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

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:

• What I accomplished yesterday
• Open errors and failed builds
• Active apps and running projects
• 2–3 next actions ranked by priority

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:

• Connector status (Pieces OS, Tailscale, GitHub, Ollama)
• Active project folders for dirty git states
• Downloads folder for accumulated clutter
• System memory pressure (I've hit 93% swap before — now I catch it early)

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:

• Kika-projects-report-hub: Automated project reports
• Mini-apps: Status menu, icon cropper, PNG tools
• AGENTS.md: Living documentation for my agent fleet

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:

• Hermes. Gateway — Telegram & Discord dispatch, cron jobs, infrastructure.
• Kartie. Code review, architecture decisions, system diagnostics.
• Neve-02. Creative tasks, brand assets, Jez V marketing.
• Codex. Code generation, project scaffolding, deployment.
• Goose. Local inference, Ollama model management, extensions.

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:

• Design system philosophy (my V8V brand shop, the "less or more" aesthetic)
• High-level product decisions
• Marketing copy and brand voice
• Shopify storefront strategy

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:

• How context flows from Pieces → agent
• How agents hand off work to each other
• How memory persists across sessions

That's where the real leverage is.

What's Next

I'm currently:

• Building V8V — a Shopify boutique for AI-agent skill bundles (28 curated collections for Codex, Claude, Cursor)
• Refining AppAudit — a macOS tool that audits installed apps and recommends keep/kill decisions using Ollama
• Standardizing my Codex-ready project structure — AGENTS.md, README.md, TASKS.md, DECISIONS.md, PROMPTS.md

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.