Deep dives into the technology behind AI voice agents — LLMs, speech-to-text, real-time voice processing, and more.
Showing 27 of 27 articles
How to build responsive AI applications using streaming, WebSockets, and SSE, with practical patterns for token streaming, agent status updates, and real-time collaboration.
How to design APIs for AI agent platforms — comparing REST, GraphQL, and gRPC for agent invocation, streaming responses, tool registration, and multi-agent orchestration.
A technical guide to building real-time voice AI agents using WebRTC for audio transport, speech-to-text, LLM reasoning, and text-to-speech in a low-latency pipeline.
DeepL's 2026 Language AI Report reveals that despite massive AI spending, 83% of enterprises still haven't deployed LLMs or agentic AI for translation, with 35% relying entirely on manual workflows.
Mastercard completes the first-ever live agentic payment transaction in Singapore with DBS and UOB, where an AI agent autonomously booked and paid for a ride to Changi Airport without human intervention.
Figure AI releases stunning video of its Helix 02 humanoid robot autonomously cleaning a living room, picking up clutter, vacuuming, and organizing — marking a breakthrough in whole-body robotics.
A practical guide to building production-grade AI pipelines using LangChain and LlamaIndex, covering when to use each framework, architecture patterns, and lessons from real deployments.
How vector databases and semantic search power AI agent memory, RAG systems, and knowledge retrieval with practical guidance on embedding models, indexing, and query strategies.
A technical guide to modern LLM inference optimization techniques — quantization, speculative decoding, KV-cache optimization, continuous batching, and PagedAttention. Make models faster and cheaper.
Design patterns for building a production LLM API gateway — including intelligent rate limiting, semantic caching, provider fallbacks, and request routing for multi-model deployments.
A guide to observability for LLM-powered applications, covering tracing frameworks, key metrics, debugging techniques, and the emerging tooling ecosystem.
A practitioner's comparison of the leading AI coding agents — Cursor, Windsurf, and Claude Code — covering architecture, capabilities, pricing, and which tool fits different workflows.
A practical guide to deploying and scaling AI agents on Kubernetes — from GPU scheduling and model serving to autoscaling strategies and cost-effective resource management.
Deep dive into the technology behind AI voice agents — ASR, NLU, dialog management, NLG, and TTS.
A practical comparison of AI-powered code review tools in 2026, evaluating CodeRabbit, Graphite, and Claude Code on accuracy, integration, pricing, and real-world developer experience.
The state of on-device LLMs in 2026: NPU hardware, model compression techniques, and real-world applications running AI locally without cloud dependency.
Explore the latest advances in automatic speech recognition and how they enable natural AI phone conversations.
How custom silicon from Groq's LPU and Cerebras' wafer-scale chips are achieving 10-50x faster LLM inference than GPU clusters — and what it means for real-time AI applications.
How to select and optimize LLMs for AI voice agent applications. Covers latency, cost, accuracy, and production deployment.
Instruction data poisoning is one of the most dangerous threats to fine-tuned LLMs. Learn how malicious training samples hijack model behavior and how to defend against them.
How modern TTS technology creates natural-sounding AI voice agents. Covers neural TTS, voice cloning, and latency optimization.
How AI voice agent platforms handle security, HIPAA compliance, PCI-DSS, SOC 2, and data protection. A guide for compliance-conscious businesses.
Advanced RAG patterns including multi-stage retrieval, hybrid search, agentic RAG, GraphRAG, and corrective RAG that are defining production AI systems in 2026.
Why latency matters for AI voice agents, how sub-500ms response times are achieved, and the technology stack behind real-time voice AI.
How AI voice agents handle multilingual conversations, language detection, and cross-language support for global businesses.
How NVIDIA is building a full-stack platform for AI agents with NIM microservices, Agent Blueprints, and purpose-built silicon beyond just GPU compute.
How to track AI voice agent performance. Covers key metrics, dashboards, sentiment analysis, and ROI measurement.
