One hundred posts. For me, writing is clarifying thinking. I built this entire site with Claude Code—designed it, deployed it, automated the Obsidian-to-Cloudflare publishing flow. Now, sixty percent of what I’ve written is about AI. The tool became the subject. That’s either profound or obvious, depending on your tolerance for meta-commentary.

Here’s what I didn’t expect: not the daily writing (I’m reading widely anyway, so ideas only compound), but the sheer pace at which AI developments demanded rethinking. Every week brought capability shifts, strategic implications, and deployment patterns worth exploring. You can’t ignore the acceleration even if you tried.

This is what emerged when you show up every day without an agenda.

The AI Emergence I Didn’t Plan

When I started in July, I knew AI would feature. Product thinking intersects with every major platform shift, and this one’s moving faster than most. But I didn’t anticipate writing fifty-nine posts with AI in the title, tags, or core argument. That’s not editorial strategy—it’s the environment forcing constant synthesis.

The vibe coding surprise compounds this. Claude Code didn’t just help build features; it architected the entire site. Pagination systems, archive layouts, newsletter integration, test coverage—all generated through conversational iteration. The flow from Obsidian draft to live Cloudflare deployment is trivial now. No friction, no deployment anxiety, no “let me check if this breaks production.”

What does that say about where we are? When the tool that builds your platform becomes the platform story itself, you’re living through the shift everyone’s theorizing about. The gap between “AI will change how we work” and “AI is how I work” closed faster than expected.

Systems thinking applies to content, too. Each post wasn’t planned in isolation—ideas connected, frameworks built on frameworks, and patterns emerged that I didn’t consciously design. The writing process became a feedback loop: publish insight, watch what resonates, follow threads that matter. Product thinking applied to product thinking itself.

Three Patterns That Surfaced

Looking across ninety-nine posts, three themes kept recurring—not by design, but by necessity.

Systems Thinking Over Task Management

The AI era demands thinking in systems, not isolated tasks. Product managers who optimize individual workflows get automated. Those who architect systems, understanding how pieces connect, where humans add irreplaceable judgment, which processes compound vs. which just scale—those PMs survive and thrive.

This pattern showed up in posts about product managers who think in systems, in frameworks for map-optimize-automate playbooks, in contrasts between AI-enabled and AI-native strategies. The throughline: atomized thinking becomes obsolete when agents can execute atomized tasks faster than humans can specify them.

Outcomes Over Everything

Projects deliver outputs. Products deliver outcomes. That distinction appeared in multiple forms across the catalog: dual-lens scorecards, North Star frameworks, DORA metrics, and roadmap review questions. The shift from project to product thinking wasn’t just a mental model—it was the foundation for every strategic decision that followed.

When you anchor on outcomes, the entire product development equation changes. Discovery isn’t a phase; it’s continuous. Metrics aren’t vanity dashboards; they’re reality checks on whether we’re moving the outcome needle. This became the organizing principle for how to evaluate strategy, prioritize bets, and structure team conversations.

Platform Dynamics Reshaping Everything

From interfaces to platforms. From distribution as afterthought to distribution as moat. From feature parity to ecosystem lock-in. The platform pattern dominated posts about OpenAI’s Atlas browser, Anthropic’s enterprise focus, go-to-market strategies for feature products competing with platforms.

The strategic question evolved from “how do we build better features?” to “how do we win when platforms commoditize features in weeks?” That shift forced exploration of wedge strategies, timing windows, switching costs, and the interface-to-platform flywheel where context becomes distribution becomes platform power.

These three patterns—systems thinking, outcome focus, platform dynamics—weren’t topics I set out to explore. They’re what surfaced when you pay attention to what’s changing and what endures.

If You’re New: Start With These 5

Ninety-nine posts is too much to consume linearly. If you’re landing here for the first time, these five posts represent the foundational frameworks worth your time.

1. Outcomes Over Outputs For Real

The comprehensive framework for product thinking beyond delivery theatre. Includes the dual-lens scorecard (outcome health + delivery health), DORA Four Keys, Flow metrics, and North Star framework integration. This isn’t “outcomes good, outputs bad”—it’s the practical implementation guide for how to measure both without losing sight of what matters.

2. From Project to Product Thinking

The mental model shift that separates project managers from product managers. Covers the four-step approach: listen widely, cluster patterns, evaluate impact vs. confidence, commit narrowly. Projects optimize for certainty and delivery. Products optimize for learning and outcomes. This post maps the transition.

3. Product Managers Who Think in Systems Will Survive the AI Era

Why task management dies in the agent era, and what replaces it. Introduces the map-optimize-automate playbook: understand the system, identify human judgment zones, architect for AI acceleration. Old PMs managed backlogs. AI-era PMs design systems where humans and agents compound each other’s strengths.

4. Jobs-to-Be-Done: Demand Creators and Demand Reducers

The complete two-part framework for understanding why customers “hire” products. Push forces (current pain) and pull forces (new solution attraction) create demand. Inertia (switching costs) and anxiety (fear of change) reduce it. Customer behavior makes sense when you see all four forces at work.

5. How I Scaled My Blog Archive with AI

Vibe coding in practice. Ninety minutes to build scalable pagination with server-side rendering, TDD approach with 200+ automated tests, no syntax memorization required. This post demonstrates the method: think in product outcomes (fast archive navigation), describe intent to AI, and validate through tests. Shows what’s possible when you treat AI as a pair programmer, not a code generator.

These five give you frameworks (outcomes, systems, JTBD), a strategic perspective (product thinking transition, AI era survival), and practical demonstration (vibe coding execution). Start here, then follow threads that resonate.

Enjoying the Flow

There’s a question people ask when they learn you’re publishing daily: “Where do you find the time?” Wrong frame. The better question is: “Why wouldn’t you capture what you’re already thinking?”

I read widely every day—industry analysis, research papers, product teardowns, strategy threads. Ideas compound. The writing isn’t manufactured; it’s a synthesis of what I’m already processing. Turning internal notes into publishable insights takes discipline, but it’s not a separate workstream. It’s making explicit what was already implicit.

No agenda here. No content calendar mapped to quarterly themes. No gaps I’m strategically filling. This is a journal that others can take advantage of if it’s useful. If not, ignore it. That freedom: writing without performance pressure makes daily publishing sustainable. The moment it becomes obligation theater, the quality dies.

The challenge isn’t ideas (those only grow). It’s the commitment to clarity. Can I explain this concept tightly? Does this framework actually work in practice? Would I reference this post in a real product conversation? That filter keeps the bar high even when the cadence is fast.

And here’s the truth: I’m enjoying this. The act of writing forces sharper thinking. Publishing daily creates feedback loops—what resonates, what falls flat, which topics deserve deeper exploration. The constraint of brevity (especially in quick-thoughts) teaches economy of language. Every post is reps for clearer communication.

Because the list is only growing, this pause to surface “quick hit posts” makes sense. One hundred posts in, it’s time to mark the trail for those arriving fresh.

What I’m Watching

Four questions occupy attention right now. No clean answers yet—these are live explorations, not settled conclusions.

Agent Capability Evolution

How fast do agents move from “interesting demo” to “production-critical”? The deployment curve looks different than previous technology shifts. Companies aren’t waiting for perfect solutions; they’re shipping agents with known limitations and iterating in production. What does product development look like when capability boundaries shift weekly, not yearly?

Platform Consolidation vs. Distribution

Does AI lead to more concentrated or more distributed internet architecture? One path: platforms consolidate (OpenAI, Anthropic, Google) and everyone else becomes thin wrappers. Another path: commoditized AI capabilities democratize creation, enabling niche products to thrive. Which flywheel wins and on what timeline shapes every strategic bet in product development.

PM Role Transformation

What does “product manager” mean in AI-native companies? Not just “using AI tools” but organizations where products are AI systems. The discovery process changes (agents generate variants faster than humans can specify them). The build process changes (natural language increasingly replaces wireframes). The measurement challenge changes (how do you instrument emergent agent behavior?). The role doesn’t disappear—it transforms in ways we’re still mapping.

AI Product Development Patterns

Where does AI-assisted development excel, and where does it break? Vibe coding works remarkably well for CRUD apps, UI layouts, test coverage, and data transformations. It struggles with novel algorithms, performance optimization, and distributed systems architecture. Understanding those boundaries helps teams deploy AI effectively without over-rotating into disappointment when it doesn’t solve everything.

These aren’t rhetorical questions. They’re active explorations that will likely generate posts in the next hundred.

The Meta-Loop

One hundred posts about product thinking, built on a platform generated through product thinking with AI, analyzing how AI changes product thinking. The loop closes.

If any of this resonates, if you’re navigating similar questions about AI integration, outcome focus, platform shifts, or systems thinking, take advantage and build on top of it.

Otherwise, carry on. The journal continues regardless. That’s the freedom of going with the flow.