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What TrustForge is designed to do
Pagariya describes TrustForge as a modular monolith: one Spring Boot application divided into modules for authentication, authorization, submissions, judging, normalization, anomalies, audit, and results. A separate React frontend communicates with it through versioned REST APIs. The stated reason for keeping these modules in one application was to retain clear boundaries without taking on distributed-systems overhead for a hackathon project.
The core idea is to connect each published outcome to the process that produced it. The article describes a chain from a submission version to an assignment, evaluation, normalization run, anomaly, audit event, and result snapshot. Rather than displaying a winner without context, the system is meant to retain the inputs and events needed to explain how the result was reached.
How judge assignments and scores are handled
Assignments account for constraints
The described assignment process considers judge capacity, minimum project coverage, declared conflicts, workload balance, and repeatability. An assignment record is intended to preserve its eligibility and conflict rationale, capacity, coverage, fairness value, algorithm version, and random seed. Pagariya reports acceptance checks for conflict exclusion and coverage; those checks are described by the author, not independently reviewed.
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Keeping the rationale and seed alongside an assignment can help organizers reconstruct why a judge was assigned to a project. It does not settle whether the constraints or the chosen fairness measure are appropriate: those are policy decisions that affect the resulting assignments.
Scores are normalized per judge
The article says TrustForge uses each judge’s mean and standard deviation to convert scores to z-scores, while retaining raw scores. The author says the system handles a zero standard deviation explicitly and leaves missing evaluations missing. This approach is intended to account for judges who use scoring scales differently, but normalized scores still depend on the method and assumptions selected. Retaining raw values makes it possible to inspect the original evaluations rather than seeing only transformed numbers.
Judging and community votes are put on compatible scales
Pagariya recounts correcting an earlier formula that added a normalized judging score directly to a raw community vote count. Those quantities are on different scales, so their sum would not have a meaningful interpretation. The described correction maps both components to a 0–100 scale, then weights judging at 80% and community voting at 20%.
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That is the method stated in the article, not an independently audited scoring standard. A common numeric range makes weighted arithmetic interpretable, but does not by itself establish that the conversion, weights, or resulting ranking are fair. Those choices need to be explicit and justified for the event using them.
What the audit chain can—and cannot—show
The article reports a SHA-256 hash chain for audit events, starting from a GENESIS value. Each event includes the previous hash, its current hash, actor, action, entity, timestamp, request ID, and payload. Verification recomputes the chain; Pagariya says a test changed an earlier payload and confirmed that verification failed.
This design can make alteration of chained event contents detectable when the chain is verified. A hash chain alone does not prove that every relevant action was logged, prevent every kind of deletion, or establish that the records are independently trustworthy. The article does not report an external security assessment.
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How access is divided between roles
Pagariya’s account distinguishes three backend roles:
- Organizer: manages assignments.
- Judge: accesses their own assigned evaluations.
- Participant: uses the public gallery and voting.
The article emphasizes that hiding an organizer control in the interface is not sufficient protection; the backend must enforce permissions. The author reports that a judge attempting to access organizer-only assignments received HTTP 403. The account also reports expiring access tokens, rotating refresh tokens, and rejection of reuse of an old refresh token. These are reported project checks, not the result of an independent security review.
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What was verified, and what remains unfinished
Pagariya reports a local API smoke test that passed ten checks, covering the seeded gallery, login, dashboard, assignment coverage, normalization, audit verification, results, certificate verification, and role isolation. The article also describes focused tests for deterministic normalization, audit tamper detection, assignment conflicts and capacity, and duplicate voting.
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The author says Docker was unavailable in the acceptance environment, so Docker Compose was not verified there. The article separates items marked VERIFIED from those marked NOT VERIFIED / BLOCKED BY ENVIRONMENT; passing local checks should not be read as proof that deployment was tested.
The demo uses a deterministic in-memory store as a replaceable persistence layer, not production persistence. PostgreSQL and Flyway appear in the deployment design, but the author lists full persistence of the judging model, assignment runs, and normalization datasets as future work. Other unfinished items include database-level immutable result snapshots, a real pairwise-ranking model in place of a read-model placeholder, property-based tests, and explicit final weights and normalization ranges in code and tests.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to conclude from the project account
TrustForge’s useful design question is how to explain a hackathon result after it has been published. The described system tries to answer it by preserving assignment decisions, original and normalized scores, audit events, and result snapshots. That is a meaningful transparency goal, but explainability depends on the completeness of the recorded process and the defensibility of its policies—not merely on having a visible formula or a hash chain.
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Pagariya’s article is a first-person account of a DogFood 2026 project. It reports particular tests and design choices, but does not establish independent verification, production readiness, adoption, performance, or improved judging fairness.
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