#ML engineering best practices
418 related articles
·2 min
GitHub Copilot Stacked Sessions: A New Paradigm for Refactoring Legacy Code
Deep dive into GitHub Copilot's Stacked Sessions and Pull Requests for systematically refactoring legacy code, elevating AI-assisted programming to task-level collaboration.
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·2 min
LangChain or LangGraph? Choosing the Right Framework for Production-Grade AI Applications
In-depth analysis of core differences between LangChain and LangGraph, exploring why more teams are migrating to LangGraph for production AI apps, with framework selection guidance.
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·2 min
LangChain or LangGraph? Framework Choices for Production-Grade AI Applications
In-depth analysis of LangChain vs LangGraph differences, why teams are migrating to LangGraph for production AI apps, and framework selection guidance based on project complexity.
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·3 min
localskills.sh: A Unified Management Platform for Cross-Tool AI Skills and MCP — Review
localskills.sh is a team-level AI skill and MCP server management platform that unifies distribution and reuse of AI Skills and Rules across Cursor, Claude Code, Windsurf, and more with a single install command.
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·3 min
localskills.sh: A Unified Management Platform for Cross-Tool AI Skills and MCP – Full Review
localskills.sh is a team-level platform for managing AI Skills, Rules, and MCP servers across Cursor, Claude Code, and Windsurf with a single install command.
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·3 min
Long Documents Don't Work: Why AI Agents Can't Follow Your Rules
Analysis of why AI Agents can't reliably follow long policy documents, covering context dilution, rule conflicts, and soft constraint limitations, with more reliable governance architectures.
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·4 min
Full-Stack Machine Learning Project Repository Structure Guide: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and configuration externalization to help ML developers move from experimental code to production-grade engineering.
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·4 min
A Guide to Full-Stack Machine Learning Project Repository Structure: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and externalized configuration to help ML developers move from experimental code to production-grade engineering standards.
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·3 min
Anthropic Breaks HAWK-256: A Security Wake-Up Call for Post-Quantum Signature Schemes
Anthropic publishes a practical key-recovery attack on HAWK-256, exposing vulnerabilities in post-quantum signature schemes and implications for PQC standardization.
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·4 min
What It Feels Like When Production Actually Runs Like Localhost
Why does production never match local? This article analyzes root causes like config gaps and dependency drift, and explores how Docker, Twelve-Factor App, and IaC practices bridge the dev-prod divide.
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·3 min
Kimi K3 Hands-On Tutorial: Claude Code Sub-Agent Configuration & GPT-5.6 Comparison Test
Complete guide to configuring Kimi K3 as a Claude Code sub-agent, including API setup, YFlow workflow building, and head-to-head comparison with GPT-5.6 and Claude Fable 5.
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·5 min
Loop Engineering Explained: The New Programming Paradigm Where AI Prompts Itself
Loop Engineering is a paradigm shift in AI usage. Learn how to build automated loops where agents explore, execute, and verify tasks autonomously, with a hands-on e-commerce case study.
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·6 min
Harness Engineering: From Context Engineering to the Third Phase of AI Programming
Explore Harness Engineering: the next evolution beyond context engineering for AI programming. Learn how to build enterprise-grade Skill systems and deliver real projects with mid-tier models.
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·3 min
What Are Agent Skills? A Detailed Guide to AI Agent Skill System Architecture and Principles
A deep dive into AI Agent Skills: understand the core concepts and technical implementation through the four key elements — SKILL.md, references, scripts, and assets — and learn how Skills differ from prompts.
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·3 min
What Are Agent Skills? A Complete Guide to AI Agent Skill System Architecture and Principles
A deep dive into AI Agent Skills: their core concepts, technical implementation, the four key elements (SKILL.md, references, scripts, assets), and how Skills differ from prompts.
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·5 min
Running GPT-5.6 Inside Claude Code: Best Practices for Subagent Orchestration and System Prompts
Deep dive into running OpenAI GPT-5.6 inside Claude Code: comparing Codex vs Claude Code on subagent orchestration, workflow design, and system prompt quality, revealing how harness engineering determines model output.
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·2 min
Impeccable Open Source Project Deep Dive: A Design Language Framework That Gives AI a Designer's Eye
Deep dive into the trending open source project Impeccable — a design language framework that enables AI tools to generate professional-grade UI. Learn how it bridges the design quality gap in AI-generated interfaces.
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·3 min
When Should You Use Machine Learning? A Pragmatic Decision Guide for Data Scientists
Not every data science problem needs ML. This guide offers a decision framework across four dimensions — rule complexity, data quality, prediction needs, and interpretability — to avoid over-engineering.
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·4 min
Claude Code Tutorial: Environment Setup, Command Reference & Git Workflow in Practice
Complete guide to Claude Code covering CLI installation, domestic model switching, core commands, Git automation workflows, and automated code review and fix loops for enterprise projects.
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·3 min
Automating GitHub Releases: Design and Practice of a Tag-Triggered Release Action
A ten-year open source maintainer shares how to build a universal tag-to-release GitHub Action, covering its opinionated design philosophy, dogfooding validation, and AI-assisted development.
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