Justin and Lily reflect on their parallel journeys diving deep into AI throughout 2025. They discuss why most AI content misses the mark for operators and system builders working within corporate constraints, share lessons from building production AI tools, and explore what's next for bringing these capabilities into the enterprise.

Guest: Lily Luo — Systems & Operations Leader, Author of Applied AI for MOps Substack

Read more of Justin's thoughts on AI Builders: The operator's roadmap for AI in 2026

KEY TOPICS

The Gap in AI Content Most resources target researchers or GTM engineers focused on outbound automation. There's little guidance for operators dealing with cloud tools, security, and corporate complexity. That creates an opportunity to define best practices for this underserved audience.

2025 Project Highlights Lily built an "Analysis Dossier" tool that generates full account research reports at the click of a button. Justin replaced a vendor intelligence tool with a custom system using Retool and a conversational agent.

Lessons Learned Start with tightly scoped AI steps in linear workflows for reliability. Pre-process insights asynchronously rather than relying on real-time agent calculations. Match tools to use cases. Failures teach more than successes.

Atlas: Lily's Autonomous Agent Runs on Google Cloud and wakes every 4 hours to research and progress projects. Uses a three-layer memory architecture: identity, temporal journal, and knowledge graph. Can push its own code and interact with other agents.

2026 Outlook Focus on scalability, reduced hallucination, and team enablement. Build infrastructure that unlocks flexible, ad-hoc use cases. Bridge the gap between AI capabilities and enterprise readiness.

The Human Side Working closely with AI changes how you think. Boundaries matter—don't let AI become a crutch.

RESOURCES

1. Applied AI for MOps — Lily's Substack
2. AI Builders Blog — Justin's Substack
3. Tools mentioned: Claude Code, Gemini, ChatGPT, Zapier, Retool, Dust, Azure AI Foundry, Letta, VS Code

ONE TIP FOR GETTING STARTED

Pick a real pain point. Start with low-code tools you already know. Test relentlessly. Expect to fail—and learn from it.

SEO used to be the ultimate growth lever—offering massive “free” traffic to marketers who could outsmart the algorithm. But in 2025, it's a different game.

In this episode, I’m joined by Gaetano DiNardi—an SEO strategist with deep experience across B2B SaaS—to unpack how AI is reshaping the search landscape.

We discuss:

• The SEO playbook for 2025
• How zero-click search is impacting real-world traffic
• Why brand is actually a competitive moat for SEO
• How to appear in AI search previews and LLM recommendations
• When a startup should consider SEO
• How to optimize content for the top, middle, and bottom of the funnel

Whether you’re running SEO for an enterprise brand or figuring out your growth strategy as a founder, this conversation will help you recalibrate your approach to search in the age of AI.

Gaetano DiNardi is an SEO expert and principal consultant at Marketing Advice.

He’s spent over a decade in B2B marketing, helping 50+ SaaS companies drive growth and demand. He’s battled in some of the most competitive trenches out there—categories like identity theft, business VoIP, employee monitoring, insider threat detection, LMS software, and more.

You can find him sharing lessons from the field on LinkedIn and Substack.

• [00:00] - Introduction
• [01:24] - Evolution of search over the past 10 years
• [04:09] - Impact of brand on rankings
• [05:36] - Did SEO degrade search quality?
• [11:30] - Impact of zero-click results
• [20:28] - How to get recommended in AI preview
• [34:15] - When should a startup focus on SEO?
• [41:39] - SEO for TOFU, MOFU, and BOFU

• Marketing Advice
• Marketing Advice Substack
• Scrunch AI - Brand monitoring for AI search
• What is Brand Authority and How Is It Calculated? - Moz

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AI agents are everywhere in conversation right now—but what actually makes them work? It’s not just slapping a large language model into a workflow and calling it a day. Under the hood, real agentic systems operate differently. They make decisions. They adapt. They break out of rigid if-this-then-that logic and enter something closer to human judgment.

In this episode, I talk with Daniel Vassilev, co-founder of Relevance AI, a platform purpose-built for building and deploying true agents. We dig deep into how agentic systems are structured—from core instructions to tool orchestration—and how that foundation changes what’s possible. Daniel explains the difference between automation and autonomy in clear, practical terms that any builder, founder, or operator can understand.

We also explore real-world use cases: where agents shine today, where they fall short, and how teams are already using them to 10x output without ballooning headcount. Whether you’re dabbling in LLM workflows or ready to rethink how your company works entirely, this conversation will level up your mental model.

If you’ve been wondering where the hype ends and the real architecture begins—this is the episode.

Daniel Vassilev is Co-Founder and Co-CEO of Relevance AI, a platform to develop commercial-grade multi-agent systems to power your business. With a background in software engineering, he previously created, grew and monetised two apps to a combined 7 million users, reaching #1 on the App Store top free.

• [00:00] - Introduction
• [01:31] - Defining agentic AI
• [03:28] - AI in linear workflows vs. agentic systems
• [08:19] - How agents work under the hood
• [11:24] - Always-on agents
• [13:43] - Selecting the right tasks for agentic AI
• [17:42] - Copilot vs. Autopilot
• [22:44] - Are there tasks we should never delegate to AI?
• [25:03] - Coolest use cases
• [34:30] - Agent memory and continual improvement
• [37:55] - Compounding effect of agent teams
• [41:39] - Relevance the company and platform

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Disclosure: I am using an affiliate link for Relevance AI, which means I earn a small bonus if you sign up through my content.