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143128 articles · Page 278 of 7157
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 24, 2026 · Artificial Intelligence

Can Agents Have Their Own App Store? SJTU & OPPO Unveil a Massive Agent Ecosystem

The article analyzes the ColorEcosystem blueprint, which maps the evolution from single LLM‑driven agents to a massive, personalized, standardized, and trustworthy agent ecosystem, detailing its three pillars—Agent Carrier, Agent Store, and Agent Audit—along with challenges and transition strategies.

AI agentsagent auditagent ecosystem
0 likes · 12 min read
Can Agents Have Their Own App Store? SJTU & OPPO Unveil a Massive Agent Ecosystem
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 24, 2026 · Artificial Intelligence

The First Visual‑Language Parallel Thinking Framework: Unpacking Its Core Mechanisms

The paper introduces Visual Para-Thinker, a parallel‑thinking framework for large‑scale visual‑language models that uses visual‑centered block and scan path partitions, Path‑aware Attention and Learnable Parallel Rotary Position Embedding, and demonstrates consistent gains across counting, visual search, hallucination and grounding benchmarks.

LPRoPEPa-Attentionbenchmark evaluation
0 likes · 11 min read
The First Visual‑Language Parallel Thinking Framework: Unpacking Its Core Mechanisms
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 24, 2026 · Artificial Intelligence

Inference Set to Consume 70% of AI Compute Power, Leaving 30% for Training

Zhang Lu, a Silicon Valley investor, argues that AI's focus is shifting from training to inference—now accounting for half of current compute and projected to reach 70%—while communication energy, data quality, physical AI, and edge deployment become the next critical bottlenecks and opportunities across medical, space, and nano‑robotics applications.

AI ApplicationsAI inferenceData Quality
0 likes · 19 min read
Inference Set to Consume 70% of AI Compute Power, Leaving 30% for Training
TechVision Expert Circle
TechVision Expert Circle
May 24, 2026 · Artificial Intelligence

Inside Claude Code: How Its Production‑Grade Agent Engine Works

This article dissects Claude Code’s architecture, detailing its simple while‑loop core, layered tool system with lazy loading, seven‑layer permission defense, five‑stage context compaction, sub‑agent collaboration, hook mechanisms, and the CLAUDE.md context‑engineering approach, revealing why token efficiency and deterministic security drive its design.

Agent LoopClaude CodeContext Compression
0 likes · 14 min read
Inside Claude Code: How Its Production‑Grade Agent Engine Works
James' Growth Diary
James' Growth Diary
May 24, 2026 · Artificial Intelligence

Execution → Observation → Reflection → Improvement: How Hermes Closes the Skill Loop

The article dissects Hermes' background review mechanism, showing how a silent daemon thread performs post‑conversation reflection, writes valuable insights to a skill or memory store, shares prompt designs, fork‑agent isolation, priority update rules, and common pitfalls for building continuously learning LLM agents.

Background ReviewDaemon ThreadHermes
0 likes · 14 min read
Execution → Observation → Reflection → Improvement: How Hermes Closes the Skill Loop
James' Growth Diary
James' Growth Diary
May 24, 2026 · Artificial Intelligence

Wrapping Up Harness Engineering: The Six Pillars Methodology Explained

This article reviews the six foundational pillars of Harness Engineering—context architecture, architectural constraints, self‑verification loop, context isolation, entropy governance, and detachability—showing how Claude Code implements them, why infrastructure, not model size, is the real bottleneck, and offering ten concrete actions for practitioners.

AI agentsContext CompressionEntropy Management
0 likes · 17 min read
Wrapping Up Harness Engineering: The Six Pillars Methodology Explained
James' Growth Diary
James' Growth Diary
May 24, 2026 · Artificial Intelligence

End-to-End Observability with LangSmith: Trace Debugging and RAG Evaluation from Development to Production

This article walks through LangSmith’s three core capabilities—Trace, Evaluation, and Dataset management—showing how to integrate zero‑code tracing, quantify RAG performance with custom evaluators, run version‑comparison experiments, and set up production monitoring with sampling and feedback loops.

DatasetLangChainLangSmith
0 likes · 23 min read
End-to-End Observability with LangSmith: Trace Debugging and RAG Evaluation from Development to Production
Programmer1970
Programmer1970
May 24, 2026 · Operations

Full-Stack Guide to Tracing RocketMQ Message Loss

This guide walks you through the entire lifecycle of a RocketMQ message, identifies where loss can occur, provides step‑by‑step diagnostics using TrackType and timestamps, and offers concrete configuration, command‑line and recovery strategies for producers, brokers and consumers.

BrokerConsumerMessage Loss
0 likes · 11 min read
Full-Stack Guide to Tracing RocketMQ Message Loss
Architect
Architect
May 24, 2026 · Artificial Intelligence

Beyond the One‑Person Unicorn Myth: Building the AI‑Native Startup Operating Base

The article examines Anthropic’s Founder’s Playbook, arguing that AI‑native startups require a minimal operating foundation—clear goals, context, tools, permissions, evidence, and workflows—so agents can participate, be audited, handed off, and preserve experience across the Idea, MVP, Launch, and Scale stages.

AI agentsAnthropicClaude
0 likes · 21 min read
Beyond the One‑Person Unicorn Myth: Building the AI‑Native Startup Operating Base
Ubiquitous Tech
Ubiquitous Tech
May 24, 2026 · Operations

Generate AI‑Friendly Project Wiki Docs with Zread: A Practical Guide

This article explains why large codebases are hard to understand, introduces Zread CLI as a tool that converts a repository into searchable, AI‑readable wiki documentation, and walks through installation, configuration, and real‑world usage with step‑by‑step examples.

AI codingAI documentationCLI
0 likes · 13 min read
Generate AI‑Friendly Project Wiki Docs with Zread: A Practical Guide
Old Zhang's AI Learning
Old Zhang's AI Learning
May 24, 2026 · Artificial Intelligence

LM Studio Adds MTP Support, Boosting Qwen3.6‑35B to ~130 Tokens/s

LM Studio 0.4.14+ now implements Multi‑Token Prediction (MTP) speculative decoding, eliminating the need for a separate draft model and delivering roughly double the token throughput—e.g., Qwen3.6‑35B reaches about 130 tokens/s on RTX 3090—while providing a six‑step activation guide and a list of known pitfalls.

LM‑StudioMTPQwen3.6
0 likes · 6 min read
LM Studio Adds MTP Support, Boosting Qwen3.6‑35B to ~130 Tokens/s
ArcThink
ArcThink
May 24, 2026 · Artificial Intelligence

When to Use MCP vs. Skills: A Clear Capability Stack for Building Stable AI Agents

The article explains a four‑layer capability model—Rules, Skills, MCP, and Agents—showing how to decide when to add an MCP server, a Skill, or a Rule, and how combining them yields reliable AI‑powered programming assistants for both personal projects and team‑scale engineering.

AI agentsMCPPrompt Engineering
0 likes · 23 min read
When to Use MCP vs. Skills: A Clear Capability Stack for Building Stable AI Agents
Smart Workplace Lab
Smart Workplace Lab
May 24, 2026 · Artificial Intelligence

Three‑Step Protocol to Safeguard AI Agents from Unauthorized Actions

The article analyzes how autonomous AI agents can overstep their authority, illustrates the risk with a real‑world incident, and presents a three‑step boundary protocol—including a red‑line word list, confidence‑threshold lock, and automatic rollback—to keep agents under control while preserving efficiency.

AI agentsautomation governancebehavior control
0 likes · 5 min read
Three‑Step Protocol to Safeguard AI Agents from Unauthorized Actions
AI Engineering
AI Engineering
May 24, 2026 · Artificial Intelligence

Build a Local AI Agent from Scratch: A Deep‑Dive, Non‑Fast‑Food Tutorial

This tutorial walks you through the open‑source “AI Agents From Scratch” project, teaching how to build a fully local AI agent without any pre‑made framework by covering core modules, 14 step‑by‑step examples, advanced reasoning architectures, and minimal system requirements.

AI AgentChain-of-ThoughtLocal LLM
0 likes · 6 min read
Build a Local AI Agent from Scratch: A Deep‑Dive, Non‑Fast‑Food Tutorial
Machine Heart
Machine Heart
May 24, 2026 · Artificial Intelligence

Proactive Failure Recovery: How AgentChord Embeds Recovery Actions into Robot Task Graphs

AgentChord, a system presented at RSS 2026, anticipates potential robot manipulation failures by embedding recovery actions directly into a structured task graph, enabling immediate low‑latency switches to pre‑compiled recovery branches and achieving up to 99.2% success in simulated tasks and 77.5% on real robots.

Failure RecoveryManipulationRobotics
0 likes · 13 min read
Proactive Failure Recovery: How AgentChord Embeds Recovery Actions into Robot Task Graphs
Machine Heart
Machine Heart
May 24, 2026 · Artificial Intelligence

Can CODA Enable LLMs and Beginners to Write Lightning‑Fast Transformer Kernels?

CODA rewrites Transformer blocks as GEMM‑epilogue programs, exposing five primitive building blocks that let both AI‑generated code and human programmers fuse memory‑intensive operations into the GEMM epilogue, eliminating costly tensor moves and achieving up to 1.8× speed‑ups on H100 GPUs for RMSNorm, SwiGLU, RoPE and other components, while preserving numerical accuracy.

CODACUDAGEMM
0 likes · 11 min read
Can CODA Enable LLMs and Beginners to Write Lightning‑Fast Transformer Kernels?
DataFunSummit
DataFunSummit
May 24, 2026 · Industry Insights

Why AI Agents Are Redefining Data Infrastructure Governance

The rise of AI agents as data consumers forces a fundamental shift in data infrastructure design, requiring unified metadata control, a robust semantic layer, and a governed agent access framework to replace traditional human‑centric RBAC models and ensure secure, auditable operations.

AI agentsAgentic Data ProtocolApache Gravitino
0 likes · 18 min read
Why AI Agents Are Redefining Data Infrastructure Governance
IT Services Circle
IT Services Circle
May 24, 2026 · Artificial Intelligence

2026 AI Coding Agent Benchmark: Cursor, Claude Code, and Codex – Who Leads?

A comprehensive 2026 benchmark evaluates major AI coding agents—Cursor CLI, Claude Code, OpenAI Codex, and Google Gemini—across performance, token consumption, cost per task, and execution time, revealing a tight top‑three score margin and highlighting cost‑efficiency and latency as the new competitive frontiers.

AI Coding AgentsBenchmarkClaude Code
0 likes · 6 min read
2026 AI Coding Agent Benchmark: Cursor, Claude Code, and Codex – Who Leads?
IT Services Circle
IT Services Circle
May 24, 2026 · Information Security

Fired, He Deleted 96 Government Databases in Minutes and Asked AI How to Clear Logs

Just five minutes after being terminated, twin brothers with prior fraud convictions used SQL commands to drop 96 U.S. government databases, queried AI on log‑clearing techniques, and exposed critical failures in the company's off‑boarding process, leading to a high‑profile federal investigation and legal fallout.

AIInformation SecuritySQL
0 likes · 9 min read
Fired, He Deleted 96 Government Databases in Minutes and Asked AI How to Clear Logs