From Prompt Engineering to Loop Engineering: The Evolutionary Chain The first three years of the large language model era followed a methodical progression. Prompt engineering dominated the conversation first — engineers spent hours crafting instructions to extract better responses from GPT-3 and Claude. Then came context engineering, shifting the focus …
Editorial team 
Three Protocols Want Your GPU Fabric. Pick Wrong, Pay 30%
On May 6, 2026, NVIDIA donated Multipath Reliable Connection (MRC) to the Open Compute Project, turning a closed Spectrum-X optimization into an open RDMA transport. That act ended the decade-long pretence that AI cluster networking was settled. Today three open transport protocols — UEC’s UET, Google’s Falcon, and NVIDIA’s MRC …
Cut GPU Costs: Kubernetes Hard Isolation Guide 2026
In June 2026, NVIDIA merged two pull requests into its open-source Kubernetes AI scheduler that finally shipped container-level GPU memory hard isolation, ending a decade where multiple tenants sharing one accelerator could silently oversubscribe each other into OOM crashes. The KAI Scheduler now relies on HAMi-core, a CUDA interception library, …
3 GPT-5.6 Tiers: Sol, Terra and Luna Strategy Guide
OpenAI announced the GPT-5.6 series on June 26, 2026, splitting the release into three capability tiers — Sol, Terra, and Luna — each with distinct pricing, speed, and reasoning profiles. The lineup delivers state-of-the-art results on coding and security benchmarks while introducing a new naming system and subagent-powered reasoning. Key …
Continuous Batching: Why 60% of Your GPU Sits Idle
Naive static batching leaves roughly 60% of an H100 GPU idle during LLM serving, because finished requests hold their slots until the slowest sequence in the batch completes. Continuous batching — iteration-level scheduling introduced in the Orca paper and now the default in vLLM, TensorRT-LLM and TGI — fixes this …
50% Lower 70B LLM Inference Costs: Quantization Guide
A 70B-parameter model in FP16 burns roughly 140 GB of VRAM just to hold its weights. Compress those weights to 4-bit integers and the footprint collapses to about 35 GB — small enough to fit on a single 80 GB GPU with room left for the KV cache. That fourfold …
Reasoning Models Cost 15x. Adaptive Depth Saves 60%
Send one complex query to OpenAI o3 and it can burn 10,000 to 50,000 reasoning tokens before emitting a single visible word — all billed at the $60-per-million output rate, all hidden in a thinking block that never appears in the response (source). Reasoning models are the single biggest line-item …
Agent Observability: 83% Build, 11% Ship, Nobody Knows Why
Cisco’s 2026 State of AI Security report found that 83% of enterprises are actively building agentic AI systems, yet a March 2026 industry survey put the share running at production scale between 11% and 14% — a 54-point gap that is widening, not closing (Synapt-AI, June 2026). McKinsey’s 2026 State …
Prefill-Decode Disaggregation: NVIDIA’s 7x Inference Fix
Every LLM inference request is two workloads pretending to be one. Prefill processes your entire prompt in a single compute-bound forward pass — it wants raw FP8 TFLOPS. Decode generates tokens one at a time by streaming KV cache tensors through memory — it wants HBM bandwidth. Running both on …
K8s GPU Clusters Waste 95% of Capacity — Top Teams Don’t
Production Kubernetes GPU clusters across AWS, GCP, and Azure average just 5% utilization — with CPU at 8% and memory at 20%. CPU overprovisioning jumped from 40% to 69% year over year. GPU prices are rising for the first time since 2006. The top-performing clusters sustain 49% GPU utilization, proving …
AI Agents Crash at Minute 15. Durable Execution Fixes It
AWS Lambda kills any process that runs longer than 900 seconds. If your research agent hits minute 15 mid-synthesis, the runtime hard-kills the container, the in-memory context evaporates, and the $4.50 of compute you just spent on 40,000 tokens of scraped and summarized content becomes a 500 error to the …
Editorial team 
LLM-as-Judge Has a Reliability Problem in Production
The headline number everyone quotes for LLM-as-Judge is 80%: GPT-4 agrees with human evaluators roughly 80% of the time, the same rate at which human annotators agree with each other. That figure comes from Lianmin Zheng and colleagues’ 2023 MT-Bench study, built on about 3,000 expert votes, and it made …