Sarah Khan@SarahKhan

AI BlogCurate is a Falcon LLM, LangChain and StreamLit based AI app for doing Top AI and Data Science Articles Search, Shortlisting, Analysis and Aspect based Rankings for given Topic. It's goal is to help Data Science and Artificial Intelligence students, enthusiasts and professionals in learning, acquiring skills learning and staying up-to-date through the best articles. AI BlogCurate searches for blogs/articles on the given topic from popular Data Science and AI articles websites including Medium/TowardsData Science, AnalyticsVidhya and KDNuggets. The articles are first short-listed by title relevance using Falcon-2 180B LLM using AI71 API. Then article content is scrapped and analyzed thoroughly through LLM. The scores of articles related to different aspects are computed and an overall rank is estimated. The Top 5 ranked articles urls and article information are displayed along with the scores for different aspects. Following are the Features of BlogCurate - Article Search, information extraction and analysis, from multiple sources. - Initial Shortlisting by Estimating article title relevance to topic. - Ranking and showing Top 5 Articles according to Overall Score. - Title, URL and Publish Date display. - Article Keywords extraction and display. - Publish Date Extraction - Score for Article Content Quality. - Score for Article Content Structure and Organization. - Score for Article Content Relevance. - Score for Article Practical Implementation Demonstration. - Score for Article Publish Date Recency. - Overall Article Score considering all aspects.

The project is designed to streamline the user form-filling process by leveraging database integration and automated validation. Users enter a unique ID, which triggers the system to fetch their relevant data from the database. The system identifies and highlights missing fields, incorrect formats, or any inconsistencies in the entered data. Upon submission, it automatically corrects errors by fetching accurate information from the database, ensuring data integrity and completeness. This process minimizes manual input, reduces errors, and improves efficiency. The project simplifies form handling, delivering a user-friendly and reliable solution for data validation and accuracy during form submission.

The Space Information AI Assistant addresses the challenge of fragmented and inaccessible space data by providing a centralized, AI-driven platform. This innovative solution fetches and organizes data from reliable sources such as NASA APIs, space news outlets, and astronomy image libraries. The platform simplifies complex datasets, making them easy to understand and actionable for researchers, educators, and the public. Users can engage in interactive space-related conversations, stay updated with real-time space news, explore upcoming events, and even track the International Space Station. By harnessing the power of AI, the assistant ensures efficient data retrieval and personalized insights, fostering a deeper understanding and appreciation of space science.