Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
LLMLingua compresses prompts up to 20x with ~1.5pt accuracy drop. We dissect LLMLingua-2's BERT-classifier approach, where it dominates (long RAG, doc Q&A) and where it breaks (tool calling), and how CallSphere blends it with prompt caching for compounding savings.
Diagnose and fix VoIP call quality issues with expert troubleshooting. Learn MOS scoring, jitter analysis, packet loss remediation, and monitoring.
OpenAI Agents SDK introduced first-class voice handoffs in 2026. Manager vs decentralized patterns, session.update events, and how they work in production.
Anthropic reports 10,000+ active public MCP servers and 97M monthly SDK downloads. Here is what MCP means for production chat agents.
About 8–10% of users will need TURN. Get it wrong and a healthy chunk of your AI calls fail with a black hole and no error. Here is the production checklist.
Index a knowledge base with text-embedding-3-large into ChromaDB, expose a retrieve tool to your voice agent, and ground every answer in real documents — full Python tutorial.
How GDPR Article 6 lawful bases interact with call recording, why voiceprints are biometric data, and what the EU AI Act August 2026 milestones mean for emotion detection on calls.
Routing the right alert to the right human at 3am is hard. Routing AI-agent alerts is harder — half are model regressions, not infra. Here's a working routing tree for voice and chat agents.
Subgraphs are the LangGraph equivalent of microservice decomposition. We unpack namespace isolation, per-subgraph checkpointers, and the MultipleSubgraphsError trap.
