The Weekly AI Marketing Ops Scoreboard
Before you publish another AI-generated post, inspect whether your marketing system is earning search visibility, AI citations, trustworthy agent output, serious replies, and CTA movement.
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Writing archive
Essays, build logs, and operating notes from the field.
What lives here
This blog is where strategy meets implementation: AI systems, founder operating principles, and what actually breaks when ideas hit real workflows.
Darwin Part 3: How Part 1's Scoring + Part 2's Discovery Became a Compounding Loop
If you're new, this recaps Parts 1 and 2. If you're continuing, this is the payoff: how scoring + discovery connect with memory and research so corrections become permanent upgrades.
The Memory Architecture: How Corrections Compound
Without a memory architecture, every AI agent correction evaporates at session end. Same mistake, next week. Borges closes the loop — a Monday correction becomes a permanent skill improvement by Wednesday.
How Hawking Does Research That Doesn't Suck
A single-pass web search produces a confident-sounding summary with six gaps and three outdated facts. Hawking runs 3-6 progressive rounds until coverage hits 80%, and it's not allowed to stop early.
Our AI Agent Scored 25% on Its Most Important Skill. Here's How We Fixed It.
We didn't invent self-improving AI. We got curious, studied five frameworks, and adapted them for a problem nobody talks about; agent skills rot.
How I Made My Agent Discover and Create New Agent Skills
Darwin doesn't just improve existing skills. Every week it scans every agent conversation to find capability gaps — and proposes new skills before anyone asks.
Why proofs beat promises in AI adoption
simonwillison.net/2024/May/27/blogmarks/ ↗Simon Willison's blogmarks format nails the speed/insight balance — quick link, clear point of view, no filler. That's exactly the writing cadence founder-operators need.
How to build a company with AI agents when you're not technical
A non-technical founder's practical guide to building with AI agents through roles, memory, review, and boundaries instead of prompt theatre.
Meet my AI team: how I'm trying to build a company with AI agents as a non-technical founder
A look inside my actual AI org chart: Jarvis, APRIL, Dev, Scout, and Zayd, and how roles, memory, review, and boundaries make the system work.
Building with AI agents means designing review, not just speed
Speed is the easy part. The harder design problem is review architecture: how correction, escalation, and quality control should work once agents enter the system.
I'm building a company where humans and AI agents have real jobs
The shift I care about is not AI as a clever tool. It is a human-agent operating model where agents carry recurring responsibilities inside a real review system.
Masaya is a proof point, not the whole story
Masaya is the hospitality AI product inside Rightful, but the larger story is the founder thesis behind it: a human-agent operating model for building companies.
What AI agents should actually own inside a company
The useful question is not whether agents can do work. It is what they should own, what humans must keep, and what needs shared review in a practical ownership matrix.
What changes when AI agents stop being demos and start becoming coworkers
The real shift is not that the tools get smarter. It is that delegation, accountability, management, and institutional memory start to feel different.
AI systems should survive contact with real operations
Most AI work fails because teams optimize for demos instead of operating reliability. Real leverage appears when workflows, owners, and review loops are explicit.
What breaks first when agents enter a company stack
Not tools. Governance, handoffs, and clarity. Agent systems fail at the seams where nobody owns decisions.
Content as infrastructure, not promotion
A founder site should compound trust over time. Publishing is not cosmetic; it is an execution layer for ideas.
