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Yes, for inference after setup. The Feedback Compiler’s author says it runs a model locally through Ollama, so once the model has been downloaded, feedback can be processed without sending it to a hosted inference API. The project is described as turning mixed feedback into reviewable actions, decisions, conflicts, questions, deadlines, duplicate relationships, and source references—not automatically updating the tools where a team works.
What “offline” means for the Feedback Compiler
According to the project author, the Feedback Compiler is a local React/Vite workspace that uses Ollama on the same computer. Internet access is needed for the initial model download; after that, the inference step can run locally without sending feedback to a hosted inference API. This describes offline inference, not a guarantee that installation, model updates, or every surrounding workflow will work without a network.
Microsoft’s separate Foundry Local documentation describes a similar general setup pattern: model files and potentially execution-provider components download on first use, then local inference can work offline. Foundry Local is not the runtime used by the Feedback Compiler; its author identifies Ollama.
What it can organize from feedback
The project is designed to accept varied inputs, including Slack-style messages, email, meeting notes, support tickets, multilingual chats, and transcript excerpts. It groups interpretations into categories intended to help a person decide what the feedback means:
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- Proposed actions: requests that might become work.
- Confirmed decisions: choices or commitments that appear explicit in the source material.
- Conflicts and open questions: contradictory or ambiguous feedback, unresolved questions, and missing information.
- Deadlines: dates or timing requirements found in the feedback.
- Duplicate relationships: requests that may refer to the same underlying need.
- Source references: links between an interpretation and the original messages that support it.
The point is not simply to summarize a pile of comments. The categories help a reviewer ask whether a request should become work, whether a choice has already been made, whether two requests are duplicates or distinct, and what evidence supports a conclusion.
How review and handoff work
The described workflow lets a user load examples, add or edit feedback, compile a batch locally, inspect the source behind each item, and edit, accept, or reject interpretations. The project description also says output is validated and that deterministic post-processing normalizes categories and relationships. Its stated design principle is: “Every proposed interpretation should remain connected to the feedback that supports it, and the person should be able to review it before it becomes work.”
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Handoff buttons prepare text for Slack, email, Linear, Jira, or Notion. They do not sign in, publish, or write to those services. The project does not currently authenticate with these platforms, modify remote work systems, or automatically create tasks, according to its author. Preparing text for a destination is therefore a manual handoff, not an integration.
A separate application, draft2craift, documents a workflow for accepting feedback as test cases and generating runnable regression suites. That is an adjacent example of a different feedback workflow, not a feature of the Feedback Compiler.
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Privacy, security, and evidence limits
The project author says that inference is local after the model download and that the active browser session is temporary: input and output are not retained after the page closes unless explicitly exported. These are the author’s descriptions; they do not amount to an independent code inspection or security test.
Local processing does not, by itself, make sensitive information safe. The project description recommends considering redaction, retention and deletion, protection of exported files, network boundaries, access control, backups and synchronization, threat modelling, and security review before real use. A team should assess those issues for its own environment and policies rather than assume that “local” settles them.
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The author says the demo uses synthetic feedback and that the repository contains no private client feedback or team transcripts. The project description does not establish quantified real-world accuracy, productivity gains, or privacy outcomes, so those should not be inferred from the demo.
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