pAiCoder — Spec-First AI Coding, Multi-Cloud & LLM Deployment (macOS)

Free 30-day evaluation · Idea → Spec → Code → Deploy · Bring your own models — Claude · Grok · GPT · Meta Muse Spark · local Ollama · or an open-source LLM you host yourself

pAiCoder turns an idea — a sentence, a document, or an architecture diagram — into a reviewable SPEC.md, then into working code, then into a real deployment. It's spec-first: you see and approve the plan before anything is built, and a deterministic audit gates every deployment. And it's model-agnostic — assign Claude, Grok, MuseSpark, GPT, a local Ollama model, or your own self-hosted open-source LLM to different roles so speed, quality, and cost are levers you control.

🚀 Quick Start — two minutes to your first win

Install the extension.
Add an API key — Cmd+Shift+P → pAiCoder: Setup → paste an Anthropic, xAI, OpenAI, or Meta key. (You can add more later and assign them by role.)
Open that CHAT panel — Cmd+Shift+P → pAiCoder: Open Panel (REPL / CHAT). This is your command center: type a command, or just chat with the agent.
Pick a first win:

You want to…	Do this
✍️ Write code faster	Open a Python file, write a docstring, press Tab to accept the ghost-text implementation.
🧱 Build from an idea	In that CHAT panel: `design a REST API for a todo app with auth` → review SPEC.md → `implement`.
☁️ Deploy infrastructure	`pAiCoder: Load Design from File/Image` (drop in an architecture diagram) → review → `aws-deploy` (or `azure-deploy`, `google-deploy`, `oracle-deploy`).
🤖 Serve an open-source LLM	In that CHAT panel: `llm-design` → pick VM or Kubernetes → answer a couple of questions → `llm-build` → `llm-deploy`.
🔗 Code with your own model	After deploying (or if you already run one): `llm-assign` → point pAiCoder's CODER role at your endpoint.

💡 Type help in that CHAT panel to see every command available in your build, and doctor to verify your setup and see which features are enabled.

Why developers like pAiCoder

You approve the plan first. Every build starts from a SPEC.md you can read, edit, and accept — no black-box code dumps.
Your models, your cost. Mix providers by role — a fast model for completion, a strong one for design, an independent one for audits. Save up to ~70% on tokens versus single-model tools.
Idea to running system, in one tool. The same assistant designs the app, writes the code, provisions the cloud, and can even stand up an open-source LLM — each step gated by a deterministic audit.
Close the loop on your own models. Deploy an open-weight LLM to your own cloud, then assign it to a pAiCoder role and build with a model that's fully private and fully yours.
It reads your diagrams. Hand it an architecture image and it produces the spec and the infrastructure.

Design principles

Protocol-first architecture. pAiCoder runs deterministic, auditable workflows rather than an autonomous "black-box" agent, which keeps its behavior predictable — every step is one you can inspect and approve.
Design-first. Rather than jumping straight into code, it generates and refines a SPEC.md first, then builds from the plan you approved.
Diagram understanding. Give it an architecture diagram and it reasons about the design to produce an implementation plan — following how the components connect, not just recognizing icons.
Multi-provider support. Anthropic, xAI, OpenAI, Meta, and local Ollama are all first-class — plus any OpenAI-compatible endpoint you host — so you pick models by capability, cost, or preference and assign them per role.
Infrastructure, not just application code. The cloud workflows generate real infrastructure-as-code — CloudFormation for AWS, Bicep for Azure, Terraform for Google Cloud and Oracle, and Kubernetes manifests for container targets — along with the parameters, modules, and deploy/destroy scripts, extending the same spec-first flow into DevOps.
Validate before you deploy. Before touching any cloud, pAiCoder checks that every module and parameter is wired up and flags unfilled secrets (passwords, connection strings, admin IDs, model tokens) up front — a practical way to cut down on failed deployments.

✨ Features

Spec-first workflow

description / doc / diagram  →  SPEC.md  →  code  →  deployment

Describe it, sketch it, or drop in a diagram — pAiCoder writes a reviewable spec, you approve it, then it implements. Add features later with plan, build with implement, and keep quality high with audit and security (each offers one-shot fixes).

Inline & docstring completion

Ghost-text suggestions as you type, powered by your chosen model. Write a docstring and let pAiCoder fill in the function.

def multiply(a, b):
    """Multiply two numbers and return the result."""
    # → press Tab to accept:  return a * b

Tab or Alt+\ to accept a suggestion.
For the cleanest experience, quiet the built-in dropdown: "editor.quickSuggestions": { "other": "off" }.

Deploy to any major cloud

Turn a diagram or a description into production-ready infrastructure — then ship it.

AWS · Azure · Google Cloud · Oracle Cloud

AWS generates a CloudFormation stack + GitHub Actions pipelines; Azure generates Bicep; Google and Oracle generate Terraform.
aws-deploy · azure-deploy · google-deploy · oracle-deploy — each pairs with a -status and a -destroy.
A broad library of deployment patterns (serverless APIs, containers, event-driven, data pipelines, AI/ML, and more).
A deterministic audit validates the templates and blocks the deploy until secrets and placeholders are filled — no half-configured launches.

Deploy open-source LLMs 🤖

Stand up your own model-serving endpoint — pAiCoder sizes it, generates the infrastructure, and gates the launch with a deterministic audit.

llm-design  →  llm-build  →  llm-deploy

llm-design first asks how you want to deploy — a GPU VM (Terraform) or Kubernetes — then how many concurrent users you expect and your use case (chat, RAG, coding, batch). From that it recommends an open-weight model (e.g. Qwen, Mistral, Llama), the right GPU, and a monthly cost estimate, and writes a reviewable SPEC.md you can tweak.
llm-build generates everything needed to serve it: for VM targets, Terraform plus a startup script that installs the NVIDIA driver and serving engine (vLLM / SGLang / Ollama); for Kubernetes, plain manifests (a GPU Deployment, a LoadBalancer Service, and a Secret) — each with ready-to-run deploy/destroy scripts.
llm-deploy runs the audit, confirms the cost, and provisions it; llm-status shows the live endpoint; llm-destroy tears it all down.

Twelve targets, one OpenAI-compatible endpoint:

GPU VM (Terraform): AWS · Azure · Google Cloud · Oracle Cloud · DigitalOcean · Nebius · Vultr · Scaleway · Lambda Cloud · Crusoe · Hyperstack
Kubernetes: CoreWeave (CKS)

Every deployment exposes an OpenAI-compatible /v1 endpoint you can point any client at — including pAiCoder itself.

Bring your model back into pAiCoder 🔗 (new)

The loop closes: once you've deployed an open-source LLM — or if you already run one — register it and build with it.

llm-assign      # register a deployed / external OpenAI-compatible LLM + assign it to a role
llm-providers   # list your registered custom providers and the roles they hold
llm-unassign    # remove one and revert its role to the default

llm-assign auto-detects an endpoint you just deployed (or lets you enter any OpenAI-compatible URL + model), then assigns it to a role — PLANNER, CODER, AUDITOR, or AUTO_AUDITOR — persisting it to ~/.paicoder/.env. Deploy Qwen or Llama on your own GPU, set it as your CODER, and you're coding with a model that's fully private, fully yours, and free of per-token cost.

Your choice, your cost

Most AI coding tools lock you into one model. pAiCoder lets you assign providers by role:

# ~/.paicoder/.env
CODER=xai                          # fast & cost-effective — implementation + inline completion
XAI_MODEL=grok-4.5
PLANNER=anthropic                  # deep reasoning — design, spec, orchestration
ANTHROPIC_MODEL=claude-opus-4-8
AUDITOR=openai                     # an independent second opinion on audits
OPENAI_MODEL=gpt-5.4
META_MODEL=muse-spark-1.1          # key: MODEL_API_KEY (from dev.meta.ai)

# Or point a role at a model you host yourself (set up via `llm-assign`):
# CODER=myqwen
# MYQWEN_BASE_URL=http://<your-endpoint>/v1
# MYQWEN_MODEL=Qwen/Qwen3-Coder-32B

Recommended: PLANNER=anthropic, CODER=xai — the best balance of speed, quality, and cost. Prefer a different coder? Set CODER=meta (Muse Spark) or CODER=anthropic. Want fully local and free? Point a role at Ollama — or at your own deployed open-source LLM.

More in that CHAT panel

A full coding-agent Project Chat, parallel implementation across a whole spec, code and security audits with one-shot fixes, safe refactors, and resource checks — all from pAiCoder: Open Panel. Type help to explore, doctor to check your setup.

🧭 Command reference

Type these in the CHAT panel. help lists everything available in your build; doctor shows which features are enabled.

Build from an idea

design <description> — generate a SPEC.md · implement — build it · plan — add a feature
audit · security · refactor — quality passes with one-shot fixes

Deploy app infrastructure — AWS · Azure · Google · Oracle

aws-deploy · azure-deploy · google-deploy · oracle-deploy
each pairs with …-status and …-destroy

Serve open-source LLMs

llm-design → llm-build → llm-deploy — size, generate, and launch a serving endpoint (VM or Kubernetes)
llm-status · llm-destroy — inspect or tear down a deployment

Use your own models

llm-assign · llm-providers · llm-unassign — register a self-hosted / external LLM and assign it to a role

Cloud and LLM-deployment commands appear only when enabled in your build — run doctor to confirm.

Evaluation License

pAiCoder is free to use for 30 days from first download. See LICENSE for details.

pAiCoder - Software Architech & Coding Agent

pAiCoder