Why Painted Wolf Code
Your machine. Your models. Your rules.
The problem we're solving
Most AI coding tools are assembled. Painted Wolf Code is built as one piece.
Coding with agents takes six layers: the evidence you check, the editor that holds your files, the agent doing the work, the context it’s given, the guardrails on what it runs and sends, and the models you pay for. Most tools build some of these well and borrow the rest from another product.
The trouble lives in the gaps between borrowed parts. An agent edit can overwrite a draft you haven’t saved. A confident summary can rest on files the agent never opened. A key in a config file can ride along in a prompt, because nothing between the agent and the provider was designed to catch it.
What building every layer buys you
- Receipts, not vibes You see every tool call, and findings cite the files and lines behind them, checked against what the agent actually read.
- Agents edit the file you have open Agent edits merge into your live document while you type, without clobbering your draft.
- Local AI makes your frontier model cheaper, faster, and smarter Bialy runs on your machine before your provider sees a word. It cuts what the work doesn't need from the prompt and your bill, puts the right code on top, and loads the right skill, so you're talking to a specialist.
- It knows what can't be undone Commands run in an OS sandbox, and consequential cloud and shell operations stop for you.
- Your keys never reach the model The model gets a reference to a credential, and outbound requests are screened before they leave.
Receipts, not vibes.
Findings and final reports cite the exact files and lines behind them, checked against what the agent actually read. A finding without evidence gets sent back. You review the evidence, not just the conclusion.
A truly AI-native code editor.
When you open a file, you and your agents can edit the same live document together. Agent edits merge into the file you have open while you type, without clobbering your draft, and every change remembers who made it, down to the chat and turn that wrote it.
You lead. Specialists do the legwork.
A coordinator runs the show and hands work to specialists who research, plan, build, review, and push back. Independent work runs in parallel, each specialist capped at the tools its role allows, on the models you assign — your strongest for the coordinator, something cheaper for the workers. You direct the work and make the calls.
Local AI makes your frontier model cheaper, faster, and smarter.
Before your provider sees a word, Bialy, our tuned local engine built on Laya, preps the turn. What the work doesn’t need stays out of the prompt, and off your bill. The right code lands on top, so the agent stops digging. And the right skill is loaded before it starts, so you’re talking to a specialist.
Limits the model can't talk its way past.
On macOS, every command starts inside an operating-system sandbox that keeps your keys and the app’s own settings out of reach. That boundary decides when you get asked: contained work just runs; reaching past it, or a consequential cloud operation, stops for approval. Real, deterministic scanners run alongside, and their findings reach the agent as evidence it can’t hand-wave.
It knows what can't be undone.
More than 300 rules across 50-plus detection packs read the operation itself — emptying a bucket, deleting a namespace, disabling an audit trail, minting a credential, publishing a release — on the AWS, Azure, Google Cloud, Kubernetes and Terraform command lines, in structured cloud calls, and in your shell. Consequential work stops for you; ordinary work inside your project stays quiet.
Your keys never reach the model.
Point at a credential — in a file, a message, a settings field — and the model gets a reference, not the bytes, resolved only inside the call that needs it. Anything you never marked is caught on the way out: model requests, searches, page fetches, commands and MCP calls are screened before they leave, and a match stops for your decision. Nothing has been sent when that card appears.
Your providers, all at once.
Connect straight to the providers you choose, cloud or local, with your own keys, and pay them at their rates. Spread your agents across several at once: no single rate limit, just raw parallel speed.
Ready to run it on your machine?
Download the app, connect your model provider, and start coding. Docs are there when you want them.