Tag feed

#nahid-mahmud555

12 posts0 followers

Explore Hashnode

Alternatives

NMNahid Mahmudnahid-mahmud555.hashnode.dev15h ago · 6 min read

Why I Built a Simple Browser-Based Cypher Terminal

Why I Built a Browser-Based Cypher Terminal (And Everything I Learned Mastering Neo4j Today) Learning graph databases like Neo4j is an essential step toward building advanced AI retrieval systems such

0

NMNahid Mahmudnahid-mahmud555.hashnode.dev1d ago · 2 min read

Knowledge Graph Architecture

A comprehensive breakdown of core concepts, system design principles, and technical architectures for building production-grade Knowledge Graphs. Key Takeaways & Mastered Concepts 1. When to Use a Kn

0

NMNahid Mahmudnahid-mahmud555.hashnode.dev5d ago · 2 min read

AI needs complex Float numbers to think... But servers need 0 and 1 to survive!

🚀 BitEmbed Engine: Vector Quantization & Semantic Search Simulator An interactive Python-based simulator built in Google Colab to explore how modern vector databases handle memory optimization using

0

NMNahid Mahmudnahid-mahmud555.hashnode.dev5d ago · 3 min read

Deep Dive into Vector Search: ANN, HNSW, and Production Optimization

Today, I took a deep dive into the core mechanics of modern AI systems, vector databases, and high-performance search architectures. Scaling search to millions of records requires smart engineering tr

0

NMNahid Mahmudnahid-mahmud555.hashnode.devAug 11 · 2 min read

Building a Hybrid Search Engine from Scratch: Combining BM25, Vector Search, and RRF

When building search systems, developers traditionally face a dilemma: Should we rely on exact keyword matching (like traditional databases or Lucene/BM25) or semantic context understanding (like mode

2

J

NMNahid Mahmudnahid-mahmud555.hashnode.devAug 10 · 3 min read

How Computers "Read" and Match Text: A Quick Vector Search Experiment

Ever wondered how modern AI systems, recommendation engines, and advanced search bars (like those on Notion, GitHub, or Google) instantly understand what you're looking for—even if you don't use the e

0

NMNahid Mahmudnahid-mahmud555.hashnode.devAug 4 · 2 min read

Building a Production-Grade Document Parsing Pipeline: From Concept to Execution

In the world of generative AI and retrieval systems, most Large Language Model applications fail not due to model limitations, but because of poor data quality at the ingestion stage. As the classic e

0

NMNahid Mahmudnahid-mahmud555.hashnode.devJul 31 · 4 min read

Deep-Dive into LLM Plumbing: Tokenization, Normalization, and RAG Architecture Mechanics

Building production-grade AI systems and Retrieval-Augmented Generation (RAG) pipelines looks glamorous from the outside, but true engineering lies deep in the invisible plumbing. If you've ever built

0

NMNahid Mahmudnahid-mahmud555.hashnode.devJul 30 · 3 min read

Building a Production-Grade RAG Pipeline from Scratch: Granularity, Stable IDs, and Parent-Child Linkages

Building a Production-Grade RAG Pipeline from Scratch: Granularity, Stable IDs, and Parent-Child Linkages When building Retrieval-Augmented Generation (RAG) systems, calling a vector database or an LL

0

NMNahid Mahmudnahid-mahmud555.hashnode.devJul 29 · 5 min read

Building a Custom BM25 Information Retrieval Search Engine From Scratch in Python

Today marked a significant milestone in my data engineering journey as I successfully built, tested, and deployed a fully functional, domain-specific information retrieval search engine from the groun

0

#nahid-mahmud555

Search Hashnode

#nahid-mahmud555

Explore Hashnode

Why I Built a Simple Browser-Based Cypher Terminal

Knowledge Graph Architecture

AI needs complex Float numbers to think... But servers need 0 and 1 to survive!

Deep Dive into Vector Search: ANN, HNSW, and Production Optimization

Building a Hybrid Search Engine from Scratch: Combining BM25, Vector Search, and RRF

How Computers "Read" and Match Text: A Quick Vector Search Experiment

Building a Production-Grade Document Parsing Pipeline: From Concept to Execution

Deep-Dive into LLM Plumbing: Tokenization, Normalization, and RAG Architecture Mechanics

Building a Production-Grade RAG Pipeline from Scratch: Granularity, Stable IDs, and Parent-Child Linkages

Building a Custom BM25 Information Retrieval Search Engine From Scratch in Python

Trending tags this week