AlignTrue
AlignTrue is an open-source reference architecture for supervised AI systems that need durable receipts, replayable state, governed actions, and fenced side effects.
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Main Character builds AI products and makes existing software more AI-native. Building is how we learn what models can do, what users adopt, and what is worth building.
Operating model
Building is how the lab learns.
01
Make products and prototypes instead of only studying AI from the outside.
02
Use hands-on work to see what models, tools, and users actually do.
03
Carry forward the product patterns and technical pieces that prove useful.
Working directly with current models, coding tools, and users gives better signal than watching from the outside.
The lab shows what is newly possible, what still breaks, and what people actually use.
The same work informs Gabe's own AI investing and conversations with founders, operators, and investors.
Work across consumer AI products, model audit tools, agent architecture, and frontline software. Different surfaces, same lab pattern: build, learn, and keep what proves useful.
These are selected public examples, not an exhaustive project history. Some related systems, client implementations, and reused patterns remain private.
AlignTrue is an open-source reference architecture for supervised AI systems that need durable receipts, replayable state, governed actions, and fenced side effects.
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GlassAlpha is an open-source toolkit for deterministic audit reports for tabular ML models, with fairness, explainability, calibration, robustness, and reproducibility metadata in one local workflow.
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A private MVP build for approved-publication retrieval, voice query, and source-cited maintenance guidance on phone, web, and wearable surfaces.
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Additional work across consumer AI, ecommerce AI, SaaS automation, developer and CMS tools, documentation software, and product leadership for technical teams. Not every engagement or implementation is publicly listed.
About the teamThe reusable parts of AI product work: interfaces, context, evaluation, workflows, review, and handoff.
AI products
AI modernization
Durable context
Workflow automation
Model evaluation
Human review
Product interfaces
Deployment handoff
Small teams, clear constraints, fast loops. Build narrowly, validate with real use, and leave behind software that can be maintained.
Make real product surfaces, test them with real constraints, and let the work show what matters.
Pay attention to where AI helps, where it fails, and how people change their behavior around it.
Turn repeated patterns into clearer product architecture, reusable tools, and better judgment for the next build.
Some projects become products. Some become reference architecture. Some become private tools or collaborations. The point is to build close enough to the technology to learn from it.
Good projects have users, workflows, data, feedback, and a reason to improve over time.
We are open to selected collaborations when the work is concrete and the learning is real.
Products built around AI
Existing product modernization
Durable context and memory
Evaluation and model behavior
Internal AI tools
Selected collaborations
Main Character is founder-led by Gabe Mays. It is a place to build AI products, learn from the work, and collaborate when there is a concrete project to build.
Before Main Character, Gabe led product teams at GoDaddy and built products across SaaS, automation, developer tools, and AI systems. Before tech, he served as a Marine Corps Captain with deployments to Iraq and Afghanistan.
The lab also informs Gabe's own AI investing and conversations with founders, operators, and investors. The common thread is firsthand product signal from building with the technology.
Talk shop
Tell us briefly what you're building, modernizing, or trying to make easier to operate.
AI projects
AI products, AI modernization, workflow software, evaluation, durable context, and selected collaborations.
What happens next
Gabe will review and follow up directly when there is a concrete project to discuss.