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  • Interactive Guide to RAG Techniques: From Vanilla to Agentic
    ragllmvector-dbaigeminipineconeembeddingsretrievalinteractivetutorial
    A hands-on interactive guide to RAG (Retrieval-Augmented Generation) techniques. Learn by doing - chat with different RAG implementations and understand how each approach works.
  • Understanding Dense Passage Retrieval (DPR): The Engine Behind Modern Search
    dprragembeddingsllmretrievalnlpvector-dbai
    A beginner-friendly deep dive into Dense Passage Retrieval (DPR) - the dual-encoder architecture that revolutionized how machines find relevant information. Learn how DPR powers modern RAG systems through intuitive analogies and interactive demos.
  • Building a Production-Ready Load Balancer from Scratch in Go
    goload-balancersystems-designbackenddistributed-systems
    I got curious about what happens between a user's request and your backend servers. So I built my own load balancer in Go—here's the journey, the gotchas, and how you can build one too.
  • Building a PyTorch RNN-Based Question Answering System
    pytorchrnnneural-networksquestion-answeringdeep-learning
    Learn how to build a simple RNN-based QA system using PyTorch from scratch - covering tokenization, embeddings, and sequence modeling.
  • What is model serialization? How to serialize and deserialize models in Python
    machine-learningpythonserializationdeploymentmlops
    Master model serialization fundamentals and learn when to use pickle vs joblib for production ML systems with hands-on examples.
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  • Arnab Mondal

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