BestHub
Sign in
Tagged articles
2011 articles
Page 4 of 21
AI Tech Publishing
AI Tech Publishing
Apr 6, 2026 · Artificial Intelligence

Six Core Components of a Coding Agent Explained with Code

The article systematically breaks down the six essential building blocks of a programming agent—live repository context, prompt shape and cache reuse, structured tool access and validation, context reduction, structured session memory, and bounded sub‑agent delegation—illustrated with a Mini Coding Agent implementation and comparisons to Claude Code, Codex, and OpenClaw.

Coding AgentLLMPrompt Caching
0 likes · 15 min read
Six Core Components of a Coding Agent Explained with Code
Senior Tony
Senior Tony
Apr 5, 2026 · Artificial Intelligence

How to Impress Interviewers with Smart Token‑Optimization Strategies for LLMs

The article explains why simply switching to cheaper large language models fails in interviews and outlines five practical techniques—prompt simplification, context management, output control, model tiering, and caching—to reduce token consumption while preserving answer quality.

Interview TipsLLMToken Optimization
0 likes · 5 min read
How to Impress Interviewers with Smart Token‑Optimization Strategies for LLMs
DeepHub IMBA
DeepHub IMBA
Apr 5, 2026 · Artificial Intelligence

Understanding ADK Multi‑Agent Orchestration: SequentialAgent, ParallelAgent, and LoopAgent Explained

The article explains ADK's three core orchestration modes—SequentialAgent for ordered pipelines, ParallelAgent for independent concurrent tasks, and LoopAgent for iterative quality‑control loops—detailing their suitable scenarios, state‑flow mechanisms, and how to build a complete order‑to‑delivery workflow without writing explicit orchestration code.

ADKAutomationLLM
0 likes · 16 min read
Understanding ADK Multi‑Agent Orchestration: SequentialAgent, ParallelAgent, and LoopAgent Explained
Machine Heart
Machine Heart
Apr 5, 2026 · Artificial Intelligence

Why Karpathy’s LLM Wiki Is Sparking a New Knowledge‑Building Approach

Karpathy’s recently released LLM Wiki, shared as a gist, demonstrates a meta‑framework where raw documents are ingested, an LLM compiles a structured, cross‑linked Markdown wiki, and agents continuously update, query, and health‑check it, offering a scalable alternative to traditional RAG pipelines.

AgentLLMMeta-framework
0 likes · 11 min read
Why Karpathy’s LLM Wiki Is Sparking a New Knowledge‑Building Approach
Old Zhang's AI Learning
Old Zhang's AI Learning
Apr 5, 2026 · Artificial Intelligence

LLM‑Powered Knowledge Management: Insights from Karpathy, Lex Fridman, and kepano

The article analyzes three leading AI experts' approaches to personal knowledge management—Karpathy’s five‑module LLM pipeline, Lex Fridman’s interactive voice‑driven consumption, and kepano’s cautionary separation of AI‑generated content—while detailing the author’s own downstream content‑production workflow that turns raw material into articles, videos, and social posts.

AI agentsContent ProductionLLM
0 likes · 13 min read
LLM‑Powered Knowledge Management: Insights from Karpathy, Lex Fridman, and kepano
PaperAgent
PaperAgent
Apr 5, 2026 · Artificial Intelligence

How Karpathy Builds a Personal Knowledge Base with LLMs: A Step‑by‑Step Blueprint

Karpathy outlines a detailed workflow for using large language models to automatically collect, organize, and continuously enrich personal research materials into an interlinked Markdown wiki, highlighting tools, architecture, and future directions for a self‑improving AI‑powered second brain.

LLMObsidianPersonal Knowledge Base
0 likes · 6 min read
How Karpathy Builds a Personal Knowledge Base with LLMs: A Step‑by‑Step Blueprint
AI Tech Publishing
AI Tech Publishing
Apr 5, 2026 · Artificial Intelligence

Why the First Token Is Slow: A Deep Dive into KV Cache for LLM Inference

The article explains how KV cache eliminates redundant computations in autoregressive LLM generation, detailing the attention mechanism, the O(n²) waste of recomputing K and V, the cache‑based solution, its impact on time‑to‑first‑token, and the memory‑vs‑speed trade‑off.

Inference OptimizationKV cacheLLM
0 likes · 7 min read
Why the First Token Is Slow: A Deep Dive into KV Cache for LLM Inference
AI Step-by-Step
AI Step-by-Step
Apr 5, 2026 · Artificial Intelligence

How Context Engineering Powers Dynamic Business Data Assembly for LLM Agents

The article explains why relying solely on handcrafted prompts leads to hallucinations in LLM agents and presents six concrete context‑engineering practices—XML isolation, hierarchical ordering, KV caching, vector reranking, async memory compression, and minimal few‑shot examples—illustrated with a full e‑commerce refund‑handling case study.

AgentContext EngineeringKV cache
0 likes · 10 min read
How Context Engineering Powers Dynamic Business Data Assembly for LLM Agents
ShiZhen AI
ShiZhen AI
Apr 4, 2026 · Artificial Intelligence

Why Sharing Ideas Beats Sharing Code: Karpathy’s LLM‑Powered Wiki Workflow

Karpathy demonstrates a three‑layer LLM‑driven Wiki that ingests raw papers, code and datasets, automatically maintains structured markdown, and continuously improves through ingest, query and lint cycles, offering a compounding knowledge base that differs fundamentally from traditional RAG retrieval.

AI agentsLLMObsidian
0 likes · 10 min read
Why Sharing Ideas Beats Sharing Code: Karpathy’s LLM‑Powered Wiki Workflow
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Apr 4, 2026 · Artificial Intelligence

Why the Best SFT Checkpoint May Hurt RL Performance: Adaptive Early‑Stop Loss (AESL) for LLM Cold‑Start

The paper reveals that over‑optimizing supervised fine‑tuning (SFT) for large language models can diminish their reinforcement‑learning (RL) potential, proposes an Adaptive Early‑Stop Loss (AESL) that balances accuracy and output diversity during cold‑start, and demonstrates across multiple LLMs that AESL consistently yields superior RL results.

AI trainingAdaptive Early‑Stop LossLLM
0 likes · 11 min read
Why the Best SFT Checkpoint May Hurt RL Performance: Adaptive Early‑Stop Loss (AESL) for LLM Cold‑Start
DeepHub IMBA
DeepHub IMBA
Apr 4, 2026 · Artificial Intelligence

Building Mini-vLLM from Scratch: KV‑Cache, Dynamic Batching, and Distributed Inference

This article walks through constructing Mini-vLLM, a from‑scratch LLM inference engine that tackles the O(N²) attention cost with KV‑cache, boosts throughput via dynamic batching, adds observability with Prometheus/Grafana, supports gRPC, and scales across multiple workers, with benchmark numbers demonstrating its CPU‑only performance.

DockerDynamic BatchingInference Engine
0 likes · 12 min read
Building Mini-vLLM from Scratch: KV‑Cache, Dynamic Batching, and Distributed Inference
AI Open-Source Efficiency Guide
AI Open-Source Efficiency Guide
Apr 4, 2026 · Artificial Intelligence

How to Deploy the Free Open‑Source Enterprise ChatGPT Platform Onyx – Complete Guide

Onyx is a fully open‑source, self‑hosted enterprise RAG platform that integrates any LLM with internal knowledge sources to provide AI chat, intelligent search, custom agents, and automation actions, and this guide walks through its core features, architecture, real‑world use cases, competitor comparison, deployment steps, configuration, best practices, and security compliance.

AI chatbotDeploymentKnowledge Base
0 likes · 15 min read
How to Deploy the Free Open‑Source Enterprise ChatGPT Platform Onyx – Complete Guide
Machine Heart
Machine Heart
Apr 4, 2026 · Artificial Intelligence

SFT Scores Don’t Predict RL Potential: Adaptive Early‑Stop Loss for LLMs

The authors show that high SFT accuracy does not guarantee strong RL performance because over‑fitting reduces output diversity, and they propose Adaptive Early‑Stop Loss (AESL), a diversity‑aware early‑stopping objective that dynamically weights token and subsequence losses, yielding consistently better RL results on multiple LLMs and math benchmarks.

AESLDiversityLLM
0 likes · 11 min read
SFT Scores Don’t Predict RL Potential: Adaptive Early‑Stop Loss for LLMs
SpringMeng
SpringMeng
Apr 4, 2026 · Artificial Intelligence

How to Build a Tencent IMA‑Style AI Knowledge Base for Under $3,000

This article details a cost‑effective AI knowledge‑base project that replicates Tencent IMA functionality using Dify’s open‑source platform, Chinese LLMs (Qwen, DeepSeek, GLM), a Java Spring Boot backend, Vue frontend, multi‑agent orchestration, hybrid on‑premise/cloud deployment, and provides concrete cost and performance estimates.

AI knowledge baseDifyDocker
0 likes · 12 min read
How to Build a Tencent IMA‑Style AI Knowledge Base for Under $3,000
Woodpecker Software Testing
Woodpecker Software Testing
Apr 3, 2026 · Artificial Intelligence

Practical Cost‑Benefit Analysis of Prompt Testing in AI‑Driven QA

The article breaks down the hidden lifecycle costs of production‑grade prompts, defines measurable benefits such as defect‑detection gain, human‑resource value and quality‑gate shift, and introduces a Prompt Investment Decision Matrix to guide when and how many prompts to use, backed by real‑world RPA project data.

AutomationLLMPrompt engineering
0 likes · 7 min read
Practical Cost‑Benefit Analysis of Prompt Testing in AI‑Driven QA
Woodpecker Software Testing
Woodpecker Software Testing
Apr 3, 2026 · Industry Insights

Five Breakthrough Trends Shaping Test Case Auto‑Generation in 2026

The article analyzes five 2026 trends—LLM‑plus‑symbolic execution, multimodal feedback loops, compliance‑embedded generation, low‑code natural‑language builders, and the shift toward AI‑driven quality culture—showing how test case auto‑generation evolves from a helper tool to a strategic quality engine.

AI testingLLMcompliance testing
0 likes · 8 min read
Five Breakthrough Trends Shaping Test Case Auto‑Generation in 2026
IT Services Circle
IT Services Circle
Apr 3, 2026 · Artificial Intelligence

What Are AI Agents? A Complete Guide to LLMs, Function Calls, MCP & A2A

This article explains the core concepts behind AI agents—including how they differ from large language models, their relationship to workflows, the various agent operating modes, and the underlying technologies such as function calls, the Model Context Protocol (MCP), Skills, and the Agent‑to‑Agent (A2A) protocol—providing clear examples and practical comparisons for developers and interviewees.

A2ALLMMCP
0 likes · 32 min read
What Are AI Agents? A Complete Guide to LLMs, Function Calls, MCP & A2A
ITPUB
ITPUB
Apr 3, 2026 · Artificial Intelligence

Why OpenClaw’s Memory Breaks and How seekdb M0 Fixes It

The article analyses OpenClaw’s single‑turn memory design, explains the two vicious cycles that cause memory bloat and forgetting, and introduces seekdb M0’s cloud‑native, two‑stage memory and experience system that decouples memory from context, reduces token costs, and shares practical knowledge across agents.

AIAgentExperience System
0 likes · 16 min read
Why OpenClaw’s Memory Breaks and How seekdb M0 Fixes It
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 3, 2026 · Artificial Intelligence

Why AI Agents Stumble at Code and How a Harness Can Make Them Reliable

The article explains why large‑language‑model agents often lose context and violate architectural rules when generating code, and proposes a Harness framework that treats the repository as an operating system, adds layered linting, pre‑validation, automated verification, and cross‑model review to keep agents on track.

Code GenerationLLMlinting
0 likes · 21 min read
Why AI Agents Stumble at Code and How a Harness Can Make Them Reliable
DataFunTalk
DataFunTalk
Apr 3, 2026 · Artificial Intelligence

How Claude’s Auto Dream Cleans Up AI Memory While You Code

Anthropic’s Claude Code introduces Auto Dream, an automated memory‑consolidation feature that triggers after 24 hours of inactivity and five dialogue exchanges, scanning, merging, and pruning project‑specific memory files to keep the agent’s knowledge base clean and up‑to‑date.

AgentAnthropicAuto Memory
0 likes · 14 min read
How Claude’s Auto Dream Cleans Up AI Memory While You Code
macrozheng
macrozheng
Apr 3, 2026 · Artificial Intelligence

Building Reliable Java AI Agents with JetBrains’ Koog Framework

JetBrains’ new Koog framework provides a native Java Builder‑style API that lets developers define annotated tools and assemble AI agents capable of handling multi‑step tasks such as banking transfers or e‑commerce customer service without writing explicit control flow, illustrating the evolving Java AI Agent ecosystem.

AI AgentAgent orchestrationJava
0 likes · 9 min read
Building Reliable Java AI Agents with JetBrains’ Koog Framework
Tencent Cloud Developer
Tencent Cloud Developer
Apr 3, 2026 · Artificial Intelligence

LLM Showdown in a Three‑Kingdoms Strategy Game: Tactics, Winners, and Surprising Insights

This article details a custom Three‑Kingdoms‑style strategy game used to benchmark nine flagship large language models, explains the game mechanics, evaluates each model's strategic decisions and diplomatic behavior, and reveals how Gemini 3.1 Pro clinched the championship with a clever "坚壁清野" tactic while also sharing the underlying engine architecture and development lessons.

Game DevelopmentLLMStrategy Evaluation
0 likes · 29 min read
LLM Showdown in a Three‑Kingdoms Strategy Game: Tactics, Winners, and Surprising Insights
AgentGuide
AgentGuide
Apr 3, 2026 · Artificial Intelligence

How to Evaluate RAG Systems: Key Metrics and the Ragas Framework

The article explains how to assess Retrieval-Augmented Generation (RAG) projects using the Ragas automated evaluation framework, detailing four key dimensions—recall quality, answer faithfulness, answer relevance, and context utilization—and describes the underlying metrics for both retrieval and generation stages.

LLMMetricsRAG
0 likes · 5 min read
How to Evaluate RAG Systems: Key Metrics and the Ragas Framework
AI Step-by-Step
AI Step-by-Step
Apr 3, 2026 · Artificial Intelligence

Why Building AI Agents Requires a Full System‑Engineering Harness

The article explains that simply scaling large language models cannot sustain long‑running, production‑grade AI agents, and that a dedicated Agent Harness—acting as an operating system with orchestration, memory, governance, tool execution, and feedback loops—is essential for reliable, industrial‑scale automation.

AI agentsAgent HarnessLLM
0 likes · 9 min read
Why Building AI Agents Requires a Full System‑Engineering Harness
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Apr 2, 2026 · Artificial Intelligence

How Large Language Models Can Self‑Improve: A Technical Review and Future Outlook

This article surveys the emerging self‑improvement paradigm for large language models, presenting a closed‑loop lifecycle comprising data acquisition, selection, model optimization, inference refinement, and an autonomous evaluation layer, and discusses current limitations and research directions toward fully autonomous LLM evolution.

AI researchLLMautonomous evaluation
0 likes · 11 min read
How Large Language Models Can Self‑Improve: A Technical Review and Future Outlook
Yunqi AI+
Yunqi AI+
Apr 2, 2026 · Industry Insights

From Code Writer to AI Conductor: How Vibe Coding Lets a Manager Build a Full Product with Just Words

The article recounts how a technically‑savvy manager used the AI‑driven Vibe Coding paradigm to create an end‑to‑end system—content generation, AI客服, ordering, shop management and token monitoring—solely through natural‑language prompts, highlighting the shift from traditional engineering to AI‑guided product development.

AI programmingLLMSoftware Engineering
0 likes · 7 min read
From Code Writer to AI Conductor: How Vibe Coding Lets a Manager Build a Full Product with Just Words
Ray's Galactic Tech
Ray's Galactic Tech
Apr 2, 2026 · Backend Development

How to Build Scalable Enterprise LLM Applications in Go with the Eino Framework

This guide walks through why enterprise‑grade LLM services need a dedicated Go framework, explains Eino’s four‑layer architecture, shows production‑ready code for model gateways, tools, RAG pipelines and graph orchestration, and provides best‑practice recommendations for performance, observability, security, testing, and deployment.

AIEinoEnterprise
0 likes · 47 min read
How to Build Scalable Enterprise LLM Applications in Go with the Eino Framework
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Apr 2, 2026 · Cloud Native

How Kthena Enables Production‑Grade LLM Inference on Kubernetes

This article analyzes the cloud‑native challenges of deploying large‑model inference on Kubernetes and presents Kthena’s architecture—ModelServing, Router, Autoscaler, and ModelBooster—along with Volcano integration, vLLM‑Ascend setup, and a real‑world Qwen3‑235B deployment case, highlighting performance gains and future directions.

Cloud NativeKthenaKubernetes
0 likes · 13 min read
How Kthena Enables Production‑Grade LLM Inference on Kubernetes
Cloud Native Technology Community
Cloud Native Technology Community
Apr 2, 2026 · Information Security

Why Traditional Kubernetes Security Isn’t Enough for LLMs – 4 Critical Risks and How to Defend Them

Running large language models on Kubernetes looks stable, but the platform’s native security cannot address the new threat model introduced by LLMs, requiring operators to recognize prompt injection, data leakage, supply‑chain, and excessive agency risks and to implement a dedicated policy layer.

KubernetesLLMPolicy Layer
0 likes · 7 min read
Why Traditional Kubernetes Security Isn’t Enough for LLMs – 4 Critical Risks and How to Defend Them
PaperAgent
PaperAgent
Apr 2, 2026 · Artificial Intelligence

Can an LLM Build a Full‑Stack Knowledge Graph System in Under 3 Hours?

Using the GLM‑5.1 large language model, the author automated the end‑to‑end development of an ontology‑based knowledge‑graph extraction and visualization platform—covering backend, frontend, and graph database—in just 2 hours 47 minutes, consuming 747 k tokens and self‑correcting multiple issues.

AI EngineeringFull-Stack DevelopmentGLM-5.1
0 likes · 12 min read
Can an LLM Build a Full‑Stack Knowledge Graph System in Under 3 Hours?
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Apr 2, 2026 · Artificial Intelligence

How Smart Chunk Splitting Boosts RAG Recall from 67% to 91%

This article examines the critical role of chunk splitting in Retrieval‑Augmented Generation systems, comparing three generations of methods—from fixed‑size token cuts to sentence‑aware and semantic‑aware strategies—showing how refined chunking, overlap tuning, and metadata design raise Recall@5 from 0.67 to 0.91 while addressing table, list, and long‑section challenges.

LLMRAGchunking
0 likes · 24 min read
How Smart Chunk Splitting Boosts RAG Recall from 67% to 91%
Java Backend Technology
Java Backend Technology
Apr 2, 2026 · Artificial Intelligence

Avoid Common Pitfalls When Designing AGENTS.md for LLM Agents

This article analyzes frequent misunderstandings about AGENTS.md files—such as treating them as encyclopedias, over‑explaining basics, bloating with full text files, poor structure, excessive permissions, and ineffective usage patterns—and provides concrete best‑practice recommendations to keep them concise, modular, and token‑efficient.

AGENTS.mdAI AgentDocumentation Best Practices
0 likes · 10 min read
Avoid Common Pitfalls When Designing AGENTS.md for LLM Agents
AndroidPub
AndroidPub
Apr 2, 2026 · Artificial Intelligence

How to Build Offline, Privacy‑First AI with On‑Device Retrieval‑Augmented Generation

This article explains how to implement on‑device Retrieval‑Augmented Generation (RAG) for large language models, covering embedding, vector indexing, model selection, quantization, data chunking, incremental updates, hybrid search, and agentic RAG to deliver fast, private, and personalized AI experiences on mobile devices.

EmbeddingLLMRAG
0 likes · 18 min read
How to Build Offline, Privacy‑First AI with On‑Device Retrieval‑Augmented Generation
ArcThink
ArcThink
Apr 2, 2026 · Artificial Intelligence

Why LLMs Forget You: Uncovering the Limits and Solutions for Long‑Term Memory

The article explains why large language models lack persistent memory due to the stateless Transformer architecture, breaks down the four dimensions of memory loss, surveys seven technical approaches, three product implementations, and emerging research, and discusses security and privacy implications.

AILLMLong-term Memory
0 likes · 22 min read
Why LLMs Forget You: Uncovering the Limits and Solutions for Long‑Term Memory
AI Step-by-Step
AI Step-by-Step
Apr 1, 2026 · Artificial Intelligence

When to Use Which Model in an Agent: Beyond the “Strongest Model” Myth

The article explains why routing every request to the most powerful LLM hurts cost, speed, and throughput, and presents a three‑layer task decomposition that assigns execution‑level tasks to cheap small models, intermediate tasks to mid‑size models, and high‑risk judgment tasks to large models, with concrete examples and a minimal routing strategy.

Agent DesignCost OptimizationLLM
0 likes · 8 min read
When to Use Which Model in an Agent: Beyond the “Strongest Model” Myth
DaTaobao Tech
DaTaobao Tech
Apr 1, 2026 · Industry Insights

How AI Turned Taobao’s Marketing Venue Testing from Manual to Intelligent Automation

This article details the AI-driven testing platform built for Taobao’s marketing venues, describing how large‑language models and multimodal agents enable visual rendering verification, price and content consistency checks, and automated multi‑device adaptation, resulting in a 40% overall efficiency boost and a 100% increase in tester productivity.

AILLMMultimodal Agent
0 likes · 12 min read
How AI Turned Taobao’s Marketing Venue Testing from Manual to Intelligent Automation
PaperAgent
PaperAgent
Apr 1, 2026 · Artificial Intelligence

How Meta‑Harness Revolutionizes LLM Harness Optimization with 10× Search Speed

Meta‑Harness introduces an external‑loop optimization framework that lets coding agents automatically search and improve large‑language‑model harnesses, achieving up to ten‑fold faster search, ten‑times token efficiency, and significant performance gains across text classification, math reasoning, and agentic coding tasks.

LLMMeta-HarnessModel Evaluation
0 likes · 11 min read
How Meta‑Harness Revolutionizes LLM Harness Optimization with 10× Search Speed
Data STUDIO
Data STUDIO
Apr 1, 2026 · Artificial Intelligence

Blackboard System: Enabling Dynamic Collaboration Among Expert AI Agents

This article compares a rigid sequential multi‑agent pipeline with a flexible blackboard architecture, showing how shared memory and a dynamic controller let specialist AI agents cooperate opportunistically, obey conditional user instructions, and achieve higher efficiency and instruction‑following scores.

Blackboard SystemDynamic SchedulingLLM
0 likes · 21 min read
Blackboard System: Enabling Dynamic Collaboration Among Expert AI Agents
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Apr 1, 2026 · Artificial Intelligence

How to Design an Effective Agent Memory System for Enterprise AI Assistants

This article explains why AI agents need a structured memory module, outlines three memory types from cognitive science, details short‑term and long‑term storage architectures using vector databases, and provides concrete code and management strategies—including conflict resolution, TTL expiration, and privacy compliance—to build a robust Agent Memory system.

Agent MemoryLLMMem0
0 likes · 23 min read
How to Design an Effective Agent Memory System for Enterprise AI Assistants
Radish, Keep Going!
Radish, Keep Going!
Mar 31, 2026 · Artificial Intelligence

Why Agent‑First Systems Fail and How Harness Engineering Fixes Them

The article analyzes OpenAI’s Harness Engineering approach, explains four systemic failure modes of LLM‑driven agents, and details five modular components—readable environment, task state machine, verification loop, architectural constraints, and loop detection—that together enable reliable, large‑scale agent development.

AIAutomationHarness
0 likes · 17 min read
Why Agent‑First Systems Fail and How Harness Engineering Fixes Them
Senior Tony
Senior Tony
Mar 31, 2026 · Artificial Intelligence

Build and Debug LangGraph Workflows with Alibaba Qwen in Minutes

This article walks through creating a LangGraph workflow in Python, first using OpenAI’s GPT‑5‑nano model, then swapping to Alibaba’s Qwen 3.5‑plus model, showing how to suppress warnings, filter out thinking responses, visualize the graph, and troubleshoot common errors, all without any prior AI coding experience.

AI workflowAlibaba QwenLLM
0 likes · 8 min read
Build and Debug LangGraph Workflows with Alibaba Qwen in Minutes
Qborfy AI
Qborfy AI
Mar 31, 2026 · Artificial Intelligence

Mastering AI Agents with the Plan-and-Solve Design Pattern

The article introduces the Plan-and-Solve design pattern for AI agents, explaining how separating planning and execution improves handling of complex tasks, compares it with ReAct, provides detailed workflow diagrams, concrete examples such as weekly report generation, and offers a full Python implementation with dynamic replanning and result aggregation.

AI agentsAgent DesignLLM
0 likes · 14 min read
Mastering AI Agents with the Plan-and-Solve Design Pattern
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Mar 31, 2026 · Artificial Intelligence

How to Build a Production‑Ready AI Memory System with Mem0 and Elasticsearch

This guide explains how to overcome the stateless nature of large language models by using the Mem0 framework together with Elasticsearch to create a persistent, vector‑searchable memory layer, covering architecture, real‑world scenarios, step‑by‑step deployment, and integration with the OpenClaw agent framework.

AI memoryElasticsearchLLM
0 likes · 15 min read
How to Build a Production‑Ready AI Memory System with Mem0 and Elasticsearch
Woodpecker Software Testing
Woodpecker Software Testing
Mar 31, 2026 · Industry Insights

2026 AI Agent Testing Trends Every Test Expert Must Know

The article outlines how software testing is shifting from functional correctness to trustworthy behavior verification for AI agents in 2026, detailing a three‑dimensional trust matrix, agent‑native CI pipelines, human‑AI collaborative testing, and compliance‑driven auditable agents with concrete industry examples and metrics.

AI complianceAI testingLLM
0 likes · 9 min read
2026 AI Agent Testing Trends Every Test Expert Must Know
Data STUDIO
Data STUDIO
Mar 31, 2026 · Artificial Intelligence

Agent Architecture: Planner → Executor → Verifier – Adding a “Quality Inspector” to Your AI

This article introduces the PEV (Planner‑Executor‑Verifier) architecture, explains why AI agents need a verification step to avoid blindly trusting faulty tool outputs, demonstrates a full implementation with LangGraph, compares its robustness to a naïve baseline, and discusses its advantages, limitations, and suitable use cases.

AI agentsLLMLangGraph
0 likes · 23 min read
Agent Architecture: Planner → Executor → Verifier – Adding a “Quality Inspector” to Your AI
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Mar 31, 2026 · Information Security

Securing LLM Code Interpreter: Sandbox Strategies and Real‑World Pitfalls

This article examines why RAG systems need a Code Interpreter, explains the dangers of executing LLM‑generated code with exec(), and presents three sandbox designs—restricted exec, Docker containers, and E2B cloud sandboxes—along with whitelist/blacklist rules, an eight‑step execution flow, and practical lessons learned from production deployment.

Code InterpreterDockerLLM
0 likes · 26 min read
Securing LLM Code Interpreter: Sandbox Strategies and Real‑World Pitfalls
AI Tech Publishing
AI Tech Publishing
Mar 31, 2026 · Artificial Intelligence

Step‑by‑Step Guide to Building Your First AI Agent from Scratch (Full Code Included)

This comprehensive guide walks you through the fundamentals of AI agents, explains the core agent loop, compares workflow patterns with autonomous agents, and provides a practical five‑step process—including tool design, memory handling, testing, and multi‑agent collaboration—complete with real code examples for Anthropic and OpenAI SDKs.

AI AgentLLMMemory
0 likes · 22 min read
Step‑by‑Step Guide to Building Your First AI Agent from Scratch (Full Code Included)
Black & White Path
Black & White Path
Mar 30, 2026 · Information Security

OWASP Top 10 Risks for LLMs Every AI Security Beginner Must Know

The article outlines the OWASP Top 10 threats for large language model applications—including prompt injection, data leakage, supply‑chain attacks, model poisoning, improper output handling, excessive agency, system prompt leakage, vector embedding weaknesses, misinformation, and unbounded consumption—plus three essential mitigation rules for newcomers.

AI securityLLMOWASP
0 likes · 6 min read
OWASP Top 10 Risks for LLMs Every AI Security Beginner Must Know
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Mar 30, 2026 · Operations

Mastering RAG Post‑Launch: A Closed‑Loop Badcase Management Blueprint

This article explains how to establish a six‑step closed‑loop workflow for operating RAG‑based question‑answer systems in insurance, covering badcase collection via three channels, four‑type classification, automated scripts, regression testing, gray‑scale rollout, and real‑world metrics that boosted answer accuracy from 76 % to 89 %.

Badcase ManagementInsurance AILLM
0 likes · 20 min read
Mastering RAG Post‑Launch: A Closed‑Loop Badcase Management Blueprint
Su San Talks Tech
Su San Talks Tech
Mar 30, 2026 · Artificial Intelligence

Mastering LLM Function Calling: Theory, Workflow, and Hands‑On Code

This article explains the fundamentals of large‑model function calling, why it’s needed to bridge language models with real‑world tools, and provides a step‑by‑step implementation in Python—including tool definition, intent extraction, local execution, and result integration—complete with code samples and diagrams.

AI AgentAPIFunction Calling
0 likes · 11 min read
Mastering LLM Function Calling: Theory, Workflow, and Hands‑On Code
Qborfy AI
Qborfy AI
Mar 29, 2026 · Artificial Intelligence

Mastering AI Agent Reflection: The Generate‑Reflect‑Refine Loop

This article explains the Reflection design pattern for AI agents, detailing how a three‑step generate‑reflect‑refine cycle can iteratively improve outputs, provides both a simple two‑call implementation and a structured class‑based version, and shares practical tips, benchmarks, and references to the original research.

AI agentsCode GenerationLLM
0 likes · 9 min read
Mastering AI Agent Reflection: The Generate‑Reflect‑Refine Loop
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Mar 29, 2026 · Artificial Intelligence

Mastering RAG Prompt Engineering: Prevent Hallucinations and Boost Accuracy

This article dissects the unique challenges of RAG prompting, presents a systematic System/User Prompt design with strong constraints and citation requirements, compares constraint strengths with quantitative hallucination rates, and offers long‑context compression strategies and rigorous testing methods to ensure reliable LLM answers.

LLMRAGSystem Prompt
0 likes · 19 min read
Mastering RAG Prompt Engineering: Prevent Hallucinations and Boost Accuracy
Java One
Java One
Mar 28, 2026 · Artificial Intelligence

Building a Vector‑Free RAG System with Hierarchical Page Indexing

This guide explains how to create a retrieval‑augmented generation (RAG) system that avoids embeddings by converting documents into a hierarchical tree, using an LLM to navigate, summarize, and retrieve answers, complete with a full Python implementation and a GitHub repository.

Hierarchical IndexingLLMPython
0 likes · 15 min read
Building a Vector‑Free RAG System with Hierarchical Page Indexing
AI Algorithm Path
AI Algorithm Path
Mar 28, 2026 · Artificial Intelligence

A Practical Guide to Building Agent Skills for Large Language Models

This guide explains the concept of LLM "Skills", shows how to organize skill directories for Claude and Copilot, walks through creating a "prepare‑pr" skill with a SKILL.md file, integrates Bash scripts for git checks, and demonstrates testing and extending the skill with additional checks and templates.

Agent SkillsBash scriptClaude
0 likes · 12 min read
A Practical Guide to Building Agent Skills for Large Language Models
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Mar 28, 2026 · Artificial Intelligence

How to Ace LLM Interview Questions: Deep Dive into Pre‑training, SFT, DPO & RLHF

This guide breaks down the four major large‑model training paradigms—pre‑training, supervised fine‑tuning, preference alignment, and RLHF—explaining which parameters are updated, how attention is reshaped, and what capabilities are gained, so you can deliver a structured, interview‑ready answer.

AI InterviewFine-tuningLLM
0 likes · 8 min read
How to Ace LLM Interview Questions: Deep Dive into Pre‑training, SFT, DPO & RLHF
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Mar 28, 2026 · Artificial Intelligence

From RNNs to Multimodal Agents: A Decade of Transformer Evolution

This article traces the evolution of sequence models from early RNN/LSTM designs through the breakthrough Transformer, its major branches, dense scaling, efficiency‑focused variants, next‑generation linear‑complexity SSMs, and finally multimodal agent architectures, highlighting each stage's strengths, weaknesses, and typical use cases.

AI ArchitectureLLMTransformer
0 likes · 12 min read
From RNNs to Multimodal Agents: A Decade of Transformer Evolution
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Mar 28, 2026 · Artificial Intelligence

Mastering Multi‑Agent Systems: Design, Parallel Execution, and Interview Strategies

This article dissects the shortcomings of single‑agent LLM pipelines, introduces the Supervisor‑based Multi‑Agent architecture with LangGraph, demonstrates parallel task execution, robust error handling, and result merging, and provides concrete interview guidance backed by real performance data.

AI ArchitectureError HandlingLLM
0 likes · 19 min read
Mastering Multi‑Agent Systems: Design, Parallel Execution, and Interview Strategies
Code Mala Tang
Code Mala Tang
Mar 28, 2026 · Artificial Intelligence

Can Claude Translate the Linux Kernel to Rust? Insights, Experiments, and Costs

This article evaluates Claude's ability to translate isolated Linux kernel modules from C to Rust, presenting a detailed analysis of translation granularity, token costs, experimental results on drivers, networking, and file‑system modules, and discussing the technical and economic challenges of a full kernel rewrite.

AIKernelLLM
0 likes · 19 min read
Can Claude Translate the Linux Kernel to Rust? Insights, Experiments, and Costs
AI Tech Publishing
AI Tech Publishing
Mar 28, 2026 · Artificial Intelligence

Designing Agent Memory Systems: Four Types, Three Strategies, and Full Python Implementation

This article breaks down agentic memory into four distinct types—In‑context, External, Episodic, and Semantic/Parametric—explains three forgetting strategies (time decay, importance scoring, periodic consolidation), shows how memory flows through an agent loop, and provides complete Python code using OpenAI embeddings and ChromaDB for a production‑ready memory layer.

Agent MemoryChromaDBLLM
0 likes · 22 min read
Designing Agent Memory Systems: Four Types, Three Strategies, and Full Python Implementation
AI Insight Log
AI Insight Log
Mar 28, 2026 · Artificial Intelligence

Anthropic’s Leaked Mythos Model Claims to Outperform Opus 4.6 – Why Release Is Delayed

A leaked internal Anthropic blog reveals the upcoming Claude Mythos (codenamed Capybara) model, touted as a step‑change over Opus 4.6 in programming, academic reasoning, and cybersecurity, while highlighting unprecedented security risks, early access for security professionals, and high compute costs that postpone a full launch.

AI SafetyAnthropicClaude Mythos
0 likes · 5 min read
Anthropic’s Leaked Mythos Model Claims to Outperform Opus 4.6 – Why Release Is Delayed
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Mar 27, 2026 · Artificial Intelligence

Weekly Quantitative Finance Paper Roundup (Mar 21‑27, 2026)

This article presents concise English summaries of four recent AI‑driven quantitative finance papers, covering an agentic AI screening platform for portfolio investment, a wavelet‑based physics‑informed neural network for option pricing, the FinRL‑X modular trading infrastructure, and the S³G stock state‑space graph for enhanced trend prediction, each with authors, links, and key experimental results.

AILLMModular Trading Infrastructure
0 likes · 12 min read
Weekly Quantitative Finance Paper Roundup (Mar 21‑27, 2026)
DataFunTalk
DataFunTalk
Mar 27, 2026 · Artificial Intelligence

Building a Production‑Ready RAG Engine: Architecture, Challenges & Solutions

This article examines the practical challenges of deploying Retrieval‑Augmented Generation in enterprise settings, outlines a layered RAG architecture with offline document processing and online query handling, and details the hybrid retrieval, multi‑stage ranking, knowledge filtering, and generation techniques that improve accuracy and reduce hallucinations.

AI EngineeringHybrid RetrievalKnowledge Filtering
0 likes · 22 min read
Building a Production‑Ready RAG Engine: Architecture, Challenges & Solutions
Qborfy AI
Qborfy AI
Mar 26, 2026 · Artificial Intelligence

Mastering ReAct: Turn LLMs into Thoughtful, Actionable AI Agents

This article explains the ReAct (Reasoning + Acting) design pattern for large language model agents, detailing its thought‑action‑observation loop, concrete examples, prompt engineering tips, full Python implementations, common pitfalls, and references to the original Google research.

AI agentsLLMOpenAI
0 likes · 11 min read
Mastering ReAct: Turn LLMs into Thoughtful, Actionable AI Agents
AI Explorer
AI Explorer
Mar 26, 2026 · Artificial Intelligence

Reinventing Financial Trading with a Multi‑Agent LLM Framework

TradingAgents introduces a multi‑agent architecture that lets specialized LLM experts—researchers, analysts, traders and risk managers—collaborate to analyse markets, manage risk and execute trades, offering a new AI‑driven collaboration paradigm for quantitative finance while providing explainable decisions and enterprise‑grade stability.

AI CollaborationFinancial AILLM
0 likes · 6 min read
Reinventing Financial Trading with a Multi‑Agent LLM Framework
AI Programming Lab
AI Programming Lab
Mar 26, 2026 · Artificial Intelligence

LLMs to the Left, Harness Engineering to the Right: Bridging the Gap

The article argues that the real bottleneck for LLM‑driven agents is not model capability but the surrounding control system—Harness Engineering—which can dramatically boost success rates, reduce failure cascades, and become the lasting moat for AI productivity.

AI OpsAgent HarnessContext Engineering
0 likes · 14 min read
LLMs to the Left, Harness Engineering to the Right: Bridging the Gap
Data STUDIO
Data STUDIO
Mar 26, 2026 · Artificial Intelligence

Metacognitive Agents: Teaching AI to Self‑Assess Before Answering

The article introduces metacognitive agents that equip AI with a self‑model to evaluate confidence, domain relevance, tool availability, and risk before acting, demonstrating a LangGraph‑based medical triage assistant with code, workflow, safety advantages, and practical test results.

AI SafetyLLMLangGraph
0 likes · 22 min read
Metacognitive Agents: Teaching AI to Self‑Assess Before Answering
Architecture Musings
Architecture Musings
Mar 25, 2026 · Information Security

Seeing AI Agent Drift in Vector Space: An Unvalidated Thought Experiment

The article imagines an AI coding agent that silently exfiltrates credentials hidden in data, explains why rule‑based and text‑level defenses miss such attacks, proposes monitoring the agent's vector‑space decision trajectory with six geometric metrics, and critically evaluates the feasibility and limitations of this approach.

AI agentsLLMSecurity
0 likes · 23 min read
Seeing AI Agent Drift in Vector Space: An Unvalidated Thought Experiment
SuanNi
SuanNi
Mar 25, 2026 · Artificial Intelligence

How to Evaluate, Optimize, and Secure Retrieval‑Augmented Generation (RAG) Pipelines

This article explains the evaluation pillar of context engineering, introduces the three core RAG metrics (context relevance, faithfulness, answer relevance), details the RAGAS automated assessment framework, shows how to build evaluation datasets, adopt evaluation‑driven development, and protect RAG systems from prompt injection and data leakage.

LLMRAGRAGAS
0 likes · 13 min read
How to Evaluate, Optimize, and Secure Retrieval‑Augmented Generation (RAG) Pipelines
Full-Stack Cultivation Path
Full-Stack Cultivation Path
Mar 25, 2026 · Artificial Intelligence

Understanding Tool Use in LLMs: How Models Leverage Tool Calls

This article explains why large language models need tool use, defines the concepts of Tool Use, Tool Call, and Function Calling, compares them, walks through a complete tool‑use workflow, and discusses architectural, safety, and design considerations for building reliable LLM agents.

AgentLLMPrompt engineering
0 likes · 17 min read
Understanding Tool Use in LLMs: How Models Leverage Tool Calls
AI Engineering
AI Engineering
Mar 25, 2026 · Artificial Intelligence

Is “Harness Engineering” Just Rebranded Engineering Common Sense?

The article examines the hype around “harness engineering” in LLM workflows, showing through SGLang’s multi‑agent experience that the approach merely repackages established software‑engineering principles such as separation of concerns, docs‑as‑code, and structured routing, and discusses its limits and future implications.

Harness EngineeringLLMMulti-Agent
0 likes · 8 min read
Is “Harness Engineering” Just Rebranded Engineering Common Sense?
Architect's Journey
Architect's Journey
Mar 25, 2026 · Artificial Intelligence

Why SKILL Makes AI Development Surprisingly Simple

The article introduces the SKILL framework, explains its file‑based structure and LLM‑driven entry point, compares it with traditional API‑centric backends, outlines its suitable use cases and limitations, and argues that mastering SKILL will become a core productivity skill for developers.

AI EngineeringLLMSKILL framework
0 likes · 8 min read
Why SKILL Makes AI Development Surprisingly Simple
Java Architecture Diary
Java Architecture Diary
Mar 25, 2026 · Artificial Intelligence

Building Java AI Agents with Koog: A Hands‑On Guide to Native Java Agent APIs

JetBrains' newly released Koog for Java provides a native Java AI Agent framework that lets developers annotate methods as tools, assemble agents with a Builder‑style API, and let large language models orchestrate multi‑step tasks without writing explicit control flow, as demonstrated with banking and e‑commerce examples.

AI AgentBuilder APIJava
0 likes · 9 min read
Building Java AI Agents with Koog: A Hands‑On Guide to Native Java Agent APIs
AI Explorer
AI Explorer
Mar 24, 2026 · Artificial Intelligence

Revolutionizing Financial Trading with a Multi‑Agent AI Framework

TradingAgents is an open‑source Python framework that uses multiple specialized LLM agents—Analyst, Researcher, Trader, and Risk Manager—to mimic a real investment bank’s workflow, offering a more robust and explainable approach to quantitative trading and financial research.

Financial AILLMPython
0 likes · 6 min read
Revolutionizing Financial Trading with a Multi‑Agent AI Framework
Tencent Tech
Tencent Tech
Mar 24, 2026 · Artificial Intelligence

Unlocking AI Power: How Skill Packages Transform Large Language Models

This article provides a comprehensive technical guide to Skill packages—standardized knowledge containers that give large language models expert-level execution capabilities—covering their definition, architecture, integration with the Model Context Protocol (MCP), creation workflow, best‑practice tips, collaborative patterns, debugging strategies, philosophical implications, and future directions.

AI toolingLLMMCP
0 likes · 18 min read
Unlocking AI Power: How Skill Packages Transform Large Language Models
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Mar 24, 2026 · Artificial Intelligence

How Hologres + Mem0 Deliver Low‑Cost, High‑Performance Long‑Memory for LLMs

This article explains how the combination of Hologres, a unified real‑time data warehouse, and Mem0, an open‑source LLM memory framework, overcomes the limited context window of large language models by providing scalable, low‑latency, and cost‑effective long‑term memory for AI applications.

AI InfrastructureHologresLLM
0 likes · 11 min read
How Hologres + Mem0 Deliver Low‑Cost, High‑Performance Long‑Memory for LLMs
SuanNi
SuanNi
Mar 24, 2026 · Artificial Intelligence

How Compression, Orchestration, and LangGraph Are Redefining LLM Context Engineering

This article analyzes the six pillars of context engineering for large language models, focusing on compression techniques, extractive vs. abstractive methods, the LLMLingua toolkit, dynamic orchestration with routing and agentic RAG, and how LangGraph enables sophisticated agent‑driven workflows.

Agentic RAGLLMLangGraph
0 likes · 14 min read
How Compression, Orchestration, and LangGraph Are Redefining LLM Context Engineering
AgentGuide
AgentGuide
Mar 24, 2026 · Artificial Intelligence

What I Learned Moving from Backend Engineering to AI Agent Development

The author, a former backend engineer turned AI Agent developer, explains how LLM uncertainty, context engineering, shifting code responsibilities, workflow standards, new failure modes, and the ReAct paradigm shape modern Agent development, and outlines tasks best suited—or unsuited—for LLMs.

AI AgentContext EngineeringLLM
0 likes · 6 min read
What I Learned Moving from Backend Engineering to AI Agent Development
DataFunTalk
DataFunTalk
Mar 24, 2026 · Artificial Intelligence

Memory‑Based Self‑Evolution: Redefining LLM Agents Beyond Parameter Updates

This article examines the limitations of traditional supervised fine‑tuning and reinforcement learning for LLM agents, introduces a memory‑based self‑evolution paradigm with technologies such as Dynamic Cheatsheet, ReasoningBank, ACE and MemGen, and shows how building an experience bank can turn static models into continuously learning agents, especially in the insurance sector.

Insurance AILLMknowledge flywheel
0 likes · 13 min read
Memory‑Based Self‑Evolution: Redefining LLM Agents Beyond Parameter Updates
Data STUDIO
Data STUDIO
Mar 24, 2026 · Artificial Intelligence

Turn LLMs into Real Assistants: Build a Tool‑Using Agent in Minutes

This article explains why large language models alone can hallucinate, introduces the tool‑using agent architecture, and provides a step‑by‑step Python tutorial using LangChain, LangGraph, and Tavily to create, run, and evaluate a real‑time web‑search capable AI assistant.

AgentLLMLangChain
0 likes · 16 min read
Turn LLMs into Real Assistants: Build a Tool‑Using Agent in Minutes
SuanNi
SuanNi
Mar 24, 2026 · Artificial Intelligence

How Memento‑Skills Enables Self‑Evolving LLMs Without Fine‑Tuning

Introducing Memento‑Skills, a novel framework that freezes LLM parameters while an external skill library iteratively reads, writes, and refines capabilities, achieving up to 116% accuracy gains on GAIA and HLE benchmarks and demonstrating scalable self‑evolution without costly model fine‑tuning.

LLMreinforcement learningself-evolution
0 likes · 11 min read
How Memento‑Skills Enables Self‑Evolving LLMs Without Fine‑Tuning
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Mar 24, 2026 · Artificial Intelligence

A Comprehensive Guide to Major Attention Mechanisms: From MHA and GQA to MLA, Sparse and Hybrid Architectures

This article reviews and compares the most important attention variants used in modern large language models—including multi‑head attention, grouped‑query attention, multi‑head latent attention, sparse and sliding‑window attention, gated attention, and hybrid designs—detailing their motivations, memory trade‑offs, example architectures, and experimental findings.

Hybrid ArchitectureLLMMHA
0 likes · 29 min read
A Comprehensive Guide to Major Attention Mechanisms: From MHA and GQA to MLA, Sparse and Hybrid Architectures
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 24, 2026 · Artificial Intelligence

Why LLMs Behave Unpredictably: From Uncertainty to Practical Agent Design

This article analyzes the sources of LLM output uncertainty, explores hardware and architectural constraints, demonstrates how to build robust AI agents with prompt engineering, tool orchestration, and memory management, and compares traditional micro‑service design with modern LLM‑centric workflows.

AI AgentHardwareLLM
0 likes · 64 min read
Why LLMs Behave Unpredictably: From Uncertainty to Practical Agent Design
Data Party THU
Data Party THU
Mar 23, 2026 · Artificial Intelligence

Boosting RAG Performance: Query Translation & Decomposition Techniques

The article explains two emerging RAG query‑optimization approaches—query translation and query decomposition—detailing fan‑out retrieval, reciprocal rank fusion, HyDE, step‑back prompting, and chain‑of‑thought retrieval, and shows how combining them can improve relevance and latency in LLM‑augmented systems.

LLMRAGRetrieval Augmented Generation
0 likes · 9 min read
Boosting RAG Performance: Query Translation & Decomposition Techniques
Full-Stack Cultivation Path
Full-Stack Cultivation Path
Mar 23, 2026 · Artificial Intelligence

What Exactly Is a Token in LLMs? A First‑Principles Explanation

The article explains that a token is the smallest discrete text unit a large language model processes, detailing why tokenization is essential, how tokenizers work, how tokens flow through the transformer, and how token counts affect context windows, cost, latency, and overall model behavior.

Context WindowCost ManagementEmbedding
0 likes · 20 min read
What Exactly Is a Token in LLMs? A First‑Principles Explanation
SuanNi
SuanNi
Mar 23, 2026 · Artificial Intelligence

Can LLMs Predict Real‑World War Outcomes? A Deep Dive into the 2026 Middle East Conflict

A research team from MBZUAI and the University of Maryland constructed an 11‑point timeline of the 2026 Middle East escalation, fed contemporaneous news to leading large language models, and evaluated their strategic reasoning, economic impact forecasts, and political signal interpretation, revealing both strengths and limitations of AI under extreme uncertainty.

AI EvaluationGeopoliticsLLM
0 likes · 12 min read
Can LLMs Predict Real‑World War Outcomes? A Deep Dive into the 2026 Middle East Conflict
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Mar 23, 2026 · Artificial Intelligence

How Agent Skills Solve LLM Development Pain Points and Gain Standard Status

The article analyses the emergence of Agent Skills as an open LLM standard, explains the technical shortcomings of current prompt‑centric workflows, describes the three‑layer skill architecture and its benefits for reuse, versioning and organization‑wide deployment, and discusses current limitations and future evolution paths.

AI StandardsAgent SkillsLLM
0 likes · 29 min read
How Agent Skills Solve LLM Development Pain Points and Gain Standard Status