#observability
543 related articles
·4 min
A Practical Guide to Loop Engineering: Pain Points and Solutions in Automated AI Coding Workflows
Deep dive into Loop Engineering: core mechanisms, three major pain points (reliability, cost, context bloat), and Harness workflow solutions including mixed model strategies and Human in the Loop.
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·4 min
Loop Engineering from Beginner to Expert: A Complete Guide to Agent Loop Development
A deep dive into Loop Engineering covering Agent Loop workflows, code implementation (While loops and Graph patterns), and how it differs from Prompt Engineering.
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·3 min
Enterprise-Grade RAG Full-Pipeline Optimization: Multi-Turn Conversations, Retrieval Tuning, and Production Engineering in Practice
A deep dive into full-pipeline optimization for enterprise RAG systems, covering multi-turn query rewriting, retrieval tuning, and quality evaluation to take RAG from demo to production.
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·4 min
Five Essential Cursor Skills Every QA Engineer Needs: A Complete Breakdown
A detailed guide to five essential Cursor Skills for QA engineers: PRD analysis, test case generation, JMeter scripting, load test reports, and web automation.
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·2 min
Intelligence Is Getting More Expensive: A Deep Dive into Google I/O 2026
Deep dive into Google I/O 2026: Gemini 3.5 Flash price hikes, RL training environments as a hidden battleground, managed agents and sandboxes, open-source model tiers, and frontier lab competition.
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·3 min
Multi-Agent Cost-Cutting Guide: 4 Documents to Slash 60-80% of Your Token Spending
Multi-agent bills out of control? This article breaks down two core token cost pain points and provides 4 actionable documents to cut multi-agent task costs by 60-80%.
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·3 min
The Ultimate Guide to Squeezing Every Drop from Mythos/Fable: Burning Through $8,000 in Inference Credits in 10 Days
Deep dive into maximizing Anthropic's Fable/Mythos model: 5-hour limit workarounds, dual account rotation, multi-Agent orchestration, and Mac Mini remote deployment to get $8,000 of inference from a $200 subscription.
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·3 min
Four-Layer Funnel Architecture: A Design Guide for Production-Grade Agent Intent Routing Systems
Deep dive into a four-layer funnel intent routing architecture (regex, vector routing, LLM FC, safety net) solving intent confusion, multi-intent gaps, emotional hijacking, and routing avalanches.
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·3 min
Firebase AI Logic Deep Dive: Hybrid Inference, Prompt Templates & Multi-Layer Security Deployment
Deep dive into Firebase AI Logic: server-side prompt templates to prevent leakage, Cloud Function triggers, four-layer security defense, AI monitoring with context caching for cost control, and cross-platform hybrid inference.
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·2 min
Agent Middleware: Adding Interceptors to Model Calls
Learn how AI Agent middleware works through two practical examples — logging and security checks. Master the Observer and Guardian design patterns to build extensible, production-grade Agents.
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·2 min
The 4-Stage Roadmap for AI Application Development: A Career Transition Guide from Beginner to Senior Engineer
A 4-stage roadmap for AI application development: from Python and RAG basics to Agent cluster architecture, covering the core skills needed for career growth.
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·3 min
AI Agent Hands-On Learning Path: A Complete Guide from Beginner to Enterprise-Level Development
A systematic AI Agent development learning roadmap covering prompt engineering, RAG, multi-Agent collaboration, tool calling, and more—with phased learning advice and 28 hands-on project references.
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·3 min
Fed Up with Every AI Coding Assistant, a Veteran Game Developer Built His Own Coding Agent
Veteran game dev Mario tried every AI coding tool including Claude Code, found them all lacking, and built Pi — a minimalist, extensible coding agent framework centered on developer control.
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·3 min
Complete Guide to LangChain 0.3: Module Architecture & Core Development in Practice
A deep dive into LangChain 0.3's module architecture, message abstraction, prompt templates, output parsers, LCEL chains, LangSmith tracing, and LangGraph for mastering LLM application development.
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·3 min
Loopcraft Explained: Loop-Stacking Architecture Design in AI Agent Development
Deep dive into Loopcraft loop-stacking architecture for AI Agent development, covering retry, self-validation, and meta-learning loops to boost reliability.
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·4 min
AI Full-Stack Development Architecture: A Three-Layer Progressive Path from Prototype to Production
A detailed guide to AI full-stack development architecture covering Node.js+TypeScript+Monorepo engineering, Docker CI/CD deployment, and AI engine design with interview tips.
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·3 min
AI Agent Learning Roadmap: A Four-Step Practical Guide from Zero to Job-Ready
A proven AI Agent learning roadmap covering four core elements, mainstream architecture patterns, multi-agent collaboration, and hands-on projects to go from zero to job-ready in three months.
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·4 min
Claude Code Codex Plugin Integration: A Practical Guide to Dual-AI Adversarial Review for Better Code Quality
Learn how to install and configure the Codex plugin in Claude Code, leveraging dual-AI adversarial review to uncover code vulnerabilities across seven attack surfaces.
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·2 min
Struggling to Deploy AI Agents? Engineering Is the Key to Going from Demo to Product
57% of projects have deployed AI Agents, but 40% will be killed. This article analyzes the engineering methodology for taking AI Agents from Demo to enterprise product, covering the full process from requirements to deployment.
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·4 min
AI Agent Development: A Complete 6-Week Systematic Learning Roadmap
A 6-week systematic learning roadmap for AI Agent development, covering core architecture, ReAct principles, multi-agent collaboration, RAG integration, and deployment.
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