I’m Yu Fan, a tech leader with deep technical expertise and managerial vision. Formerly at Motorola, now at Mavenir, I’ve led teams for years, focusing on backend architecture and cloud-native solutions, staying abreast of AI and other frontier fields, and championing personal growth and lifelong learning.
The Claude Code team’s engineering director shares five core principles—shifting bottlenecks, Just‑In‑Time planning, pervasive automation, trust‑but‑verify code review, and a flat, role‑agnostic team structure—illustrated with concrete examples and actionable guidelines for AI‑native organizations.
The article analyzes Huawei’s ternary‑logic chip and the τ (tau) law, tracing their historical roots, technical claims, patent details, performance numbers, ecosystem challenges, market reactions, and the intense industry debate over whether they represent genuine breakthroughs or elaborate marketing narratives.
The article examines PayPal’s open‑source JunoDB, a high‑performance distributed key‑value store built in Go, detailing its motivation, three‑tier proxy architecture, sharding strategy, quorum‑based consistency, aggressive replication, security mechanisms, real‑world PayPal use cases, and the path to open‑source release.
AI development teams now face a shift from accelerating code generation to tackling the hidden challenges of automated testing inference and workflow orchestration, which have become the primary constraints on delivery speed and quality.
This guide walks through constructing production-grade AI agents by combining reusable Skills for domain knowledge, the Model Context Protocol (MCP) for secure integration, and a token‑efficient CLI, detailing architecture layers, progressive tool loading, code‑mode orchestration, performance benchmarks, and future roadmap considerations.
In 2026 China’s job market, 12.7 million graduates face fierce competition, 53% of firms drop degree requirements, AI‑related skills add an average salary premium of 78,000 CNY, and the most successful talent combines a solid degree with continuously updated practical skills.
This article compiles top community shares, official blogs, research papers, open‑source projects and real‑world engineering experience into a step‑by‑step AI Agent learning todo list, organized into phases, practical usage tips, and core learning principles for beginners to advanced practitioners.
The article analyzes why large organizations suffer from architectural decision fatigue, explains the lack of a formal decision‑making process, and presents a practical solution using a technical decision matrix combined with Architectural Decision Records (ADR) to create transparent, repeatable, and low‑overhead decision workflows.
The article explains how five open‑source Claude Code Skills—Backtest Expert, Data Pipeline, Signal Generator, Risk Manager, and Live Signal Monitor—combined with the EODHD API can automate the entire quant‑trading workflow from strategy definition to live monitoring, detailing each skill's workflow, code examples, advantages, limitations, and ideal users.
Fiona Fung explains how the Claude Code team shifted from code‑centric bottlenecks to verification, review, cross‑functional collaboration and safety, cutting outdated processes, flattening the org, leveraging Claude for PR automation, shifting left testing, and measuring impact with new onboarding, PR‑cycle, and AI‑assisted commit metrics.
