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AI Architecture Hub
AI Architecture Hub
May 6, 2026 · Artificial Intelligence

Google’s Five Core Agent Skill Design Patterns: Elevating Agent Skills to a New Design Paradigm

The article explains how, after format standardization removed the bottleneck for enterprise AI agents, the real challenge shifted to internal logic design, and presents five reusable Agent Skill design patterns—Tool Wrapper, Generator, Reviewer, Inversion, and Pipeline—complete with code samples, typical use cases, and best‑practice guidelines for combining and selecting them.

AI AgentsAgent SkillDesign Patterns
0 likes · 18 min read
Google’s Five Core Agent Skill Design Patterns: Elevating Agent Skills to a New Design Paradigm
Old Meng AI Explorer
Old Meng AI Explorer
May 5, 2026 · Artificial Intelligence

Free AI APIs That Won’t Break Your Budget: A Complete Global Guide

This article compiles a comprehensive list of free AI APIs from China and abroad, explains the three hard truths about free tiers, details each provider’s token limits, rate limits, and ideal use cases, and offers practical tips for handling rate‑limiting, key management, and fallback strategies.

AICloud AIFree API
0 likes · 21 min read
Free AI APIs That Won’t Break Your Budget: A Complete Global Guide
Architect
Architect
May 5, 2026 · Artificial Intelligence

From Anthropic to Google: Agent Skills Enter the Design‑Pattern Era

Google Cloud Tech’s recent article outlines five Agent Skill design patterns, building on Anthropic’s earlier work that standardized Skill format and loading, and shows how the community is shifting from merely defining Skill syntax to engineering robust, reusable workflow structures for AI agents.

AI EngineeringAgent SkillsDesign Patterns
0 likes · 25 min read
From Anthropic to Google: Agent Skills Enter the Design‑Pattern Era
Java Architect Essentials
Java Architect Essentials
May 5, 2026 · Artificial Intelligence

Can GPT‑5.5 Really Do Your Work? My Hands‑On Test Shows It Can

After a colleague handed me an error log, I used GPT‑5.5 to trace the problem, discovered it clarifies the troubleshooting path, and then compared ChatGPT Plus and Pro, showing how clear prompts and task intensity determine which tier truly boosts daily productivity.

AI productivityChatGPT PlusChatGPT Pro
0 likes · 6 min read
Can GPT‑5.5 Really Do Your Work? My Hands‑On Test Shows It Can
DataFunTalk
DataFunTalk
May 4, 2026 · Artificial Intelligence

Engineering and Algorithm Innovations for RAG Engines in Office Applications

This article analyzes the challenges and practical solutions of building a Retrieval‑Augmented Generation (RAG) system for office scenarios, covering background issues, modular architecture, offline and online pipelines, hybrid retrieval, ranking models, knowledge filtering, prompt design, and two‑stage generation techniques.

AIDocument ParsingHybrid Retrieval
0 likes · 22 min read
Engineering and Algorithm Innovations for RAG Engines in Office Applications
AI Engineer Programming
AI Engineer Programming
May 4, 2026 · Artificial Intelligence

RAG in the Long-Context Era: Challenges, Benchmarks, and Context Engineering

The article analyzes how expanding LLM context windows to millions of tokens reshape Retrieval‑Augmented Generation, detailing chunking trade‑offs, embedding retrieval limits, attention U‑shaped distribution, benchmark results, and the emerging practice of Context Engineering for optimal end‑to‑end pipelines.

Embedding RetrievalLLMRAG
0 likes · 10 min read
RAG in the Long-Context Era: Challenges, Benchmarks, and Context Engineering
AI Explorer
AI Explorer
May 3, 2026 · Artificial Intelligence

Why a 55k‑Star Open‑Source ‘Skills’ Repo Is a Must‑Have for Engineers Working with AI

The article analyzes Matt Pocock’s highly starred open‑source “skills” repository, explaining how its lightweight Markdown‑based protocols solve common AI coding tool problems—misunderstanding intent and verbosity—by enforcing clear communication, context sharing, and a quick three‑step setup for developers.

AI codingDeveloper WorkflowOpen Source
0 likes · 5 min read
Why a 55k‑Star Open‑Source ‘Skills’ Repo Is a Must‑Have for Engineers Working with AI
AI Architecture Path
AI Architecture Path
May 3, 2026 · Artificial Intelligence

How Matt Pocock’s Open‑Source ‘Skills’ Turns AI Coding from Vibe to Engineer‑Level Precision

Matt Pocock’s open‑source ‘Skills’ framework tackles four common AI‑coding pitfalls—misaligned requirements, verbose output, non‑runnable code, and architectural decay—by providing lightweight, composable skills such as deep‑questioning, domain‑language generation, test‑driven development, and architecture‑guardrails, enabling engineers to guide AI with disciplined, reproducible workflows.

AI codingMatt PocockTypeScript
0 likes · 15 min read
How Matt Pocock’s Open‑Source ‘Skills’ Turns AI Coding from Vibe to Engineer‑Level Precision
Smart Workplace Lab
Smart Workplace Lab
May 2, 2026 · Industry Insights

Prompt Engineer Layoffs: How to Re‑Engineer Your Career Path

As large language models mature, prompt‑writing roles are disappearing, prompting engineers to shift from crafting prompts to designing end‑to‑end AI workflows; this article outlines a three‑step system‑reconstruction protocol, common pitfalls, and practical guidelines for transitioning into workflow architecture.

AI workflowLLMSystem Design
0 likes · 6 min read
Prompt Engineer Layoffs: How to Re‑Engineer Your Career Path
Su San Talks Tech
Su San Talks Tech
May 2, 2026 · Artificial Intelligence

Why GPT-Image-2 Outshines Nano Banana in Every Way

The article reviews the full release of GPT-Image-2, showcases dozens of Chinese prompt examples that generate travel guides, recipe flowcharts, scientific infographics, portrait photography, and Chinese‑style posters, and distills five practical prompt‑engineering rules while linking to a popular GitHub prompt repository.

AI image generationChinese promptsGPT Image 2
0 likes · 18 min read
Why GPT-Image-2 Outshines Nano Banana in Every Way
SpringMeng
SpringMeng
May 2, 2026 · Artificial Intelligence

10 Essential AI Prompt Templates Every Programmer Needs

This article presents ten practical AI prompt templates that help programmers efficiently handle requirement clarification, unit test generation, code explanation, refactoring, exception troubleshooting, performance tuning, SQL creation, knowledge documentation, design review, and cross‑language translation, each illustrated with concrete examples and usage tips.

AI promptingBackend DevelopmentCode Review
0 likes · 13 min read
10 Essential AI Prompt Templates Every Programmer Needs
IT Services Circle
IT Services Circle
May 1, 2026 · Artificial Intelligence

10 Essential AI Prompt Templates Every Programmer Should Use

The article presents ten practical AI prompt templates that cover the full software development workflow—from requirement clarification and code generation to testing, refactoring, debugging, performance tuning, SQL optimization, documentation, design review, and cross‑language translation—helping developers get accurate, production‑ready results from AI.

AI promptingDebuggingJava
0 likes · 12 min read
10 Essential AI Prompt Templates Every Programmer Should Use
AI Explorer
AI Explorer
May 1, 2026 · Artificial Intelligence

Taming AI Code Generators: Essential Shell Skill Set for Real Engineers

The article introduces mattpocock/skills, an open‑source collection of lightweight shell “skills” that structure prompts and shared context to keep AI coding assistants like Claude Code or Codex from misinterpreting requirements, offering quick installation and configuration steps for engineers seeking reliable, controllable AI‑augmented development.

AI assistantsAI codingShell skills
0 likes · 6 min read
Taming AI Code Generators: Essential Shell Skill Set for Real Engineers
AI Waka
AI Waka
Apr 29, 2026 · Artificial Intelligence

Mastering Agent Harness: The Core Architecture Behind Modern AI Systems

The article explains how Agent Harness structures the interaction between user intent and LLM output, detailing its components, long‑conversation handling, layered memory, tool integration, and a four‑stage pipeline demonstrated by an Essay Harness prototype, highlighting design trade‑offs and practical implementation details.

Agent HarnessContext ManagementLLM
0 likes · 22 min read
Mastering Agent Harness: The Core Architecture Behind Modern AI Systems
Architect
Architect
Apr 29, 2026 · Artificial Intelligence

How Claude Code Subagents Keep Context Clean by Isolating Exploration

Long Claude Code sessions get polluted when exploratory commands, logs, and temporary files share the main window, so Subagents run those steps in independent workspaces, returning only concise results and preserving the main context for decision‑making.

AI AgentsAgent HarnessClaude Code
0 likes · 26 min read
How Claude Code Subagents Keep Context Clean by Isolating Exploration
Woodpecker Software Testing
Woodpecker Software Testing
Apr 29, 2026 · Artificial Intelligence

Leveraging ChatGPT to Transform Software Development

The article explains how large language models like ChatGPT can assist software engineers across the entire development lifecycle—requirements, design, coding, testing, and operations—while emphasizing the need for human review due to hallucinations, and presents a PDCA‑style iterative workflow for effective human‑AI collaboration.

AI-assisted testingChatGPTPDCA
0 likes · 4 min read
Leveraging ChatGPT to Transform Software Development
Data Party THU
Data Party THU
Apr 29, 2026 · Artificial Intelligence

Claude Opus 4.7 System Prompt Leak: Decoding Its 10 Core Design Decisions

The article dissects the leaked Claude Opus 4.7 system prompt, revealing ten intertwined design decisions—from treating psychological reconstruction as a danger signal to dynamic safety‑policy upgrades—that together shape the model’s self‑restraint, tool‑use, memory handling, and risk‑aware behavior.

AI safetyClaudeSystem Design
0 likes · 8 min read
Claude Opus 4.7 System Prompt Leak: Decoding Its 10 Core Design Decisions
Selected Java Interview Questions
Selected Java Interview Questions
Apr 29, 2026 · Artificial Intelligence

How to Write Your Own Claude Skill

This guide explains the simple file structure of a Claude Skill, compares it with CLAUDE.md, shows where to store skills at personal, project, or plugin level, and provides detailed best‑practice tips, code examples, and validation steps for creating effective, on‑demand AI agent skills.

Agent SkillsClaudeGit
0 likes · 13 min read
How to Write Your Own Claude Skill
Huolala Tech
Huolala Tech
Apr 29, 2026 · Artificial Intelligence

From MVP to 1.0: A Practical Roadmap for AI‑Powered Test Case Generation

The article analyses the structural bottlenecks of manual test case creation, validates an MVP that keeps human testing logic while automating repetitive steps, identifies three core limitations of the MVP, and then details a 1.0 upgrade that adds multimodal input parsing, prompt engineering, knowledge‑graph RAG and retrieval loops, culminating in measurable productivity gains and a reusable framework for AI‑driven testing.

AI testingMVPknowledge graph
0 likes · 17 min read
From MVP to 1.0: A Practical Roadmap for AI‑Powered Test Case Generation
Su San Talks Tech
Su San Talks Tech
Apr 29, 2026 · Artificial Intelligence

10 Essential AI Prompt Templates Every Programmer Should Use

The article explains why well‑crafted prompts are crucial for AI‑assisted programming, introduces the STAR principle for prompt design, and provides ten ready‑to‑use prompt templates covering requirement analysis, test generation, code explanation, refactoring, debugging, performance tuning, SQL design, documentation, architecture review, and language translation.

AI promptingJavabackend
0 likes · 12 min read
10 Essential AI Prompt Templates Every Programmer Should Use
AI Architecture Hub
AI Architecture Hub
Apr 29, 2026 · Artificial Intelligence

How Subagents Keep Claude Code Context Clean

Long Claude Code sessions quickly become cluttered with every grep, find, and ls command lingering in the context, but using subagents—independent assistants that run tasks in separate windows and return only final results—keeps the context tidy; this article explains what subagents are, how to create them, built‑in options, and context‑forking techniques.

AI assistantsClaude CodeContext Management
0 likes · 8 min read
How Subagents Keep Claude Code Context Clean
AI Explorer
AI Explorer
Apr 28, 2026 · Artificial Intelligence

Open‑Source Skill Pack that Helps AI Engineers Tame Large‑Model Code Assistants

The article introduces the open‑source project mattpocock/skills, which equips developers with interactive “grill” commands to interrogate AI code assistants, align expectations, use a shared ubiquitous language, and integrate the skills in under 30 seconds, aiming to close the communication gap between engineers and large‑model generators.

AI code assistantsGitHubopen-source tools
0 likes · 5 min read
Open‑Source Skill Pack that Helps AI Engineers Tame Large‑Model Code Assistants
IT Services Circle
IT Services Circle
Apr 28, 2026 · Artificial Intelligence

Agent Tool Calls vs. Regular Function Calls: Key Differences Explained

The article explains how LLM‑driven agent tool calls differ from traditional function calls in timing, parameter sourcing, error handling, call‑chain observability, and performance, and it provides concrete examples, failure modes, and interview‑ready summaries.

AI InterviewAgentError Handling
0 likes · 14 min read
Agent Tool Calls vs. Regular Function Calls: Key Differences Explained
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Apr 28, 2026 · Artificial Intelligence

Why Bigger Context Fails for Deep Research Agents and How IterResearch Fixes It

Interviewers point out that simply enlarging the LLM’s context window cannot prevent forgetting early conclusions in long‑step Deep Research tasks; the article explains the ReAct context issues, introduces the IterResearch framework with evolving reports, and compares its accuracy, cost, and scalability against ReAct and ReSum.

Context ManagementIterResearchLLM
0 likes · 17 min read
Why Bigger Context Fails for Deep Research Agents and How IterResearch Fixes It
IoT Full-Stack Technology
IoT Full-Stack Technology
Apr 28, 2026 · Artificial Intelligence

Why Claude Code Feels Like an OS: Inside Anthropic’s 510k‑Line Source

A security researcher uncovered Claude Code’s full 512,000‑line TypeScript source, revealing a sophisticated OS‑like architecture with dynamic prompt assembly, 42 lazily‑loaded tools, multi‑layer security reviews, memory management, and three‑stage compression that together explain why it feels more usable than other AI coding assistants.

AI AgentsAnthropicClaude Code
0 likes · 17 min read
Why Claude Code Feels Like an OS: Inside Anthropic’s 510k‑Line Source
High Availability Architecture
High Availability Architecture
Apr 28, 2026 · Artificial Intelligence

40 Engineered Prompt Templates for Claude, ChatGPT, and Gemini to Generate Expert‑Level Output

After testing over 500 prompts, the author curates 40 structured prompt templates—covering writing, analysis, development, productivity, data interpretation, and communication—that work reliably on Claude, ChatGPT, and Gemini and turn vague instructions into expert‑grade AI output.

AI productivityChatGPTClaude
0 likes · 23 min read
40 Engineered Prompt Templates for Claude, ChatGPT, and Gemini to Generate Expert‑Level Output
Test Development Learning Exchange
Test Development Learning Exchange
Apr 27, 2026 · Artificial Intelligence

30 AI Prompts to Double Office Efficiency and End Overtime

The article presents 30 practical AI prompts covering Excel data handling, document drafting, and general productivity tasks, showing how office staff can copy‑paste these commands to automate formula creation, data cleaning, report writing, meeting summarization, and more, dramatically boosting efficiency and reducing overtime.

AIExcelOffice Automation
0 likes · 7 min read
30 AI Prompts to Double Office Efficiency and End Overtime
DevOps Coach
DevOps Coach
Apr 27, 2026 · Artificial Intelligence

Can You Cut Claude Code’s Token Usage by 75%? A Simple Plugin Shows How

The article demonstrates that Claude Code’s verbose responses waste hundreds of tokens, but a free “caveman” plugin can slash token consumption by up to 75% while preserving answer quality, backed by benchmark data and a research paper on concise replies.

ClaudeLLM cost reductioncaveman plugin
0 likes · 6 min read
Can You Cut Claude Code’s Token Usage by 75%? A Simple Plugin Shows How
SuanNi
SuanNi
Apr 27, 2026 · Artificial Intelligence

Agent Skills Explained: Definition, Structure, and Engineering Practices

This article breaks down the official Anthropic definition of Agent Skills, shows how they are simple file‑system‑based, composable units stored in SKILL.md, scripts, references and assets, and explains the three‑layer progressive‑disclosure loading model, discovery, selection, execution, composition patterns, security, version‑control integration and evaluation practices.

AIAgent SkillsComposable
0 likes · 14 min read
Agent Skills Explained: Definition, Structure, and Engineering Practices
Old Zhang's AI Learning
Old Zhang's AI Learning
Apr 27, 2026 · Artificial Intelligence

Taming Claude Code: A Simple Skill Slashes Unnecessary Code Bloat

The author evaluates a community‑crafted “Karpathy Skills” plugin for Claude Code, applying four concise coding principles, and shows through a controlled experiment that the skill‑guided model produces far fewer superfluous changes—38 lines versus 95—while still fixing the targeted bug and improving code quality.

Claude CodeLLMcode quality
0 likes · 12 min read
Taming Claude Code: A Simple Skill Slashes Unnecessary Code Bloat
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Apr 27, 2026 · Artificial Intelligence

Can Your RAG Pass the Demo? Scaling to 5,000 Docs for Reliable Answers

The article walks through the practical challenges of turning a RAG demo into a production system for 5,000 insurance documents, covering knowledge‑base chunking, embedding model selection, recall‑threshold tuning, hybrid vector‑BM25 retrieval, intent‑aware query routing, prompt constraints, confidence scoring, and operational scaling, with concrete metrics and code examples.

EmbeddingHybrid RetrievalRAG
0 likes · 16 min read
Can Your RAG Pass the Demo? Scaling to 5,000 Docs for Reliable Answers
IoT Full-Stack Technology
IoT Full-Stack Technology
Apr 27, 2026 · Artificial Intelligence

Cut Token Usage by Up to 80% in Claude Code, Codex, and OpenCode

The article explains how to dramatically reduce token consumption in Claude Code, GitHub Copilot's Codex, and the open‑source OpenCode by tightly controlling input, trimming context, filtering files, leveraging tools, caching, and model selection, offering concrete commands, configuration files, and a ten‑step checklist that can cut usage by up to 80%.

AI coding assistantClaudeCodex
0 likes · 11 min read
Cut Token Usage by Up to 80% in Claude Code, Codex, and OpenCode
Wuming AI
Wuming AI
Apr 26, 2026 · Artificial Intelligence

13 Practical Ways to Cut AI Tool Costs

The article outlines thirteen actionable strategies—ranging from choosing the right billing plan and trimming context to using layered models, caching, and proper output prompts—to dramatically reduce token consumption and overall expenses when working with AI services.

AICachingContext Management
0 likes · 10 min read
13 Practical Ways to Cut AI Tool Costs
AI Waka
AI Waka
Apr 26, 2026 · Artificial Intelligence

Unlocking Reliable AI Agents: A Deep Dive into Harness Engineering

The article examines why raw LLM models fail as autonomous coding agents and introduces Harness Engineering—a disciplined scaffold of prompts, tools, context policies, hooks, and sub‑agents—that mitigates context corruption, long‑task collapse, and security risks while cutting token costs by up to 50%.

AI agentHarness EngineeringLLM safety
0 likes · 14 min read
Unlocking Reliable AI Agents: A Deep Dive into Harness Engineering
DataFunTalk
DataFunTalk
Apr 26, 2026 · Artificial Intelligence

Building an Enterprise‑Grade RAG 2.0 System: Architecture, Challenges, and Best Practices

This article analyses the practical construction of an enterprise‑level Retrieval‑Augmented Generation (RAG) 2.0 system, covering background issues of large models, a modular architecture, layered offline/online pipelines, hybrid retrieval, ranking strategies, prompt engineering, and deployment insights drawn from China Mobile’s production experience.

Hybrid RetrievalRAGRanking Models
0 likes · 22 min read
Building an Enterprise‑Grade RAG 2.0 System: Architecture, Challenges, and Best Practices
AI Illustrated Series
AI Illustrated Series
Apr 26, 2026 · Artificial Intelligence

Build Your First LangChain Agent: A Hands‑On Framework Tutorial

This article walks through a practical, step‑by‑step construction of a LangChain agent—from basic concepts and a simple weather‑query agent to a more complex market‑research agent, adding memory and RAG capabilities, and finally comparing LangChain with LangGraph.

AI agentLangChainMemory
0 likes · 15 min read
Build Your First LangChain Agent: A Hands‑On Framework Tutorial
Java Backend Technology
Java Backend Technology
Apr 26, 2026 · Artificial Intelligence

Why Claude Code Says Nothing Unnecessary: Inside Its Minimalist Prompt Design

The article dissects Claude Code’s lean output by exposing the meticulously crafted system prompts that enforce a strict engineering‑assistant role, safety boundaries, concise output rules, and disciplined Git workflows, revealing how each rule curtails hallucination and over‑engineering while enabling reliable, task‑focused code generation.

AI code assistantClaude Codegit-workflow
0 likes · 9 min read
Why Claude Code Says Nothing Unnecessary: Inside Its Minimalist Prompt Design
Test Development Learning Exchange
Test Development Learning Exchange
Apr 26, 2026 · Artificial Intelligence

20 Must‑Know AI Large‑Model Interview Questions for Test Managers (with Answers)

This article examines how AI, especially large language models, is reshaping software testing, covering fundamental concepts, token economics, prompt‑engineering, strengths and limitations, practical use‑cases, ROI calculations, tool selection, data‑security measures, and strategies for upskilling test managers and their teams.

AI testingROITool Evaluation
0 likes · 19 min read
20 Must‑Know AI Large‑Model Interview Questions for Test Managers (with Answers)
Old Meng AI Explorer
Old Meng AI Explorer
Apr 25, 2026 · Artificial Intelligence

Stop Using Vague Prompts – Master GPT Image 2 with Top‑Tier Prompt Templates to End ‘Waste’ Images

The guide explains why GPT Image 2 dramatically reduces low‑quality outputs, outlines five essential prompt elements, provides eight ready‑to‑use scene templates, shares advanced tricks, common pitfalls, and concrete examples to help users generate professional AI images reliably.

AI image generationCJK renderingGPT Image 2
0 likes · 16 min read
Stop Using Vague Prompts – Master GPT Image 2 with Top‑Tier Prompt Templates to End ‘Waste’ Images
PaperAgent
PaperAgent
Apr 25, 2026 · Artificial Intelligence

86K‑Star Repo Turns Karpathy’s Coding Wisdom into Practical AI‑Coding Rules

The article shares four concrete principles distilled from Andrej Karpathy’s experience—captured in the 86.1k‑star "andrej‑karpathy‑skills" repository—to help developers steer large language models toward reliable, concise, and goal‑driven code changes, with installation tips for Claude Code and other AI assistants.

AI codingClaude CodeKarpathy
0 likes · 7 min read
86K‑Star Repo Turns Karpathy’s Coding Wisdom into Practical AI‑Coding Rules
PMTalk Product Manager Community
PMTalk Product Manager Community
Apr 25, 2026 · Product Management

3 Pitfalls I Encountered When Transitioning from Traditional to AI Product Management

A former traditional product manager shares how a naive AI feature request exposed his lack of AI knowledge, why learning programming, algorithms, or certificates didn’t help, and the three practical paths—using AI, building an AI feature, and filling essential basics—to successfully become an AI product manager.

AI fundamentalsAI product managementcost structure
0 likes · 11 min read
3 Pitfalls I Encountered When Transitioning from Traditional to AI Product Management
Woodpecker Software Testing
Woodpecker Software Testing
Apr 25, 2026 · Artificial Intelligence

5 Common Pitfalls in Prompt Testing and Practical Ways to Fix Them

The article analyzes five frequent mistakes teams make when testing LLM prompts—confusing pass with robustness, ignoring implicit assumptions, relying on subjective judgments, lacking version‑aware CI/CD, and missing a human‑AI feedback loop—while offering concrete, data‑backed remedies.

AI quality assuranceLLM testingadversarial testing
0 likes · 8 min read
5 Common Pitfalls in Prompt Testing and Practical Ways to Fix Them
Senior Tony
Senior Tony
Apr 25, 2026 · Industry Insights

Why GPT-Image-2 Outshines Midjourney and Nano Banana and Lowers Design Barriers

The article showcases GPT-Image-2's impressive ability to generate accurate visual and textual content from prompts, explains how its structural understanding resolves previous AI image flaws, and analyzes the disruptive impact on the design industry, including job displacement, cost efficiency, and market oversupply.

AI image generationGPT Image 2design automation
0 likes · 5 min read
Why GPT-Image-2 Outshines Midjourney and Nano Banana and Lowers Design Barriers
AI Illustrated Series
AI Illustrated Series
Apr 25, 2026 · Artificial Intelligence

From "Can Talk" to "Can Act": Deep Dive into Function Calling for AI Agents

The article explains how Function Calling enables large language model agents to overcome knowledge staleness and hallucination by invoking external tools—such as search, email, code execution, and databases—to fetch real‑time data, perform actions, and deliver verifiable, multi‑step responses.

AI AgentsFunction CallingLLM
0 likes · 25 min read
From "Can Talk" to "Can Act": Deep Dive into Function Calling for AI Agents
James' Growth Diary
James' Growth Diary
Apr 25, 2026 · Artificial Intelligence

Choosing the Right AI Memory: Truncation, Summarization, or Vector Retrieval

This article breaks down LangChain.js's three memory strategies—window truncation, summary compression, and vector‑store retrieval—explaining their inner workings, code setup, trade‑offs in token cost and information retention, and provides a decision guide for selecting the best approach in multi‑turn LLM conversations.

Conversation MemoryLLMLangChain
0 likes · 14 min read
Choosing the Right AI Memory: Truncation, Summarization, or Vector Retrieval
Su San Talks Tech
Su San Talks Tech
Apr 25, 2026 · Backend Development

35 Practical Claude Code Tips to Supercharge Your Development

This guide presents 35 hands‑on Claude Code techniques—from project initialization and session management to code quality, architecture reviews, automation, and debugging—each with ready‑to‑copy commands or prompts that help developers streamline AI‑assisted software creation.

AI code assistantClaude CodeDevOps
0 likes · 17 min read
35 Practical Claude Code Tips to Supercharge Your Development
Lao Guo's Learning Space
Lao Guo's Learning Space
Apr 25, 2026 · Artificial Intelligence

30 Proven Prompt Templates to Unlock Tongyi Lingma’s Full Potential

This guide compiles the 30 most effective prompt templates for Alibaba's Tongyi Lingma code‑assistant, explains its three interaction modes, and offers concrete examples—from code generation and unit‑test creation to multi‑file refactoring—plus five universal tips to double output quality.

AI coding assistantDebuggingTongyi Lingma
0 likes · 13 min read
30 Proven Prompt Templates to Unlock Tongyi Lingma’s Full Potential
AI Architecture Path
AI Architecture Path
Apr 25, 2026 · Artificial Intelligence

Claude Design Shakes Up Design Tools Market as Prompt Library Leaks on GitHub

Claude Design’s preview launch on April 17 triggered a sharp drop in major design‑tool stocks, showcases a natural‑language driven workflow that generates interactive UI, PPT and code, reveals high token consumption and uniform styling limits, and has its full system prompts publicly leaked on GitHub, signaling a major shift in the AI‑design landscape.

AI designClaude Designdesign automation
0 likes · 8 min read
Claude Design Shakes Up Design Tools Market as Prompt Library Leaks on GitHub
ZhiKe AI
ZhiKe AI
Apr 25, 2026 · Industry Insights

Harness Engineering: The Hottest New AI Engineering Paradigm of 2026

Harness Engineering, now buzzing across the tech community, promises a ten‑fold productivity boost by replacing hand‑written code with a structured AI‑driven system, and the article breaks down its definition, evolution from Prompt to Context to Harness, core components, real‑world examples, and the associated risks and debates.

AI SystemsAI safetyHarness Engineering
0 likes · 9 min read
Harness Engineering: The Hottest New AI Engineering Paradigm of 2026
Model Perspective
Model Perspective
Apr 24, 2026 · Artificial Intelligence

GPT-Image-2 Shows Near-Perfect Chinese Text Rendering and Dominates Arena.ai Rankings

OpenAI’s GPT‑Image‑2, released on April 21, instantly topped the Arena.ai leaderboard with an Elo of 1512, dramatically improving multilingual text accuracy to over 99%, introducing a planning‑based “Thinking Mode”, supporting arbitrary aspect ratios up to 2K, while still facing spatial‑precision limits and a paid‑only advanced mode.

AI image generationArena.ai leaderboardGPT Image 2
0 likes · 16 min read
GPT-Image-2 Shows Near-Perfect Chinese Text Rendering and Dominates Arena.ai Rankings
AI Explorer
AI Explorer
Apr 24, 2026 · Artificial Intelligence

Hands‑On Large‑Model Tutorial: From Fine‑Tuning to Security Attacks (34k‑Star Repo)

This article introduces the open‑source "Dive into LLMs" tutorial (34k+ GitHub stars) that offers a complete, hands‑on workflow for large language models—from fine‑tuning and deployment to prompt engineering, knowledge editing, math reasoning, watermarking, and jailbreak security experiments—along with step‑by‑step Jupyter notebooks and easy setup instructions.

AI securityJupyter NotebookLLM tutorial
0 likes · 6 min read
Hands‑On Large‑Model Tutorial: From Fine‑Tuning to Security Attacks (34k‑Star Repo)
Woodpecker Software Testing
Woodpecker Software Testing
Apr 24, 2026 · Artificial Intelligence

Transforming Testing Teams for Large Language Models: A Practical Guide

The article explains why traditional deterministic testing fails for LLMs, introduces the ‘trust triangle’ quality model, describes data‑centric and lifecycle‑shifted testing practices, and outlines organizational structures—embedded test scientists or central evaluation centers—that enable reliable, safe AI deployment.

AI trustworthinessAdversarial EvaluationLLM testing
0 likes · 7 min read
Transforming Testing Teams for Large Language Models: A Practical Guide
Design Hub
Design Hub
Apr 24, 2026 · Industry Insights

Anthropic Postmortem: Claude Code Decline Due to Product‑Layer Changes

Anthropic’s detailed postmortem explains that recent user‑perceived declines in Claude Code’s reasoning depth, context retention, and response length stemmed from three product‑layer adjustments—a lowered default reasoning effort, a caching bug that repeatedly cleared thinking, and an overly restrictive system prompt—rather than any degradation of the underlying model itself.

AI product engineeringAnthropicClaude Code
0 likes · 15 min read
Anthropic Postmortem: Claude Code Decline Due to Product‑Layer Changes
AI Waka
AI Waka
Apr 24, 2026 · Artificial Intelligence

One Loop, Three Modes: A Practical Guide to Multi‑Agent Orchestration

The article explains how treating an AI system as multiple specialized agents—delegator, worker, and reviewer—running the same loop but with different configurations can prevent context overload, and it details three orchestration patterns (delegation, swarm, coordinator) along with tool partitioning to ensure reliable, scalable multi‑agent workflows.

AI AgentsOrchestrationmulti-agent
0 likes · 15 min read
One Loop, Three Modes: A Practical Guide to Multi‑Agent Orchestration
SpringMeng
SpringMeng
Apr 24, 2026 · Backend Development

35 Practical Claude Code Tips with Ready‑to‑Use Commands

This guide presents 35 concrete Claude Code techniques—each with a ready command or prompt—to streamline project bootstrapping, session handling, code quality, architecture, API design, automation, debugging, and recovery for faster, more reliable software development.

AI coding assistantClaude Codeautomation
0 likes · 15 min read
35 Practical Claude Code Tips with Ready‑to‑Use Commands
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 24, 2026 · Artificial Intelligence

How Hermes Agent Achieves Self‑Evolution: A Deep Dive into Prompt, Context, and Harness Design

This article provides a detailed technical analysis of Hermes Agent, explaining how its dynamic skill generation and reinforcement‑learning loop enable true self‑evolution, and examines the prompt engineering, context compression, memory architecture, harness mechanisms, error handling, and plugin ecosystem that differentiate it from OpenClaw and Claude Code.

Agent FrameworkContext CompressionHermes Agent
0 likes · 41 min read
How Hermes Agent Achieves Self‑Evolution: A Deep Dive into Prompt, Context, and Harness Design
AI Engineer Programming
AI Engineer Programming
Apr 24, 2026 · Artificial Intelligence

From Prompt to Context to Harness Engineering: The Next Evolution of AI Agent Design

The article traces the shift from Prompt Engineering to Context Engineering and now Harness Engineering, analyzing their origins, methods, limitations, and future directions such as Coordination, Intent, Ecosystem, and Cognition engineering, while emphasizing the decreasing human involvement and increasing system autonomy.

AI AgentsAgent SystemsContext Engineering
0 likes · 24 min read
From Prompt to Context to Harness Engineering: The Next Evolution of AI Agent Design
Wuming AI
Wuming AI
Apr 23, 2026 · Artificial Intelligence

Redefining OpenClaw’s Soul: From Obedience to Assertiveness

The article explains why many OpenClaw users overlook core configuration files, then shows how customizing SOUL.md and IDENTITY.md can give the AI personality, judgment, and boundaries, providing sample settings and practical advice to turn the agent into a collaborative, assertive personal assistant.

AI agentIDENTITY.mdOpenClaw
0 likes · 9 min read
Redefining OpenClaw’s Soul: From Obedience to Assertiveness
PMTalk Product Manager Community
PMTalk Product Manager Community
Apr 23, 2026 · Product Management

From Manufacturing to AI Product Management: My Journey and Lessons

The author recounts a six‑month transition from traditional manufacturing to AI product management, outlining how AI reshapes workflows, the pitfalls of superficial efficiency, and four key transformations that turn a product manager into a workflow architect who defines tools, delegates prompts, and flips the prototype‑first development model.

AI product managementAI toolscareer transition
0 likes · 10 min read
From Manufacturing to AI Product Management: My Journey and Lessons
Data Party THU
Data Party THU
Apr 23, 2026 · Artificial Intelligence

The Complete 2026 Agentic AI Engineer Roadmap: A Systematic Learning Path

This guide presents a step‑by‑step roadmap for becoming an Agentic AI engineer in 2026, covering Python fundamentals, LLM concepts, framework selection, advanced memory management, tool integration, production deployment, and interview preparation with concrete examples and best‑practice recommendations.

LLMLangGraphPython
0 likes · 10 min read
The Complete 2026 Agentic AI Engineer Roadmap: A Systematic Learning Path
PaperAgent
PaperAgent
Apr 23, 2026 · Artificial Intelligence

Stop RAG, Navigate Enterprise Knowledge Directly with CORPUS2SKILL

The article critiques traditional RAG’s blind spots, introduces CORPUS2SKILL’s offline‑compile, online‑navigate two‑stage architecture that builds a hierarchical topic tree and progressive‑disclosure skill files, and shows through WixQA benchmarks that this approach outperforms dense retrieval and Agentic RAG on F1, factuality and recall while highlighting cost and hierarchy quality trade‑offs.

Hierarchical ClusteringRAGagentic AI
0 likes · 7 min read
Stop RAG, Navigate Enterprise Knowledge Directly with CORPUS2SKILL
Su San Talks Tech
Su San Talks Tech
Apr 23, 2026 · Artificial Intelligence

The Ultimate AI‑Powered Coding Workflow

The author details a two‑month experiment that combines Claude Code, Codex, and Gemini into a four‑step AI‑driven development pipeline, showing how each model’s strengths complement the others to double coding efficiency for medium‑to‑large projects.

AI coding workflowClaude CodeCodex
0 likes · 11 min read
The Ultimate AI‑Powered Coding Workflow
AndroidPub
AndroidPub
Apr 23, 2026 · Fundamentals

Why Computational Thinking Is the Must-Have Skill for Programmers in the AI Coding Era

As AI code generators master syntax, the article argues that programmers must cultivate computational thinking—decomposition, abstraction, pattern recognition, and algorithm design—to stay indispensable, offering concrete examples, research findings, and practical guidelines for effective AI collaboration.

AI CollaborationAI programmingalgorithm design
0 likes · 14 min read
Why Computational Thinking Is the Must-Have Skill for Programmers in the AI Coding Era
ShiZhen AI
ShiZhen AI
Apr 22, 2026 · Artificial Intelligence

35 Practical Claude Code Tips with Ready‑to‑Use Commands

This article presents 35 hands‑on Claude Code techniques, each paired with a ready‑to‑use command or prompt, covering project initialization, session management, code quality, architecture review, automation, documentation, dependency handling, debugging, and recovery to help developers harness the AI coding assistant efficiently.

AI assistantClaude CodeDebugging
0 likes · 18 min read
35 Practical Claude Code Tips with Ready‑to‑Use Commands
Sohu Tech Products
Sohu Tech Products
Apr 22, 2026 · Artificial Intelligence

What Is Harness Engineering and How to Use It in Your Projects?

Harness Engineering, the set of systems that surround and extend a large‑language‑model‑based agent, determines real‑world performance far more than the model itself, and mastering its six‑layer architecture, bottlenecks, and practical rollout steps is essential for AI‑agent development and interview preparation.

AI AgentsAgent ArchitectureContext Engineering
0 likes · 20 min read
What Is Harness Engineering and How to Use It in Your Projects?
DataFunSummit
DataFunSummit
Apr 22, 2026 · Artificial Intelligence

Why the Overlooked Agent Harness Is the Real Reason AI Projects Fail

The article explains that the hidden infrastructure layer called Agent Harness—responsible for prompt, context, and tool orchestration—determines whether impressive AI agent demos can survive production, highlighting issues like context rot, compounding errors, verification loops, and concrete benchmark improvements.

AI AgentsAgent HarnessContext Management
0 likes · 14 min read
Why the Overlooked Agent Harness Is the Real Reason AI Projects Fail
IT Services Circle
IT Services Circle
Apr 22, 2026 · Artificial Intelligence

GPT-Image-2 Launches: How Designers Can Ditch Old‑School Workflows

OpenAI's newly released ChatGPT Images 2.0 (GPT‑Image‑2) lets users generate photorealistic screenshots, posters, and even homework from ultra‑short prompts, outperforms the previous Nano Banana model, supports 2K resolution, multi‑language input, and is already available via API with pricing details.

AI modelChatGPT Images 2.0OpenAI
0 likes · 7 min read
GPT-Image-2 Launches: How Designers Can Ditch Old‑School Workflows
Tech Minimalism
Tech Minimalism
Apr 22, 2026 · Artificial Intelligence

14 Reusable Agent Skill Design Patterns from Anthropic’s Official Best Practices

Anthropic’s official skill authoring guide outlines fourteen reusable design patterns for Agent Skills—grouped into discovery & selection, context economy, instruction calibration, workflow control, and executable code—each with concrete examples, trade‑offs, and practical tips to help developers craft effective, token‑efficient Claude extensions.

AIAgent SkillsAnthropic
0 likes · 21 min read
14 Reusable Agent Skill Design Patterns from Anthropic’s Official Best Practices
AI Illustrated Series
AI Illustrated Series
Apr 22, 2026 · Artificial Intelligence

Mastering AI Agent Skills: From Concept to Hands‑On Implementation

This guide explains what Agent Skills are, how they differ from traditional prompts, the three core design mechanisms, step‑by‑step creation of a Skill—including file structure, YAML metadata, and markdown instructions—plus advanced tips, real‑world use cases, and troubleshooting advice.

AI AgentsAgent SkillsWorkflow orchestration
0 likes · 30 min read
Mastering AI Agent Skills: From Concept to Hands‑On Implementation
AI Waka
AI Waka
Apr 22, 2026 · Artificial Intelligence

Unlock Better AI Results: Harvard‑Backed Prompt Skills You Can Apply Today

Drawing on Harvard research, BCG studies, and major AI platform guidelines, this article reveals three concrete prompt‑engineering skills—task definition, contextual grounding, and output testing—plus actionable checklists that let everyday users instantly boost the quality, speed, and reliability of generative AI outputs.

AI productivityHarvard researchLLM best practices
0 likes · 13 min read
Unlock Better AI Results: Harvard‑Backed Prompt Skills You Can Apply Today
PMTalk Product Manager Community
PMTalk Product Manager Community
Apr 22, 2026 · Product Management

What Real AI Product Managers Look Like: Insights from Analyzing 200 Job Listings

Analyzing 200 AI product‑manager job ads reveals that 70% of roles don’t require deep AI knowledge, salaries depend more on industry expertise than technical depth, prompt engineering has become a baseline skill, and the most valuable talent are those who can ship end‑to‑end AI products.

AI product managercareer advicejob market analysis
0 likes · 17 min read
What Real AI Product Managers Look Like: Insights from Analyzing 200 Job Listings
ZhiKe AI
ZhiKe AI
Apr 22, 2026 · Artificial Intelligence

Why Harness Engineering Is the Hottest AI Engineering Paradigm in 2026

The article explains how the emerging "Harness Engineering" paradigm—highlighted by OpenAI, Stripe and Anthropic—shifts AI development from prompt tweaking to building full control systems, promising ten‑fold efficiency gains, new architectural components, and both opportunities and risks for developers.

AI EngineeringHarness EngineeringRisk analysis
0 likes · 9 min read
Why Harness Engineering Is the Hottest AI Engineering Paradigm in 2026
CodeTrend
CodeTrend
Apr 21, 2026 · Artificial Intelligence

AI Agents for Beginners: A Zero‑Prerequisite Course Overview

This article breaks down Microsoft’s open‑source AI‑Agent learning repository, explaining core concepts, five design patterns, production deployment considerations, and emerging protocols, while offering practical engineering guidance for building reliable multi‑agent systems from scratch.

AI AgentsAgentic RAGmetacognition
0 likes · 10 min read
AI Agents for Beginners: A Zero‑Prerequisite Course Overview
Code Mala Tang
Code Mala Tang
Apr 21, 2026 · Artificial Intelligence

Turn a Simple AGENTS.md into a Senior Engineer’s Playbook for AI Coding Assistants

AGENTS.md is a concise, project‑root file that guides AI coding assistants like Claude Code, Codex, and Cursor to behave like senior engineers by enforcing non‑negotiable rules, minimal changes, verification‑first execution, and clear communication, all distilled from Karpathy’s failure principles and Boris Cherny’s workflow.

AI coding agentsLLM best practicesagentic AI
0 likes · 22 min read
Turn a Simple AGENTS.md into a Senior Engineer’s Playbook for AI Coding Assistants
Ops Community
Ops Community
Apr 21, 2026 · Artificial Intelligence

How to Tame Unstable LLM Prompts: Causes and Fixes

This article explains why large‑model prompts can yield inconsistent answers, examines the roles of temperature, top‑p/top‑k, tokenization, context windows, position bias, and model randomness, and provides a step‑by‑step debugging workflow and production‑grade best‑practice checklist to achieve stable outputs.

DebuggingLLM stabilityTemperature
0 likes · 13 min read
How to Tame Unstable LLM Prompts: Causes and Fixes
AI Programming Lab
AI Programming Lab
Apr 21, 2026 · Artificial Intelligence

Mastering Claude Code’s 1M Context: Anthropic’s Five Essential Management Strategies

The article breaks down Anthropic’s official guidance on handling Claude Code’s expanded 1‑million‑token context window, explaining the concept of context rot and detailing five concrete actions—Continue, Rewind, Clear, Compact, and Subagents—along with when and how to apply each to keep the model focused and cost‑effective.

AI coding assistantAnthropicClaude Code
0 likes · 11 min read
Mastering Claude Code’s 1M Context: Anthropic’s Five Essential Management Strategies
Yunqi AI+
Yunqi AI+
Apr 21, 2026 · Artificial Intelligence

What We Learned Building Production‑Grade AI Agents: A Retrospective

The article reviews a year of production‑grade AI agent deployments, revealing that engineering challenges—data handling, rule governance, workflow integration, context quality, and clear boundaries—are far more critical than model performance for successful real‑world adoption.

AI Agentsproduction engineeringprompt engineering
0 likes · 9 min read
What We Learned Building Production‑Grade AI Agents: A Retrospective
Frontend AI Walk
Frontend AI Walk
Apr 21, 2026 · Artificial Intelligence

How to Distill Any Expert into an AI Skill: Elon Musk SOP Guide

This article walks you through a complete knowledge‑distillation workflow that turns Elon Musk’s decision‑making logic into a reusable AI skill, covering source collection, Obsidian setup, a six‑step prompting chain, adding personal commentary, and packaging the result for manual or automated AI use.

AI workflowClaudeElon Musk
0 likes · 21 min read
How to Distill Any Expert into an AI Skill: Elon Musk SOP Guide
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Apr 21, 2026 · Industry Insights

Is Vibe Coding the Next Revolution in Software Development?

The article analyzes how AI‑driven "Vibe Coding" is shifting programming from line‑by‑line logic to intent‑driven natural‑language interaction, presents data on developer adoption, compares three programming eras, examines tool ecosystems, showcases real‑world case studies, and outlines the skills developers must master to stay relevant in 2026.

AI programmingLow‑codeVibe Coding
0 likes · 25 min read
Is Vibe Coding the Next Revolution in Software Development?
Su San Talks Tech
Su San Talks Tech
Apr 21, 2026 · Artificial Intelligence

How to Turn Bad Prompts into High‑Scoring AI Prompts: A Step‑by‑Step Guide

This article walks through a complete prompt‑engineering workflow—starting from a weak baseline, building an evaluation pipeline, and applying four concrete techniques (clarity, specificity, XML structuring, and examples) that lift a Claude score from 3.4 to over 9, with code, metrics, and real‑world examples.

AIClaudeXML
0 likes · 19 min read
How to Turn Bad Prompts into High‑Scoring AI Prompts: A Step‑by‑Step Guide
Architect's Must-Have
Architect's Must-Have
Apr 21, 2026 · Artificial Intelligence

30 Essential AI Agent Concepts: From LLMs to Multi‑Agent Systems

This comprehensive guide systematically explains thirty core terms of AI agents—covering foundational large language models, fine‑tuning techniques, multimodal vision‑language models, agent architectures such as ReAct and CoT, tool‑calling protocols, retrieval‑augmented generation, workflow orchestration, and emerging product forms like autonomous and embodied agents—while detailing the reasoning, trade‑offs, and concrete examples that shape modern agent engineering.

AI AgentsEmbodied AIRAG
0 likes · 36 min read
30 Essential AI Agent Concepts: From LLMs to Multi‑Agent Systems
AI Waka
AI Waka
Apr 21, 2026 · Artificial Intelligence

Why Massive Prompts Fail and How Skills Transform AI Agents

The article explains how monolithic system prompts become costly, unreliable, and hard to maintain as AI agents grow, and demonstrates a modular Skill‑based architecture that loads knowledge on demand, improves scalability, debugging, and reuse.

AIAgentModular Design
0 likes · 13 min read
Why Massive Prompts Fail and How Skills Transform AI Agents
AI Architecture Hub
AI Architecture Hub
Apr 21, 2026 · Artificial Intelligence

Why Harness Architecture Turns LLMs into Production‑Ready Agents

This article explains why the Harness architecture—linking prompts, context, and runtime support—is the decisive factor that turns large language models from demo prototypes into reliable production agents, detailing its core capabilities, structural components, execution loop, design trade‑offs, and industry trends.

AI OperationsAgent HarnessContext Management
0 likes · 35 min read
Why Harness Architecture Turns LLMs into Production‑Ready Agents
FunTester
FunTester
Apr 20, 2026 · Artificial Intelligence

Why Self‑Evaluating Agents Fail and How to Build Reliable Multi‑Agent Systems

The article analyzes why letting the same AI Agent generate and self‑evaluate results in over‑confident but flawed outputs, especially for subjective tasks, and proposes a three‑stage multi‑agent architecture with independent evaluation, concrete standards, and prompt‑based calibration to improve reliability as models evolve.

AISystem Designevaluation
0 likes · 9 min read
Why Self‑Evaluating Agents Fail and How to Build Reliable Multi‑Agent Systems
DeepHub IMBA
DeepHub IMBA
Apr 20, 2026 · Artificial Intelligence

What 10 Core Design Decisions the Claude Opus 4.7 Prompt Leak Reveals

The leaked Claude Opus 4.7 system prompt exposes ten intertwined design choices—ranging from treating psychological reconstruction as a danger signal to prohibiting over‑politeness, treating tool calls as cost‑free, using natural language as memory cues, and dynamically upgrading safety—illustrating a pattern of self‑regulation rather than pure capability enhancement.

AI safetyBehavioral ConstraintsClaude
0 likes · 8 min read
What 10 Core Design Decisions the Claude Opus 4.7 Prompt Leak Reveals
AI Code to Success
AI Code to Success
Apr 20, 2026 · Artificial Intelligence

Why Identical LLMs Behave So Differently: Inside the Agent Harness Architecture

The article dissects the Agent Harness concept—covering its definition, three engineering layers, twelve production‑grade components, detailed orchestration loops, context‑management tricks, verification strategies, and how frameworks like Anthropic, OpenAI, LangChain, CrewAI and AutoGen implement these patterns, revealing why the same model can yield wildly different results.

AI AgentsAgent HarnessContext Management
0 likes · 21 min read
Why Identical LLMs Behave So Differently: Inside the Agent Harness Architecture
Architect's Ambition
Architect's Ambition
Apr 20, 2026 · Artificial Intelligence

How to Turn GitHub‑Trending AI Skills into Real‑World Agents with Knowledge Distillation

The article explains why generic AI is insufficient, defines a Skill as the minimal unit of specialized AI, and details a three‑layer knowledge‑distillation methodology—knowledge, logic, style—to build practical person‑ and book‑based AI Skills, illustrated with a complete Wang Yangming Skill implementation and common pitfalls.

AI SkillKnowledge Distillationagent development
0 likes · 12 min read
How to Turn GitHub‑Trending AI Skills into Real‑World Agents with Knowledge Distillation
Baobao Algorithm Notes
Baobao Algorithm Notes
Apr 20, 2026 · Industry Insights

From Prompt Writer to Harness Architect: Redefining the Algorithm Engineer in the LLM Era

The article analyzes how the rise of foundation models shifts algorithm engineers from hand‑crafting models to building robust Harness environments, detailing OpenAI’s agent‑first experiments, the new "Model + Harness" formula, and practical steps for staying valuable in a prompt‑centric world.

AI EngineeringHarness architectureLLM
0 likes · 9 min read
From Prompt Writer to Harness Architect: Redefining the Algorithm Engineer in the LLM Era
AI Waka
AI Waka
Apr 20, 2026 · Artificial Intelligence

How to Build Powerful Claude Skills: A Step‑by‑Step Guide

Learn how to design, write, test, and deploy reusable Claude Skills—custom instruction sets that automate document processing, code review, content creation, and data handling—by defining goals, crafting SKILL.md, adding scripts, creating trigger phrases, and measuring performance with concrete examples.

AIClaudeautomation
0 likes · 15 min read
How to Build Powerful Claude Skills: A Step‑by‑Step Guide