pAiCoder — Your Autonomous Engineering Team, on macOS
Free 30-day evaluation · Hand off the build — or write code yourself · Bring your own models — Claude · Grok · GPT · Meta Muse Spark · local Ollama · or an open-source LLM you host yourself
pAiCoder gives you one or more autonomous engineers you can hand a project to — and a manager who takes
your biggest idea, breaks it down, dispatches the pieces to those engineers, and keeps orchestrating until the
whole thing is assembled and audited. You direct them by chat, by voice, or from
your inbox; you approve every plan before a line is written; and a deterministic audit gates every deployment.
It is spec-first — nothing is built until you have seen and approved SPEC.md. It is model-agnostic —
assign Claude, Grok, GPT, MuseSpark, a local Ollama model, or your own self-hosted LLM to different roles, so
speed, quality and cost are levers you control.
And when you want to do the work yourself, the same engine is a spec-first coding assistant in your editor:
ghost-text completion, design → implement, multi-cloud deployment, LLM hosting, even RTL. That is all
further down.
🚀 Two minutes to your first hand-off
manager-deploy # 1. start Aria, the manager
engineer-deploy Ada # 2. start your engineers — TWO is the sweet spot
engineer-deploy Ava
manager-recruit all # 3. Aria adopts them — AFTER they are running
engineer-status # 4. who is up, and their private panel URLs
Each command prints a 127.0.0.1 URL protected by a one-time token. Open Aria's, describe what you
want, and she plans it, splits it into components, and dispatches them. Open an engineer's and you
are talking to her directly. /help in either panel lists everything they accept.
No setup needed. The first manager-deploy writes a working manager — and an engineer, if you
have none — and starts them.
manager-recruit adopts the engineers it can find, so it goes last. engineer-deploy all
starts the ones on this machine and skips any you have dismissed — name her to bring her back.
Two engineers is usually right. They share one LLM quota, so a third mostly competes with the
other two rather than finishing sooner.
🤖 Autonomous Engineer — hand off the build, stay in control
An engineer takes a project end to end: design → review → implement → audit → fix-audit,
then packages the result. She works in her own directory, on her own port, with her own panel.
She writes code and never runs it. Automated passes may read and write files — no shell, no
package installs, no test runs. What she produces is yours to run.
Talk to her however suits you:
- her panel — chat, watch the task list, answer questions, enter secrets safely;
- email — put the command in the SUBJECT as
/<command>(<argument>), for example
/new-project(url-shortener) or /zip-project(). She replies with progress and artifacts, so you
can start work from a phone and read the result later.
Several at once, each on her own panel. Open as many as you like in one browser — each holds her
own session.
🧑💼 Autonomous Manager — one idea, a whole system
Aria takes a description, asks a planner to split it into components, writes a portfolio SPEC.md,
and sends it to you for review. On approval she dispatches each component to a free engineer,
collects the artifacts, assembles them into one tree and audits the result.
Pieces with no free engineer are queued and go out as soon as one finishes. She reaches local
engineers directly, and remote ones by email.
Start a BIG project with the manager, not an engineer. An engineer plans one SPEC in a single
pass — directory tree, file names and phases all at once — and that gets thin when the project is
large or spans several stacks. Aria splits it first, so each engineer plans a component she can hold
in her head. A small, single-stack project is fine handed straight to an engineer.
Type /help in her panel — or an engineer's — for everything they accept.
manager-status · manager-roster · manager-inbox — what she is doing, who she has, what arrived.
manager-design reconfigures her; it is not needed to start.
Both ship DORMANT. Nothing runs until you deploy it, and everything stays on 127.0.0.1.
Full details: agent/manager/README.md and agent/engineer/README.md.
📋 Common workflows
Eight things people actually do, start to finish. Commands are typed in the pAiCoder panel
(Cmd+Shift+P → pAiCoder: Open Panel (REPL / Chat)) unless a step says otherwise.
1 · Install the extension
git clone <your pAiCoder checkout> && cd coding-agent-v16
./build-binary.sh # builds dist/pAiCoder (Nuitka, 3–5 min)
./install-vscode-extension.sh # installs the VS Code extension
Reload VS Code. Cmd+Shift+P → type pAiCoder — you should see nine commands.
2 · Add or change your API keys
Cmd+Shift+P → pAiCoder: Setup — Configure API Keys & Roles.
One place for all of them, and for which model plays which ROLE (planner, coder, auditor). Keys are
written to ~/.paicoder/.env and never leave your machine. In the panel, llm-providers lists what
is registered and llm-status shows the current assignment.
3 · Your first project, from a design
From the Explorer: right-click an architecture diagram, a screenshot or a written brief →
pAiCoder: Load Design from this File.
Or in the panel:
load-design ./docs/architecture.png
load-design(claude) ./docs/architecture.png # Claude Vision — better on images
pAiCoder reads the design, classifies the stack, writes SPEC.md, and shows it for review before
anything is built. Approve, and it implements against that spec.
4 · Drive it yourself — the coding assistant
Open the panel and talk to it. It reads your workspace, edits files and runs commands, asking before
each write unless AUTO_APPROVE=true is set in ~/.paicoder/.env.
implement SPEC.md build what the spec describes
audit-code what is wrong with this codebase
fix-audit fix what the audit found
doctor health check — providers, tools, config
5 · Hand a whole project to Ada — the autonomous engineer
engineer-design one wizard: her name, channels, contacts, safety
engineer-deploy starts her and prints her web panel URL
engineer-status running? her URL, mode, budgets
engineer-stop kill switch
Open her URL. Give her a project in her panel and she plans, builds, audits and fixes it on her own,
reporting as she goes. /help in her panel lists what she understands.
6 · A bigger system — Aria and a team of engineers
manager-design configure Aria (identity, channels, the engineers she manages)
manager-deploy starts her and prints her panel URL
manager-recruit --all enrol the engineers on this machine
manager-status running? her URL and current project
Give Aria one idea in her panel. She splits it into tech-stack sub-projects, sends each to an
engineer, collects the artifacts and assembles them. /status shows every component and who holds
it; /audit-code and /fix-code work on the assembled tree.
One engineer, one transport. An engineer configured with email is not recruited locally — that
is what stops duplicate requests and duplicate deliveries.
7 · Serve your own model
llm-design describe users, budget and latency → a serving SPEC
llm-build generate the GPU-VM IaC and startup script
llm-deploy provision it (audit-gated; this one bills)
llm-status endpoint URL and IP
llm-destroy tear it down
llm-assign point a ROLE at the deployed model
8 · Hardware — SystemVerilog
verilog-design an HDL SPEC from a description
verilog-build generate the RTL
Off by default. Enable hdl_design.enable_verilog in agent/config.py first; the commands tell
you so if it is not.
🧹 Housekeeping
purge-logs 30 delete logs older than 30 days (projects untouched)
purge-logs 30 --dry-run show what would go, delete nothing
reset-system soft the agents forget their work — configs, .env, projects all stay
reset-system agents the agents are REMOVED; pAiCoder is the coding agent it started as
reset-system hard agents, and the project registry too
.env and every project FILE survive all three resets. Each asks you to type I understand
three times, and refuses while an agent is running.
🧠 Models — including local ones
Set these in ~/.paicoder/.env. Roles are assigned separately, so a fast local model can code while
a stronger one reviews.
ANTHROPIC_MODEL=claude-opus-5
XAI_MODEL=grok-4.6
OPENAI_MODEL=gpt-5.6-luna
META_MODEL=muse-spark-1.2-contributor
OLLAMA_MODEL=muse-glimmer:30b-mlx # local, via Ollama
Local models via Ollama. Install Ollama, pull a model, name it above — chat, streaming and tool
calling work with no further setup:
ollama pull muse-glimmer # Meta's open-source agentic model, ~18 GB quantized
Use muse-glimmer:30b-mlx on Apple Silicon — the same model on Ollama's MLX engine.
llama3.1:8b is the smaller default. Ollama is expected at http://localhost:11434.
Local vision. muse-glimmer reads images, so load-design works entirely on your machine —
drop in an architecture diagram and it never leaves the network. That matters when privacy outranks
raw capability, which for many teams it does. JPEG, PNG and WebP; convert a GIF first.
Models without vision (llama3.1, most coder models) still handle text and tools — a diagram is
simply skipped, with a note. load-design images also work with Anthropic, OpenAI, xAI, Meta, and
any llm-assign endpoint.
Changing a model, live. set-anthropic-model, set-xai-model, set-openai-model and
set-meta-model take effect in the REPL immediately — no restart.
A running engineer or manager is a separate process, so those do not reach her. Change hers from
her own panel instead:
/engineer-coder meta # in an engineer's panel — her CODER, right now
/engineer-planner openai # her PLANNER (writes SPEC.md and the phases)
/manager-planner anthropic # in Aria's panel — the planner that splits a portfolio
Each affects only that engineer or manager, takes effect on her next call, and lasts until she
restarts — nothing is written to your .env. Ideal for trying a model on one engineer without
disturbing the others. To make a change permanent, edit ~/.paicoder/.env.
A short command palette. Shift+Cmd+P → "pAiCoder" lists the nine commands you actually reach
for from there — the toggles, Open Panel, Setup, Checkpoint, Load Design from this File and
Download / Update Binary. The rest are terminal-CLI commands and stay hidden. Nothing is removed;
set show_vscode_menus: True in agent/config.py to list them all again.
A short REPL help. The provider-key and model setters (set-anthropic-key,
set-openai-model, …) and the parallel* family are hidden from help, because the VS Code panel
and the TUI config screen already do all of it — and eight one-per-provider setters grow the list
with every provider added. The commands still work when you type them. Set
agent.show_repl_advanced: True, or PAICODER_REPL_ADVANCED=on, to list them again. setup is
the one entry point that replaces all eight, and every llm* command stays listed.
Big codebases. pAiCoder reads up to 400,000 characters of your workspace into an audit or a
design pass — enough for a multi-component project whole. Raise or lower it for your setup:
PAICODER_CONTEXT_BUDGET=1200000 # a large monorepo on a model with a big window
PAICODER_MAX_FILE_CHARS=300000 # one very large generated file
Lower it if you run a small local model — an 8K-token window holds roughly 32,000 characters, and
overflowing it fails rather than costing money.
Any other model — llm-assign. Register a self-hosted or external OpenAI-compatible endpoint and
point a role at it:
CUSTOM_PROVIDERS=myllm
MYLLM_BASE_URL=http://your-endpoint/v1
MYLLM_MODEL=your-model
Then llm-assign → choose the role. llm-providers lists what is registered; llm-unassign undoes
it.
Want to host one yourself? llm-design → llm-build → llm-deploy sizes, generates and launches
a serving endpoint — twelve clouds as a GPU VM, six as managed Kubernetes — then llm-assign points
pAiCoder at it.
🧰 The coding assistant — when you want to drive
Everything above is the team working for you. The rest of this page is pAiCoder as a spec-first coding
assistant in your editor — the same engine, driven by you, one command at a time. The engineer and the manager
run these commands on your behalf, so anything here is also something you can ask them to do.
Quick Start — two minutes to your first win (solo mode)
- 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 |
right-click your diagram in the Explorer → pAiCoder: Load Design from this File → 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. |
| 🔧 Design hardware (RTL) |
In that CHAT panel: verilog-design → answer a few questions → review the hardware SPEC.md → verilog-build writes modern SystemVerilog + a testbench for your own sim/synth flow. |
💡 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).
One idea, twelve stacks 🌐
Describe what you want — pAiCoder picks the language that fits, or you just name it ("an iPhone game in Swift," "a Go CLI," "a Spring Boot API"). It recognizes the stack from your description and tells you what it chose, then scaffolds a modern, production-shaped project — a package-by-feature layout, an adapter layer for external systems, idiomatic tests in that language's own framework, and an explicit DEPLOYMENT.md. Not a toy scaffold you outgrow in a week — a structure built to scale to a real project.
| Language |
Great for |
Tests |
Ship it |
| 🐍 Python |
data & ETL pipelines, ML/AI, backends, automation, glue |
pytest |
wheel / container |
| 🍎 Swift / SwiftUI |
native iOS · iPhone · iPad · macOS apps & games |
swift-testing |
App Store · TestFlight · binary |
| 🟦 TypeScript |
Node.js services, CLIs, tooling |
vitest |
process manager / container |
| 💜 C# / .NET |
Windows & cross-platform services, Web APIs |
xUnit |
dotnet publish |
| ☕ Java / Spring Boot |
JVM services & REST APIs |
JUnit 5 |
bootJar / container |
| 🐹 Go |
services, CLIs, single static binaries |
go test |
cross-compiled binary |
| 🦀 Rust |
systems, performance-critical, memory-safe |
cargo test |
cross-compiled binary |
| ⚙️ C++ |
high-performance & embedded |
GoogleTest |
host binary · flash to device |
| 🟪 Kotlin |
JVM services & Android |
Kotest |
shadowJar / container |
| 🔴 Scala |
JVM, functional, data-heavy |
MUnit |
sbt assembly |
| 🔵 F# / .NET |
functional-first .NET (records, unions) |
Expecto |
dotnet publish |
| ⚛️ React |
browser front-ends (TypeScript + Vite) |
Vitest + jsdom |
static dist/ — any host or CDN |
Building a full-stack app? React and the service behind it are two components — pAiCoder builds
each in the stack that suits it and keeps the type contract between them consistent.
Every project is designed the same spec-first way — you approve the SPEC.md before a line is written — and audited the same way, in whichever language fits the job. Not sure which stack? Just describe the project and let pAiCoder choose; name a language explicitly and it honors your call.
Inline & docstring completion
Ghost-text suggestions as you type in any language, 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) tailored to your provider's GPU classes and node pools — 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 · CoreWeave
- Managed Kubernetes: AWS EKS · Google GKE · Azure AKS · Oracle OKE · DigitalOcean DOKS · CoreWeave CKS
Pick Kubernetes on any of those six and you get plain manifests for that provider — the right GPU node
selector, the right cluster CLI, the right node-pool commands — not a generic template you have to
translate.
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.
Design hardware, too — idea → synthesizable SystemVerilog 🔧 (new)
Not just software. Describe a hardware block and pAiCoder turns it into a reviewable hardware SPEC.md, then into modern, synthesizable SystemVerilog with a self-checking testbench — ready for your own simulation and synthesis flow.
verilog-design → verilog-build
verilog-design runs a short wizard — target device (AMD, Intel, Lattice, or portable), clock + reset scheme, and one of 13 hardware block types:
- Memory — FIFO, RAM, direct-mapped cache, on-chip SRAM controller
- Compute — ALU, SIMD engine, systolic MAC / GEMM tile, DSP datapath
- Control & glue — FSM controller, arbiter, AXI4-Lite / AXI4-Stream / APB / Wishbone SoC peripheral
- Reference blocks — a SHA-256-style hash core and an educational RV32I CPU subset
From your answers it writes a deterministic SPEC.md that pins every module's ports, parameters, clocking, and interface contracts up front — plus a rough resource + timing estimate so you catch an over-budget design before you build it.
verilog-build generates the RTL to that spec: IEEE-1800 SystemVerilog, synthesizable subset — always_ff / always_comb, logic-typed, `default_nettype none, no inferred latches, clean reset / CDC discipline — plus a self-checking testbench, a README, and an exact, device-aware DEPLOYMENT.md with the precise lint / simulate / synthesize commands for your part. A fast, read-only static audit flags anti-patterns before you ever open a tool.
You verify — on your toolchain, your way. pAiCoder writes clean, review-ready RTL and hands you the exact commands; it never runs a simulator or synthesizer itself, so nothing surprising happens to your environment. Lint with Verilator or Verible, simulate with Verilator / Icarus, synthesize with Vivado, Quartus, or Yosys + nextpnr — the generated DEPLOYMENT.md spells out each command for your device.
Honestly scoped. Each block is bounded to what a generator can produce correctly. The two reference blocks say so up front, right at the top of their spec: the hash core is a learning implementation to check against the standard test vectors (not production crypto, not side-channel hardened), and the RISC-V core is an educational RV32I subset (not a conformant CPU — no CSRs, traps, or pipeline). No block oversells what it is.
verilog-design writes the spec, then offers to build ([y/N]) — nothing is generated until you say so.
Your choice, your cost
Most AI coding tools lock you into one model. pAiCoder lets you assign providers by role:
# ~/.paicoder/.env
PLANNER=anthropic # deep reasoning — design, spec, orchestration
ANTHROPIC_MODEL=claude-opus-5
CODER=openai # implementation + inline completion
OPENAI_MODEL=gpt-5.6-luna
AUDITOR=anthropic # an independent second opinion on audits
META_MODEL=muse-spark-1.2-contributor # key: MODEL_API_KEY (from dev.meta.ai)
OLLAMA_MODEL=muse-glimmer:30b-mlx # local — reads diagrams too (see below)
# 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 (claude-opus-5) and CODER=openai (gpt-5.6-luna) — planning quality matters most, and this pairing has been the most reliable in practice. CODER=meta (muse-spark-1.2-contributor) is a strong alternative and reads architecture diagrams. 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 featureaudit · 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- twelve clouds as a GPU VM, six of them as managed Kubernetes (EKS · GKE · AKS · OKE · DOKS · CKS)
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
Design hardware (SystemVerilog)
verilog-design → verilog-build — idea → reviewable hardware SPEC.md → modern synthesizable SystemVerilog + a self-checking testbench, with device-aware lint/sim/synth commands you run yourself
Hand work to an autonomous engineer, or a manager
engineer-deploy · engineer-status · engineer-inbox · engineer-stop — one engineer, or several by namemanager-deploy · manager-status · manager-roster · manager-purge — a manager who plans a whole product and dispatches the piecesengineer-design · manager-design — change any of it; neither is needed to start- In their own panels:
/engineer-coder <provider> · /engineer-planner <provider> · /manager-planner <provider> — swap a model live, for that one, until she restarts
Cloud, LLM-deployment, hardware-design, engineer and manager 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.
Links
