I started AdLlama after leaving a startup where I ran Google Ads as part of my role. They wanted an AI way to manage it after I left, so I started building — first with MCP for campaign creation, then ad copy. I realized campaign creation wasn't sticky enough for agencies. Pivoted to search term QA after a design partner showed me that's where the real pain was. Built the whole thing solo with Claude as my engineering partner. Now live and used in real client calls weekly, catching things human analysts miss. Ad copy is next!

We got scammed for an apartment in San Antonio, and that led to creating an apartment marketplace for college students — which then led to the inspiration to create RentBamboo, AI agents to help property managers scale.

I grew up in Uvalde. No CS class. No coding club. No one telling me this world was for me. That's why I built this. Through a partnership with MakeWater and the WeTeachCS initiative (Texas Advanced Computing Center at UT Austin), I designed and plan to deliver a 28-hour, 4-day virtual professional development program for K-12 teachers in Region 20 — San Antonio and the surrounding districts that need it most: Title I schools, rural campuses, schools that have never offered a CS course. The program runs teachers through computational thinking, Python and Scratch fundamentals, hardware (each teacher leaves with a micro:bit water quality kit), and on Day 3 — AI. Not AI as a buzzword. AI as pattern recognition, data interpretation, and a tool they can demystify for a 7th grader. The teachers who go through this leave with TEKS-aligned lesson plans, a pathway toward Texas CS certification, and the confidence to say "let me show you something cool" to students who are exactly where I was.

I was a Director/VP of marketing for 15 years, and founded the company in order to solve a problem I felt for a long time. Everything had become much more competitive, and yet there was no structured way of tracking competitive activity across all of the channels I cared about as a startup marketer. So we built KeepTabz to help companies track everything their competitors are up to in a single app — news, reviews, social changes, pricing and messaging shifts, traffic spikes and paid advertising campaigns — then let them get alerts when something important happens.

Streaming my music makes me feel a lack of ownership over my music library. I recently found an old hard drive with all my MP3s from high school and decided to build a digital DJ to dive into my old library — helping me resurface the songs I grew up listening to!

I used to book shows but I couldn't work for artists that made less than $700/event because I only make 10% of what they make. EEVET can. EEVET is an AI Talent Buyer and Booking Agent for independent artists.

Three curves are colliding: compute availability is exploding (pushing machine-to-machine traffic past human-originated traffic), data centers are leaving Earth, and quantum compute is close enough that classical cryptography should be considered readable within the decade. Today's stacks were built for a different world. We built the runtime primitive that still works when none of those assumptions hold. The key insight: "human in the loop" is the wrong abstraction for autonomous infrastructure. In a retraining pipeline, an orbital node, or a high-frequency M2M chain, a human touching the system is usually the anomaly — not the safeguard. MagenTrust's behavioral scoring gave us a primitive we could invert: inside a Human Exclusion Zone, human presence triggers INTERRUPT. Every signed artifact carries a versioned algorithm field and supports hybrid classical-plus-post-quantum signatures, so a data center — terrestrial or orbital — can migrate one service at a time without re-architecting.

The system was built using a core-first architecture focused on identity, data normalization, and provenance tracking. Multimodal AI was integrated to extract and interpret data from documents, images, and human narrative — going beyond basic OCR into semantic understanding. Throughout development, I learned to design around AI unpredictability by implementing validation, normalization, and user confirmation layers. Handling duplicate and conflicting data required building a lightweight identity resolution approach that preserves source context while maintaining a usable data model. Additional challenges included managing AI token usage, handling rate limits, and designing the system to remain resilient to model variability and ongoing updates.

Most generative AI projects fail somewhere between a polished demo and a system a real customer-facing team will trust — so we set out to build the framework that closes that gap, using apartment locating as the testbed. The hardest problems weren't the model: they were context management, mixing client/staff/system messages, multi-channel ingestion, and making the whole pipeline observable enough for humans to actually rely on it.