#concurrent data structures
165 related articles
·4 min
From 50% to 92%: A Practical Methodology for Teaching AI Agents to Master Spreadsheets
An engineering team spent four months raising an AI coding agent's spreadsheet accuracy from 50% to 92%. Deep dive into REPL architecture, validation loops, and domain knowledge injection.
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5 Web Search APIs Compared: How to Cho…
·2 min
5 Web Search APIs Compared: How to Choose the Right Data Source for Your AI App
A deep comparison of 5 mainstream Web search APIs across latency, result quality, and pricing — helping AI app developers find the best data source for RAG and LLM use cases.
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·2 min
AI Application Developer Skills Breakdown: Four Core Modules and a Clear Learning Roadmap
Calling an API isn't enough. This article breaks down the full AI application developer skill structure — Python, deep learning, fine-tuning, Agents, and enterprise projects — with a clear learning roadmap.
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·3 min
pgrust: Rewriting Postgres in Rust — 100% Regression Tests Passing
pgrust is an open-source project rewriting PostgreSQL entirely in Rust, claiming 100% regression test passage. This article analyzes the technical motivation, implementation approach, and key challenges.
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·3 min
PostgreSQL Lock Contention: The Root Cause and Solutions for High-Concurrency Scalability Bottlenecks
An in-depth analysis of PostgreSQL's lock scalability bottlenecks under high concurrency: LWLock contention, the cost of the multi-process model, negative scaling, and practical strategies like connection pooling, transaction optimization, and horizontal scaling.
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·3 min
Biome's Missing ESLint Rule: A Community Voting Guide for padding-line-between-statements
Biome.js currently lacks the popular ESLint rule padding-line-between-statements. This article explains the rule's purpose and configuration, and explores how community voting can drive Biome's feature completeness and aid ESLint migration decisions.
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·7 min
Breaking Down the Core Skills of an AI Application Engineer: From Beginner to Industrial-Grade Deployment
Knowing how to call an API doesn't make you an AI engineer. This article breaks down the complete skill structure of an AI application engineer, covering Python fundamentals, LLM fine-tuning, Agent development, and enterprise projects.
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·6 min
Spec-Driven Development in Practice: The New AI Coding Paradigm from Andrew Ng × JetBrains
Andrew Ng partners with JetBrains to launch a Spec-Driven Development course, teaching how to direct AI coding agents via spec files to boost intent fidelity and build maintainable production apps.
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·3 min
Deep Dive into gRPC: Building High-Performance Microservice Communication with HTTP/2 + Protobuf
An in-depth analysis of gRPC's core architecture: HTTP/2 multiplexing, Protocol Buffers serialization, and unified multi-language implementation, covering microservice communication and cloud-native integration.
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·4 min
Running Out of Codex Quota? An In-Depth Review of HeyRoute's GPT-5.5 Integration
Codex quota running out too fast, or API costs too high? HeyRoute offers a low-cost AI coding relay via GPT-5.5, with ~1.08s first response and 99.95% success rate. This in-depth review covers its selling points, pricing, and caveats.
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·3 min
Multi-Agent Collaboration with Claude Code and Codex: A Guide to Architecture Design and State Management
A deep dive into Claude Code and Codex multi-Agent collaboration: architecture design, state-driven coordination, private/public zone division, and building efficient AI programming teams.
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·5 min
Browser Use: A Deep Dive into the Event-Driven Browser AI Agent Framework
Browser Use is an open-source AI Agent framework that lets LLMs autonomously drive browser operations via natural language. This article breaks down its four-layer architecture, core Agent loop, CDP perception layer, dynamic Tools dispatch, and its Skills, Sandbox, and MCP extension capabilities.
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·3 min
Designing MPMC Queues: Balancing Bounded Waiting and High-Performance Concurrency
An in-depth look at the core design challenges of MPMC queues: bounded waiting, lock-free mechanisms, sequence-number slots, dual-path strategies, and helping. Helping engineers balance throughput, fairness, and latency.
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·4 min
100,000 Lines of Code in 7 Days: A Solo Developer's Hands-On Guide to AI-Assisted iOS Development
A solo developer iterated an iOS app to 100,000 lines of code in 7 days and shipped it. This article breaks down the core methodology: delegate the frontend to AI, control the backend by hand.
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·6 min
AI Agent Development in Go: A Hands-On Guide to ByteDance's Eino Framework and Its Seven Core Capabilities
Build production-grade AI Agents with a pure Go stack using ByteDance's Eino framework. A deep dive into seven core capabilities: multi-Agent orchestration, long-task execution, command approval, RAG, MCP, Skills, and database reporting.
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·6 min
Andrew Ng × Anthropic: A Hands-On Claude Code Tutorial and Core Methodology
Andrew Ng partners with Anthropic to launch a hands-on Claude Code course, revealing its simple architecture, local security edge, and core context methodology across three cases: RAG chatbot, Jupyter analysis, and Figma-to-frontend.
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·5 min
Getting Started with LangChain: LLM Invocation, Agent Practice, and Environment Setup Guide
Starting from the three limitations of LLMs, this guide systematically explains LangChain's core positioning, environment setup, API key prep, model init, and the message system. Learn init_chat_model and AIMessage/HumanMessage/SystemMessage.
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·4 min
4 Skills to Completely Transform Claude Code: Say Goodbye to Token Waste and Cut Daily Coding to 2 Hours
Does Claude Code always give irrelevant answers? This article breaks down 4 core Skill plugins: Project Context Engine, PRD Requirements Translator, Code Review tool, and Daily Report Generator—showing you how to transform Claude Code from a money-burner into a true AI coding assistant.
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·4 min
The Three Eras of AI Agents: The Capability Leap from Tool Calling to Autonomous Orchestration
An in-depth look at the three core eras of AI Agent development: reliable tool calling, coherent long-task execution, and autonomous orchestration with metacognition. Helps developers match tasks to model capabilities.
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·6 min
GPT-5.6 Now Widely Available: The Era of Three-Tier Models and Multi-Agent Orchestration Arrives
OpenAI releases GPT-5.6 (SOUL/TERRA/LUNA), with Ultra mode running four agents in parallel; Meta launches Muse Spark 1.1 with million-token context; ChatGPT desktop unifies Chat, Work, and Codex.
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