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timsankara/README.md

Tim Sankara: Software Engineer & AI Enthusiast

About Me

I am Tim Sankara, a Software Engineer with a deep interest in creating and deploying cutting-edge AI technologies. Currently, I am expanding my expertise in AI safety to enhance my contributions to the community focused on releasing safe and impactful tech to the world. I am passionate about transforming theoretical AI research into practical, innovative tools and solutions.

Technologies I Work With

  • Python (ML and data-intensive workloads)
  • Rust (Full ecosystem)
  • JavaScript (React, Node)
  • Go (High-performance systems)
  • Java (Scalable back-end systems and large-scale data processing)
  • Cloud (AWS, Azure, GCP, Vercel, Heroku)

Current Focus

Technical AI Engineering at Rookih.com

At Rookih, we have built an awesome interface to help students become super productive in their education. My work also involves developing tools for AI researchers and learners to boost their productivity and streamline processes. By understanding the intricacies of AI research, I am working to translate theoretical concepts into practical, technical implementations. My efforts lie at the intersection of AI capabilities and AI safety, ensuring that advancements in AI are both powerful and secure.

Collaboration

Partnering with Innovators

I am actively seeking collaboration opportunities with researchers and engineers to develop high-quality tools that facilitate AI research and implementation. If you are working on AI safety or related fields and need support in creating practical applications from theoretical concepts, I would be thrilled to work with you.

I am open to new roles and collaborations to help release safe and innovative AI technologies to the world.

Contact Information

I am available for meetings and happy to provide advice on tech-related topics. Let's connect and create something amazing!

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  1. Scientific-Paper-Recommender-System Scientific-Paper-Recommender-System Public

    This repository provides recommendations for research papers based on user queries. It utilizes an embedded model to interpret the user's input, retrieve the most relevant research papers, and disp…

    CSS

  2. ArxivPaperEmbeddings ArxivPaperEmbeddings Public

    Python