WAL-Fix Forensics for SQLite Apps

Who it's for

Developers and ops engineers running SQLite in production (e.g., Tailscale, self-hosted apps) who today cope by manually inspecting WAL files, restoring from backups, or shipping hotfixes with data loss.

The problem

time (debugging corruption can take days/weeks) and legal (data loss in regulated industries like health/finance can trigger compliance breaches).

How to build it

CLI tool + library; scans a SQLite directory, parses WAL headers/frames, detects known corruption fingerprints (WAL reset, torn writes), produces a diff of affected pages and a repair SQL script with a rollback journal; integrates via a one-command hook in backups/monitoring.

How it makes money

SRE/platform teams at startups with production SQLite pay $100–500/month for a support contract that includes the CLI, a Slack alert on corruption signatures, and priority repair-script generation; they'd pay because a single data-loss incident costs far more in downtime and regulatory fines, and the free option (SQLite's own `PRAGMA integrity_check`) doesn't repair corrupted WALs.

Why it doesn't exist yet

Incumbents (SQLite team, cloud DB providers) skip it because SQLite is embedded, so there's no central vendor to sell tooling; the bug is rare enough that they focus on core engine fixes. An indie can packaging the forensic playbook into a reusable diagnostic tool for the long tail of affected apps.

First users

Tailscale's post-mortem is #1 on HN today, so every SQLite-heavy project maintainer is suddenly aware of WAL corruption risk; they'll try the tool to audit their own DBs.

Build size

1 person x 6 weeks (CLI parsing WAL format, corruption detection heuristics, repair script generator, rollback logic; excludes GUI or multi-database orchestration).

Biggest risk

The SQLite core team ships an official `wal-repair` utility in the next release, making a third-party tool niche.

Conditions for a hit (all 3 required)

• Given a SQLite database directory with a WAL file, outputs a JSON report listing the exact WAL header fields that deviate from spec (e.g., checksum mismatch, page count reset) within 10 seconds per GB of WAL.
• Generates a `repair.sql` script that restores the database to the last consistent checkpoint, with a `--rollback` flag that reverts changes atomically, and prints the number of lost transactions (e.g., 'lost 3 transactions from 2026-08-12 14:32:00')
• After running, prints a one-page human-readable diff of which tables/rows changed, plus a timestamped backup of the original WAL and DB files to a `./wal-fix-forensics/<timestamp>/` folder.

How it's judged (in 6 months)

GitHub 500 stars or 100 paying users (Stripe revenue) or public mention in a major SQLite-related blog post (e.g., sqlite.org forum, Tailscale blog)(judgment date 2027-02-13)

AI self-confidence 50/100 — self-reported likelihood of meeting the criterion, not a business success rate