LLM Basics: RNNs and Sequential Processing
Explains how RNNs — the dominant architecture before Transformers — process sequences token by token, and the fundamental limitations that motivated moving beyond them.
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LLM Learning Basics: Masked Language Model
Explains BERT's core training objective — the Masked Language Model — with formulas, commentary, and examples.
LLM Learning Basics: Pre-training and Fine-tuning
Explains large-scale pre-training and task-specific fine-tuning through the lens of the BERT workflow.
RedisBloom in Production: Fast, Safe Duplicate-ID Checking for User Signup
A practical guide to integrating RedisBloom into a signup duplicate-check flow, covering request routing, sharding, synchronization, rebuild strategies, and monitoring.
Transformer Basics: Encoder and Decoder
A clear explanation of what the Transformer encoder and decoder each do, grounded in the original architecture and illustrated with simple examples.