Multi-agent orchestration in 2026 — MCP for tools, A2A for agent-to-agent, LangGraph for stateful flows. Reference architecture, picking criteria, and production patterns.
Vibe coding moved from demos to production in 2026 — what works, what blows up, eval-driven loops, repo-context patterns, and the eight failure modes to instrument against.
Federated learning for IoT — FedAvg vs FedProx vs FedOpt aggregation, secure aggregation, differential privacy budgets, and a 2026 deployment blueprint for edge fleets.
Q2 2026 LLM inference benchmark across vLLM, TGI, SGLang, and Triton — throughput, p50/p99 TTFT/TPOT, KV-cache efficiency, and which engine wins per workload class.
Auditing the viral 2025 claim that AI replaced half of software engineers — BLS data, layoff trackers, GitHub Copilot adoption surveys, and what the numbers actually show in 2026.
What is publicly known about Anthropic Claude Opus 4.6 — capability tier, agentic coding, computer use, MCP integration, and how it positions against GPT-5 and Gemini 3 in 2026.
Inside NVIDIA GB300 NVL72 — Blackwell Ultra GPUs, 288GB HBM3e, NVLink 5 fabric, liquid cooling, and how the rack-scale system reshapes 2026 AI training and inference.
What is publicly known about Anthropic Claude Opus 4.6 — capability tier, agentic coding, computer use, MCP integration, and how it positions against GPT-5 and Gemini 3 in 2026.
Inside Anthropic Cowork mode — desktop agent architecture, MCP plugin model, sandboxed shell, computer use tier system, and how it differs from Claude Code in 2026.
How NVIDIA Spectrum-X re-architects Ethernet for AI training fabrics — adaptive routing, congestion control, BlueField-3 DPUs, and why it competes with InfiniBand at 100K-GPU scale.