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schulerj89/README.md

Josh Schuler

Applied AI & agent systems · Engineering leader · Hands-on builder

I build local-first AI agents, voice interfaces, and developer tools that turn model capability into safe, testable products. I bring 15+ years of software experience to the work—from architecture and delivery to the details of implementation, verification, and release.

Portfolio · Featured AI project · Open-source contributions

Featured build

A local-first Gmail cleanup workbench with optional OpenAI classification, human review, and reversible actions.

  • Go API, React workbench, Gmail OAuth, SQLite, and the OpenAI Responses API.
  • Metadata-only Gmail reads and bounded AI classification batches.
  • Preview-and-confirm flows for Trash and standards-based one-click unsubscribe actions.
  • Demo data, guided onboarding, responsive UI, automated tests, and documented trust boundaries.

Gmail Organizer dashboard

Agent systems and AI tools

A local Windows supervisor that routes Telegram text and voice through OpenAI or tracked Codex tasks.

Focus: agent routing, SQLite memory, background jobs, recovery, and local execution boundaries.

A voice-first Windows desktop agent built with Electron and OpenAI Realtime.

Focus: live voice, tool approvals, resumable task planning, and browser interaction.

A Rust and Tauri desktop runner for persistent Codex workflows.

Focus: event-stream parsing, SQLite thread history, cancellation, retry, and typed lifecycle state.

A 20-season college-football simulation with 70 fictional programs and deep browser verification.

Focus: product depth, simulation systems, TypeScript, cross-browser tests, and long-loop reliability.

How I build

My working loop is Frame → Build → Verify → Ship:

  1. Turn ambiguity into constraints, risks, and a testable first slice.
  2. Keep architecture and interfaces understandable enough for another person to own.
  3. Validate with tests, screenshots, telemetry, and real usage.
  4. Release software that can be explained, operated, and improved after launch.

The projects above are working labs for agent reliability, human approval flows, persistent memory, AI-assisted development, browser performance, and evidence-led quality.

Open source

My upstream work includes merged framework and documentation improvements across:

I am currently returning to regular open-source participation in the AI agent, MCP, TypeScript, Rust, and developer-tooling ecosystems.

Current focus

  • Reliable local and desktop agent systems.
  • Human-in-the-loop controls for tools that can change external state.
  • Evaluation, observability, memory, and recovery for long-running agent work.
  • Practical uses of OpenAI, Codex, TypeScript, Go, Rust, React, Electron, and Tauri.

I am always interested in thoughtful collaboration around applied AI, agent infrastructure, developer experience, and ambitious interactive systems.

Pinned Loading

  1. campus-gridiron-dynasty campus-gridiron-dynasty Public

    A 20-season college football dynasty simulator built with React and TypeScript, deterministic systems, and cross-browser verification.

    TypeScript

  2. github-page github-page Public

    Engineering leadership and applied AI portfolio built around the Frame → Build → Verify → Ship loop.

    CSS

  3. soup_ai soup_ai Public

    Local Windows AI supervisor that routes Telegram text and voice through OpenAI and Codex with SQLite memory and recoverable jobs.

    JavaScript 1

  4. agent_top agent_top Public

    Rust and Tauri desktop runner for persistent Codex workflows, event-stream monitoring, retry, and SQLite thread history.

    Rust

  5. chet-bot chet-bot Public

    Voice-first Windows desktop agent using OpenAI Realtime, tool approvals, browser automation, and resumable task planning.

    TypeScript

  6. gmail-organizer gmail-organizer Public

    Local-first Gmail cleanup assistant with optional OpenAI classification, human review, and reversible actions.

    Go