My AI Build Stack Is Boring. That Is the Point.

My AI stack is boring.

That's the point. Often time when things just work, it's not anything flashy.

I don't want tools that look impressive in a demo. I want tools that help me think, build, review, and ship with less friction.

That distinction matters. A lot of AI tooling conversations still focus on novelty. Which model is newest? Which agent looks the most autonomous? Which demo feels the most magical?

Those questions are interesting, but they aren't the questions that change how work gets done.

The better question is this:

Does this tool make the work easier to move from idea to outcome?

For me, the useful stack isn't one tool trying to do everything. It is a small set of tools with clear jobs. Each layer supports a different part of the work.

Here is what that looks like right now.

1. Antigravity: Workspace

Antigravity is where ideas start turning into structure.

This is the workspace layer. It helps keep work visible, organized, and easy to move through. When I am exploring an idea, reviewing files, or thinking through how a project should take shape, I need a place where the pieces are not scattered across random conversations and half-finished notes.

The value isn't that the workspace is flashy. The value is that it gives the work a place to live.

Good AI work needs orientation. What are we trying to do? What files matter? What decisions have already been made? What needs review? What should happen next?

The watch-out: a workspace can become another place where things pile up. If it doesn create clarity, it becomes clutter.

2. Claude Code and Codex: Build

Claude Code and Codex are the execution layer.

This is where ideas turn into working changes. I use these tools to explore options, make edits, review tradeoffs, and push work forward faster than I could alone.

The important part isn't simply that they write code. The important part is that they shorten the distance between intent and implementation.

A good coding agent can inspect a repo, understand patterns, propose an approach, make changes, and explain tradeoffs. That is useful. But it still needs direction.

The better the context, the better the output.

I don't treat these tools as magic developers. I treat them as capable collaborators that need clear tasks, constraints, and review.

The watch-out: speed can create false confidence. AI can produce something that compiles and still misses the business intent. Review still matters.

3. Hermes: Orchestrate

Hermes is the layer I care about most.

This is the orchestration layer. It tracks projects, routes work, captures context, and turns loose ideas into repeatable workflows.

Most real work isn't a single prompt. It's a chain of activity:

• Capture the idea
• Clarify the goal
• Find the context
• Create the plan
• Assign the work
• Review the output
• Save what matters
• Follow up later

Without orchestration, AI work becomes a pile of disconnected conversations. Useful in the moment, but hard to reuse and hard to govern.

Hermes helps connect the dots. It becomes the operating layer around the tools, not just another chatbot.

The watch-out: orchestration should simplify the system, not make it feel heavier. If the workflow becomes too complicated, people will route around it.

4. GitHub: Govern

GitHub is the source of truth.

This is the governance layer. Versioning, branches, reviews, issues, commits, and audit trails make AI-assisted work durable and recoverable.

When AI accelerates the pace of change, governance becomes more important, not less. You need to know what changed, why it changed, who approved it, and how to roll it back if needed.

GitHub gives the work a durable record. It turns AI-generated output into something that can be reviewed, compared, tested, merged, and recovered.

That is the difference between experimenting with AI and operating with AI.

The watch-out: governance cannot just be ceremony. If the process is too heavy, it slows the work down. If it is too loose, the work becomes risky. The goal is lightweight control.

The Lesson

AI tools are strongest when each one has a clear job.

One tool doesn't need to do everything.

The stack needs to support the way work actually moves:

Idea. Context. Build. Review. Ship. Learn.

That is the workflow that matters.

Not the demo. Not the hype. Not the longest list of tools.

The practical advantage comes from reducing friction at each step while preserving enough structure to keep the work reliable.

For leaders, this is the real question:

Are your AI tools organized around how work actually gets done?

If not, the stack will feel busy but not productive.

The best AI systems I have seen aren't the most complicated. They're the ones where every tool has a clear role, every handoff has enough context, and every important output has a place to live.

That's what I'm optimizing for.

A boring stack that helps me ship is far more valuable than an impressive stack that creates more noise.