frontend design
Improving Frontend Design with Claude Skills If you’ve ever asked a large language model (LLM) like Claude to quickly produce a web page — industry slang: “landing page” — chances are you’ve seen the same tired formula: * Inter font * White background with a purple gradient * Optional and forgettable animations Why
SpringAI
# Introduction As a seasoned Java professional, I’ve closely followed the language's role in the AI space. Due to **inherent limitations**, however, **Java is unlikely to become a mainstream AI development language**. That said, Java **does** have a place in the AI ecosystem — especially as a **backend request-forwarding
AI testing
0. One-Sentence Summary By leveraging the Aone Copilot Agent with well-crafted prompts, AI can automate test case generation and code modifications. Practice results show an AI code adoption rate of around 50%, with further improvement requiring continuous prompt optimization. --- 1. Project Background & Requirements 1.1 Business Context In
AI engineering
 # AI-Assisted Engineering vs. Vibe Coding: Balancing Speed and Quality Atmosphere (or **Vibe**) coding emphasizes **rapid prototyping and exploration**, while **AI-assisted engineering** insists on **human control of architecture, code review, and production quality**. This guide explores how AI reshapes
LLM architecture
Table of Contents * What is an Agent? * Why did Agents emerge? * How to implement an Agent * Summary --- Introduction Since the launch of ChatGPT, the industry has actively explored practical applications of LLMs (Large Language Models). This article outlines the evolution of LLM implementation approaches — from Prompt Engineering → Chain Orchestration
LLM
# Context Engineering *Part of the “Machine Learning for Engineers” series*  **Previous recap:** *Vector Distance Metrics* --- ## Introduction: From Chatbot to Decision Engine Large Language Models (LLMs) have shifted from being **casual chatbots** to **core decision-making components** in complex
Google Cloud
Google Launches LLM-Evalkit for Structured Prompt Engineering Date: 2025-10-29 08:24 Beijing Google has introduced LLM-Evalkit, a new open-source framework designed to bring structure, measurability, and collaboration to prompt engineering for large language models. --- Overview Built on the Vertex AI SDK, LLM-Evalkit replaces guesswork-driven workflows with data-backed, unified processes.
context engineering
# 2025-10-24 · Zhejiang   --- ## Introduction With the rapid growth of AI Agents, a new term — **Context Engineering** — has emerged. Many are asking: - How does it differ
AI agents
How to Actually Use Agents — A Practical Guide This is a real-world, production-level story about implementing AI Agents to boost a team’s speed and efficiency. It includes: * Background context and strategy * Before-and-after cost and productivity metrics * Failures, challenges, and lessons learned * A linked public handbook for production-ready Agent implementation
LLM-Evalkit
Google Launches LLM-Evalkit: A Structured Approach to Prompt Engineering LLM-Evalkit is Google’s new open-source framework for prompt engineering, built on Vertex AI SDKs. It’s designed to replace scattered notes, disorganized experiments, and trial-and-error guesswork with a unified, data-driven workflow. --- Why It Matters As Michael Santoro points out,
AI coding
AI-Assisted Java Backend Development — Workflow & Best Practices --- Business Scenario Back-end Java business code generation for a financial-grade system with accelerated iteration cycles. AI Solution Overview * System design analysis → * Core element extraction → * Task list generation → * AI tools with tailored prompts for end-to-end code generation. Tooling * CodeFuse IDE * CodeFuse IDEA
AI programming
# Table of Contents 1. [The Birth and Challenges of VibeCoding](#the-birth-and-challenges-of-vibecoding) 2. [Returning to the “Origin” to Examine Communication Challenges](#returning-to-the-origin-to-examine-communication-challenges) 3. [Writing Code vs. Reading Code](#writing-code-vs-reading-code) 4. [Prompts vs. Context Engineering](#prompts-vs-context-engineering) 5. [Some Insights](#some-insights) --- Have you ever experienced this: AI outputs feeling like a **“random