Cline vs. Muse Code on Privacy: A Contributor Discount Is the Wrong Trade for a New Proprietary Codebase

Say your team is building Ledgerline: a new B2B billing product with a TypeScript API, a Postgres schema, Stripe integration code, and the first version of its authorization model. The repository is not public, the product does not yet have customers, and its useful details are concentrated in exactly the material a coding agent asks to read: migrations, tests, architecture notes, stack traces, environment variable names, and the code around an unfinished feature. In that situation, Meta’s discounted Muse contributor tier is not appropriate if its discount is conditioned on letting Meta use prompts and completions to train future models. Choose Cline instead—but configure it deliberately, because installing Cline alone does not make a workflow private.

The decision is about what leaves the repository

A contributor tier changes the role of the API from inference service to data contributor. Inference means a provider processes a request to return an answer. Training use means the request and response may become material for improving a later model. Those are materially different permissions even if the same agent command, model name, and diff view sit in front of you.

For Ledgerline, the prompt is rarely just “add invoice reminders.” The agent will likely read the reminder job, customer tables, test fixtures containing realistic account shapes, the policy rules that distinguish an admin from a bookkeeper, and terminal output from a failed migration. An agent that works well gathers this context because guessing is worse. That also means a training-enabled plan receives the parts of a young codebase that explain what the company is building and how it works.

This is not an argument that code sent to a model automatically appears in another customer’s output. It is a simpler procurement point: if your company has not explicitly decided that this code may be supplied for model improvement, an individual engineer should not make that decision in exchange for lower token prices. A pricing checkbox is a poor substitute for a data-use policy, a security review, and authority to grant that permission.

Cline is a routing choice, not a privacy guarantee

Cline’s useful property here is separability. It supports multiple model providers and OpenAI-compatible endpoints, so the agent harness and the inference provider do not have to be the same vendor. Cline’s terms say that when you bring your own API keys and send model calls through infrastructure you control, Cline does not receive or store input tokens, output tokens, underlying code, or other user content. That gives Ledgerline a clean boundary: Cline can read and edit the checkout-service repository locally, while your selected model provider receives only the context that Cline sends for a task.

That last clause matters. Cline being out of the path does not mean the model provider is out of the path. If a developer configures Cline with a personal key for a consumer-oriented model account, the repository’s prompts and outputs are governed by that provider’s terms and settings. If the team routes requests through an organization-managed API account, cloud model service, or internal gateway, the team can choose a provider agreement and retention setting that match its rules. Cline gives you that option; it does not choose it for you.

For Ledgerline, make the provider boundary boring and explicit: use a company-owned account, prohibit personal keys in the repository’s agent setup, and record the approved endpoint and model in the engineering handbook. If the company needs a central endpoint, Cline’s enterprise configuration supports organization-wide providers, including AWS Bedrock, Google Vertex AI, LiteLLM, and OpenAI-compatible endpoints. That is more setup than pasting a discounted key into an agent on day one. It is also easier to explain during a customer security questionnaire six months later.

Turn off the telemetry you do not need

There are two data paths to check in Cline: model requests and product telemetry. They are not the same thing. Cline documents telemetry as anonymous usage events rather than code, file contents, file paths, command arguments, conversation content, credentials, or personal information. Its terms nevertheless say telemetry is enabled by default. On a proprietary project, open Cline Settings, find “Cline Telemetry,” and switch it off before the first agent task; the documentation says the change takes effect immediately.

That setting does not stop model calls. It only stops Cline’s product analytics. Do not confuse it with a network control, and do not treat an ignore file as one either. A file exclusion can keep obvious material such as .env, production exports, and signing keys out of the agent’s workspace view, but it cannot make a task private after the agent has been given a copied secret, a pasted log, or a broad terminal command. The practical rule for Ledgerline is to remove secrets from fixtures and logs first, then use exclusions as a second line of defense.

What Muse Code is still good for

Muse Code can still be a reasonable evaluation tool. Meta’s Muse Spark API is designed for agentic workflows and Meta describes support for tool use, long-context work, and multi-agent orchestration. That may make Muse Code worth a trial on a repository whose disclosure is already acceptable: an open-source project, an intentionally public benchmark fixture, a throwaway internal demo built from synthetic data, or a carefully reduced reproduction of a bug.

Keep that evaluation separate from Ledgerline’s actual repository. Create a small test project with fake customer IDs, fake API keys, and a deliberately generic domain model. Measure the things that affect daily use: whether the agent makes a coherent plan, how often it needs correction, whether it leaves a runnable test suite, and whether its shell commands are safe enough to approve. Do not use real migrations or a redacted production incident as the benchmark. Redaction tends to leave the valuable structure intact.

The contributor tier is especially bad for a new codebase because the amount of sensitive context is largest when the least process exists around it. Early repositories mix product strategy into implementation: TODOs name future integrations, tests reveal intended permissions, and comments explain the shortcuts the team has not yet had time to replace. Later, the company may consciously decide that a particular provider’s training terms are acceptable for a limited class of work. That is a policy decision with a scope, not a default for every engineer trying to make an agent run cheaper.

A workable default for Ledgerline

1. Use Cline with a company-controlled provider account or gateway, not Cline-provided credits or an engineer’s personal API key.
2. Before adding the repository, verify the selected provider’s current retention, training, and opt-out terms in writing; classify that provider as approved for proprietary source code or not approved.
3. In Cline Settings, disable “Cline Telemetry.” Do not enable prompt storage unless the team has chosen its S3 or Cloudflare R2 destination, retention period, and access controls.
4. Keep .env files, credentials, production data dumps, signing material, and generated customer exports out of the workspace available to the agent. Use synthetic fixtures for agent experiments.
5. Allow Muse Code contributor-tier trials only on deliberately non-sensitive code. If an evaluation needs the real codebase to be meaningful, pay for a plan whose contract does not grant training use—or do not run the evaluation.

The bad news is that this removes the easiest discount. The good news is that it puts the cost where it belongs: on the inference budget, not on an unpriced right to reuse the source code of a product that does not exist in public yet. Cline is the better fit not because it is inherently private, but because it lets Ledgerline keep the agent, model provider, telemetry, and storage decisions separate—and make each one on purpose.