Aneesa Shahmand@Aneesa
4
Events attended
2
Submissions made
Pakistan
1 year of experience
About me
I am a MERN stack developer and 3rd year Computer Science student, who builds full-stack web applications and AI-powered products from idea to deployment. I got into development because I had ideas I couldn't build yet — so I learned untilL I could, and that drive still shapes everything I work on.
🤝 Top Collaborators
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🤓 Latest Submissions
BiasTrace
ML and data science students routinely train models on datasets with undocumented demographic imbalance, sampling bias, or geographic skew — and only discover it during peer review, a thesis defense, or after deployment, when it's too late to fix cheaply. BiasTrace catches this before training starts. Users paste a dataset's README, description text, or a Kaggle/HuggingFace URL. GPT-4o analyzes the methodology for known bias red flags — small or single-source samples, missing demographic documentation, no external validation set — and returns a 0-100 bias-risk score with severity-rated, evidence-backed flags. What makes BiasTrace different from a simple AI classifier: when a URL is provided, it uses Bright Data's live web integration to pull the dataset's actual public discussion or issues page and check whether bias has already been reported by other researchers. This grounds the analysis in real evidence instead of pure model judgment. Every audit closes with a concrete verification checklist and is saved to the user's private history for future reference. Built entirely in native.builder — the frontend, Supabase-backed auth and database, and Edge Functions were all generated and iterated on inside the platform, from PRD through deployment, with GPT-4o and Bright Data wired in as external integrations. Target users: ML/data science students, thesis writers, and early-career researchers who need a fast, evidence-based check before they train on or cite a dataset.
10 Aug 2026
LITTLE HERO AI
Storytelling has always been a fundamental part of childhood, helping to nurture imagination, creativity, and learning. Traditional books and stories, however, often follow a generic narrative that may not fully engage every child. Children naturally seek personalized experiences that make them feel important and involved. Recognizing this gap, Little Hero AI was developed as an innovative platform that combines AI-powered storytelling with visual personalization, allowing children to become the heroes of their own stories.By enabling users to upload a child’s photograph, select a role, and generate a customized story and avatar, Little Hero AI creates an immersive experience that stimulates imagination while offering educational and motivational benefits. This project not only provides entertainment but also helps children explore various roles, visualize themselves achieving success, and develop a deeper interest in reading and learning.Little Hero AI is primarily designed for children aged 3–12 years, but its appeal extends beyond the child alone. The target audience includes: 1. Children (3–12 Years) The primary users benefit from engaging, interactive stories that help improve imagination, literacy, and cognitive skills. Personalized narratives keep them interested and motivated to read more frequently.2. Parents and Guardians Parents can use Little Hero AI to encourage reading and creativity in their children. The platform offers a fun and educational activity that strengthens parent-child interaction.3. Educators and Schools Teachers can integrate Little Hero AI into classrooms or reading programs to provide a personalized and creative learning experience. By using the platform, educators can encourage students to explore different roles, develop empathy, and improve literature by using this agentic software build by using coral protocol.
21 Sep 2025
