Hack-A-Gent is an autonomous hackathon coding agent that transforms a Devpost competition into a working software project. It analyzes the competition brief, plans a winning strategy using multiple AI agents, generates production-ready code, and can build and deploy the final application through a simple CLI.
Major progress today! I completed the Phase 1 stabilization of Hack-A-Gent by resolving all remaining test and build issues (1721/1721 tests passing, clean TypeScript build). I also performed a deep forensic investigation into the AI generation pipeline and discovered a critical issue where the router was rejecting recoverable LLM responses before they reached the JSON extraction layer. With the core architecture now stable, the focus shifts to eliminating unnecessary template fallbacks and improving the quality of AI-generated hackathon projects.
Today’s work focused on stabilizing Hack-A-Gent for a production-ready release. I fixed the Improvement Pass so it now completes the full critique -> patch -> build -> judge cycle with measurable score improvements, strengthened template fallback generation with complete project scaffolding and improved styling, added automatic generation of deployment assets like LICENSE and vercel.json, and implemented comprehensive child-process lifecycle management to ensure the CLI exits cleanly after execution. I also streamlined the interview flow by removing unnecessary prompts, improved validation, and verified the project with a clean TypeScript build and extensive unit and feature test coverage.
This is session I added a better parser so I get almost 99% accuracy and also made many improvements in the code generation. Almost done and drained out from all this
Spent this session improving Hack-A-Gent’s reliability and generation pipeline. Fixed a major architectural issue where generated apps were being overwritten by fallback templates, allowing AI-generated pages to be preserved correctly. Also resolved several runtime and build issues, improved validation, and verified the full pipeline by generating, building, and running a real hackathon project successfully. The next focus is improving project quality so the generated apps are more original, context-aware, and competitive in real hackathons.
Today was mostly about making Hack-A-Gent more reliable instead of adding new features. I finally tracked down a long-standing parser issue that turned out to be an architectural problem: the CLI and the analysis pipeline were using two completely different parsers. I merged everything into a single parser, updated the production build to include it, and verified that all parser regression tests and the full test suite still pass. After that, I confirmed the CLI now extracts real judging criteria like Impact and Innovation instead of mistakenly treating navigation links as judging criteria. With parsing in a much better place, I shifted focus toward hardening the overall pipeline by planning phased reliability work around generation, auto-repair, validation, and recovery so the goal is no longer just generating code, but consistently producing projects that typecheck, build, run, and are ready for submission.
This week I focused on making Hack-A-Gent stable and release-ready instead of adding new features. I cleaned up dead code, removed unused files, improved CLI logging and error messages, and verified the project with a full build and test pass. The CLI now parses real hackathon pages, plans projects, and gracefully falls back when AI providers are unavailable. I also ran end-to-end tests on real hackathon URLs to identify the final issues before release. The remaining work is improving template fallback and validation so every generated project is immediately usable, bringing Hack-A-Gent closer to its first stable release.
Might be on final stages, making sure the agent doesn’t blows up in users hands. Taking time but needs to be done, Hope I can ship it by this week
Spent today improving Hack-A-Gent’s long-term direction instead of just fixing bugs. I mapped out a roadmap to turn it into a real Devpost hackathon agent, including a hackathon qualification engine, real Devpost benchmark suite, autonomous repair loop, browser validation, and organizational learning. Also audited the current architecture to identify what already exists, what needs refactoring, and what should be built next before writing more code.
I built Hack-A-Gent, a CLI-based AI agent that helps developers turn a Devpost hackathon challenge into a working project in minutes. Simply paste a Devpost competition URL into the terminal, and Hack-A-Gent analyzes the challenge, extracts the judging criteria and submission requirements, plans an appropriate architecture, and generates a production-ready project scaffold.
You bring your own LLM API key (Anthropic, OpenAI, Google Gemini, NVIDIA NIMs, Ollama, and more), and Hack-A-Gent orchestrates the rest—from understanding the hackathon prompt to generating code, documentation, and a submission-ready starting point.
The goal isn’t to replace developers, but to eliminate the repetitive setup work so builders can spend more time refining their ideas, polishing their projects, and creating a great demo.
