#AI engineer
5259 related articles
·3 min
How AI Can Curb Knowledge Decay in Software Engineering: Insights from IBM's Perspective
Exploring IBM's perspective on AI curbing software engineering knowledge decay, analyzing AI's role in code comprehension, decision recording, and knowledge retrieval, plus how enterprises can build the right habits around AI.
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·2 min
Reverse Engineering Apple's ANE: Open-Source Project Enables On-Device Training on the Neural Engine
Deep dive into the maderix/ANE GitHub project that reverse engineers Apple's private APIs to enable neural network training on the Apple Neural Engine, exploring its technical approach, efficiency gains, compliance risks, and implications for on-device AI.
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·4 min
Harness Engineering: The Core Infrastructure for Taking AI Agents from Demo to Production
Deep dive into Harness Engineering: why AI Agents need memory management, durable execution, guardrails & approvals to go from demo to production.
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·4 min
Harness Engineering: The Core Infrastructure for Taking AI Agents from Demo to Production
Deep dive into Harness Engineering: why AI Agents need memory management, durable execution, guardrails & approvals to reach production. Based on Scott Moss's workshop.
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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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·2 min
Breaking Through CI/CD Storage Bottlenecks: Why Senior Engineers Are Returning to the Storage Domain
Exploring overlooked storage and caching bottlenecks in CI/CD pipelines, how Blacksmith redesigns storage architecture to accelerate builds, and why storage is a rebirth opportunity in cloud-native.
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·3 min
The Real Daily Life of MLE and MLOps Engineers: Key Differences Between Three Roles and Career Paths
An in-depth look at the real daily work of data scientists, MLEs, and MLOps engineers — covering responsibilities, essential tools, and career paths to help you find your direction in AI.
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·5 min
Complete Breakdown of Core Competencies for AI Agent Engineers: From Demo to Production-Grade Systems
Deep breakdown of 4 core AI Agent engineer competencies: business decomposition, multi-Agent architecture, quantitative evaluation, and engineering delivery—bridging the gap from Demo to production.
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·3 min
The Jargon Trap: Why Engineers Can Never Seem to Speak Plain Language
Why do engineers struggle to communicate in plain language? Exploring the curse of knowledge, jargon as identity, and practical tips for clearer technical communication.
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·4 min
Frontend Engineer Driven to Homelab by AI: From React Specialist to Full-Stack Self-Rescue
A 7-year frontend engineer, fearing AI-driven job loss, builds a homelab to learn Docker, databases, and networking. A pragmatic roadmap for developers building breadth in the AI era.
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·3 min
The Software 3.0 Era: An AI Programming Evolution Guide for Frontend Engineers
Natural language programming is reshaping frontend development. This article explains AI code generation, Prompt formulas, pitfall avoidance, RAG, Agent orchestration, and skill maintenance.
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·3 min
Context Engineering: Why Does More Context Make AI Dumber?
Anthropic introduces Context Engineering, revealing Context Rot: the more tokens in the window, the worse AI retrieval accuracy. Learn the three principles, just-in-time retrieval, and three moves against context overflow.
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·3 min
Introduction to How AI Large Models Work: A Must-Read Guide for Test Engineers
A clear explanation of how AI large models work: from concept hierarchy and Transformer mechanics to probabilistic traits, helping test engineers grasp AI testing.
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·3 min
Introduction to AI Large Language Models: A Must-Read Guide for Test Engineers
A thorough explanation of the essence of AI large language models: from conceptual hierarchy and Transformer mechanics to probabilistic nature, helping test engineers understand LLM strengths and weaknesses.
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·2 min
Harness Engineering: An Advanced Methodology for Programmers to Master AI Coding
From prompt engineering to context engineering to Harness engineering, this article breaks down the three evolutions of AI coding and offers engineering solutions to pain points like hallucinations, non-standard code, and infinite loops.
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·2 min
The Age of AI Coding: Why Tinkering Engineers Are Worth More Than Ever
Claude Code creator Boris and developer Theo reveal: in the AI Agent era, tinkering habits like automation, building small tools, and writing CLAUDE.md are becoming the core edge for reaching Staff engineer level.
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·2 min
Harness Engineering: The Core Methodology for Enterprise-Grade AI Coding
From prompt engineering to Harness Engineering, a deep dive into the three-stage evolution of AI coding. Learn how enterprises use engineered frameworks to harness AI models for production-ready code.
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·2 min
Harness Engineering: The Core Methodology for Enterprise-Grade AI Coding
From prompt engineering to Harness Engineering: a deep dive into the three-stage evolution of AI coding. Learn how enterprises use engineering frameworks to harness LLMs and ship production-ready code.
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·1 min
Becoming an AI Agent Engineer: A Detailed 4-Week Hands-On Learning Roadmap
Want to become an AI Agent engineer? This article breaks down a 4-week roadmap: from core agent architecture and ReAct, to multi-agent collaboration and real projects.
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