Trust the Result: Inside the 72-Hour Development of ForgeJudge at DogFood 2026

Main Facts

In the high-stakes environment of modern hackathons, the infrastructure supporting the event is often taken for granted. However, the mechanism by which winners are chosen remains one of the most critical and complex components of any competitive coding event. During the intensive DogFood 2026 hackathon, developer and engineer Anand Rajput tackled this very challenge head-on by building ForgeJudge—a comprehensive hackathon judging and management platform—within a grueling 72-hour timeframe.

Rather than relying on existing third-party software or simple spreadsheets, Rajput set out to design a system capable of managing the intricate lifecycle of hackathon evaluations. The core objective of ForgeJudge was not merely to automate calculations, but to establish absolute integrity and trust in the final results. The platform addresses multifaceted judging workflows, including automated and manual judge assignments, strict role-based access isolation, weighted multi-criteria scoring, real-time progress tracking, score normalization, and dynamic leaderboard generation.

Built as a modular monolith to maximize velocity without sacrificing structural clarity, ForgeJudge represents a masterclass in rapid prototyping, agile decision-making, and under-pressure software architecture. The project underscores a growing industry demand for transparent, tamper-resistant, and logically sound evaluation systems in competitive technology spaces.

Chronology: The 72-Hour Sprint

Building a production-grade evaluation platform from scratch in just three days requires rigorous time management, uncompromising prioritization, and a strict adherence to development milestones. Rajput’s 72-hour sprint unfolded through distinct phases of conceptualization, execution, and stabilization.

Hours 0–12: Scoping and Architectural Foundation

The hackathon began with defining the boundaries of the problem. Instead of falling into the trap of feature bloat—a common pitfall in short-duration hackathons—Rajput focused heavily on core necessities. The primary architectural decision was to utilize a modular monolith pattern. This approach avoided the distributed system complexities of microservices (such as network latency, complex orchestration, and fragmented debugging) while maintaining clean code boundaries that would allow components to be scaled or separated later if necessary.

During this initial window, the foundational database schemas were designed. The data model needed to seamlessly link users, roles (participants, judges, and administrators), projects, submission metadata, assignment matrices, and scoring criteria.

Hours 12–36: Core Logic and Role Isolation

With the database schema and framework setup complete, the middle phase of the project focused on the engine room of ForgeJudge: the assignment and evaluation logic.

Hackathon judging often breaks down logistically when judges are accidentally assigned projects they have conflicts of interest with, or when scores are unevenly distributed due to varying grading stringency among judges. Rajput implemented strict role isolation protocols. Judges needed a streamlined interface where they could only view projects specifically assigned to them, completely walled off from accessing the evaluations, notes, or scores submitted by other judges. This prevented groupthink and bias.

Simultaneously, the scoring engine was developed to handle weighted criteria. Rather than relying on flat numerical grades, judges were guided through multi-dimensional rubrics (such as innovation, technical complexity, utility, and presentation) that fed into a centralized normalization algorithm.

Hours 36–60: UI/UX, Tracking, and Real-Time Leaderboards

As the backend functionality solidified, attention shifted toward the user experience for both administrators and judges. Administrators needed a comprehensive dashboard to monitor judging progress in real-time—answering critical questions like: Which judges have completed their reviews? Which projects are still unassigned? How close are we to finalizing the scores?

To solve this, Rajput engineered progress-tracking visualizations and automated score aggregation pipelines. Once all judges completed their assigned evaluations, the system automatically executed normalization scripts to account for harsh versus lenient grading biases across different judging panels, culminating in a trustworthy, dynamically updated leaderboard.

Hours 60–72: Stabilization, Git Discipline, and Deployment

The final 12 hours were dedicated entirely to bug fixing, edge-case testing, and code stabilization. In a 72-hour build, there is no margin for error or luxury of extensive refactoring.

A critical element of this final phase—and indeed, the entire project—was rigorous Git discipline. Rather than treating version control as an afterthought to be pushed once at the end of the hackathon, Rajput used Git as an active safety net. Operating on a relentless loop of git status, git add ., descriptive commits, and frequent pushes, the repository served as an immutable history of progress, ensuring that if an experimental feature broke the build just hours before the deadline, a stable fallback was always moments away.

Supporting Data and Technical Architecture

While the constraints of a 72-hour hackathon meant keeping the technology stack lean and familiar, the choices made for ForgeJudge reflect modern, pragmatic software engineering principles.

The Modular Monolith Approach

In contemporary software architecture, microservices are often touted as the default solution for scalability. However, for a 72-hour development window, microservices introduce an unacceptable overhead of infrastructure configuration, inter-service communication, and deployment pipelines.

By opting for a modular monolith, Rajput achieved the best of both worlds:

• Development Velocity: Code could be written, tested, and integrated locally without complex mocking or distributed tracing.
• Logical Boundaries: Even though the application resided within a single deployment unit, internal packages and modules were strictly separated by domain (e.g., User Management, Project Submissions, Assignment Engine, Scoring & Normalization). This ensures that if ForgeJudge is ever expanded into a commercial product, extracting modules into independent services will be a straightforward refactoring task rather than a rewrite.

Key Functional Modules

1. Assignment Engine: Automatically or manually distributes project submissions across available judges while preventing bias, conflicts of interest, and redundant grading.
2. Role Isolation Layer: Enforces strict access control, ensuring judges operate within a secure sandbox where external scores and peer evaluations remain hidden until official publication.
3. Multi-Criteria Weighted Scoring: Allows administrators to define custom grading rubrics with varying weights per criterion, transforming subjective opinions into quantifiable metrics.
4. Normalization and Aggregation Pipeline: Combines raw scores, adjusts for judge grading variances (standardization), and outputs a mathematically sound final score.
5. Progress & Leaderboard Dashboard: Gives organizers real-time visibility into the judging pipeline and instantly generates public or private leaderboards upon completion.

Official Insights and Developer Perspective

Reflecting on the whirlwind experience of building ForgeJudge for DogFood 2026, Anand Rajput shared key insights regarding the realities of rapid application development and the philosophy of software trust.

"What if the platform responsible for judging a hackathon was itself the thing you had to build?" Rajput noted, highlighting the meta-nature of the project. This self-referential challenge elevated the stakes of the hackathon, turning it from a standard coding exercise into a stress test of software reliability.

Rajput emphasized that the hardest problems in software development are rarely the ones you anticipate at the outset. While writing UI components and database queries is straightforward, designing a system where the output can be implicitly trusted requires deep consideration of human behavior and statistical fairness.

"Can I build a judging system where the result isn’t just calculated, but can actually be trusted?"

This central question guided every architectural choice made during the 72 hours. By removing human error in score tallying, neutralizing grading bias through statistical normalization, and securing data via strict role isolation, ForgeJudge transformed hackathon judging from a opaque black box into a transparent, verifiable process.

Furthermore, Rajput pointed to developer workflow habits as a deciding factor in project survival during tight deadlines. The discipline of micro-commits via Git was not just about backup; it served as a psychological framework that kept development organized, focused, and iterative.

Implications and Future Outlook

The successful delivery of ForgeJudge in just 72 hours carries significant implications for the broader hackathon and technical competition ecosystem.

Raising the Bar for Event Infrastructure

Historically, hackathons have prioritized the participant experience—providing good food, fast Wi-Fi, and generous APIs—while treating the judging infrastructure as an afterthought. Spreadsheets, ad-hoc forms, and manual calculations are still widely used, often leading to administrative bottlenecks, delayed results, and post-event controversy regarding fairness.

Platforms like ForgeJudge demonstrate that robust, trustworthy evaluation infrastructure can be rapidly prototyped and deployed. As hackathons continue to scale in size and prize pools—often involving tens of thousands of dollars—organizers will face increasing pressure to adopt transparent, verifiable judging systems.

Potential Evolution of ForgeJudge

While ForgeJudge was conceived under the pressure cooker of a 72-hour hackathon, its foundational architecture leaves ample room for future expansion. Potential roadmaps for the platform include:

• AI-Assisted Preliminary Screening: Integrating large language models (LLMs) to perform automated code quality checks, README evaluations, or plagiarism detection to assist human judges.
• Cryptographic Result Verification: Utilizing cryptographic hashing or blockchain-inspired ledgers to permanently record final scores, ensuring that leaderboards cannot be altered post-submission.
• Multi-Event Multi-Tenancy: Scaling the modular monolith into a multi-tenant Software-as-a-Service (SaaS) product capable of hosting multiple concurrent hackathons globally.

Conclusion

ForgeJudge stands as a testament to what can be achieved when clear problem definition meets disciplined execution. In compressing complex access control, statistical normalization, and real-time dashboarding into a 72-hour window, Anand Rajput delivered more than just a functional hackathon tool—he provided a blueprint for how technical competitions can achieve absolute trust in their results.