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· 8 min read · Systems

How I Orchestrate My Entire Workflow: From Pieces Memory to Codex Agents to ChatGPT

I don't code alone anymore. I have a fleet — a stack of AI agents, a memory system that never forgets, and a routing layer that decides who does what, when, and with what context.

I don't code alone anymore. And I don't mean I have a co-founder. I mean I have a fleet — a stack of AI agents, a memory system that never forgets, and a routing layer that decides who does what, when, and with what context.

Here's how it actually works.

The Foundation: Pieces OS Is My Brain

Everything starts with Pieces OS. It's not just a snippet manager. It's my long-term memory graph. Every clipboard event, every screenshot OCR, every browser tab, every Codex session, every ChatGPT conversation — Pieces captures it, indexes it, and makes it searchable.

This matters because context is everything. When I open a project I haven't touched in 3 weeks, I don't have to remember where I left off. I ask my system to pull a morning brief, and it synthesizes:

I didn't build this from scratch. Pieces OS already does the heavy lifting. I just wired it into my orchestration layer.

The Routing Layer: Lapapi — My Personal Mission Control

Lapapi is the hub. Think of it as my own lightweight AI workspace — a macOS-first dashboard that connects to Pieces, Ollama, Codex, and whatever model I need.

Every morning, Lapapi runs a health check:

If something's wrong, it tells me. If everything's green, it gets out of the way.

Codex: The Builder Agent

Codex is where the real work happens. I use it as my primary code agent — not just for quick snippets, but for full project scaffolding, refactoring, and deployment.

Here's my actual Codex project dashboard right now:

I recently made a big pivot: I removed Claude completely from my local-first tooling stack. My apps now only track what matters: Pieces, Ollama, Apple's local AI capabilities, and Codex project context. Build passed. App relaunched. No regrets.

Codex gets context from Pieces OS, so it knows my recent work without me copy-pasting. It reads my workstream summaries, checks my active window titles, and pulls relevant files. That's not magic — that's just good plumbing.

The Agent Fleet: Specialization Over Generalization

I don't use one AI for everything. I use agents — specialized personas with specific skills:

Each agent gets primed with skills — documented at agentskills.io — so they don't waste tokens discovering tool names. They already know: ollama run, codex --model, eagle-mcp search.

ChatGPT: The Strategic Layer

So where does ChatGPT fit? It's my strategic partner.

When I'm architecting something new — a design system, a business model, a brand pivot — I go to ChatGPT. It has the broadest context window and the deepest reasoning. I use it for:

ChatGPT is where I think. Codex is where I build. Pieces remembers everything.

The Daily Flow: An Actual Example

Here's what a real day looks like:

  1. Morning brief (Lapapi + Pieces) → "Yesterday you refactored the UI, worked on V8V storefront, and hit a swap crisis. Today: fix ShieldCheck error, publish Complete Collection product."
  2. Deep work (Codex) → I open a project, Codex reads AGENTS.md, README.md, and CONTEXT.md, then implements the next smallest useful feature. No prompting required.
  3. Creative sprint (ChatGPT + Lovart/GPT Image 2) → Design assets for Jez V, iterate on brand positioning, or draft Shopify product descriptions.
  4. Evening wrap (Pieces memory capture) → The entire day's context is saved. Tomorrow, any agent can pick up exactly where I left off.

Why This Works: Local-First Sovereignty

I'm obsessed with local-first. My M4 Max runs Ollama with qwen3.6:27b, gemma4:31b, nemotron3:33b. I don't need cloud APIs for most tasks. When I do use cloud (GPT-5, Claude), it's a conscious choice — not a default.

I removed MongoDB backends from apps and replaced them with local JSON + Tauri. I self-host my project dashboards. My skills collections are organized in a local directory that any agent can read.

The result: my workflow is fast, private, and resilient.

The Philosophy: Workflow Over Model

Here's what I've learned after running this system for months:

The model matters less than the workflow.

A mediocre model with perfect context routing beats a frontier model with zero memory. Pieces OS gives me the memory. Lapapi gives me the routing. Codex and ChatGPT give me the execution.

I don't chase the latest GPT release. I optimize the pipeline:

That's where the real leverage is.

What's Next

I'm currently:

If you're building with AI agents, my advice is simple: invest in memory and routing before you invest in better models. The model will change next month. Your workflow is what compounds.

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