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Prompt Engineering

1599 articles · Page 7 of 16
Golang Shines
Golang Shines
Apr 24, 2026 · Artificial Intelligence

Can One Developer Do the Work of Five? Exploring GitHub Copilot CLI’s /fleet Parallel Agent Feature

GitHub Copilot CLI’s /fleet command turns the AI assistant into an orchestrator that splits a large task into parallel subtasks, runs multiple agents with separate context windows, and aggregates the results, letting a single developer modify several files, run tests, and update documentation simultaneously.

AI orchestrationCLIGitHub Copilot
0 likes · 6 min read
Can One Developer Do the Work of Five? Exploring GitHub Copilot CLI’s /fleet Parallel Agent Feature
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 AgentsMulti-agentOrchestration
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 CodePrompt Engineering
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 FrameworkHermes AgentPrompt Engineering
0 likes · 41 min read
How Hermes Agent Achieves Self‑Evolution: A Deep Dive into Prompt, Context, and Harness Design
Ubiquitous Tech
Ubiquitous Tech
Apr 24, 2026 · Artificial Intelligence

Why GPT‑Image‑2 Is the New King of AI Image Generation

OpenAI’s GPT‑Image‑2 model dramatically upgrades visual generation with high‑resolution output, automatic fidelity, lower cost, and versatile prompt‑to‑image capabilities, and the author demonstrates its performance through ten detailed prompts, cost analysis, installation steps, and comparisons with previous models.

AI image generationGPT Image 2OpenAI
0 likes · 11 min read
Why GPT‑Image‑2 Is the New King of AI Image Generation
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 AgentsContext EngineeringLLM
0 likes · 24 min read
From Prompt to Context to Harness Engineering: The Next Evolution of AI Agent Design
Linyb Geek Road
Linyb Geek Road
Apr 24, 2026 · Artificial Intelligence

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

The article distills Anthropic’s official skill‑authoring guidance into fourteen reusable design patterns—grouped into discovery & selection, context economy, instruction calibration, workflow control, and executable code—explaining their purpose, trade‑offs, and practical examples for building effective Claude Agent Skills.

AI AgentAgent SkillsAnthropic
0 likes · 19 min read
14 Reusable Agent Skill Design Patterns from Anthropic’s Official Best Practices
Linyb Geek Road
Linyb Geek Road
Apr 24, 2026 · Artificial Intelligence

How Anthropic Engineers Turn Skills into Stable, Reusable Agent Assets

AI teams often waste effort on prompts that break in new scenarios, but Anthropic engineers show that upgrading prompts to engineered "Skills"—standardized work units with documentation, scripts, and validation—enables agents to reliably reuse team experience across workflows.

AI AgentAnthropicClaude
0 likes · 12 min read
How Anthropic Engineers Turn Skills into Stable, Reusable Agent Assets
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 toolsPrompt Engineering
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.

LLMLangGraphProduction Deployment
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.

Prompt EngineeringRAGagentic 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 programmingPrompt Engineering
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 AgentsContext EngineeringPrompt 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 HarnessPrompt Engineering
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 SkillsPrompt Engineering
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 managerPrompt EngineeringSalary Trends
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 engineeringPrompt 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 RAGProduction Deployment
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 practicesPrompt Engineering
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 stabilityPrompt Engineering
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 programmingPrompt EngineeringVibe 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.

AIClaudeEvaluation
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 AILarge Language Models
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 HarnessLLM engineering
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.

AIEvaluationMulti-agent
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 HarnessLLM infrastructure
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 SkillPrompt Engineeringagent 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.

AIClaudePrompt Engineering
0 likes · 15 min read
How to Build Powerful Claude Skills: A Step‑by‑Step Guide
Top Architecture Tech Stack
Top Architecture Tech Stack
Apr 20, 2026 · Artificial Intelligence

Using Claude Code in VS Code: Install, Setup, and First Tasks

This guide walks developers through installing Claude Code, integrating its VS Code extension, configuring the local environment, opening real projects, and assigning concrete tasks—such as code explanation, small feature addition, bug fixes, or demo generation—while offering best‑practice tips for prompt specificity, workflow division, and long‑term usage options.

AI coding assistantClaude CodePrompt Engineering
0 likes · 9 min read
Using Claude Code in VS Code: Install, Setup, and First Tasks
Frontend AI Walk
Frontend AI Walk
Apr 20, 2026 · Artificial Intelligence

Build an AI‑Powered Content Creation Workflow: Distill Experts, Master a Niche in 7 Days, and Let AI Do the Heavy Lifting

This guide shows content creators how to set up a practical AI toolchain—using Obsidian to distill expert knowledge into a searchable vault, applying a 7‑day vertical‑niche sprint to define positioning, and allocating 50% of daily work time to Claude and other AI tools as a topic radar and second brain—complete with SOPs, checklists, and common pitfalls.

AIClaudeObsidian
0 likes · 15 min read
Build an AI‑Powered Content Creation Workflow: Distill Experts, Master a Niche in 7 Days, and Let AI Do the Heavy Lifting
大转转FE
大转转FE
Apr 20, 2026 · Industry Insights

What’s Driving the Next Wave of AI Agents? A Deep Dive into OpenClaw, DeerFlow, YC Insights, and Card‑Based Dialogues

This newsletter curates five cutting‑edge industry analyses covering ByteDance’s open‑source Agent evolution framework, OpenClaw’s Prompt/Context/Harness design, DeerFlow 2.0’s Super Agent runtime, YC’s architecture‑first efficiency lessons, and a systematic protocol for card‑based conversational interfaces.

AI AgentsPrompt Engineeringagent architecture
0 likes · 5 min read
What’s Driving the Next Wave of AI Agents? A Deep Dive into OpenClaw, DeerFlow, YC Insights, and Card‑Based Dialogues
Qborfy AI
Qborfy AI
Apr 20, 2026 · Artificial Intelligence

How Harness Engineering Lifted LangChain Agents into the Top 5 on Terminal Bench 2.0

LangChain’s Harness Engineering framework tuned system prompts, tool selection, and middleware to turn a rank‑30 programming agent into a top‑5 performer on Terminal Bench 2.0, using trace‑driven analysis, inference‑sandwich scheduling, and context engineering without changing the underlying model.

AI AgentsMiddlewarePrompt Engineering
0 likes · 12 min read
How Harness Engineering Lifted LangChain Agents into the Top 5 on Terminal Bench 2.0
Java One
Java One
Apr 20, 2026 · Artificial Intelligence

From Bad Prompts to 9.5 Scores: A Step‑by‑Step Prompt Engineering Guide

This article walks through an iterative prompt‑engineering workflow—starting with a weak baseline, applying four concrete techniques (clarity & directness, specificity, XML structuring, and examples), evaluating each change with a PromptEvaluator, and showing how scores jump from 3.4 to over 9.5 using real code snippets and concrete data.

AIClaudeEvaluation
0 likes · 20 min read
From Bad Prompts to 9.5 Scores: A Step‑by‑Step Prompt Engineering Guide
ArcThink
ArcThink
Apr 19, 2026 · Artificial Intelligence

From Repetitive Prompts to One‑Click Execution: A Complete Guide to Writing Claude Skills

Learn how to turn daily repetitive Claude Code prompts into reusable Skills by identifying repeatable workflows, extracting five key Skill traits, applying a four‑step creation process, and iterating through observation, refinement, structuring, validation, and continuous improvement, illustrated with a real code‑review case study.

AI WorkflowClaudePrompt Engineering
0 likes · 19 min read
From Repetitive Prompts to One‑Click Execution: A Complete Guide to Writing Claude Skills
Architect
Architect
Apr 19, 2026 · Artificial Intelligence

Why Your AI Agent’s Success Depends on the Harness, Not Just the Model

The article explains that an Agent Harness is the complete runtime system surrounding a language model—handling the main loop, tools, context, state, permissions, and validation—and shows why this engineering layer, not the model itself, determines the stability and scalability of AI agents.

AI AgentPrompt EngineeringSafety Guard
0 likes · 23 min read
Why Your AI Agent’s Success Depends on the Harness, Not Just the Model
Design Hub
Design Hub
Apr 19, 2026 · Artificial Intelligence

What’s Inside the Leaked 70K‑Word Claude Design System Prompt?

The article verifies the authenticity of a 73 KB, 422‑line Claude Design system prompt leaked by the CL4R1T4S project, provides a faithful translation of its contents, and dissects the five‑layer design that enables high‑quality AI‑assisted design output.

AI designAnthropicClaude
0 likes · 23 min read
What’s Inside the Leaked 70K‑Word Claude Design System Prompt?
AI Architect Hub
AI Architect Hub
Apr 19, 2026 · Artificial Intelligence

Mastering RAG: From Data Cleaning to Vector DBs in AI Applications

This article introduces the second stage of a large‑model application series, detailing the value of Retrieval‑Augmented Generation (RAG), its architecture, and a step‑by‑step outline covering data cleaning, text chunking, vectorization, vector‑DB selection, recall strategies, reranking, and prompt construction.

AILLMPrompt Engineering
0 likes · 4 min read
Mastering RAG: From Data Cleaning to Vector DBs in AI Applications
AI Explorer
AI Explorer
Apr 19, 2026 · Artificial Intelligence

How Claude’s Design System Prompt Turns AI into an Expert Designer

The article reveals Claude’s design system prompt, detailing its role as an expert designer, a six‑step workflow, context‑driven methodology, exploration modes, strict technical rules, built‑in collaboration tools, and ethical content guidelines that together enable the AI to produce high‑quality, user‑centric designs.

AI designClaudePrompt Engineering
0 likes · 6 min read
How Claude’s Design System Prompt Turns AI into an Expert Designer
DataFunTalk
DataFunTalk
Apr 19, 2026 · Industry Insights

From ChatBI to DataAgent: Turning AI Demos into Trusted Enterprise Decision Engines

The live discussion breaks down the practical challenges of building enterprise‑grade Data Agents—from unified semantic layers and prompt engineering versus model fine‑tuning, to table discovery, multi‑turn memory, trust, cost control, and continuous improvement—showing why real‑world AI success hinges on system reliability rather than raw model power.

AIData AgentEnterprise AI
0 likes · 17 min read
From ChatBI to DataAgent: Turning AI Demos into Trusted Enterprise Decision Engines
LuTiao Programming
LuTiao Programming
Apr 19, 2026 · Artificial Intelligence

Master These 5 Core AI Concepts to Outperform 90% of Users

The article explains five fundamental AI concepts—Token, Context Window, Temperature, Hallucination, and Retrieval‑Augmented Generation—detailing how they affect cost, memory limits, output style, reliability, and knowledge sourcing, and offers practical guidance for effective prompt engineering.

AI FundamentalsPrompt EngineeringRAG
0 likes · 8 min read
Master These 5 Core AI Concepts to Outperform 90% of Users
TonyBai
TonyBai
Apr 19, 2026 · Artificial Intelligence

Thin Harness, Fat Skills: Garry Tan’s AI Engineering Secrets for 1000× Efficiency

Garry Tan explains that the massive productivity gap among AI agents stems from bloated harness layers and weak skills, proposing a "Thin Harness, Fat Skills" architecture—four‑function harnesses, reusable markdown skill files, resolvers, latent‑vs‑deterministic separation, propensity analysis, and perpetual skill upgrades—to achieve up to a thousand‑fold efficiency gains.

AI AgentsGarry TanMarkdown Skills
0 likes · 10 min read
Thin Harness, Fat Skills: Garry Tan’s AI Engineering Secrets for 1000× Efficiency
AgentGuide
AgentGuide
Apr 18, 2026 · Artificial Intelligence

How to Write High‑Quality Skills for Your Agent System

The article outlines a five‑step process for creating robust Agent Skills, covering when to encapsulate a task, extracting decision logic and anti‑patterns, writing concise instructions, provisioning workflows and verification loops, and iterating with real‑world testing to ensure reliability.

AI developmentAgentPrompt Engineering
0 likes · 8 min read
How to Write High‑Quality Skills for Your Agent System
Architect's Tech Stack
Architect's Tech Stack
Apr 18, 2026 · Artificial Intelligence

What’s New in Claude Opus 4.7? Deep Dive into Capabilities and Migration Tips

Anthropic’s Claude Opus 4.7 launches with enhanced handling of complex, long‑running tasks, higher‑resolution visual analysis, stricter instruction compliance, improved benchmark scores, expanded file‑system memory, new effort levels (xhigh), API task‑budget beta, reinforced security measures, and migration guidance on tokenization and prompt adjustments.

AI modelAnthropicClaude Opus
0 likes · 4 min read
What’s New in Claude Opus 4.7? Deep Dive into Capabilities and Migration Tips
PMTalk Product Manager Community
PMTalk Product Manager Community
Apr 18, 2026 · Product Management

5 Harsh Truths Uncovered from Analyzing 200 AI Product Manager Job Descriptions

A data‑driven study of 200 AI product manager JD listings reveals that 70% of roles don’t require deep AI knowledge, salary ceilings depend on industry expertise, the 3‑5‑year experience band is the toughest competition, Prompt engineering is now a baseline skill, and delivering end‑to‑end AI products is the most scarce capability.

AI product managementPrompt Engineeringcareer advice
0 likes · 16 min read
5 Harsh Truths Uncovered from Analyzing 200 AI Product Manager Job Descriptions
Mingyi World Elasticsearch
Mingyi World Elasticsearch
Apr 18, 2026 · Artificial Intelligence

How an Easysearch AI Assistant Beats RAG Without Using Retrieval‑Augmented Generation

The article details a step‑by‑step case study showing that a well‑engineered AI assistant—built with Flask, DeepSeek, structured prompts, strict output rules, and a lightweight SQLite session store—can achieve high answer quality, traceability and user experience comparable to RAG systems without the overhead of vector retrieval.

AI assistantEasysearchFlask
0 likes · 11 min read
How an Easysearch AI Assistant Beats RAG Without Using Retrieval‑Augmented Generation
ArcThink
ArcThink
Apr 17, 2026 · Artificial Intelligence

Why Opus 4.7 Demands a Workflow Overhaul, Not Just Smarter AI

Anthropic's Claude Opus 4.7 introduces a 1 M token context window, Auto Mode, adaptive thinking, and a new default xhigh setting, but the real breakthrough lies in how you must redesign your workflow—from pair‑programming to delegating tasks to a capable AI engineer.

AI coding assistantAuto ModeClaude
0 likes · 30 min read
Why Opus 4.7 Demands a Workflow Overhaul, Not Just Smarter AI
DataFunSummit
DataFunSummit
Apr 17, 2026 · Artificial Intelligence

From Manual Agents to Self‑Improving AI: My OpenClaw vs Hermes Experiment

A senior Google Cloud AI product manager shares a hands‑on study comparing OpenClaw and the open‑source Hermes agent, revealing how a disciplined prompt‑engineering feedback loop can turn static agents into self‑improving systems while highlighting ownership, back‑tracking, and practical deployment considerations.

AI AgentsHermesOpenClaw
0 likes · 7 min read
From Manual Agents to Self‑Improving AI: My OpenClaw vs Hermes Experiment
Machine Heart
Machine Heart
Apr 17, 2026 · Artificial Intelligence

Can π0.7 Unlock Compositional Generalization and Cross‑Embodiment Transfer for VLA?

The new π0.7 model from Physical Intelligence demonstrates emergent compositional generalization and cross‑embodiment transfer in visual‑language‑action (VLA) robots by leveraging massive heterogeneous data and richly structured prompts, outperforming specialist Recap models on tasks such as air‑fryer cooking, clothing folding, and coffee making.

Cross-embodiment TransferPrompt EngineeringRobotics
0 likes · 11 min read
Can π0.7 Unlock Compositional Generalization and Cross‑Embodiment Transfer for VLA?
James' Growth Diary
James' Growth Diary
Apr 17, 2026 · Artificial Intelligence

Advanced System Prompt Design Patterns & Few-Shot Techniques for Reliable LLM Outputs

This article breaks down System Prompt engineering into a five‑layer contract, presents four design patterns—role anchoring, output schema, chain‑of‑thought steering, and guardrails—explains how to select effective few‑shot examples, provides production‑grade prompt templates with code snippets, and warns about common pitfalls such as token length, sample bias, and contradictory constraints.

AIFew-shotLLM
0 likes · 16 min read
Advanced System Prompt Design Patterns & Few-Shot Techniques for Reliable LLM Outputs
James' Growth Diary
James' Growth Diary
Apr 17, 2026 · Artificial Intelligence

How to Encode Project Conventions into AI Memory with CLAUDE.md

This article explains why the .cursorrules file is limited, introduces the cross‑tool CLAUDE.md specification, shows its hierarchical structure, provides a complete Vue 3 + TypeScript example, and lists common pitfalls and best‑practice tips for keeping AI assistants aligned with project standards.

AI code assistanceCLAUDE.mdPrompt Engineering
0 likes · 10 min read
How to Encode Project Conventions into AI Memory with CLAUDE.md
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Apr 17, 2026 · Backend Development

How Claude Code’s Memory System Works: From SHA‑256 Storage to Coalescing Extraction

This article dissects Claude Code’s Memory subsystem, explaining the distinction between Session logs and persistent Memory, the SHA‑256‑based storage layout, file indexing, four memory types, prompt injection steps, two write pathways, the ExtractionCoordinator’s coalescing strategy, and how to explain the design in interviews.

Claude CodePrompt Engineeringbackend architecture
0 likes · 19 min read
How Claude Code’s Memory System Works: From SHA‑256 Storage to Coalescing Extraction
Wuming AI
Wuming AI
Apr 16, 2026 · Artificial Intelligence

Why Claude Opus 4.7 Is Shifting From Smart Answers to Real Work Execution

Anthropic’s Claude Opus 4.7 moves the competition from raw cleverness to reliable task completion, boosting complex coding, long‑running agents, high‑resolution visual understanding, stricter instruction following, and safety guardrails, while urging developers to retest prompts, budgets, and real‑world workflows.

AIAgentPrompt Engineering
0 likes · 11 min read
Why Claude Opus 4.7 Is Shifting From Smart Answers to Real Work Execution
AI Waka
AI Waka
Apr 16, 2026 · Artificial Intelligence

Why Modern AI Systems Should Compile Knowledge Instead of Just Retrieving It

Traditional RAG pipelines forget everything after each query, but the LLM Wiki mode proposed by Andrej Karpathy compiles source material into a version‑controlled, cross‑referenced Markdown wiki, enabling knowledge to compound over time, reduce query costs, and provide a transparent, human‑readable knowledge base for AI engineers.

AI engineeringLLMPrompt Engineering
0 likes · 23 min read
Why Modern AI Systems Should Compile Knowledge Instead of Just Retrieving It
Architect
Architect
Apr 16, 2026 · Artificial Intelligence

Mastering Claude Code: Session Management Strategies for 1M Context Windows

This article analyzes Anthropic's Claude Code session‑management features, explaining how context rot limits effective token usage, what the 1 M‑token window actually stores, and when to use the five built‑in actions—Continue, /rewind, /clear, Compact and Subagent—to keep long‑running AI tasks reliable and efficient.

AI AgentsClaude CodePrompt Engineering
0 likes · 18 min read
Mastering Claude Code: Session Management Strategies for 1M Context Windows
AI Code to Success
AI Code to Success
Apr 16, 2026 · Artificial Intelligence

Master Claude Code’s 1M‑Token Context: Proven Strategies to Manage, Compact, and Rewind

Claude Code now supports a 1 million‑token context window, but effective use hinges on disciplined context management—choosing when to continue, rewind, clear, compact, or delegate to sub‑agents, and applying three core concepts of context windows, compaction, and context rot to avoid performance pitfalls.

AI WorkflowClaudePrompt Engineering
0 likes · 10 min read
Master Claude Code’s 1M‑Token Context: Proven Strategies to Manage, Compact, and Rewind
Tech Verticals & Horizontals
Tech Verticals & Horizontals
Apr 16, 2026 · Artificial Intelligence

Harness Engineering Explained: From Concept to Real‑World Implementation

Leveraging Harness Engineering—a control‑system framework for AI agents—requires defining constraints, feedback loops, memory, and acceptance mechanisms, then integrating tools, execution environments, orchestration, and gating layers, enabling engineers to turn tacit knowledge into enforceable rules that guide AI safely from design to production.

AI control systemsLLM AutomationPrompt Engineering
0 likes · 18 min read
Harness Engineering Explained: From Concept to Real‑World Implementation
AI Engineering
AI Engineering
Apr 16, 2026 · Artificial Intelligence

How Meta-Harness Enables AI to Self‑Optimize Its Own Harness

Meta-Harness, an open‑source framework from Stanford's IRIS lab, lets large language models access their full code, execution traces, and evaluation scores to autonomously improve prompting pipelines, achieving state‑of‑the‑art results on TerminalBench‑2 while exposing challenges such as long evaluation time, massive token generation, and specialized storage needs.

LLM self‑optimizationMeta-HarnessPrompt Engineering
0 likes · 6 min read
How Meta-Harness Enables AI to Self‑Optimize Its Own Harness
AI Software Product Manager
AI Software Product Manager
Apr 16, 2026 · Artificial Intelligence

How to Leverage Google NotebookLM for Efficient Research and Summaries

Google NotebookLM, powered by Gemini, lets you upload PDFs, web pages, and other documents, automatically extracts their content, and answers questions with citations, while also generating audio overviews and PPTs, making research, report writing, and exam preparation faster and more reliable.

AI research toolArtificial IntelligenceAudio Overview
0 likes · 11 min read
How to Leverage Google NotebookLM for Efficient Research and Summaries
AI Waka
AI Waka
Apr 16, 2026 · Interview Experience

40 Must‑Know GenAI Interview Questions: From RAG Pipelines to Multi‑Agent Orchestration

This comprehensive guide compiles 40 senior‑level GenAI interview questions covering LLM fundamentals, retrieval‑augmented generation, prompt engineering, multi‑agent orchestration, fine‑tuning, evaluation, system design, NL‑to‑SQL, and knowledge‑graph retrieval, providing concise, accurate answers and practical trade‑off insights.

GenAILLMPrompt Engineering
0 likes · 31 min read
40 Must‑Know GenAI Interview Questions: From RAG Pipelines to Multi‑Agent Orchestration
Frontend AI Walk
Frontend AI Walk
Apr 16, 2026 · Artificial Intelligence

Hands‑On Guide to Karpathy’s Autoresearch: From Setup to Custom Research Loops

This article walks through Karpathy’s open‑source Autoresearch system, explaining its core design principles, file layout, and workflow, and then demonstrates practical AI‑agent applications for code optimization, bug fixing, and article writing, complete with setup commands, code snippets, and example experiment logs.

AI AgentKarpathyPrompt Engineering
0 likes · 25 min read
Hands‑On Guide to Karpathy’s Autoresearch: From Setup to Custom Research Loops
Big Data and Microservices
Big Data and Microservices
Apr 16, 2026 · Artificial Intelligence

Why Perfect Prompts Crash After Days: Uncovering the Limits of Context Engineering

An AI‑driven customer‑service bot that answered perfectly for two days suddenly started hallucinating because single‑turn prompt engineering ignored the continuous, stateful nature of real‑world conversations, revealing the hidden token, memory, and retrieval challenges that demand a new context‑engineering approach.

Context EngineeringConversation StateLLM
0 likes · 14 min read
Why Perfect Prompts Crash After Days: Uncovering the Limits of Context Engineering
ZhiKe AI
ZhiKe AI
Apr 15, 2026 · Artificial Intelligence

Build Your AI Superpowers from Scratch: A Hands‑On Guide to Creating Claude Code Skills

This article walks you through what Claude Code skills are, how they work, the four skill types, the exact file format, a step‑by‑step process for building your first skill, best‑practice design principles, testing methods, and ongoing maintenance, enabling you to automate repetitive AI tasks efficiently.

AI SkillsClaude CodePrompt Engineering
0 likes · 15 min read
Build Your AI Superpowers from Scratch: A Hands‑On Guide to Creating Claude Code Skills
AI Algorithm Path
AI Algorithm Path
Apr 15, 2026 · Artificial Intelligence

8 Must-Collect Agent Skills Repositories for Claude and AI Agents

This article explains what Agent Skills are, why a curated skill library is valuable, and reviews eight actively maintained GitHub repositories—detailing their structure, core capabilities, integration points, and practical usage examples for building production‑grade AI agents.

AI AgentsAI toolsAgent Skills
0 likes · 11 min read
8 Must-Collect Agent Skills Repositories for Claude and AI Agents
Woodpecker Software Testing
Woodpecker Software Testing
Apr 15, 2026 · Artificial Intelligence

When Large‑Model Testing Becomes the AI Delivery Lifeline: 2026 Cost‑Benefit Threshold

The article analyzes how large‑model testing has shifted from a peripheral step to a core economic lever in AI delivery, detailing 2026 cost‑structure changes, new benefit metrics such as compliance resilience and decision‑trust gains, and four ROI‑boosting levers that can turn testing into a strategic asset.

AI cost analysisPrompt EngineeringROI strategies
0 likes · 8 min read
When Large‑Model Testing Becomes the AI Delivery Lifeline: 2026 Cost‑Benefit Threshold
Big Data and Microservices
Big Data and Microservices
Apr 15, 2026 · Artificial Intelligence

Prompt vs Skill: Why Skill Engineering Is the Next Leap in AI Productivity

This article compares Prompt engineering and Skill engineering, explaining their fundamental differences, design goals, reusability, context usage, security, and best‑fit scenarios, and shows how moving from one‑off prompts to reusable Skill packages can dramatically boost AI efficiency and scalability.

AI AgentsProductivityPrompt Engineering
0 likes · 11 min read
Prompt vs Skill: Why Skill Engineering Is the Next Leap in AI Productivity
Linyb Geek Road
Linyb Geek Road
Apr 15, 2026 · Artificial Intelligence

How to Optimize Prompts for Multi‑Turn Large‑Model Dialogues

The article outlines practical methods for designing and refining prompts in multi‑turn conversations with large language models, covering task definition, contextual information, structured templates, step‑by‑step guidance, knowledge‑graph integration, dynamic adjustments, and real‑time data incorporation, each illustrated with concrete examples and code snippets.

AIMulti-turn DialoguePrompt Engineering
0 likes · 11 min read
How to Optimize Prompts for Multi‑Turn Large‑Model Dialogues
AI Explorer
AI Explorer
Apr 14, 2026 · Artificial Intelligence

Enhance Claude Code with Karpathy‑Inspired Optimization Guidelines

The article examines common pitfalls of AI coding assistants like Claude Code, then presents the Karpathy‑inspired CLAUDE.md project’s four guiding principles—think before coding, prioritize simplicity, make precise edits, and execute goal‑driven tests—to improve code quality, reduce unwanted changes, and streamline prompt engineering.

AI coding assistantCLAUDE.mdClaude Code
0 likes · 6 min read
Enhance Claude Code with Karpathy‑Inspired Optimization Guidelines
AI Software Product Manager
AI Software Product Manager
Apr 14, 2026 · Artificial Intelligence

7 Design Principles to Build High‑Impact Claude Code Skills

This article extracts the core methodology of Anthropic's skill‑creator tool and presents seven practical design guidelines—progressive three‑layer loading, aggressive description writing, explaining the why, test‑driven development, avoiding over‑fitting, delegating repetitive work to scripts, and domain‑specific reference splitting—to help developers craft LLM‑driven skills that are both efficient and generalizable.

AIClaudeLLM
0 likes · 18 min read
7 Design Principles to Build High‑Impact Claude Code Skills
Su San Talks Tech
Su San Talks Tech
Apr 14, 2026 · Artificial Intelligence

10 Proven Claude Code Hacks to Supercharge Your AI Development

This guide shares ten practical Claude Code techniques—including CLAUDE.md contracts, context‑management commands, Plan Mode, model switching shortcuts, session rewind, code simplification, and a HUD plugin—helping developers boost productivity and avoid common pitfalls when using the AI coding assistant.

AI coding assistantClaude CodeCommand Line
0 likes · 9 min read
10 Proven Claude Code Hacks to Supercharge Your AI Development
JavaGuide
JavaGuide
Apr 14, 2026 · Artificial Intelligence

Interview Question: How to Build Prompt Engineering for an Agent and Defend Against Malicious Prompt Injection

The article explains how industrial‑grade AI agents require structured prompt engineering, chain‑of‑thought reasoning, task decomposition, and a three‑layer defense (sandbox, prompt isolation, and human approval) to prevent prompt‑injection attacks, while also covering context engineering, retrieval‑augmented generation, and tool design best practices.

Agent designChain-of-ThoughtContext Engineering
0 likes · 23 min read
Interview Question: How to Build Prompt Engineering for an Agent and Defend Against Malicious Prompt Injection
Data STUDIO
Data STUDIO
Apr 14, 2026 · Artificial Intelligence

Can ChatGPT Deep Research Double Your Research Efficiency?

The article explains how ChatGPT Deep Research transforms ordinary web searches into full‑fledged research reports, compares three leading Deep Research tools, outlines nine practical use cases, warns of common pitfalls, and offers prompt‑engineering tips for both individual and enterprise adoption.

AI researchChatGPTDeep Research
0 likes · 16 min read
Can ChatGPT Deep Research Double Your Research Efficiency?
DeepNoMind
DeepNoMind
Apr 14, 2026 · Artificial Intelligence

Harness Revolution: A Comprehensive 2026 Overview of Agent Engineering

In 2026, as large‑model capabilities plateau, the article argues that Harness—strategic infrastructure that wraps models—has become the decisive factor for successful Agent systems, tracing its evolution from prompt engineering through context engineering to the emerging Harness engineering, and detailing its core components, ecosystem, and the required shift from coder to system engineer.

AgentArtificial IntelligenceContext Engineering
0 likes · 19 min read
Harness Revolution: A Comprehensive 2026 Overview of Agent Engineering
AI Waka
AI Waka
Apr 14, 2026 · Artificial Intelligence

From Prompt Chains to Python State Machines: Evolving Production‑Grade AI Orchestration

This article chronicles three generations of production‑grade AI orchestration—from fragile Claude Code skill chains, through adversarial sub‑agent pipelines with explicit judges, to a deterministic Python state‑machine built on the Claude Agent SDK—highlighting how structured control flow, task splitting, and budget enforcement dramatically improve reliability over raw prompt‑driven workflows.

AI orchestrationClaude Agent SDKLLM
0 likes · 19 min read
From Prompt Chains to Python State Machines: Evolving Production‑Grade AI Orchestration
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Apr 14, 2026 · Artificial Intelligence

The First Principle of Context Engineering: Mastering the “Just‑Right” Art for AGI

The article explains that as large language models approach their capacity limits, performance is now bounded by the quality of the supplied context, advocating a “just‑right” approach that balances over‑ and under‑feeding through a three‑layer architecture, dynamic context agents, and a central router to enable scalable multi‑agent AI systems.

AI architectureContext EngineeringLLM
0 likes · 9 min read
The First Principle of Context Engineering: Mastering the “Just‑Right” Art for AGI
Java One
Java One
Apr 13, 2026 · Artificial Intelligence

How to Build a Complete Prompt Evaluation Pipeline for Reliable AI Outputs

This guide walks you through constructing a full prompt‑evaluation workflow—from drafting prompts and generating a test dataset to running Claude, scoring responses with model‑ and code‑based metrics, and iterating until your prompts are data‑driven and trustworthy.

AI modelClaudeEvaluation Pipeline
0 likes · 25 min read
How to Build a Complete Prompt Evaluation Pipeline for Reliable AI Outputs
Senior Tony
Senior Tony
Apr 13, 2026 · Artificial Intelligence

5 Advanced Codex Tips to Supercharge Your Development Workflow

This guide presents five practical, intermediate‑level techniques for using OpenAI's Codex—writing explicit prohibitions, breaking tasks into fine‑grained steps, generating multiple solutions with a "Best‑of‑N" approach, analyzing before coding, and prioritizing requirements—to help developers steer AI assistance toward reliable, low‑risk code changes.

AI programmingCodexPrompt Engineering
0 likes · 6 min read
5 Advanced Codex Tips to Supercharge Your Development Workflow
Old Zhang's AI Learning
Old Zhang's AI Learning
Apr 13, 2026 · Artificial Intelligence

How Harness Engineering Makes or Breaks AI Agents – Lessons from Hsu’s 2026 Lecture

The article explains Harness Engineering—a set of tools that control an AI agent’s cognitive framework, capability boundaries, and behavior flow—showing how proper harnesses can turn modest models into high‑performing agents, while poor harnesses cause failures, with concrete examples, benchmarks, and research citations.

AI AgentAgent LoopContext Engineering
0 likes · 12 min read
How Harness Engineering Makes or Breaks AI Agents – Lessons from Hsu’s 2026 Lecture
Programmer XiaoFu
Programmer XiaoFu
Apr 13, 2026 · Artificial Intelligence

Why Prompt‑Based JSON Extraction Breaks LLM‑Driven Intent and NER

The article explains how using plain prompts to force large language models to output JSON for intent recognition and named‑entity extraction leads to unpredictable extra text, key mismatches, and hallucinated fields, and it presents three robust alternatives—placeholders, function calling, and constrained decoding—to achieve reliable structured outputs.

Function CallingLLMNamed Entity Recognition
0 likes · 8 min read
Why Prompt‑Based JSON Extraction Breaks LLM‑Driven Intent and NER