00:00 — 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

00:00 — 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-up

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

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

00:23 — 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

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

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

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

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

Key Themes

1. Innovation Requires Accepting Failure

• 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

2. The Real Work Isn't Writing Code — It's Solving Problems

• 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

3. AI Has Fundamentally Changed Innovation Speed (2026)

• 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

4. Enabling Teams, Not Replacing Them

• 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)

5. Bespoke Software is Now Accessible

• 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

6. AI Security & Control Challenges

• 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

7. The Future of AI Innovation

• 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

Takeaways for Listeners

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

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

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

• AI 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.

Summary

In 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.

Chapters

00:00 The Evolving Landscape of AI

02:58 Agentic Models and Their Impact

05:40 Understanding AI Components: Agents, Skills, and Tools

08:48 Use Cases for AI in Business

11:59 Navigating AI Security Risks

15:47 The Role of CISOs and CTOs in AI Safety

18:53 Future Trends in AI Development

25:52 Experimentation and Best Practices in AI Usage

30:47 Choosing the Right AI Models

43:53 Getting Started with AI Tools

Keywords

AI, agentic models, OpenAI, Claude, security risks, AI components, business use cases, experimentation, AI models, OpenCode

In 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.

Takeaways

• The 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.

Chapters

00:00 Technical Setup and Initial Challenges

03:45 Build vs. Buy: The Dilemma

08:37 Real-World Examples of Building Solutions

13:33 Iterative Development and User Feedback

18:27 Automation in Business Operations

21:38 Building Solutions for Unique Problems

23:43 The Evolution of Software Solutions

25:26 Navigating the Build vs. Buy Dilemma

35:45 Understanding Maintenance and Costs

49:25 The Importance of Control in Building Software

55:35 Concluding Thoughts on Building vs. Buying

Keywords

build vs buy, software solutions, automation, iterative development, APIs, open source, business processes, software purchasing, technical expertise, user feedback

Takeaways

• Off-sites are powerful for strategic alignment and decision-making
• Transparency is essential for team trust and collaboration The importance of authentic rituals in business culture
• Challenges and successes of organizing in-person and remote events

Chapters

• 00:00 The Power of Off-Sites
• 20:34 Rituals That Sometimes Work
• 28:22 The Importance of Transparency
• 38:53 The Value of Proximity in Business
• 44:52 Company Events and Team Building
• 52:33 Challenges of Remote Collaboration
• 59:00 Summary of Business Rituals