1 million as they leverage AI to automate marketing tasks. The discussion highlights the importance of converting market signals into actionable sales strategies and establishing a growth repository to enhance marketing efforts. We also delve into the necessity of regular tool analysis and the integration of diverse skills to effectively utilize AI for driving startup growth.Wed 26 AugWebMCP clearly explained (and how to make $$)In this episode, we explore WebMCP, a groundbreaking feature from Google and Microsoft that enhances AI agent interactions with websites, streamlining user engagement. As this technology gains traction in Chrome, it presents significant opportunities for businesses to improve customer experiences and drive sales, particularly in complex purchasing sectors. We also discuss how startups can capitalize on WebMCP by offering setup services and user journey reports, creating new revenue streams in the evolving digital landscape.Tue 25 AugScreensharing top takes in AI/startupsIn this episode, Greg Eisenberg discusses the essential steps for making a company AI native, emphasizing the need for clear documentation and gradual integration of AI tools. The conversation highlights the success of the baby food app "Solid Starts," illustrating a trend toward premium offerings in niche markets. Additionally, the potential of iMessage agents and the evolving role of customer support in product development are explored, underscoring the importance of balancing technology with human interaction in business.Fri 21 AugGrok Bot: make a 1 person company with agentsBilly Howell makes the case that Grok Bot, priced between $200 and $300 per month, is the strongest available platform for running agent teams and that a single person can use it to operate a real business, as he does with his Arlington Bagel newsletter serving 6,000 subscribers.Wed 19 AugYou need to be skillsmaxxing (10x your Claude/Codex)Remy makes the case that agent skills, defined as SOPs packaged as markdown files that Claude reads to execute a process consistently, are the highest-leverage thing someone can build with AI right now, estimating a single well-built skill saves two hours a week with compounding returns as more are stacked. He walks through his thin agents, thick skills philosophy and specific examples including proposal generation, brand voice, and a YouTube publish workflow structured as a skill chain.Mon 17 AugHow to use Claude Code better than 99% of PeopleMost users treat Claude Code as a chat interface and get mediocre results, but the episode argues that configuring it as a structured AI employee across nine defined areas is what separates effective use from wasted potential.Wed 12 AugHow to Build an AI-Native Company in 2026Ali Miller, who once managed roughly 100 people at AWS, now runs her company with 34 AI agents organized under six director-level roles, including a chief dreaming officer whose sole job is asking how to 10x any output. She explains that models like GPT 5.6 now respond meaningfully to vague prompts such as her three-word favorite, do smart things, a capability she says did not exist a year ago.Mon 10 AugMaking $$$ selling to AI AgentsCloudflare's new pay-per-crawl system, built on the HTTP 402 status code and a payment rail called X402, lets site owners charge AI agents directly at the edge before a request reaches the origin server, replacing the broken bargain where crawlers traded access for human traffic.Wed 5 AugThese AI Marketing Agents Get You CustomersCody Schneider argues that marketing agents are the direct successor to coding agents, making customer acquisition on demand the same way coding agents made software development on demand.Mon 3 AugGraph Engineering Clearly ExplainedGraph engineering, defined as designing how work moves between steps in an AI system, solves a core problem with single-prompt AI: one model is forced to research, interpret, write, and grade its own confidence simultaneously, producing unchecked overconfidence. The solution is a graph structure where parallel nodes handle separate tasks, a skeptic node checks claims, and a human gate precedes expensive decisions.Tue 28 JulJack Dorsey's Buzz: The New Hermes Agent?Jack Dorsey's Buzz is a team collaboration tool built on the open Nostr protocol that positions itself as a competitor to both Slack and GitHub by storing all chats, agent interactions, and data on user-controlled relays rather than locking data inside a proprietary platform. Agents are first-class team members in Buzz, with a swappable harness supporting Claude Code, Codex, and Goose without losing conversation history, and tight Git integration lets agents spin up parallel work trees simultaneously.Mon 27 JulHow I use Claude Code + MCPs to run my marketingCody Schneider draws a hard line between genuine marketing agents and the linear automation workflows most builders are actually shipping in tools like Zapier or N8n. A real agent, he argues, requires a unified data pipeline connecting Facebook ads, Google Analytics, HubSpot, and Stripe into a warehouse like ClickHouse so the system can tie specific creative to revenue, then autonomously run a thinking loop on a recurring cadence.Fri 24 JulMost Valuable Skill of 2026: Managing AI AgentsRyan Carson, the sole employee of AI divorce startup Untangle, makes the case that managing teams of AI agents will be the most valuable skill for founders and solopreneurs in 2026, drawing on his experience scaling Treehouse to 110 employees as a direct point of comparison. Carson runs five to ten cloud-based agents simultaneously through Devin, shipping up to 40 pull requests per day, and warns that working locally produces roughly ten times less output.Fri 24 JulHow I run a team of AI Agents 24/7Ryan Carson, solo founder of Untangle, an AI divorce agent serving family law firms, explains how running five to ten parallel cloud-based AI agents simultaneously allows him to ship an average of 22 to 25 pull requests per day. He argues that local development cannot scale to multi-agent workflows because code copies collide, making cloud environments from tools like Devin, Codex, and Claude Code essential.Mon 20 JulFDE: The $1M/Year AI Job ExplainedForward-deployed engineers connect AI models to companies’ specific workflows, with compensation described as starting at $150,000 base plus equity and reaching as much as $1 million a year. The role blends software, communication, and business skills: FDEs study how work actually gets done, build and evaluate tailored systems, and decide where AI adds value versus deterministic software or human review. The episode explains why measurable business outcomes—not AI spending alone—drive successful deployments.Wed 15 JulThe $1,000/hour Solo AI business (Full Course)Corey Gannon built a solo consulting business around a $999 AI tools assessment targeting small businesses with two to twenty employees and $500,000 to $5 million in revenue, requiring no coding, no audience, and no capital. The workflow uses Claude to analyze discovery call transcripts, match pain points to off-the-shelf tools from directories like Futurepedia.io and There's An AI For That, and generate a client-facing report in Claude Artifacts.Mon 13 JulMaking $$$ with Loop EngineeringLoop engineering, a build-verify-iterate cycle for AI agents drawing on lean startup principles, is the central focus of this episode. Ellie walks through his production SEO loops for Inbox Zero and draftfantasy.com, where agents pull real ranking data from Google Search Console and the Data for SEO API, make page-level changes, and run every two weeks to one month for under five dollars in API costs.Fri 10 JulGrok 4.5 is a bigger deal than FableNick from Orgo argues that Grok 4.5 is the most significant model upgrade his Hermes and OpenClaude agent stack has seen, offering intelligence comparable to Anthropic Opus 4.8 at roughly one tenth the cost and ten to fifteen times the speed of Fable for equivalent tasks. In a live demo, his Dewey agent completed a full landing page build in about 40 seconds while a simultaneously running GPT 5.6 agent was still processing.