Tyler Mayberry

Animas AI · Tulsa, OK · Remote

verified PRACTICAL AI SYSTEMS

I build AI systems for messy real-world work.

I turn scattered context into working software: agentic workflows, internal tools, automation, and product systems people can actually use.

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Agents and internal tools Automation Product-minded builder

What Animas AI is for

I build the layer between people, agents, tools, and messy operations.

Most AI work does not fail because the model is weak. It fails because nobody captured the context, workflow rules, handoffs, review loops, and ownership model. That is the layer I like building.

psychology

Agentic workflows

Agents with durable context, task state, tool access, and judgment boundaries.

dashboard

Internal tools

Dashboards, admin panels, intake flows, and control surfaces for repeated work.

auto_awesome_motion

AI media systems

Avatar, voice, video, content, and review pipelines using modern AI media APIs.

api

Product engineering

React, TypeScript, SQLite, Supabase, PocketBase, APIs, and pragmatic glue code.

Selected work

Current builds, not AI theater.

Selected builds that show how I think, build, and turn ambiguity into working systems. Masthead is the current flagship.

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Current flagship product

Masthead

Masthead is a local-first session memory layer for AI-agent work. It imports local harness history, makes prior sessions searchable, and exposes bounded context through read-only MCP tools.

It is the clearest current example of the Animas AI pattern: turn messy agent context into durable product infrastructure people and future agents can actually use.

Tauri / TypeScript SQLite session graph Local daemon Read-only MCP Agent memory

See the Masthead build arrow_forward View Masthead open_in_new

Generated Masthead product visual showing Board, Logbook, and MCP retrieval surfaces.

Pip app screens showing Spendable Cash Today and chat.

Second featured product

Pip

Pip is an AI-native spending companion built around one daily number: Spendable Cash Today. It pairs deterministic financial logic with an agent that explains, routes, and presents approved actions.

It stays prominent as the clearest consumer-product proof point beneath Masthead's agent-infrastructure story.

Next.js / TypeScript Supabase Plaid OpenAI Agents SDK

See the Pip build arrow_forward View Pip open_in_new

Product build

Executioner

A deployed personal execution system with onboarding, payments, email, and AI-assisted planning flows.

View Executioner open_in_new

Central Brain

Private infrastructure

Nova OS / Central Brain

A private pattern for agent memory, project state, decisions, incidents, follow-ups, and dashboards so AI work has continuity.

databaseSQLite-backed project context

hubAgent coordination patterns

fact_checkHuman-readable task state

Workflow board

Client environment

Brander Group

Contracted AI systems work in a real operating environment. Public details stay high-level while still showing the operating pattern: context, workflow state, agentic support, and practical delivery.

AI media systems

Julian / media workflows

Control panels and experiments around AI avatar video, voice, generated media, content review, and API-driven production loops.

Playable game

Rat Detective Online

A playable multiplayer browser game showing 3D, TypeScript, physics, and real-time interaction.

Play live open_in_new

Workflow tool

Paycheck Sanity Checker

A focused tool for turning confusing pay questions into a simple review flow.

Decision support

AML Risk Navigator

A compliance-oriented prototype for making structured review easier to follow.

Operating thesis

The person using the system is part of the system.

Good AI work is not just prompts. It is context capture, interfaces, review loops, permissions, data shape, failure handling, and the human confidence to use the thing when the work gets noisy.

groups

Strong with nontechnical operators

Hospitality and customer-facing work taught me how to translate messy human context into tools people can trust.

construction

Comfortable without clean specs

Most valuable systems start as fog. I can explore, prototype, test, and tighten until the shape appears.

manage_accounts

Ownership beats micromanagement

I do my best work with room to own the problem, make decisions, and show progress through working artifacts.

verified

Honest about AI limits

AI does not fix chaos by magic. It needs rules, memory, context, and maintenance to become useful infrastructure.

Built directly

Animas AI is Tyler Mayberry turning ambiguous AI work into working systems.

My best work is turning unclear operational problems into concrete systems: interfaces, workflows, data shape, automations, and agent loops that people can trust. Available for aligned full-time remote roles, contract work, and consulting where practical AI tooling matters.

Need someone who can turn ambiguous AI work into something real?

Send me the context. I will tell you what I would build first, what I would leave alone, and where a useful version probably starts.

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