Technical Insights & Architecture Papers
Deep-dives on high-throughput backend architecture, Python & Django performance, real-time Voice AI, and resilient database modeling.
Self-Hosting vLLM on a Single Cloud GPU: Sub-Second Token Streaming & Continuous Batching
Proprietary LLM APIs present severe data privacy risks, rate limits, and unpredictable costs under sustained traffic. Learn how to self-host open-weights models using vLLM, PagedAttention, and continuous batching on a single cloud GPU with sub-second streaming latency.
Semantic Caching for LLMs with Redis & `pgvector`: Slashing API Costs & Sub-20ms Latency
Identical and semantically equivalent LLM queries waste massive API budgets and introduce 1.5s+ latency. Build a high-throughput semantic caching layer using embeddings, cosine distance thresholds, and Redis vector indexing for sub-20ms instant responses.
Multi-Agent Workflow Orchestration: LangGraph State Machines vs. Linear Pipelines with Deterministic Fallbacks
Linear LLM chains break unpredictably when tools fail or models hallucinate argument structures. Explore how to build resilient multi-agent supervisors using LangGraph cyclical state machines, typed schemas, and deterministic human-in-the-loop fallback gates.
Hybrid Search in PostgreSQL: Combining Full-Text Search with pgvector via Reciprocal Rank Fusion
Pure vector cosine distance misses exact alphanumeric SKU/ID matches, while keyword search misses semantic intent. Learn how to architect a native hybrid search engine inside PostgreSQL using tsvector, pgvector, and Reciprocal Rank Fusion (RRF) in a single CTE query.
PostgreSQL `pgvector` in Production: HNSW vs. IVFFlat Indexes for Low-Latency RAG Search
Vector search in high-dimensional embedding spaces degrades query latency without optimized indexing. Learn how to configure HNSW graphs and memory parameters in pgvector for sub-10ms semantic retrieval.