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OmniJudge’s post-mortem describes four separate failure classes: floating-point noise that destabilized score normalization, an undefined Prisma filter that removed a judge-scope restriction, ranking that let unreviewed projects outrank reviewed ones, and container startup problems involving Alpine, Prisma, and SQLite. The incidents and fixes below are Vineet Wagh’s account and the project’s own implementation descriptions—not independently reproduced results or a third-party security audit. Wagh’s October 1, 2026 post-mortem and the OmniJudge repository provide the account and project context.
How did a one-ULP difference become a huge normalized score?
OmniJudge was designed to combine judges’ weighted criterion scores and normalize them across judges, whose scoring strictness can differ. Wagh frames the reported incident around an event scenario of 40 projects, 30 judges, and four tracks; those figures are from his account, not independently verified event statistics. The post-mortem says mathematically equivalent composite scores differed by approximately 2.22 × 10−16—about one double-precision ULP around the described value.
The project uses a Modified Z-score based on the median absolute deviation (MAD): 0.6745 × (score − median) / MAD. The multiplier and formula are described in the project README. Because the denominator was tiny but not exactly zero, an exact mad === 0 test did not catch the near-zero spread. The post-mortem reports that division magnified the rounding difference into a score of roughly 3 × 1015. That is an incident-specific result reported by Wagh, not a general benchmark.
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Mean-and-standard-deviation normalization can be strongly influenced by extreme scores. Median and MAD are more robust to outliers, which can make them useful when judge score distributions differ. Neither method establishes that judges are unbiased: normalization changes scores relative to the comparison group, and its meaning still depends on which judges, projects, and tracks are grouped together.
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| Approach | What it does | Important edge case |
|---|---|---|
| Mean and standard deviation | Centers scores on the mean and scales by standard deviation. | Extreme observations can pull the mean and inflate the spread. |
| Median and MAD | Centers on the median and scales by median absolute deviation. | A zero or extremely small MAD makes division undefined or unstable unless the implementation handles low spread. |
What the epsilon guard changes
The repository describes validating that values are finite and treating MAD below 10⁻⁹ as low spread, returning neutral zero normalized scores in that case rather than dividing by a tiny denominator. The README gives that threshold; it is an implementation choice, not a universal statistical constant. This guard addresses the reported numerical failure, but it does not by itself validate the cohort design or correct biased judging.
Why did Prisma expose projects outside a judge’s track?
Wagh reports that the judge-page query used an undefined trackId when a judge had no assigned tracks. In the described Prisma query, that undefined value caused the predicate to be omitted, so the result was not an empty set: the unassigned judge could see projects across tracks. The post-mortem attributes this behavior to the incident; the repository describes the project’s access-control approach.
The stated repair makes authorization explicit rather than relying on a possibly absent filter:
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- Pass an explicit track-scope
infilter, including when the allowed-track list is empty, so an unassigned judge receives no projects. - Enforce scope in server-side routes, not only in the page or client interface.
- Check team membership to prevent a judge from evaluating their own team’s project; the repository also describes relational conflict-of-interest defenses.
These are project-described safeguards, not evidence of an independent security audit. The key design distinction is that “no assignment” must resolve to deny-all, not to an omitted condition that broadens access.
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Why should reviewed projects rank ahead of unreviewed ones?
A separate reported bug came from treating an unreviewed project as having a neutral normalized score of zero. If reviewed projects had negative normalized scores, sorting only by score could place the unreviewed project above them despite its lack of evidence. The project describes a ranking invariant that puts projects with at least one review first, then compares scores and applies deterministic tie handling. The repository describes this behavior; Wagh recounts the original ranking issue in the post-mortem.
| Ordering rule | Consequence |
|---|---|
| Reviewed-first, then score and deterministic tie handling | Projects with actual reviews remain ahead of projects with no reviews, even when reviewed normalized scores are negative. |
| Raw score-only ordering | A neutral default for an unreviewed project can outrank reviewed projects with negative scores. |
What went wrong when the app started in Alpine containers?
The deployment account centers on an Alpine-based Node image and Prisma’s native engine requirements. Wagh reports a mismatch involving Alpine’s musl libc and OpenSSL 3 after choosing node:20-alpine. The described remedy was to declare a musl/OpenSSL Prisma binary target and install OpenSSL in both build and runtime stages. The post-mortem also reports three distinct startup issues:
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| Reported symptom | Described cause or remedy |
|---|---|
| Prisma engine binary did not match the container environment | Declare the relevant musl/OpenSSL binary target and provide OpenSSL in build and runtime stages. |
| Shell entrypoint failed at startup | CRLF line endings interfered with the shell script’s shebang; the described remedy was line-ending normalization. |
| SQLite database could not be created at its expected path | The /data directory was absent; the described fix was to create it. |
| Fresh ephemeral containers did not initialize the database as expected | The migration and database initialization sequence was changed. |
These are historical project-specific reports, not a universal Docker recipe. Prisma engine targets, Alpine releases, and OpenSSL packaging can change; verify the current compatibility requirements for the exact Prisma, Node, Alpine, and OpenSSL versions before applying the same settings elsewhere. The repository is the project source for its stated architecture and setup: OmniJudge on GitHub.
What does the offline, single-container design trade away?
The project account says the team removed WebSockets and Redis to meet a single-container, air-gapped runtime goal. It describes HTTP transactions, an append-only SQLite audit log, and asynchronous webhooks in their place. These are stated design goals and implementation choices, not independently verified guarantees of complete offline operation or “zero failure states.” Wagh’s post-mortem and the project repository describe the design.
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| Design choice | Operational benefit | Trade-off described by the project |
|---|---|---|
| Offline, single-container approach using HTTP transactions and SQLite | Fewer external services are required for the intended air-gapped deployment. | SQLite serializes writes through a single writer, limiting concurrent write capacity. |
| Redis-backed distributed approach | Redis can provide shared state across application instances. | It adds an external operational dependency, which conflicts with the stated single-container, air-gapped goal. |
| In-memory rate limiting | Does not require a separate shared service for a single instance. | Limits do not coordinate across multiple application instances, as the repository notes. |
The sources provide no independent head-to-head performance benchmark, so these are architectural trade-offs rather than a claim that either design is universally faster or more secure.
How does the project describe CSV formula-injection protection?
The post-mortem says CSV export prefixes formula-like text inputs while preserving negative numeric values, then applies RFC 4180 quoting for commas, quotation marks, and newlines. The repository lists formula-injection protection in the export route. The account describes the approach, but it does not establish that every spreadsheet application interprets the output identically or that every potentially dangerous input is covered. Quoting handles CSV structure; prefixing formula-like text addresses a separate spreadsheet interpretation risk.
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