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A project is not safe just because its save function returned successfully. Data can be changed or lost while it is imported, edited, written, exported, backed up, restored, or migrated. A practical no-loss campaign maps each boundary, defines what must survive it, and verifies that the right writer committed the right data.
Here, “no loss” is an engineering objective supported by explicit invariants and evidence—not a universal warranty. The campaign discussed in the October 1 DEV Community article is a case study, not a formal standard or independently audited proof that every path works. Its #553 lineage records safeguards in some paths while identifying residual restore-ingress work.
What does “no loss” mean across a project’s lifecycle?
It means specifying what must remain true as a project moves through each boundary, then checking that behavior rather than treating “save succeeded” as proof of recoverability. A single project may exist as imported text, an in-memory model, a durable record, a portable export, or a historical backup. Those representations can have different owners and different guarantees.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →The campaign’s central engineering insight is that persistence bugs often arise between these stages. A successful operation at one boundary cannot establish that the next boundary preserves the same content or that a later restore can recover it.
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How should each persistence boundary be handled?
1. Import or restore: admit data before replacing active state
Treat incoming content as a candidate, not as the active project. Validate it against the representation and identity expected at that boundary before allowing it to replace current state. If validation fails or the restore is ambiguous, fail closed rather than silently treating uncertain data as authoritative.
2. Editing: bind the writer to current authority
A project identifier alone may not show that an open editor still contains the current data. An import or restore can replace a project while an older editor remains open; that editor may later write its stale state over the replacement. Where replacement can race with editing, bind the editor’s baseline to the storage target, its authority, and a replacement epoch or equivalent version signal. Before a write, confirm that the editor still has authority to update that target.
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3. Durable commit: advance the baseline only after the write commits
Displaying a replacement in the interface does not make it durable. Keep the prior baseline until the replacement’s durable write has committed; only then should later edits treat the replacement as the saved starting point. If the commit fails, surface the failure and preserve a recoverable state instead of presenting an uncommitted replacement as safely stored.
4. Export and backup: define separate contracts
A canonical export may intentionally omit machine-local metadata so the result is portable. A stored snapshot may instead be intended to recover the project as it existed on that device or at that point in time. Do not assume that two JSON outputs should contain the same fields or satisfy the same equality test.
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For each output, document which fields are portable user data, which local-only fields are omitted, which edits are applied, and what a restore is expected to recover. A reconstructed model can preserve meaning without preserving the original text byte for byte; an exact-source promise requires retaining the original carrier rather than relying on parse-and-serialize alone.
5. Migration: retain the old form until the candidate is validated
Migration should not destroy the only recoverable representation before its replacement is known to be valid. Keep the old representation, validate the migrated candidate, confirm that the migration still has authority over the target, and replace only after those checks succeed. A failed or interrupted migration should leave a path back to the retained form.
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Which kind of fidelity does a boundary promise?
Parsing and serializing can normalize whitespace or key order and may drop fields the model does not support. That may be acceptable for a semantic export, but it is not exact textual preservation. Make the fidelity contract explicit for every path:
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- Textual fidelity: Must the original input remain byte-for-byte available, or may formatting and ordering change?
- Semantic fidelity: Which model fields and user-owned edits must survive even if the serialized text changes?
- Portability: Which data belongs to the user’s project, and which metadata is specific to one machine?
- Writer authority: How does a save detect that a restore or import has made its editor baseline stale?
- Replacement safety: Is the previous representation retained until the new one is validated and durably committed?
These distinctions prevent a misleading test: exact equality is appropriate only when the boundary promises exact equality. If an export intentionally filters local-only metadata, test that the required portable fields survive and that the omitted fields are omitted by design.
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What can browser storage guarantee?
IndexedDB operations run in transactions, and a transaction can abort because of an invalid request, explicit abort, an uncaught handler exception, an I/O error, quota exhaustion, or a user-agent crash. Applications should observe transaction outcomes, surface failures, and exercise recovery paths. See MDN’s IDBTransaction reference, last modified September 22, 2026.
Transaction completion is not an identical disk-flush guarantee in every browser configuration. A completed transaction is meaningful evidence of a successful storage operation, but it should not be presented as a universal promise against every later failure.
Browser storage quotas and eviction behavior vary. MDN distinguishes persistent from best-effort storage; browser storage is not automatically a backup. An application needs an explicit export or backup and recovery story suited to its users. See MDN’s storage quotas and eviction criteria guide.
How can a team audit a persistence campaign?
For each boundary, record the incoming representation, its owning authority, the identity or epoch that binds a writer, allowed transformations, required exact fields, intentional omissions, failure behavior, and evidence that a durable transition occurred. Then test failures that could invalidate those assumptions:
- Interrupted or aborted writes, including quota exhaustion.
- Project replacement while an asynchronous save is in flight.
- Malformed imports and restores whose intended target is ambiguous.
- Recovery from a retained backup after a failed migration or replacement.
- Exports that must omit local metadata while preserving required project data.
These are recommended audit cases, not evidence that every case was independently tested in the #553 campaign. Its acceptance record identifies safeguards in some paths and remaining restore-ingress work, so it should not be read as a finished universal solution.
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