AI Engineering
13 articles exploring AI Engineering. Expert analysis and insights from our editorial team.
Latest in AI Engineering
Hugging Face Skills: Pretrained Agent Capabilities
Hugging Face Skills are standardized, self-contained instruction packages that give coding agents—Claude Code, Codex, Gemini CLI, and Cursor—procedural expertise for AI/ML tasks. Launched in November 2025, the Apache 2.0-licensed library reached 7,500 GitHub stars by early 2026 and provides nine composable capabilities from model training to paper publishing.
Superpowers: The Agentic Framework Replacing Your Dev Process
Superpowers is an open-source agentic skills framework by Jesse Vincent that enforces structured software development workflows—brainstorming, planning, TDD, and subagent coordination—on top of AI coding agents like Claude Code, turning them from reactive assistants into disciplined developers capable of autonomous multi-hour sessions.
How AI Agents Remember: Memory Architectures That Work
AI agents use four distinct memory tiers—working, episodic, semantic, and procedural—stored across context windows, vector databases, knowledge graphs, and model weights. Choosing the right architecture determines whether your agent stays coherent across sessions or forgets everything the moment a conversation ends.
Vibe Coding One Year Later: What Actually Survived
One year after Andrej Karpathy coined 'vibe coding,' the evidence is clear: rapid prototyping and non-developer productivity are genuine wins, but production security and organizational-level gains remain elusive. Here's what the data shows.
Browser-Use Agents: AI That Browses Like a Human
A comprehensive guide to browser-use AI agents, exploring OpenAI Operator, Claude Computer Use, Browser-Use framework, and Google Project Mariner with benchmarks and capabilities.
AI Testing Automation: Agents That Write and Run Tests
AI agents can now generate, execute, and maintain test suites with minimal human intervention. While unit tests and regression suites achieve 60-80% automation rates, exploratory testing and complex business logic validation still require human oversight.
Function Calling Best Practices: LLMs That Actually Use APIs Correctly
Function calling enables LLMs to interact with external systems through structured API calls, but reliability requires careful schema design, error handling patterns, and validation strategies to prevent hallucinated parameters and malformed requests.
AI-Orchestrated Systems: The Rise of Multi-Agent Development Frameworks
AI-orchestrated development systems like AutoGen, CrewAI, and ChatDev are emerging as comprehensive platforms for managing end-to-end software development through coordinated multi-agent workflows, revealing both significant capabilities and critical limitations in AI-managed software engineering.
AI Code Review Agents: Catching Bugs Before Humans Do
AI code review agents can reduce review time by 50% and catch security vulnerabilities human reviewers miss, but they augment rather than replace human expertise in 2025.
AI That Debugs Production Systems: From Logs to Root Cause
AI-powered observability platforms can analyze logs, traces, and metrics to identify root causes automatically, but they augment rather than replace on-call engineers. Organizations report significant MTTR improvements and alert noise reduction while maintaining human oversight for critical decisions.
The Art of AI Pair Programming: Patterns That Actually Work
AI pair programming is a collaborative coding methodology where developers work alongside AI coding assistants like Claude Code and GitHub Copilot. The most effective approach involves understanding when to delegate routine tasks to AI while maintaining human oversight for complex architecture decisions, security-critical code, and quality validation.
Breaking the Spell of Vibe Coding: A Fast.ai Critique of AI-Assisted Development
Fast.ai's Rachel Thomas warns that unchecked AI-assisted coding creates 'dark flow'—a dangerous state where developers feel productive while producing unmaintainable code, with research showing AI tools can actually slow development by 19%.
Multi-Agent Coordination Protocols: When AI Agents Work Together
Multi-agent coordination protocols are standardized communication frameworks that enable autonomous AI agents to delegate tasks, share information, and resolve conflicts in distributed systems. These protocols are essential infrastructure for modern AI systems from autonomous vehicles to LLM-based agent frameworks.