[Review] AutoBE Hackathon 2025-09-12 - AI based IT Business Allocation Service

[yeo99]

Conversation Links

https://hackathon.autobe.dev/?session-id=01993e91-7307-745d-808b-09c22c193dd7

1. Requirements Analysis

The requirements were generally clear and well-documented. Functional priorities were specified in the docs/analysis folder, including user roles, exception handling, and performance/security expectations. This level of documentation is impressive for a hackathon project. However, the RBAC model was simplified and may not reflect realistic production needs. More granular permission handling (per project, per resource) should be introduced. Non-functional needs such as HTTPS and JWT were mentioned, but scalability, fault tolerance, and disaster recovery planning were not deeply addressed.

2. Database Design

The Prisma schema was modularized by domain (actors, tasks, projects, notifications) and linked through main.prisma. The ER relationships were valid and easy to understand, especially with the ERD document provided. There was little duplication, but certain schemas seemed over-normalized, potentially causing performance overhead with frequent joins. For production readiness, query performance should be improved with indexes and caching strategies. Handling high-traffic workloads requires optimization that is not yet evident.

3. API Design

The APIs followed RESTful principles for endpoints, methods, and status codes. Swagger documentation was generated, which was useful for understanding the endpoints. The main weakness was the lack of a consistent error response model. Different endpoints might produce differently structured error messages, which complicates client-side handling. Standardizing error codes and response structures, and documenting them clearly, would make the API more reliable. Versioning was not clearly established, which could become an issue for long-term maintenance.

4. Test Code

Unit tests and integration tests were not clearly visible in the repository, suggesting limited test coverage. Business logic validation may exist at a minimal level, but failure paths (invalid parameters, authentication errors, expired tokens) and edge cases were not explicitly tested. Without these, stability cannot be guaranteed. To strengthen the codebase, contract testing and broader coverage of both positive and negative scenarios should be prioritized.

5. Implementation Code

The implementation code was modular and easy to navigate. Files like MyBackend.ts, MyConfiguration.ts, and MyGlobal.ts provided a clear entry point and global configuration. The structure was layered and reasonably readable. However, duplicated logic should be refactored into shared utilities. Performance considerations such as N+1 query prevention and caching strategies were not evident. Input validation and stricter sanitization are required to address security concerns. The directory structure and CI setup were solid, but production-level readiness is not yet achieved.

Estimated Code Proficiency Level

mid

🙌 Overall Review

The strongest aspect of this AutoBE deliverable is the level of documentation and initial architecture design. The docs/analysis folder contained detailed requirement breakdowns, user scenarios, performance expectations, and exception handling. This demonstrates a mature approach to planning, which is rare in hackathon projects. The modular Prisma schema and the availability of Swagger API documentation also contributed positively, allowing participants to quickly understand the system’s structure.

In terms of code quality, the structure is clean, modular, and easy to navigate. The use of Prisma and NestJS conventions shows awareness of modern backend practices. However, the actual implementation aligns more closely with a junior developer level. Testing was minimal, with almost no evidence of coverage for failure paths or edge cases. Error response models were inconsistent, which would make production clients harder to build. Security and performance considerations (input validation, N+1 prevention, caching, scalability) were acknowledged but not fully addressed in the code.

Additionally, from the user experience perspective, the platform itself showed some critical issues: conversations disappeared and reappeared unexpectedly, refreshing replayed previous agent actions, and generated artifacts could not be downloaded. These directly affected workflow during the hackathon.

Overall, AutoBE is a very promising tool for rapid prototyping and hackathon scenarios. To evolve into a production-grade backend generator, the next steps should be:

• Standardizing error responses and introducing API versioning.
• Expanding RBAC and security validation.
• Adding robust test coverage for both success and failure cases.
• Optimizing Prisma schema for query performance.
• Fixing UX issues in conversation management and artifact downloading.

If these improvements are made, AutoBE could bridge the gap between hackathon prototyping and enterprise-level backend reliability.