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GraphRAG: Knowledge-Graph Retrieval Architecture (2026)

GraphRAG: Knowledge-Graph Retrieval Architecture (2026)

Posted by By MPRAUTO MPRAUTO July 24, 2026Posted inAINo Comments
GraphRAG explained: how knowledge-graph-augmented retrieval beats naive vector RAG - entity extraction, community summarization, graph traversal, hybrid retrieval and the indexing cost trade-offs for enterprise RAG in 2026.
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Google Gemma 3 Explained: Architecture, Benchmarks & Deployment (2026)

Google Gemma 3 Explained: Architecture, Benchmarks & Deployment (2026)

Posted by By MPRAUTO MPRAUTO July 24, 2026Posted inAINo Comments
Google Gemma 3 explained: the open-weights small model family - sizes, context window, multimodality, architecture, benchmark scores, licensing and the VRAM and quantization options for on-device and edge deployment in 2026.
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Multi-LoRA Serving: Architecture for Thousands of Adapters (2026)

Multi-LoRA Serving: Architecture for Thousands of Adapters (2026)

Posted by By MPRAUTO MPRAUTO July 23, 2026Posted inAINo Comments
Multi-LoRA serving explained: how S-LoRA and Punica serve thousands of fine-tuned adapters on one base model - unified paging, heterogeneous batching, adapter routing and the multi-tenant cost trade-offs in 2026.
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Claude Sonnet 5 Explained: Architecture, Benchmarks & Pricing (2026)

Claude Sonnet 5 Explained: Architecture, Benchmarks & Pricing (2026)

Posted by By MPRAUTO MPRAUTO July 23, 2026Posted inAINo Comments
Claude Sonnet 5 explained: Anthropic's most agentic mid-tier model - the 1M-token context window, adaptive thinking, 82.1% SWE-bench Verified, $3/$15 pricing, and how it compares to Opus 4.8 and GPT-5.6 in 2026.
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Expert-Parallel MoE Inference: Serving Sparse Models at Scale (2026)

Expert-Parallel MoE Inference: Serving Sparse Models at Scale (2026)

Posted by By MPRAUTO MPRAUTO July 22, 2026Posted inAINo Comments
Expert-parallel MoE inference explained: how sparse MoE models are served with expert parallelism, all-to-all routing, load balancing, expert-cache and the latency/throughput trade-offs behind GLM, Kimi and DeepSeek in 2026.
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FLUX Explained: Black Forest Labs’ Image-Generation Model (2026)

FLUX Explained: Black Forest Labs’ Image-Generation Model (2026)

Posted by By MPRAUTO MPRAUTO July 22, 2026Posted inAINo Comments
FLUX image generation model explained: Black Forest Labs' rectified-flow transformer, the dev/pro/schnell weight tiers and licenses, GenEval and ELO benchmarks, VRAM to self-host and inference cost in 2026.
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Inkling Explained: Thinking Machines Lab’s 975B Open-Weights MoE (2026)

Inkling Explained: Thinking Machines Lab’s 975B Open-Weights MoE (2026)

Posted by By MPRAUTO MPRAUTO July 21, 2026Posted inAINo Comments
Inkling architecture explained: Thinking Machines Lab's 975B/41B-active open-weights multimodal MoE with 1M context, controllable thinking effort, Apache 2.0 weights, benchmarks and deployment cost in 2026.
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Reasoning-Effort Control in LLM Serving: Thinking Budgets (2026)

Reasoning-Effort Control in LLM Serving: Thinking Budgets (2026)

Posted by By MPRAUTO MPRAUTO July 21, 2026Posted inAINo Comments
Reasoning-effort control lets one model span cheap and deep modes: thinking-budget APIs, token accounting, routing policy, SLO impact and the failure modes of adaptive test-time compute in 2026.
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Kimi K3 Explained: Architecture, Benchmarks, and Deployment (2026)

Kimi K3 Explained: Architecture, Benchmarks, and Deployment (2026)

Posted by By MPRAUTO MPRAUTO July 18, 2026Posted inAINo Comments
Kimi K3 architecture explained: Moonshot's 2.8T open MoE with 16/896 experts, Kimi Delta Attention, 1M context, GPQA 93.5, Terminal-Bench 88.3, pricing and how it compares in 2026.
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Prefill/Decode Disaggregation for LLM Serving: Architecture (2026)

Prefill/Decode Disaggregation for LLM Serving: Architecture (2026)

Posted by By MPRAUTO MPRAUTO July 18, 2026Posted inAINo Comments
Prefill/decode disaggregation splits LLM inference into separate compute-bound and memory-bound pools: KV-cache transfer, SLO isolation, and when it beats co-located serving in 2026.
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  • Ambient IoT in 3GPP Release 19 vs Release 20: Battery-Free Device Architecture for 2026 and Beyond
  • OPC UA Companion Specifications as MCP Tools: Wiring Industrial Semantics into AI Agents (2026)
  • LeRobotDataset v3.0: Chunked Parquet, MP4 Shards and Streaming for Robot Learning Data at Scale (2026)
  • Zephyr vs FreeRTOS vs NuttX in 2026: Choosing an RTOS for Industrial Edge Devices
  • OpenUSD Core Specification 1.1 and v26.08: What CAD, PLM and Digital Twin Teams Decide in 2026
  • ExecuTorch 1.5 On-Device LLM Serving (2026): Batched Scheduling, Cancellation and Off-Graph KV Cache
  • Apache Iceberg v4 vs v3 in 2026: Root Manifests, Single-File Commits, and What to Decide Now
  • AAS Metamodel 3.2 (IDTA Release 26-01): What Changes for Digital Twin Teams in 2026
  • Newton vs MuJoCo Warp vs Isaac Lab: Choosing a GPU Physics Stack for Robotics in 2026
  • AI Agent Sandboxes Compared: Firecracker vs gVisor vs Kata for Untrusted Code in 2026
  • Apache Iceberg v3 Explained: 7 Spec Features That Change Your Lakehouse Upgrade Plan (2026)
  • STEP AP242 vs JT vs QIF: Choosing an MBD Exchange Format in 2026
  • MCP 2026-07-28 Spec Migration: 8 Breaking Changes Stateless Servers Must Handle
  • Kubernetes 1.37 DRA Explained: 6 Changes That Retire the GPU Device Plugin in 2026
  • EU Cyber Resilience Act 24-Hour Reporting: A 5-Step Compliance Architecture for IIoT Vendors (2026)
  • IEC/IEEE 60802 TSN Profile: 7 Decisions Industrial Network Architects Must Make in 2026
  • Asset Administration Shell Submodels in Practice (2026 Guide)
  • Jetson Thor vs Jetson Orin AGX: 2026 Edge AI Upgrade Guide
  • OTel Collector vs Vector vs Fluent Bit: 2026 Telemetry Pipeline
  • ROS 2 Kilted Kaiju to Lyrical Luth Migration (2026): What Breaks
  • Karpenter vs Cluster Autoscaler for GPU Nodes: 2026 Cost Guide
  • Postgres 18 vs TimescaleDB vs ClickHouse for IoT Time-Series (2026)
  • LangGraph vs CrewAI vs OpenAI Agents SDK vs Pydantic AI (2026)
  • Isaac Lab vs Isaac Sim vs Gazebo Harmonic: 2026 Robot Sim Stack
  • vLLM vs SGLang vs TensorRT-LLM in 2026: The Serving Engine Pick
  • OpenAI o3 and o4-mini Explained: The Reasoning-Model Lineage (2026)
  • pgvector vs Qdrant vs LanceDB: On-Prem RAG Vector Search (2026)
  • OTLP vs Prometheus Remote Write: The 2026 Metrics Pipeline Decision
  • TensorRT-LLM vs llama.cpp on Jetson: Throughput, VRAM & Setup (2026)
  • INT4 vs INT8 vs FP8 on Edge NPUs: The 2026 Quantization Trade-off
  • Sparkplug B vs Plain MQTT Topics: Do You Actually Need Sparkplug? (2026)
  • PROFINET vs EtherCAT vs OPC UA FX+TSN: The 2026 Deterministic Ethernet Decision
  • K3s at the Edge: A Production Kubernetes Guide for 2026
  • ArgoCD vs Flux for GitOps at Scale: An Architecture Decision Record
  • Agentic RAG Architecture Patterns: When Plain RAG Is Not Enough
  • OPC UA vs MQTT Sparkplug B: The Industrial Connectivity Decision (2026)
  • Unified Namespace (UNS) Reference Architecture for Industrial IoT in 2026
  • Ollama vs LM Studio vs Jan (2026): Local LLM Runner Compared
  • containerd vs CRI-O (2026): Kubernetes Runtime Decision Guide

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