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What a local feedback compiler should do
A feedback compiler turns submissions into organized working material—such as issue drafts or recurring themes—without silently replacing source reports or transmitting them to a service. “Local” describes the processing path, not an automatic privacy guarantee: machine access, backups, extensions, telemetry, and optional network features can still expose data.
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Before implementation, write down which decisions the tool supports. For example, it might help a maintainer triage bug reports or identify repeated usability concerns. Give every input field a reason to exist, and make the data path understandable to contributors: what stays on the device, what is written to disk, and what leaves the machine.
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These are architectural options, not rankings of vendors or legal conclusions.
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| Approach | Privacy and exposure | Offline behavior | Complexity | Control and trade-offs |
|---|---|---|---|---|
| Local files with deterministic scripts | The design requires no remote processing, though dependencies still need checking. | Strong fit. | Lowest starting burden. | Easy to inspect and export; the maintainer remains responsible for file permissions and backups. |
| Local database with local processing | Data stays on the machine unless another path is added. | Strong fit. | Requires schema and migration work. | Supports indexing and workflow state; plan for backups and standard-format export. |
| Local processing with explicit remote export | Raw processing can remain local until selected output is shared. | Local work is offline; export requires connectivity. | Moderate. | Preview, redaction, and a clear consent boundary are essential. |
| Hosted intake or remote summarization | Data is transmitted to a service and handled under that service’s terms and settings. | Depends on network availability. | May reduce infrastructure work. | Explain destination, payload, retention, and controls. The examples cited here do not establish provider guarantees beyond each provider’s own statements. |
A simple first version can use one directory of source records, a script that creates normalized records, and a separate output directory for drafts. Move to a local database if searching, workflow state, or concurrent editing becomes difficult to manage in files. In either case, use an ordinary documented export format such as JSON so the data remains portable.
Define a minimal input contract
Begin with only the fields that support the decisions you named. A practical baseline is feedback text, an optional category, a submission date or software version when useful, and a stable source identifier. Keep contact details, account identifiers, IP addresses, and environment information out unless the project has a concrete need for them.
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Some intake channels attach identifying data even when the form does not ask for it. Decide whether to discard, isolate, or redact those fields before analysis. Document the purpose of each retained field; availability alone is not a reason to collect it.
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Do not overwrite the original submission with cleaned text, an inferred category, or a generated summary. Store raw input separately from normalized records and derived artifacts. Give each derived record a stable reference to its source so an authorized maintainer can inspect context and correct mistakes.
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A small workflow can use states such as “new,” “triaged,” “accepted,” and “dismissed.” Pair those states with explicit retention and deletion controls appropriate to the project. A useful implementation pattern appears in OpenSelf’s repository: its documentation describes source-attributed records, scope and sensitivity filters, owner approval for writes, and receipts explaining which records were selected or skipped. These are adaptable ideas, not a standard or independent evaluation.
Automate predictable cleanup, not judgment
Use ordinary code for routine, deterministic work: normalize whitespace and dates, validate known fields, and apply explicit rules for identifiers that the project recognizes. If deduplicating, begin conservatively with exact matches or carefully reviewed near-matches, and preserve references to every original submission represented by a combined record.
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Do not silently discard unusual reports because they are hard to classify. If the tool infers a category, show the assigned category and the reason or rule behind it. Keep original text available to an authorized reviewer under the project’s retention policy. Redaction rules should be inspectable and subject to human review; a model should not be the sole judge of whether personal information is safe to retain or share.
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Add summarization only if it helps—and put it behind review
A local model can be an optional helper for grouping reports or drafting themes, but no particular model, accuracy level, or need for a model is established here. Prefer a local option if the privacy promise requires local processing. If remote inference is offered, make it opt-in and state the exact content sent and the destination service.
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Show generated summaries alongside their source records. A maintainer should verify the summary before it becomes an issue draft or public report; a summary must not silently stand in for its evidence. Zed’s privacy page offers one product-specific example of scoped consent: “AI feedback sharing requires an explicit rating.” Zed also says that sharing once does not grant future permissions. See Zed’s privacy statement; its policy is an example, not a universal legal rule.
Make export a deliberate boundary
Before creating a GitHub issue, CSV, JSON file, or other export, show a preview of the exact included content and any redactions. Let the user decide whether to proceed, and make clear whether the action sends data to a remote destination. Keep the export format ordinary and documented, and explain how to read it.
The local-first paper Local-First Software: You Own Your Data, in spite of the Cloud recommends standard-format export for longevity and clear communication about local storage versus backend transmission. Its practitioner guidance says: “For mobile or desktop applications, try to make clear to users when the data is stored only on their device versus being transmitted to a backend.”
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Inventory network paths and test offline operation
List every outbound action, its destination, the event that triggers it, and its payload. Distinguish a user-initiated export from automatic telemetry, update checks, or remote inference. An example of this style of documentation is YazSes’s privacy statement, which describes its defaults, opt-in paths, and an egress inventory. It is an account of that project’s implementation, not independent certification.
- Run the compiler with network access disabled and confirm that intake, normalization, grouping, review, and local export still work.
- Inspect the application’s dependencies and configuration for network requests that could run automatically.
- For each remaining outbound path, record the destination, trigger, and data sent in user-facing documentation.
- For any sharing action, show the payload before sending and require a deliberate, scoped confirmation.
Local-first does not mean risk-free. Protect access to the machine and its files, account for backups, and disclose optional features that transmit data. The local-first paper also discusses privacy and longevity trade-offs when adding sync; synchronization introduces another data path that needs clear handling and review.
Quick Recap
A practical build sequence
- Specify the purpose and fields. Name the decisions the tool supports, define a minimal input schema, and document why each field is retained.
- Choose local storage. Start with files or a local database based on the project’s workflow. Keep raw submissions, normalized records, and generated artifacts separate.
- Add provenance and review states. Give records stable identifiers, link derived items to their sources, and provide states for triage and disposition.
- Implement transparent deterministic transforms. Normalize known fields, apply reviewable redaction rules, and preserve all source links when combining duplicates.
- Test the complete local workflow offline. Confirm that core processing works without a network and inventory any requests that remain.
- Add optional analysis only when justified. Keep local summarization optional where feasible, and make any remote inference opt-in with an explicit destination and payload.
- Build preview, export, and deletion controls. Let users inspect what will be shared, export in a documented stable format, and manage retained records deliberately.
- Keep a source reference for each derived theme or issue draft.
- Require human review for redaction, grouping, and generated summaries.
- Make backup, deletion, and export workflows visible.
- Explain what is stored locally and what is transmitted.
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