This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
Parakh turns developer skill claims into evidence-backed, cryptographically verifiable assessments.
Developers often list frameworks, technologies, and skills on resumes, but evaluating those claims is time-consuming and vulnerable to superficial keyword matching. Recruiters, engineering hiring managers, and evaluators need a reliable way to understand what a developer has actually built and tested.
Parakh addresses this problem by evaluating public GitHub repositories against specific job descriptions or opportunity briefs. Rather than generating ungrounded AI summaries, Parakh connects skill requirements to code citations, including file paths and line ranges, associated with a specific Git commit.
Crucially, Parakh distinguishes between what the evidence demonstrates, what is partially demonstrated, and what is absent. Missing evidence in a repository is not treated as proof that a candidate lacks a skill. Instead, it is reported as Not Demonstrated in Supplied Evidence.
Who benefits from Parakh?
- Developers and freelancers: Demonstrate hands-on expertise with code evidence and receive actionable recommendations to strengthen their portfolios.
- Recruiters and engineering managers: Explore a more transparent, evidence-oriented approach to technical screening.
- Open-source contributors: Highlight architectural patterns, implementation details, and tested components in their repositories.
Demo & Links
- 🌐 Live Web Application: https://parakh-web.onrender.com
- 🔌 API Health Probe: https://parakh-api-kyyz.onrender.com/healthz
- 💻 GitHub Repository: https://github.com/puneetnith28/Parakh
- 🎥 Demo Video: https://www.youtube.com/watch?v=YJlloKOkdi8
The demo walks through the Parakh interface, the assessment workflow, the evidence map, the action plan, and the system architecture.
How I Built It
Parakh is built as a TypeScript monorepo combining repository analysis, AI-assisted reasoning, structured evidence retrieval, citation validation, and persistent storage.
Core Technology Stack & Integrations
- Frontend: React 18 and Vite, with interactive evidence maps, code citation views, and an audio player interface.
- Backend: Node.js and Express, with typed assessment orchestration, request validation, and security controls.
- Google AI Studio / Gemini: AI-assisted requirement extraction and qualitative evaluation, with structured outputs, rate-limit handling, and deterministic fallbacks.
- Google Gemma: Support for open-weight model configurations where the appropriate model is configured and operational.
- MongoDB Atlas: Persistent storage for reports and assessment data, with indexing and document lifecycle management.
- Sentry: Assessment workflow tracing and privacy-oriented scrubbing of sensitive data.
- ElevenLabs: An audio briefing integration with an in-app playback interface and fallback handling.
- Render: Hosting for the frontend and backend services.
The Five-Step Assessment Workflow
1. Opportunity Ingestion
Parakh processes job descriptions, client requirements, or opportunity briefs into structured skill requirements.
2. Repository Security and Commit Pinning
The system validates repository URLs, applies security checks, and associates repository evidence with a specific Git commit so that findings can be traced back to a defined code version.
3. Multi-Stage Evidence Retrieval
Repository content is examined using lexical ranking and code-structure extraction to identify potentially relevant functions, classes, and code snippets.
4. Citation Validation
Assessment findings are checked against repository evidence and source locations. The goal is to ensure that conclusions can be traced to relevant files and line ranges instead of relying solely on unsupported AI-generated statements.
5. Report Storage and Verification
Assessment reports are stored persistently. Cryptographic integrity mechanisms and controlled report access are intended to make subsequent verification and sharing more trustworthy.
A core design principle runs throughout the workflow: AI should help interpret evidence, not replace it. When a repository does not demonstrate a particular capability, the assessment should communicate that limitation rather than make an unsupported judgment about the developer.
Why Does Open Innovation Matter?
An application built around evidence, auditability, and fairness should itself be open to inspection.
By developing Parakh in the open under the Apache-2.0 license, the project invites developers and evaluators to inspect its implementation, review its assessment logic, examine citation validation, and contribute improvements.
Open-source development also creates opportunities to challenge assumptions about AI-assisted hiring and technical evaluation. Contributors can investigate how evidence is selected, how uncertainty is communicated, and how assessment workflows can avoid treating an incomplete repository as a complete picture of someone's abilities.
For an AI-powered product, openness matters because people should have an opportunity to question how conclusions are produced instead of simply accepting a black-box score.
Prize Categories
Primary Track
- Hacktoberfest Open-Source AI Challenge - Week 2: Side Hustle: An evidence-oriented developer capability assessment platform designed to help connect technical requirements with demonstrated repository work.
Sponsor Prize Tracks
- Google AI Studio: Structured AI-assisted reasoning, requirement extraction, rate-limit handling, and support for Gemma configurations where verified.
- MongoDB Atlas: Persistent assessment storage, indexing, and report lifecycle management.
- Sentry: Assessment workflow tracing and privacy-conscious data handling.
- ElevenLabs: Audio briefing generation and an in-app playback interface.
- Render: Deployment of the frontend and backend services.
These are the integrations associated with the project. Final eligibility for each sponsor category depends on the official rules and the functionality demonstrated by the submitted code and running application.
Closing Thoughts
Parakh was built on a simple conviction: a skill claim is only as credible as the evidence backing it.
By grounding AI-assisted evaluation in repository evidence, traceable code locations, and structured validation, Parakh explores a more transparent way to connect developer capabilities with opportunities.
The project is an ongoing open-source effort, and there is room to improve evidence coverage, assessment quality, verification, and the contributor experience.
Parakh - Prove your skills. Back every claim.
- GitHub: https://github.com/puneetnith28/Parakh
- Live Demo: https://parakh-web.onrender.com
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