ai-singhal is an AI/ML researcher and early-career developer with a strong focus on natural language processing, LLM bias mitigation, and algorithmic accountability. Their portfolio demonstrates a keen interest in academic research and rapid prototyping, frequently utilizing Python, Jupyter Notebooks, and modern frontend tools like TypeScript and React. While they excel at conceptualizing and documenting scientific methodologies, their codebase reflects typical research-focused patterns that prioritize experimentation over production-ready software engineering.
Excellent at writing academic abstracts, defining problem statements, and detailing methodology in repository READMEs.
Tends to write monolithic, procedural scripts and single-file applications with high code duplication instead of modular components.
Research repositories frequently lack source code, rely on hardcoded local file paths, and omit dependency management files (e.g., requirements.txt).
Serverless API implementations expose paid AI endpoints to the public web with wildcard CORS and no authentication.
Successfully adopts modern, high-quality stacks (React Query, shadcn/ui, Supabase) to build responsive and robust user interfaces, as seen in Forkprint.
Demonstrates deep understanding of NLP, bias mitigation, and LLM evaluation through well-documented research methodologies and targeted prompt engineering.
Effectively uses standard data manipulation libraries to process CSVs and unstructured data, though implementations often suffer from O(N^2) inefficiencies.
Capable of building functional applications like GeoMazer, but heavily relies on monolithic architectures and fixed-size arrays rather than object-oriented principles.
Able to integrate external AI APIs via serverless functions, but lacks critical understanding of API security, CORS restrictions, and rate limiting.
