Tag: llm

A technical comparison of STATE-Bench, AMA-Bench, GroupMemBench, MemoryArena, and EvoMemBench — five benchmarks proving AI agents cannot remember and the graph architectures that might fix it.

Evidence-based analysis of GraphRAG's failure modes — benchmarks where it underperforms vanilla RAG — with concrete mitigations and scenarios where graph-based retrieval dominates.

How LazyGraphRAG collapses GraphRAG indexing costs from $30,000 to $30 by deferring entity extraction to query time — with a practical guide to when lazy beats eager.

Empirical analysis of token consumption in LLM-based multi-agent systems reveals that 59.4% of tokens go to code review, not generation — and a 2:1 input-to-output ratio exposes the 'communication tax' haunting agentic workflows.

How knowledge graphs solve the structural context gap that vector databases leave open — GraphRAG architecture with Cypher traversal patterns and LLM context serialization.

GraphRAG enhances retrieval-augmented generation by grounding LLM responses in structured knowledge graphs instead of flat vector embeddings.

Build AI-powered applications with Neo4j as the knowledge layer — from graph-backed RAG to autonomous graph agents.

Comparing the five major approaches to building agentic AI workflows — when to use monolithic frameworks, multi-agent orchestration, or the emerging LLM router pattern for autonomous tool selection.

Microsoft's DELEGATE-52 benchmark proves frontier models corrupt documents beyond 20 interactions. One week later, Google confirmed criminals used AI for a real zero-day exploit. The two findings describe the same gap from opposite ends.

Run 3 specialised LLMs on a single DGX Spark in under 2 minutes with 100+ tok/s throughput. Production orchestration patterns revealed.

DeepSeek V4 ships two open-weight MoE models — a 1.6T Pro and a 284B Flash — with novel sparse attention, FP4 quantisation, 1M token context, and validated Huawei Ascend NPU support. Here's what actually changed.

Alibaba released Qwen3.6-35B-A3B on 16 April 2026, the first open-weight model in the Qwen3.6 series. The benchmarks show real gains in agentic coding, but the architecture is unchanged from Qwen3.5 and the red flags warrant scrutiny.

How CoreCoder reverse-engineered Anthropic's Claude Code from 512K lines into a minimal 950-line implementation, revealing the essential architecture of modern AI coding agents.

A 26-person startup spent $20M training a 400B MoE model on 2,048 B300 GPUs — and produced the strongest open reasoning model outside China. Trinity-Large-Thinking ranks #1 on τ²-Airline at 1/28th the cost of Claude Opus 4.6.

A technical comparison of vLLM and SGLang, the two leading open-source LLM inference engines, covering architecture, performance, and when to pick each one.

The fastest-growing CNCF category — ML serving, vector databases, and the open AI stack running on Kubernetes.

Gemma 4 brings frontier-level multimodal intelligence to open-source — with models ranging from 2B to 31B parameters, MoE efficiency, and native audio support for edge devices.

How LiteLLM, OpenCode, and Oh-My-OpenAgent form a multi-agent system where 10 specialised agents route through 25+ models across 3 providers with automatic fallback.

Professional guide to implementing LiteLLM proxy for multi-provider LLM integration in GraphWiz.AI, featuring production deployment, cost optimization, and advanced routing strategies.

A practical guide to engineering prompts for autonomous AI systems that plan, act, and iterate toward goals.

Deploy Qwen's latest agentic coding model with vLLM on NVIDIA DGX Spark. Complete configuration for tool calling, extended context, and optimal performance on the GB10 Grace Blackwell Superchip.

A practical guide to deploying production-ready LLM inference using vLLM on NVIDIA DGX Spark hardware, covering configuration, troubleshooting, and performance optimization.

Comprehensive guide to prompt engineering techniques that work reliably in production environments, including chain-of-thought, few-shot learning, and output formatting strategies.

Comprehensive guide on training artificial intelligence for software testing: architectures, pedagogical strategies, and validation frameworks

Interactive exploration of prompt engineering techniques for Knowledge Graph generation using LLMs