webcoderspeed.com
10 articles
Optimize LLM inference speed by 10×. Master quantization tradeoffs, speculative decoding, KV cache management, flash attention, and batching strategies.
Reduce API payload sizes and latency through compression, streaming, pagination, and field selection. Master bandwidth optimization for global users.
A comprehensive performance checklist across all layers—database, application, caching, network, and edge.
Master Postgres query optimization using EXPLAIN ANALYZE, covering index types, query rewriting, and plan analysis for production databases.
Deploy open-source LLMs at scale with vLLM. Compare frameworks, optimize GPU memory, quantize models, and run cost-effective inference in production.
Techniques for manually and automatically optimizing prompts including structured templates, chain-of-thought, few-shot selection, compression, and DSPy automation.
Master Qdrant collections, payload filtering, quantization for cost savings, batch operations, and backup strategies for production AI systems.
Explore chunking strategies from fixed-size to semantic splitting, including sentence-window retrieval and late chunking techniques that dramatically improve retrieval quality.
Solve tiling (domino, triomino) and optimal string splitting problems combining backtracking insight with DP optimization.
Week 2 mock: solve medium problems then optimize. Practice the full interview loop of brute force → optimal → follow-up questions. Includes common interviewer follow-up prompts.