Build an AI Agent Company from Scratch: A Complete Guide to 6 Autonomous Agents Core Question: How can you build and operate an automated system of 6 AI agents from scratch without relying on complex frameworks like LangChain and requiring deep programming skills? With the assistance of an AI coding assistant and without needing to be an expert coder, you can build an automated system consisting of 6 AI agents. This system can autonomously execute tasks such as intelligence scanning, content writing, tweet posting, and data analysis. It holds 10-15 meetings a day, learns from experience, adjusts relationships, and even …
Accelerating Opus 4.6 Responses: A Deep Dive into Claude Code’s Fast Mode Mechanics and Use Cases The core question this article answers: What exactly is Claude Code’s Fast Mode, how does it significantly boost response speed while maintaining model quality, and when should developers enable it versus when they should disable it? Fast Mode is essentially not a new AI model, but a specific API configuration of the Opus 4.6 model. When you type /fast and hit Tab in the Claude Code CLI, you are activating the same intelligent system, but it is reconfigured to prioritize speed over cost efficiency. …
★Trellis: The Architectural Framework for AI Coding – Making Claude Code & Cursor Controlled, Collaborative, and Persistent★ When using Claude Code or Cursor for AI-assisted development, have you ever faced this dilemma: Yesterday you taught the AI your project’s coding standards, but today, in a new session, it has forgotten everything? Or, when handling complex features, does the randomness of AI-generated code force you to conduct repetitive code reviews and corrections? This section answers the core question: Compared to using Cursor or Claude Code directly, what fundamental efficiency and quality pain points does introducing the Trellis framework solve? Trellis is …
The Complete Guide to OpenAI Skills: Supercharge Your AI Coding Assistant with 38 Powerful Tools In the era of AI-assisted development, developers are no longer satisfied with AI generating simple code snippets. We expect it to act like a senior engineer capable of executing complex tasks, from deploying applications to conducting security audits. This guide provides an in-depth analysis of the OpenAI Skills repository, a powerful ecosystem containing 38 skills designed to extend the capabilities of Codex (OpenAI’s coding agent). We will explore how these skills work, how they are categorized, and how they can transform a generic AI assistant …
Bridging the Gap: How to Transform DeepSeek Free Chat into OpenAI & Claude Compatible APIs with DS2API Image Source: Unsplash Introduction: Unlocking Programmatic Access to Free AI Resources Core Question: How can developers bridge the gap between the free, interactive DeepSeek web interface and the standardized, programmatic requirements of modern AI application development? For developers and product engineers, the availability of powerful Large Language Models (LLMs) like DeepSeek is an exciting opportunity. However, the friction arises when these models are initially offered only through a web-based chat interface. Building production-grade applications requires standard APIs—specifically those compatible with the ubiquitous OpenAI …
Stop Failing at “Vibe Coding”: The Documentation-First System for Shipping Real Software Why is it that despite using the most advanced AI coding agents like Cursor or Claude Code, you still end up with a pile of broken, non-functional code? The core answer is simple: The problem isn’t AI “hallucinating.” The problem is you, the operator, lacking structured thinking and constraints. AI is a translator that converts your intent into code; if your intent is vague and unstructured, the output will inevitably be chaotic. By establishing a strict “Documentation-First” system that pre-sets all specifications, workflows, and context, you can eliminate …
The Ultimate Guide to Advanced Claude Code Usage: Parallel Development, Plan Mode, and Hooks Summary: Based on official Claude Code documentation and internal team best practices, this comprehensive guide covers advanced workflows including Git worktree parallel sessions, Plan Mode for complex task planning, CLAUDE.md knowledge management, Skills automation, Subagents for multi-threading, Hooks for event-driven automation, and 10 core technical strategies for data analysis and terminal optimization. Core Claude Code Workflows Understanding New Codebases Claude Code provides streamlined workflows for rapidly comprehending unfamiliar codebases. When you join a new project, you can master its structure through several key steps: Get a …
Google Whisk with Antigravity AI: The Seamless Fusion of Design and Development Reshaping How We Build Have you ever been excited by a brilliant product idea, only to be held back by the complexity of prototyping, tedious coding, and a disjointed toolchain? Today, we stand at an inflection point: artificial intelligence is no longer just an辅助 tool but is becoming the central hub connecting creativity with execution. Google’s combination of Google Whisk and Antigravity AI is the concrete embodiment of this shift. This is more than the sum of two tools; it represents a complete “creative operating system” from visual …
Comprehensive Guide to PolyMCP: Unlocking AI-Driven Development Efficiency Core Value Analysis What is PolyMCP? PolyMCP represents a groundbreaking toolkit designed to streamline the development of modular command platforms (MCP). It integrates Python functions, third-party services, and large language models (LLMs) through a unified interface supporting HTTP, stdio, and in-process communication. This solution empowers developers to create automated workflows across heterogeneous tools while ensuring production-grade security and observability[^1.1^][^3.2^]. Key Technical Advantages: Dual Language Support: Compatible with both Python and TypeScript ecosystems. LLM Integration: Native support for OpenAI, Anthropic (Claude), Ollama, and other providers. Visual Monitoring: PolyMCP Inspector enables real-time tracking of …
Qwen3-Max-Thinking: The Next Evolution in Reasoning-Capable Large Language Models Image source: Unsplash What exactly is Qwen3-Max-Thinking, and what tangible breakthroughs does it deliver in the large language model landscape? Qwen3-Max-Thinking represents the latest flagship reasoning model from the Tongyi Lab, engineered through expanded parameter scale and intensive reinforcement learning training to deliver significant performance improvements across factual knowledge, complex reasoning, instruction following, human preference alignment, and agent capabilities. Benchmark evaluations across 19 authoritative tests demonstrate its competitive standing alongside industry leaders including GPT-5.2-Thinking, Claude-Opus-4.5, and Gemini 3 Pro. Beyond raw performance metrics, this model introduces two pivotal innovations that enhance …
「The “Bash-First” Revolution: A Deep Dive into the Claude Agent SDK and the Future of Autonomous Agents」 「Snippet/Summary」: The Claude Agent SDK is a developer framework by Anthropic, built on the foundations of Claude Code, designed to create autonomous agents that can manage their own context and trajectories. It advocates for a “Bash-first” philosophy, prioritizing Unix primitives over rigid tool schemas. By utilizing a core loop of gathering context, taking action, and verifying work through deterministic rules and sub-agents, the SDK enables AI to execute complex, multi-step tasks in isolated sandboxes. 「I. Beyond Chatbots: The Shift to Autonomous AI」 If …
Mastra is a TypeScript framework designed for building AI-powered applications and agents. It enables developers to connect to over 40 model providers through a single interface, featuring autonomous agents, graph-based workflows, human-in-the-loop capabilities, and built-in observability for reliable production deployment. Building Production-Grade AI Applications with Mastra: The Ultimate TypeScript Framework In the rapidly evolving landscape of software development, the integration of Artificial Intelligence (AI) has shifted from a competitive advantage to an absolute necessity. Developers today are not just asked to write code; they are asked to orchestrate intelligence. However, the journey from a simple prototype to a robust, production-ready …
When AI Assistants Meet Reality: A Cloud vs Bare Metal Showdown for Big Data Can AI programming assistants truly handle production-grade data analytics? My experiment analyzing Common Crawl data reveals they excel at code generation but fail at system-level judgment, making human oversight critical for architecture decisions. The Experiment: Pitting Claude Against Codex What happens when you let two AI coding assistants choose your infrastructure? I tasked Claude Code (Opus 4.5) and GPT-5.2 Codex with the same goal—analyze the latest Common Crawl dump for URL frequency counts—then stepped back to let them lead. The result was a masterclass in AI …
How to Choose the Right Multi-Agent Architecture for Your AI Application: A Clear Decision Framework When building intelligent applications powered by large language models, developers face a critical design decision: should you use a single, “generalist” agent, or design a collaborative system of multiple specialized “expert” agents? As AI applications grow more complex, the latter is becoming an increasingly common choice. But multi-agent systems themselves come in several design patterns. How do you choose the one that meets your needs without introducing unnecessary cost and complexity? This article delves into four foundational multi-agent architecture patterns. Using concrete, quantifiable performance data, …
Exploring the “Big Three Realtime Agents”: A Voice-Controlled AI Agent Orchestration System Have you ever imagined directing multiple AI assistants to work together with just your voice? One writes code, another operates a browser to verify results, and all you have to do is speak? This might sound like science fiction, but the “Big Three Realtime Agents” project is turning this vision into reality. It’s a unified, voice-coordinated system that integrates three cutting-edge AIs—OpenAI, Anthropic Claude, and Google Gemini—to seamlessly dispatch different types of AI agents for complex digital tasks through natural conversation. This article will provide an in-depth analysis …
Google Antigravity Now Supports Agent Skills: Easily Extend Your AI Agents with Reusable Knowledge Packs Meta Description / Featured Snippet Candidate (50–80 words) Google Antigravity’s Agent Skills feature lets you extend AI agent capabilities using an open standard. Place a SKILL.md file (with YAML frontmatter and detailed instructions) inside .agent/skills/ for project-specific workflows or ~/.gemini/antigravity/skills/ for global reuse. Agents automatically discover skills at conversation start, evaluate relevance via the description, and apply full instructions when appropriate—delivering consistent, repeatable behavior without repeated prompting. Have you ever found yourself typing the same detailed instructions into your AI coding assistant over and over …
How to Build Reliable Evaluations for AI Agents: A Complete Practical Guide (2025–2026 Edition) If you’re building, shipping, or scaling AI agents in 2025 or 2026, you’ve probably already discovered one hard truth: The same autonomy, tool use, long-horizon reasoning, and adaptability that make powerful agents incredibly valuable… also make them extremely difficult to test and improve reliably. Without a solid evaluation system, teams usually fall into the same reactive cycle: users complain → engineers reproduce the bug manually → a fix is shipped → something else quietly regresses → repeat. Good evaluations break this loop. They turn vague feelings …
Let AI Ship Features While You Sleep: Inside Ralph’s Autonomous Coding Loop A step-by-step field guide to running Ralph—an 80-line Bash loop that turns a JSON backlog into shipped code without human interrupts. What This Article Answers Core question: How can a single Bash script let an AI agent finish an entire feature list overnight, safely and repeatably? One-sentence answer: Ralph repeatedly feeds your agent the next small user story, runs type-check & tests, commits on green, and stops only when every story is marked true—using nothing but Git, a JSON queue, and a text log for memory. 1. What …
Mastering Context Engineering for Claude Code: A Practical Guide to Optimizing LLM Outputs In the realm of AI-driven coding tools like Claude Code, the days of blaming “AI slop” on the model itself are long gone. Today, the onus falls squarely on the user—and the single most controllable input in these black-box systems is context. So, how do we optimize context to unlock the full potential of large language models (LLMs) like Claude Code? This comprehensive guide will break down everything you need to know about context engineering, from the basics of what context is to advanced strategies for maximizing …
Vibe Coding from Zero: Build Your First App with No Experience Using a Dual-AI Setup Have you ever opened your social media feed to see hundreds of posts about “vibe coding,” where everyone seems to be building crazy tools, dashboards, and even full production apps that make money, and felt completely overwhelmed? Don’t worry. It’s actually much simpler than it looks. While the sheer volume of information can be paralyzing, the core pathway can be strikingly clear. This article reveals a proven, beginner-friendly method that leverages powerful AI tools, allowing you to start building real projects—be it bots, dashboards, tools, …