Not Brothers

In this episode of Not Brothers, Ryan and Mark dig into a major shift inside modern teams: AI now lets non-technical people prototype, build, and express product ideas in ways that used to require developers, designers, and long handoff cycles.That's powerful. It's also messy.The upside is real. AI can take someone from a vague idea to an interactive prototype incredibly quickly, compressing wireframing, design, and prototyping into a tighter feedback loop. Designers, strategists, and PMs can create something tangible enough for the team to test and improve.But a slick UI can create the illusion that something is "done" when there's no real infrastructure, no secure backend, and no maintainable architecture. AI is great at making something that feels real — but often it's a house of cards no responsible team can simply deploy.The team shares what Oodle has worked through internally: oversized pull requests, skipped requirements, one-off solutions, spaghetti code, missing docs, and unclear handoffs. The big theme — AI doesn't remove the need for product thinking, it makes it more important. Ryan's example: flexible custom fields in Cortex beat hard-coding for one client. And his best metaphor: AI will bore through a concrete wall with a spoon if you ask it to, so planning still matters.Rather than banning the tools — unrealistic, since "life finds a way" — Oodle builds guardrails: project instructions, agent rules, standards, and an internal assistant, Sheldon, that asks clarifying questions and turns vague bugs into actionable reports.Handoff quality matters too. If you build something with AI, you still own it: explain the problem, document intent, set success criteria, and make review easy. The workflow is also inverting — technical people now ask non-technical teammates to carry prototypes further before development takes over.The takeaway is simple: write things down. Clear writing, intent, and documentation are becoming core skills for turning ideas into real software.Chapters00:00 — The collision between technical and non-technical teams  01:11 — AI gives non-technical people a new way to express ideas  03:12 — Why “done” is harder to define now  05:34 — When a polished UI creates the illusion of progress  07:46 — Why AI prototypes often are not deployable  11:20 — Guardrails, standards, and responsible AI workflows  15:02 — Existing products make the collision more complicated  17:22 — Product design versus one-off feature requests  20:25 — AI will dig through concrete with a spoon  22:05 — The problem with huge AI-generated pull requests  24:47 — Why smaller chunks beat massive code drops  27:57 — Compression, cleanup, and maintainability  30:28 — Better pull requests need demos, screenshots, and context  36:01 — What open source is teaching us about AI-generated code  40:02 — Why banning AI is not the answer  42:31 — How agents can improve bug reports and feedback loops  45:17 — Don’t clean up everyone else’s AI mess  49:09 — The workflow has flipped for idea people  52:59 — Writing clearly is now a core AI-era skill  55:10 — Final takeaway: write it down

Short podcast summaryMark and Ryan dig into Herald, Oodle’s developer-native changelog and release notes platform built for teams that ship through GitHub but hate writing product updates from scratch. Ryan explains the gap he found in existing changelog tools, why release notes usually get skipped, and how Herald uses GitHub history plus AI to turn commits and pull requests into editable release drafts. They also cover GitHub sync, nested projects, scheduled releases, customizable widgets, email notifications, and user segmentation — all aimed at making product updates easier to publish and easier for users to discover.YouTube descriptionMost teams ship more than they communicate.In this episode of Not Brothers, Mark and Ryan talk through Herald — Oodle’s changelog and release notes platform for software teams that live in GitHub but hate writing release notes from scratch.Ryan explains why changelogs are usually skipped, why existing tools did not quite fit the workflow he wanted, and how Herald turns GitHub activity into draft release notes using AI. Instead of starting with a blank page, teams can connect a repository, pull in commits and pull requests, draft a release, edit the important parts, and publish across Herald, GitHub, email, and an in-app widget.They also get into two-way GitHub sync, public and private repositories, nested projects for related repos, scheduled releases, customizable changelog widgets, user groups, segmentation, and why discoverability matters just as much as authorship.Herald is built for developers, product teams, indie founders, and small SaaS teams that want to keep users informed without turning release notes into another full-time job.Try Herald: https://sendherald.comChapters00:00 — Why Oodle built Herald 00:44 — What Herald is and the changelog problem it solves 03:02 — Release notes for users, engineers, and bigger feature launches 04:56 — Using AI to turn GitHub activity into draft changelogs 06:21 — Moving from creator to editor of release notes 07:22 — Two-way GitHub sync and avoiding duplicate work 09:31 — Custom categories and tuning the AI import prompt 10:25 — Public/private repos and nested projects 11:31 — Multi-repo product families and parent changelogs 13:08 — Scheduled releases 14:22 — Getting started without a blank canvas 15:30 — Drafting a release from everything since the last GitHub release 16:43 — Customizable in-app changelog widgets 17:36 — Making product updates discoverable 19:28 — In-app updates vs. noisy notifications 19:59 — Groups, JWT, and segmented changelog visibility 21:44 — Internal users, client users, and beta release use cases 22:10 — A simple tool that adds value in the right capacity 23:07 — The three user types Herald is built for 23:48 — Real release notes, testing, and future feedback 24:35 — Website demo and interactive examples 24:59 — Try Herald and let us know what you think

DescriptionThe AI gold rush is hitting its first real hangover.In Episode 12 of Not Brothers, Mark and Ryan talk through the gap between what AI companies promised, what executives bought into, and what the tools are actually proving they can do. The conversation starts with cloud-license cancellations, token spend, AI data-center bets, and the realization that “AI will solve everything” is not the same thing as a useful operating plan.Ryan argues that AI is still an incredible tool — even if it never gets dramatically smarter — but the fantasy of universal automation, effortless AGI, and instant economic transformation is starting to crack. Mark pushes on the business side: why executives accepted the hype, how fiscal pressure may be changing the story, and why the next phase of AI value may come from practical application layers instead of frontier-model moonshots.They also get into AI dopamine loops, hallucinated research, agentic coding tools, the iPhone analogy for model progress, Sam Altman softening job-replacement claims, data-center and memory-market ripple effects, Google’s AI distribution advantage, Google Workspace integration, and what AI search might do to SEO.The takeaway: AI is not going away. The useful version is probably less magical, more embedded, more specialized, and much more dependent on human judgment than the hype cycle promised.Chapters00:00 — The AI economics hangover 01:24 — Executives, overpromising, and shareholder-value promises 02:40 — Why AI hype is easy to sell upstairs 04:30 — Token drunkenness and the cost reality check 05:54 — Fiscal pressure, Microsoft, Claude, and Copilot 07:26 — Finding the limits of agentic AI tools 09:44 — Goalposts, model progress, and AI fatigue 11:55 — The iPhone analogy for frontier-model improvement 14:18 — AGI goalpost shifting and useful-but-not-magical agents 16:49 — Model economics and better autonomous coding loops 18:26 — Dopamine machines, fake confidence, and verification 20:48 — Reddit, authenticity, and trust in AI training data 21:56 — Sam Altman, job disruption, and the softer economic view 23:29 — Is AI a bubble or an early overbuild? 24:38 — Data centers, memory prices, and supply-chain ripples 26:48 — Infrastructure bets and consumer/app-layer demand 29:03 — Google’s distribution advantage in AI 30:02 — Gemini, coding models, and different model strengths 31:04 — Google Workspace as the AI surface area 32:34 — AI search, generated answers, and SEO disruption 33:20 — Actual content people want may finally matter 35:36 — The echo chamber vs. mainstream adoption 36:33 — Untapped users and the application layer 37:39 — AI inside existing tools, not only standalone chatbots 38:02 — Better chatbots would still be a win 38:32 — Wrap-upPinned comment / hookAI is still powerful. The fantasy version is what’s getting repriced.Tags/topicsAI, AI economics, AGI, token costs, AI agents, OpenClaw, OpenAI, Anthropic, Google Gemini, Google Workspace, AI search, SEO, data centers, jobs, automation, future of work, Not Brothers Podcast

AI can make building products faster. It does not make people care. Distribution, trust, and attention are still the real game.DescriptionBuilding software is easier than ever. Getting anyone to care is still the hard part.In Episode 11 of Not Brothers, Mark and Ryan dig into the modern version of “if you build it, they will come” — and why that idea breaks down fast in an AI-driven product world. Vibe coding, faster prototyping, and smaller teams have made niche software products more realistic than they used to be. But the same tools also make it easier for competitors, clones, and half-baked alternatives to show up overnight.The real debate: has the power shifted from developers to distributors, or was distribution always the thing that separated products that survived from products that disappeared?Mark and Ryan talk through AI-era distribution tactics including AI-friendly tools and CLIs, MCP servers, programmatic SEO, answer-engine optimization, free tools, shareable product outputs, niche newsletters, cold email ethics, and content repurposing engines. Along the way, Ryan gets predictably fired up about MCP bloat, AI slop, automated outreach, and bots talking to bots until everyone involved is just burning tokens.The takeaway: AI can help you build faster, but it does not magically create trust, attention, demand, or distribution. If you build it, they probably will not come — unless you give them a damn good reason to.Chapters00:23 — Why AI changes the product-development conversation 01:08 — Has the Silicon Valley pecking order flipped? 02:27 — Distribution was always the hard part 04:20 — When product moats get easier to copy 05:04 — Salesforce, Oracle, and the power of incumbency 06:41 — Niche products in the AI era 07:16 — Why small markets used to be hard to serve 09:38 — The new case for niche software businesses 10:23 — Using AI-friendly tools for distribution 11:05 — Ryan's problem with MCP servers 12:42 — Distribution paths beyond MCP 12:54 — Programmatic SEO and the slop problem 14:02 — LinkedIn, AI content, and the loudest voice in the room 15:58 — Answer-engine optimization vs content spam 17:10 — Good old-fashioned inbound marketing, now AI-readable 19:10 — Free tools as top-of-funnel distribution 20:17 — Why interactive tools build brand equity 21:10 — Making product outputs shareable 22:28 — Buying niche newsletters and owned audiences 23:27 — Ryan draws the line on spam 25:27 — AI agents, cold outreach, and inbox overload 27:27 — When sender bots meet screener bots 28:17 — Why AI does not belong in every communication layer 29:56 — AI content repurposing engines 32:07 — Using AI to extract the useful five minutes 33:44 — Social volume, quality, and the For You page 35:27 — The final answer: building is easier, distribution still wins

Does AI make us dumber, or does it just move the line between what humans need to know and what tools can handle?In Episode 10 of Not Brothers, Mark and Ryan pick up the thread from their previous conversation about AI in education and push it further: if AI can write the paper, build the CLI app, summarize the research, and automate the busy work, what exactly are humans supposed to learn, practice, and protect?The conversation gets into education, critical thinking, memorization, work, hobbies, purpose, the future of AI adoption, and the difference between delegating execution and outsourcing your brain.The short answer: yes, AI can make you dumber at the thing you delegate. But that may be fine if you’re using the saved time and leverage to get smarter about the thing that actually matters.00:00 — Does AI make us dumber? 01:02 — AI in education vs AI at work 02:27 — Delegating your brain 03:44 — What are schools actually measuring? 06:04 — Real-world skills vs academic restrictions 06:54 — Resourcefulness vs intelligence 09:23 — AI, memorization, and what we call “smart” 10:31 — Does learning need to be hard? 12:26 — Are we at another inflection point? 14:23 — Human purpose when work changes 16:23 — Universal high income and building for fun 18:00 — Retiring without a backup plan 20:25 — What do we do with AI-created time? 21:17 — Are AI models plateauing? 24:08 — The IKEA example: AI plus human judgment 25:43 — Acceptable AI use in education 27:48 — When does AI work become “mine”? 29:42 — Building blocks and the 10-year-old problem 31:07 — Handwriting, typing, and obsolete skills 33:51 — Brain development and hard things 35:52 — Why AI adoption feels faster than the internet 37:53 — Can AI or the internet be regulated? 40:15 — So, does AI make us dumber? 40:30 — Dumber at one thing, smarter at another 42:45 — Critical thinking vs subject matter expertise 44:18 — AI is best at patterned execution 46:27 — The final answer: maybe 48:24 — Are papers even the right test? 50:06 — Education needs to figure this out

The conversation delves into the ethical implications of AI use in academia, particularly in the context of overuse patterns and the balance between critical thinking and plagiarism. It also explores the role of AI as a tool in education and the challenges associated with setting boundaries and rules for its use. The conversation delves into the impact of AI on academia, particularly in the context of academic projects, senior theses, and ethical considerations. It explores the use of AI as a tool for persuasion and argumentation, its role in academic integrity, its application in business and work environments, and the maturity and ethical use of AI in education. The discussion also addresses the development of writing skills in the context of AI use.TakeawaysEthical implications of AI useOveruse patterns in AI AI in academiaImpact of AI on learningEthical considerations in AI useChapters00:00 Ethics and AI in Academia08:55 Critical Thinking vs. Plagiarism17:53 AI as a Tool in Education24:29 Academic Projects and Senior Theses32:10 AI in Business and Work Environments38:10 AI and Writing Skills Development

In this episode of the Not Brothers Podcast, Mark and Ryan dig into one of the most important questions for entrepreneurs: are you working in your business, or on it? Using Oodle’s long-running offsite rhythm as the backdrop, they break down how stepping away from daily execution creates space for alignment, strategic thinking, and better decision-making.They cover how their offsites have evolved over the years, what preparation looks like, how to spot when you’ve become the bottleneck in your own business, and why intentional time away can be one of the best investments you make as a business owner. Along the way, they mix in stories from past offsites, lessons from hard pivots, and the frameworks they use to keep the business moving forward.Chapters00:01 Intro, working on vs. in your business01:09 What offsites are and why they matter03:14 What Oodle offsites actually look like06:28 How they prepare and gather leadership input09:23 Early offsites, tactical work, and the shift to strategy12:51 Asking, “If we started today, would we build this business the same way?”14:00 Offsites as alignment and board-meeting time15:00 How to tell if you’re stuck working in the business18:35 Why true offsites need zero distractions20:27 The “seesaw” framework and removing yourself as the bottleneck22:15 Family, tax write-offs, and why they avoid turning offsites into vacations26:15 The artifacts and strategic documents that come out of offsites27:56 Most memorable and most impactful offsite stories34:14 Planning 3 years out, even when tactics change fast37:00 Final takeaway, when to change structure and create space to work on the business

The conversation explores the concept of Dead Internet Theory and the impact of agentic workflows on social networks. It delves into the future of human interaction on the internet and the implications of AI-first vs human-first product design. The conversation explores the integration of AI and human interaction, emphasizing the importance of AI in making human lives easier. It delves into the impact of AI on content management, decision-making, and human roles, highlighting the democratization of content creation and the concept of Jevons Paradox in AI.TakeawaysDead Internet TheoryAI vs Human-Centric Product Design AI and human interaction are both importantAI should be used to make human lives easierChapters00:00 Dead Internet Theory and Agentic Workflows08:53 The Future of Human Interaction on the Internet21:12 AI-First vs Human-First Product Design27:08 API vs. Interface32:24 Agentic CMS Workflow38:19 Empowering Humans with AI43:20 Interactive Prototypes

Why innovation is difficult for small and medium businesses — and how AI is changing the gameKey Themes1. Innovation Requires Accepting FailureInnovation is like "setting money on fire" — but necessary for long-term winsMost experiments fail; the learning is the value, not the outputR&D tax credits exist specifically because the government wants businesses to invest in uncertain outcomesAnalogy: Innovation is like working out — everyone wants the results, nobody wants the 5-year grind2. The Real Work Isn't Writing Code — It's Solving ProblemsWriting code is fast; architecture and problem-solving are the hard partsLosing a day's work and recreating it in 30 minutes proves: the code isn't the value, the thinking isAI can write code extremely quickly, but still struggles with novel architecture and business-specific problems3. AI Has Fundamentally Changed Innovation Speed (2026)What took weeks to build now takes daysThe barrier to entry for innovation has never been lowerSmall/mid-sized businesses are the biggest winners — they can now do what only enterprises could afford beforeExample: Building interactive, regional data visualizations that would have been "cost-prohibitive" before4. Enabling Teams, Not Replacing ThemThe goal isn't to replace workers with AI — it's to eliminate the work nobody wants to doNon-technical team members can now build React artifacts and interactive toolsThe focus shifts from "writing code" to architecture, ideas, and oversightPeople still need to learn through failure (like touching the hot stove)5. Bespoke Software is Now AccessiblePreviously, custom software required $2-3M+ investment for dev teamsNow, small teams with AI tooling can build tailored solutionsExample: Instead of begging enterprise vendors for features, just build what you needModern frameworks (Rails, etc.) allow deployment in minutes6. AI Security & Control ChallengesAI agents will try to work around restrictions (digging tokens out of logs, attempting DNS changes)Balancing innovation with security is an ongoing tensionLocal/on-premise models offer a path for sensitive data processingThe future: purpose-built, domain-specific models that don't need general knowledge7. The Future of AI InnovationFrontier models are being compressed to run on consumer hardware (RTX 6000, etc.)Next evolution: slicing off specialized capabilities for specific use casesSmall, tuned models for narrow tasks (OCR, customer service, etc.) instead of massive general-purpose modelsTakeaways for ListenersBudget for failure — Innovation requires experiments that won't workAI lowers the barrier — What cost millions now costs a fractionEmpower your team — Give them AI tools and let them experimentFocus on architecture — Let AI handle code output; humans own the thinkingStay curious — The landscape changes weekly; ride the wave or get left behindEpisode Length: ~47 minutesTone: Conversational, technical but accessible, optimistic about AI's potential with realistic caveats about challenges

SummaryIn this episode, Ryan and Mark discuss the latest developments in AI, focusing on the ongoing model wars, the emergence of OpenClaw, and the implications for SaaS companies. They explore the ethical dilemmas surrounding AI, the challenges of context management, and the potential for innovation in AI interactions. The conversation highlights the rapid evolution of AI technologies and the need for organizations to adapt to these changes while managing risks effectively.TakeawaysThe model wars continue with new innovations from various labs.Distillation attacks raise ethical questions about AI development.OpenClaw is revolutionizing how organizations interact with AI.Context management is crucial for effective AI usage.SaaS companies face new challenges from AI advancements.Ethical dilemmas in AI revolve around the use of stolen data.Organizations must balance innovation with security risks.The future of SaaS may involve more in-house development.AI tools are becoming more accessible to non-technical users.Living in a beta environment is the new norm for AI software.Chapters00:00 This Week in AI: Updates and Insights12:00 The Model Wars: Innovations and Challenges22:05 OpenClaw: Revolutionizing AI Interaction38:49 The Future of SaaS: Threats and OpportunitiesKeywordsAI, OpenAI, Anthropic, model wars, OpenClaw, SaaS, innovation, security, context, technology

SummaryIn this episode, Ryan and Mark discuss the challenges and dynamics of meetings in the workplace, particularly in a remote setting. They explore the balance between synchronous and asynchronous work, the impact of open office environments, and the importance of unstructured time for creativity and productivity. The conversation highlights innovative communication strategies and the illusion of productivity that often accompanies busy schedules. Ultimately, they emphasize the need for more effective meeting structures and the value of informal discussions in fostering collaboration and innovation.'TakeawaysMeetings can often hinder productivity rather than enhance it.Asynchronous communication can be more effective than constant meetings.The challenge of open office dynamics can disrupt deep work.Innovative communication strategies can help reduce unnecessary meetings.Unstructured time can lead to more creative and productive outcomes.The illusion of productivity can stem from a busy calendar.Finding balance in communication styles is crucial for team dynamics.Informal meetings can lead to significant breakthroughs and ideas.It's important to capture the essence of discussions in meetings for clarity.The unstructured nature of certain meetings can be a superpower for teams.Chapters00:00 The Shift from Work Management to Innovation05:01 The Meeting Dilemma: Productivity vs. Distraction09:48 Asynchronous vs. Synchronous Work: Finding Balance14:50 The Power of Informal Collaboration19:51 Rethinking Communication: Texts, Emails, and Meetings24:50 The Illusion of Productivity: Busy Calendars vs. Real Work30:03 The Unstructured Meeting: A Superpower?34:50 Level 10 Meetings: Structure Meets FlexibilityKeywordsmeetings, productivity, asynchronous work, communication, team dynamics, innovation, work management, remote work, collaboration, technology

TakeawaysAI is evolving rapidly, with new models emerging frequently.Agentic models allow for more autonomy and longer task execution.Understanding the components of AI—agents, skills, and tools—is crucial.AI can enhance business processes, but human oversight is essential.Security risks associated with AI tools are significant and must be managed.CISOs and CTOs need to establish guidelines for safe AI usage.Future AI developments will focus on orchestration and managing multiple agents.Experimentation with AI should be approached cautiously and incrementally.Choosing the right AI model depends on the specific task at hand.OpenCode is a user-friendly tool for experimenting with various AI models.SummaryIn this episode of the Knot Brothers podcast, Ryan and Mark discuss the rapidly evolving landscape of AI, focusing on the emergence of agentic models and their implications for business and security. They explore the components of AI, including agents, skills, and tools, and highlight the importance of human oversight in AI applications. The conversation also delves into the security risks associated with AI tools, the role of technology leaders in ensuring safe usage, and the future trends in AI development. Listeners are encouraged to experiment with AI cautiously and to choose the right models for their specific needs, with OpenCode being recommended as a user-friendly starting point.Chapters00:00 The Evolving Landscape of AI02:58 Agentic Models and Their Impact05:40 Understanding AI Components: Agents, Skills, and Tools08:48 Use Cases for AI in Business11:59 Navigating AI Security Risks15:47 The Role of CISOs and CTOs in AI Safety18:53 Future Trends in AI Development25:52 Experimentation and Best Practices in AI Usage30:47 Choosing the Right AI Models43:53 Getting Started with AI ToolsKeywordsAI, agentic models, OpenAI, Claude, security risks, AI components, business use cases, experimentation, AI models, OpenCode

SummaryIn this conversation, Ryan and Mark discuss the ongoing debate of whether to build or buy software solutions for business needs. They share personal experiences and insights on the challenges and benefits of both approaches, emphasizing the importance of understanding organizational needs, iterative development, and the potential pitfalls of software purchasing. The discussion also highlights the significance of APIs, open-source solutions, and the necessity of ongoing maintenance for built solutions.TakeawaysThe layout issues can impact the workflow.Building solutions can be tailored to specific needs.Buying software often leads to unmet expectations.Iterative development allows for flexibility and adaptation.Automation can save significant time in business processes.Evolving solutions can lead to better outcomes over time.APIs and open-source solutions provide flexibility.Buyer beware: sales promises may not be fulfilled.Maintenance costs can add up over time for built solutions.Understanding organizational needs is crucial for decision-making.Chapters00:00 Technical Setup and Initial Challenges03:45 Build vs. Buy: The Dilemma08:37 Real-World Examples of Building Solutions13:33 Iterative Development and User Feedback18:27 Automation in Business Operations21:38 Building Solutions for Unique Problems23:43 The Evolution of Software Solutions25:26 Navigating the Build vs. Buy Dilemma35:45 Understanding Maintenance and Costs49:25 The Importance of Control in Building Software55:35 Concluding Thoughts on Building vs. BuyingKeywordsbuild vs buy, software solutions, automation, iterative development, APIs, open source, business processes, software purchasing, technical expertise, user feedback

The conversation explores the impact of rituals in business, focusing on the effectiveness of off-sites, the value of transparency, and the significance of water cooler meetings. It delves into rituals that work, those that sometimes work, and those that don't work, providing insights into the impact of daily standups and status meetings. The conversation delves into the value of proximity in business, the impact of rituals on company culture, challenges of remote events, and the transition to remote work. It also explores the difficulties of remote collaboration and communication, as well as a summary of various business rituals and their impact.TakeawaysOff-sites are powerful for strategic alignment and decision-makingTransparency is essential for team trust and collaboration The importance of authentic rituals in business cultureChallenges and successes of organizing in-person and remote eventsChapters00:00 The Power of Off-Sites20:34 Rituals That Sometimes Work28:22 The Importance of Transparency38:53 The Value of Proximity in Business44:52 Company Events and Team Building52:33 Challenges of Remote Collaboration59:00 Summary of Business Rituals