Technical Insights & Architecture Papers
Deep-dives on high-throughput backend architecture, Python & Django performance, real-time Voice AI, and resilient database modeling.
Opus Codec Optimization for Real-Time Telephony: Forward Error Correction (FEC), Packet Loss Concealment (PLC), and 20ms Frame Sizing
Maximize speech intelligibility and survive up to 30% packet loss in real-time WebRTC and SIP voice AI streams by fine-tuning Opus in-band FEC, DTX, and dynamic bitrate adaptation.
Dynamic Prompt Prefix Caching in Multi-Turn LLM APIs: Structuring Breakpoints for Sub-100ms TTFT and 80% Cost Reduction
Dramatically accelerate multi-turn LLM agent responsiveness and slash inference billing by engineering deterministic prompt prefix breakpoints across Anthropic and OpenAI caching layers.
Turn-Taking Prediction in Conversational Voice AI: Combining Acoustic VAD with Semantic End-of-Thought (EoT) Classifiers
Eliminate awkward conversational latency and premature interruptions in real-time voice agents by orchestrating acoustic Voice Activity Detection with streaming semantic End-of-Thought classifiers.
Real-Time Token Stream Transformation: Mid-Flight PII Redaction & Aho-Corasick Multi-Pattern Filtering in LLM Pipelines
Streaming LLM responses character-by-character exposes sensitive data before safeguards can intervene. Build zero-latency sliding-window streaming token sanitizers with Aho-Corasick automaton algorithms.
Vector Quantization in pgvector: Scaling to 50M+ Embeddings with Scalar (SQ8) & Product Quantization (PQ)
Storing uncompressed 1536-dimensional embeddings in PostgreSQL explodes RAM requirements and crashes cache hit ratios. Implement Scalar Quantization (SQ) and Product Quantization (PQ) in pgvector 0.7+.
Speculative Decoding in Real-Time Voice Agents: Accelerating LLM Inference with Draft-Verification Pipelines
Sequential autoregressive token generation creates an unavoidable latency bottleneck for large LLMs. Discover how speculative decoding uses lightweight draft models to achieve 2x to 3x token generation speeds in vLLM without quality degradation.