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Exploring how users evolve trust in Gemini and generative AI—from verifying everything to selective delegation, analyzing trust patterns, key factors, and the trust drift trap.

Why do AI Agents hallucinate more as they grow more complex? This article analyzes the causes from error accumulation, context noise, and model completion nature, with 5 practical production strategies.

Moonshot AI's Kimi-K3 model is set to launch on HuggingFace. A deep dive into its architecture, licensing, community reactions, and strategic implications for the global open-source AI landscape.

Build high-quality AI projects on a budget. Learn how to use Ollama, Groq, Chroma, and other free open-source tools to build RAG systems and multi-Agent workflows from scratch.

Build high-quality AI projects on a budget. Learn how to use Ollama, Groq, Chroma, and other free open-source tools to build RAG systems and multi-Agent workflows from scratch.

last30days-skill is a 50K+ Star AI Agent skill on GitHub that performs cross-platform research across Reddit, X, YouTube, Hacker News, and Polymarket to generate grounded 30-day summary reports.

Hermes Agent is a mature AI Agent framework with built-in Claude Code and Codex coding capabilities, supporting 200+ models, multi-platform deployment, and WeChat integration. Its layered memory and self-evolution features enable low-Token automated task execution.

Build a personal website with Vibe Coding even from scratch! Practical tips for collaborating with AI via Codex and DeepSeek: have AI restate requirements, use screenshots to locate, change one thing at a time, and handle long Chinese text.

Build a personal website with Vibe Coding even with zero coding background! Practical tips for collaborating with AI via Codex and DeepSeek: have AI restate needs, use screenshots, change one thing at a time, and deploy your site with ease.

NanoClaw founder David Boyd breaks down the core engineering of enterprise autonomous Agents: a triple security isolation model, LLM Wiki memory design, and the real-world path from personal Agents to team-scale deployment.

An in-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing methods and the transition path for test engineers.

In-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing and the transition path for test engineers.
Intelligent Model Routing: The Core Te…
Intelligent Model Routing is becoming key AI infrastructure. This article explores its principles, solution types, technical challenges, and implementation considerations to help developers balance cost, latency, and quality.
Ego-lite: An Open-Source Browser Desig…
ego-lite is an open-source AI-native browser by citrolabs that lets humans and AI Agents work in parallel in the same environment. Explore its design and value.

A developer fine-tunes a small model with LoRA to extract conversation state, tackling the LLM long-conversation memory problem. A deep dive into the technical approach, dataset design, and the real trade-offs between fine-tuning and prompt engineering.

An in-depth look at using AI LLMs to audit Cloudflare's open-source cryptographic library CIRCL, covering constant-time detection, side-channel vulnerabilities, and human-AI collaboration.

Spring AI 1.0 is here — Java developers can now build AI apps without switching to Python. This guide covers LLM integration, RAG, intelligent customer service, and Agent patterns for enterprise deployment.

AI agents underperforming? The root cause usually isn't the model. This guide breaks down Loop, Harness, and Context Engineering so you can diagnose the real issue fast.

Context engineering is the core methodology for building efficient AI Agents, covering query enhancement, RAG retrieval, prompt design, memory management, and tool invocation. Master Write, Select, Compress, and Isolate to solve LLM hallucination at its root.

Tongyi Qianwen Qwen-Image-3.0 image generation model gets a comprehensive upgrade: supporting 4,500-token ultra-long instructions, pixel-level detail rendering, 12-language knowledge understanding, and ancient painting restoration. This article analyzes its three core capabilities.