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Full-Stack Software Engineer | Product Design | Cross-Platform Development

Sayantan Paul • Peerlist Profile

CVCompass: Accelerate talent matches, find right talents, amplify Quality. This project aims to address the limitations of traditional ATS (Applicant Tracking System) scanners while providing a more intelligent, user-centric solution for job seekers and hiring teams.How CVCompass will help?It compares the candidate's resume with the specific job requirements and background fit needs and provides a more accurate analysis of the candidate's strengths and relevancy with the job requirement.Some Quick Features (Beta Launch):Different options for LLM: Our application relies on different large language models (currently: Llama3, DeepSeek r1) to review and score the candidate's strengths.No Login Flow: Breaking the extra barrier between the user and the application. Get started instantly; no sign-up hassle.Local Processing: As there's no user-centric database, most of your data is stored and managed from your cached memory.Interactive Charts: Dynamic, interactive charts to visualize the candidate's strengths, weaknesses, and key insights to make quick decisions.Personalized Feedback: Job description-specific feedback and recommendations to improve the candidate's performance.

CVCompass

SpaceDev is a blogging platform that facilitates users to publish articles and cultivate connections with the global community, similar to medium platform architecture.
The features of the projects include:
-Integrated Firebase with the Next.js app, allowing for features like user authentication, image storage, and data manipulation.
-Implementing rich text editor for writing articles can enhance the user experience by allowing formatting options like bold, italic, underline, hyperlink, etc.
-Complete responsive design to ensure a seamless user experience across various devices.

SpaceDev.

Deck is a real-time communication platform designed to deliver seamless and efficient messaging experiences, with plans to integrate whiteboard collaboration features in the future.

Key Features
-&gt; Utilised pusher subscriptions and WebSockets to enable instant delivery of messages.
-&gt; Users can send, receive, and accept friend requests via email, facilitating direct communication once the request is accepted.
-&gt; Implemented JWT-based authentication with access token renewal and refresh token verification, ensuring secure and continuous user sessions.
-&gt; Multi-media Sharing: Users can share media files (photos, videos, audio, and documents) alongside messages, enhancing versatility in conversations.

Deck App

Orbit-Space is a Lofi music application designed to enhance focus, relaxation, and productivity. The concept for Orbit-Space was inspired by the Lofi Girl YouTube channel. The goal was to create a scalable full-size app maintaining this Lofi ambiance integrated throughout and adaptable to different features. Key features of the current release:<ul><li>Spotify Authentication: Implemented NextAuth-based Spotify authentication to access the full music playlist and user management.</li><li>Customizable Backgrounds: Users can select from some default live wallpaper options or upload their own, personalizing the visual experience to their liking.</li><li>User-Defined Playlists: Users are not restricted to the default playlists. They can add their customized playlists.</li><li>Custom Themes: There are four accessible custom themes: Nightowl, Countryside, Ghibli, and Cosy Home. By choosing them, the theme-based backgrounds, music effects, and internal Spotify playlists within the app will be updated.</li><li>Sound Effect Controls: Users can control over the volume of each background sound effect, allowing them to create the perfect auditory environment.</li><li>Interactive Cards: Add-ons for users include random quotation cards and a real-time digital clock, these cards are draggable so that users can place them anywhere they wish to.</li></ul><h3>Share your ideas, and let's make the application better:</h3>This project is on continuous integration, which means we will be expanding more new features as well as optimizing the current version. Got ideas or feature requests? Feel free to create a new issue in this repository! I’m always open to fresh ideas and enthusiastic about collaborative development. GitHub repository: <a target="_blank" rel="noopener noreferrer nofollow" class="text-green-500 rich-link break-all link-break" href="https://github.com/SayantanmPaul/orbit-space">https://github.com/SayantanmPaul/orbit-space</a>

Orbit Space

The project VidBhasa's goal is to develop a video dubbing automation that can translate from English or any other foreign language to any of the Indian regional languages that are being targeted. By utilizing existing package libraries and APIs, we are reducing the process of training a multilingual dataset while retaining high accuracy.

The solution implementation involves:
- Converting spoken language in the video into text using Automatic Speech Recognition.
- Translate the text into the target language using Machine Translation Technology.
- Convert the translated text back into speech in the target language using Text-to-Speech (TTS).
- Replacing the original audio in the video with the dubbed speech, ensuring synchronization with the video.
- Add subtitles using the translated text, ensuring they are time-aligned with the video.

Vid Bhasha

The project NoSpam aims to develop a machine learning algorithm that can accurately classify comments on YouTube videos as either spam or legitimate, based on various features such as the text content, the user's history, and the frequency of the comments. By identifying and filtering out spam comments, the project seeks to improve the user experience for viewers and creators on the platform, reduce the amount of unwanted content, and mitigate the negative effects of spam such as phishing attempts, scams, and malware.The project involves the following key steps:<ol><li>Literature Review: Understanding existing spam detection techniques.</li><li>Design of User Interface: Creating wireframes and prototypes for the front end using tools like Figma.</li><li>Implementation: Developing and testing models using frameworks like scikit-learn, numpy, pandas, gunicorn, flask, skllearn, etc.</li><li>Results &amp; Discussion: Analyzing the effectiveness of the spam detection system.</li><li>Future Outcome Expectations: Identifying factors for improving spam detection mechanisms.</li></ol>