Short podcast summary
Mark 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 description
Most 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.com

Chapters

Description
The 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.

Chapters

Pinned comment / hook
AI is still powerful. The fantasy version is what’s getting repriced.

Tags/topics
AI, 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.

Description
Building 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.

Chapters

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.

Takeaways

• Ethical implications of AI use
• Overuse patterns in AI AI in academia
• Impact of AI on learning
• Ethical considerations in AI use

Chapters

• 00:00 Ethics and AI in Academia
• 08:55 Critical Thinking vs. Plagiarism
• 17:53 AI as a Tool in Education
• 24:29 Academic Projects and Senior Theses
• 32:10 AI in Business and Work Environments
• 38:10 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.

Chapters
00:01 Intro, working on vs. in your business
01:09 What offsites are and why they matter
03:14 What Oodle offsites actually look like
06:28 How they prepare and gather leadership input
09:23 Early offsites, tactical work, and the shift to strategy
12:51 Asking, “If we started today, would we build this business the same way?”
14:00 Offsites as alignment and board-meeting time
15:00 How to tell if you’re stuck working in the business
18:35 Why true offsites need zero distractions
20:27 The “seesaw” framework and removing yourself as the bottleneck
22:15 Family, tax write-offs, and why they avoid turning offsites into vacations
26:15 The artifacts and strategic documents that come out of offsites
27:56 Most memorable and most impactful offsite stories
34:14 Planning 3 years out, even when tactics change fast
37:00 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.

Takeaways

• Dead Internet Theory
• AI vs Human-Centric Product Design AI and human interaction are both important
• AI should be used to make human lives easier

Chapters

• 00:00 Dead Internet Theory and Agentic Workflows
• 08:53 The Future of Human Interaction on the Internet
• 21:12 AI-First vs Human-First Product Design
• 27:08 API vs. Interface
• 32:24 Agentic CMS Workflow
• 38:19 Empowering Humans with AI
• 43:20 Interactive Prototypes

Why innovation is difficult for small and medium businesses — and how AI is changing the game

• Innovation is like "setting money on fire" — but necessary for long-term wins
• Most experiments fail; the learning is the value, not the output
• R&D tax credits exist specifically because the government wants businesses to invest in uncertain outcomes
• Analogy: Innovation is like working out — everyone wants the results, nobody wants the 5-year grind

• Writing code is fast; architecture and problem-solving are the hard parts
• Losing a day's work and recreating it in 30 minutes proves: the code isn't the value, the thinking is
• AI can write code extremely quickly, but still struggles with novel architecture and business-specific problems

• What took weeks to build now takes days
• The barrier to entry for innovation has never been lower
• Small/mid-sized businesses are the biggest winners — they can now do what only enterprises could afford before
• Example: Building interactive, regional data visualizations that would have been "cost-prohibitive" before

• The goal isn't to replace workers with AI — it's to eliminate the work nobody wants to do
• Non-technical team members can now build React artifacts and interactive tools
• The focus shifts from "writing code" to architecture, ideas, and oversight
• People still need to learn through failure (like touching the hot stove)

• Previously, custom software required $2-3M+ investment for dev teams
• Now, small teams with AI tooling can build tailored solutions
• Example: Instead of begging enterprise vendors for features, just build what you need
• Modern frameworks (Rails, etc.) allow deployment in minutes

• AI agents will try to work around restrictions (digging tokens out of logs, attempting DNS changes)
• Balancing innovation with security is an ongoing tension
• Local/on-premise models offer a path for sensitive data processing
• The future: purpose-built, domain-specific models that don't need general knowledge

• Frontier models are being compressed to run on consumer hardware (RTX 6000, etc.)
• Next evolution: slicing off specialized capabilities for specific use cases
• Small, tuned models for narrow tasks (OCR, customer service, etc.) instead of massive general-purpose models

1. Budget for failure — Innovation requires experiments that won't work
2. AI lowers the barrier — What cost millions now costs a fraction
3. Empower your team — Give them AI tools and let them experiment
4. Focus on architecture — Let AI handle code output; humans own the thinking
5. Stay curious — The landscape changes weekly; ride the wave or get left behind

Episode Length: ~47 minutes
Tone: Conversational, technical but accessible, optimistic about AI's potential with realistic caveats about challenges

Summary

In 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.

Takeaways

• The 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.

Chapters

00:00 This Week in AI: Updates and Insights

12:00 The Model Wars: Innovations and Challenges

22:05 OpenClaw: Revolutionizing AI Interaction

38:49 The Future of SaaS: Threats and Opportunities

Keywords

AI, OpenAI, Anthropic, model wars, OpenClaw, SaaS, innovation, security, context, technology

Summary

In 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.'

Takeaways

• Meetings 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.

Chapters

• 00:00 The Shift from Work Management to Innovation
• 05:01 The Meeting Dilemma: Productivity vs. Distraction
• 09:48 Asynchronous vs. Synchronous Work: Finding Balance
• 14:50 The Power of Informal Collaboration
• 19:51 Rethinking Communication: Texts, Emails, and Meetings
• 24:50 The Illusion of Productivity: Busy Calendars vs. Real Work
• 30:03 The Unstructured Meeting: A Superpower?
• 34:50 Level 10 Meetings: Structure Meets Flexibility

Keywords

meetings, productivity, asynchronous work, communication, team dynamics, innovation, work management, remote work, collaboration, technology