Projects & Ventures

The receipts behind the claim.

Businesses I’ve run, products I’ve shipped, and the open-source work in between.

Business Ventures

Ship small. Measure. Iterate.

Ravi

Co-Founder · Jan 2026 – May 2026

Agent-native SaaS that lets AI agents manage their own accounts and secrets.

  • Raised a $400K pre-seed from South Park Commons
  • Grew to hundreds of users
  • Shipped the product end to end: a web SaaS and a cross-platform Flutter mobile app on the Apple App Store, built on prototypes started in 2024
  • Designed agent-native account, secret, and 2FA management so AI agents can operate their own identities

The Onward Store Steakhouse

Co-Owner with my wife · Oct 2021 – Jan 2024

A full-service steakhouse we owned and ran outright: the part of business most engineers never see.

  • Executed the full business lifecycle together, from opportunity identification and acquisition to a $500K annual revenue operation
  • Built and led a high-performing team of 7 through hands-on leadership, creating a culture of excellence, accountability, and pride
  • Established operational systems and feedback loops that drove consistent quality and customer satisfaction
  • Applied a tech product mindset to a traditional business: rapid iteration, data-driven decisions, continuous improvement

SocialDrizzle

Founder · 2013 – 2015

Social media engagement platform for stadium video boards at public events.

  • Built a complete microservices architecture with Docker, Ansible, and Jenkins: data collection bots, routing, processing, and the web app
  • Launched the MVP at Mississippi State University in just 12 weeks using the Meteor framework
  • Provided a turnkey solution for game-day production crews: hardware integration, custom templates, on-site support
  • Developed a fleet of social media bots to aggregate and curate fan content for real-time stadium display

Open Source & Technical Projects

Agent infrastructure: the control planes, identity systems, and protocol plumbing that let AI agents do real work.

Fountain

A multi-tenant developer platform for running fleets of sandboxed coding agents. Running Claude instances with worktrees and hand-shuffled MCP configs was slow and error-prone, so I built the tool.

  • Spin up sandboxed agent instances with preconfigured env vars, MCP servers, skills, repos, and packages
  • Every feature on three surfaces: web UI for debugging, REST API for CI/CD, and a Homebrew-distributed CLI with manifest-driven fountain apply
  • Agents onboard themselves: every instance serves llms.txt and a drop-in skill, so any agentic IDE learns the whole API from one fetch
  • The fleet itself is code: agent-specs declares every agent, environment, and vault as typed resources, applied in one command
  • Streams every conversation over SSE, so agent runs are observable and debuggable, not fire-and-forget

Built agent-first, in the open: BinaryBourbon is the GitHub identity my agent fleet ships under. I set the roadmap, review the work, and operate the system. Fountain is itself developed by the agents it orchestrates.

The Homelab

A production-grade home cloud: three M4 Mac minis running k3s over a Tailscale mesh, every manifest reconciled from git by Flux.

  • Full platform stack: Longhorn, Garage, CloudNativePG, Prometheus/Grafana/Loki, SOPS-encrypted secrets
  • Serves real production traffic — ai.jakegaylor.com and the agent fleet’s infrastructure run on it
  • Photo tour, network topology, and the war stories behind the design

infisical-chant

A self-hosted Infisical deployment you drive from the repo, for any Kubernetes cluster kubectl can reach. The shippable artifact behind my secrets-management writing.

  • Five components, three tiers (light / production / production-ha), generated CI, and a naming scheme that lets many instances coexist across clusters
  • Agent-operable by design: clone it and your agent already knows how to run it by its verbs
  • Built with chant, a friend’s intent-aware infrastructure toolchain I use across my stack

ai.jakegaylor.com

A live, hosted MCP server that teaches AI assistants about me. Try it on this candidate.

  • Connect any MCP client and it can read my resume, score my fit against your job description, and build the interview
  • Your assistant can even email me directly from the conversation — you review it, the server sends it
  • Streamable HTTP and SSE transports, an npm package for stdio-only clients, and llms.txt for everyone else

Accessible Ops

A spec I co-authored: a short list of properties that make infrastructure safe to hand off, to a new engineer or to an AI agent.

  • Properties like one path to prod, documentation is law, and named secrets with least privilege, each written to be audited against
  • The judgment stays with a human; the rest becomes safe to delegate
  • It’s the lens behind this site’s headline, and the standard I hold my own systems to

OTFL — On The Fly List

A free, open service for shareable checklists. Every list is a UUID and knowing the link is the only key — no account required.

  • Programmatic by design: a small JSON REST API to create and drive lists, plus a generated llms.txt so an assistant learns the whole API from one fetch
  • A built-in, stateless MCP server at /mcp lets AI assistants create lists, toggle items, and clear checked ones directly
  • Optional GitHub sign-in names and tracks your own lists on a “My lists” dashboard; the session-less API and MCP only ever touch open, anonymous lists
  • Node/Express + SQLite in one container — deployed to the k3s homelab via Flux GitOps, multi-arch image built in CI, TLS via cert-manager, built agent-first with Claude Code

Marginalia

A hobby project my wife and I are building together: it pairs novelists with critique partners, then has them swap chapters a few thousand words at a time.

  • Matches on mutual fit, ranking writers for whom the trade works in both directions
  • Pages burn after fourteen days, so drafts stay private; the notes you received are yours forever
  • Live now, and my wife is its first and toughest user

MCP Working Group Contributions

Early participant in Model Context Protocol (MCP) community development.

  • Was a member of the MCP Hosting Working Group
  • Developed strategies for deploying and running MCP servers at scale
  • Contributed to the Dart SDK and an Express tool for binding to MCP servers
  • Built MCP servers for Hirebase, candidate job search, and text extraction
  • Created a GitOps tool for MCP deployments; still operate a public Echo MCP server for community testing

CareerFlick

Flutter mobile app with a Tinder-like LLM interface.

  • Built an intuitive swipe-based interface for interacting with LLMs using Flutter
  • Implemented AI-driven content generation and response system
  • Shipped cross-platform for iOS and Android

CleanJobData MCP Server

An MCP server for the CleanJobData Job API, published on PyPI in August 2026.

  • Runs as stdio for local clients or as one hosted HTTP server where every user authenticates with their own API key per request
  • The newest entry in a line of MCP servers I’ve shipped since the protocol’s early days

Hirebase MCP Server

A Python MCP server for Hirebase.org’s API.

  • Built a Python-based MCP server for searching jobs on Hirebase.org
  • Dockerized the service and set up GitOps CI/CD pipelines for automated builds and releases

Express MCP Handler

Package to simplify MCP handling in Express.js.

  • Developed Express middleware in TypeScript to streamline MCP request handling
  • Authored full type definitions and robust error handling for production-grade reliability

Node Candidate MCP Server

A TypeScript library to build customizable candidate MCP servers.

  • Authored a reusable library so developers can integrate their own candidate data into MCP flows
  • Published as an npm package for easy adoption and extension by other teams

mcp_dart

Dart SDK implementation of the Model Context Protocol.

  • Contributed stream-based support for in-process MCP servers
  • Added support for running MCP servers in Flutter apps
  • Authored Dart examples to accelerate SDK adoption

“Everyone else told us how to work around problems. Jake built us better tools.”

Operator, Magic

Want this on your team?

Every project above started the same way: find what’s slow, build the tool, measure the result. Let’s talk about doing it for you.

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