#smoke testing
28 related articles
·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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·4 min
Advice from the Creator of Claude Code: Automation Is an Engineer's True Moat
Claude Code creator Boris argues top engineers should embrace AI-era automation leverage. By encoding domain knowledge into infrastructure, preview environments, and lint rules, engineers multiply output—the core path to Staff Engineer.
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·2 min
The Truth Behind Codex 'Build a Website in 5 Minutes': AI Isn't Creating Sites—It's Helping You Copy Them
Exposing the truth behind viral Codex 5-minute website videos: creators aren't building original sites with AI—they're copying shared prompts or scraping others' work. Learn AI coding tools' real limits.
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·3 min
Cross-Region Packet Loss Monitoring in Practice: Tool Selection and Deployment Guide
A systematic guide to cross-region packet loss monitoring covering core challenges, tool comparison (MTR, SmokePing, PRTG, Zabbix, ThousandEyes), and a self-hosted deployment solution using Prometheus + Grafana.
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·7 min
DIY Companion Robot: Building Your AI Desktop Buddy with Jetson Orin
A maker builds a DIY companion robot with NVIDIA Jetson Orin and 4S LiPo battery. Explore the full development journey from first power-up to AI interaction, including edge computing, power design, and companion robot trends.
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·3 min
In-Depth Analysis of Three AI Test Automation Approaches: Code Scripting, DOM Parsing, and Visual Models
A systematic breakdown of the three mainstream test automation approaches in the AI era: AI-generated code scripts, DOM parsing driven, and LVM visual model driven. In-depth comparison of principles, pros/cons, and use cases.
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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
6-Day Open Source AI Agent: Natural Language-Driven Android Automation Testing
Veta is an open source AI testing agent: just describe your test goal in natural language and it autonomously plans, executes, verifies, and reports Android test results — no scripts needed.
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Zig Creator Calls Out Anthropic: The G…
·2 min
Zig Creator Calls Out Anthropic: The Gap Between AI Hype and Technical Reality
Zig creator Andrew Kelley publicly criticizes Anthropic for "blowing smoke" in AI marketing. A deep dive into the tension between AI hype and engineering integrity.
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·7 min
Base44 in Practice: How Claude Code Powered Rapid Scaling from 1 to 80 People
How Base44's product team scaled from a single founding engineer to an 80-person team with Claude Code. Covers AI-assisted onboarding, code review, user evaluation, and QA automation.
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·7 min
The Trap of AI Visibility Dashboards: Why Most Metrics Are Meaningless
Can a brand's "visibility" in AI answers really be quantified? This article deeply dissects the methodological flaws of AI visibility dashboards—from LLM output randomness and black-box mechanisms to vanity metric traps.
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·5 min
In Praise of Observational Evidence: When Randomized Controlled Trials Can't Answer Everything
Are RCTs really the only standard for scientific evidence? This article explores the scientific value of observational evidence, the rise of causal inference methods, and how data scientists can draw reliable conclusions from observational data when A/B testing isn't feasible.
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·6 min
America's 250th Anniversary: Behind the Fireworks, Founding Ideals Still Face Serious Challenges
As the U.S. marks its 250th anniversary with France lighting the Eiffel Tower and Japan setting off fireworks, its founding ideals of liberty and democracy face ongoing threats.
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·4 min
pytest Testing Patterns in Practice: Building a Systematic Automated Testing Framework
A deep dive into pytest patterns: layered fixture management, parameterized coverage, mock isolation, coverage gates, and CI integration — upgrade your team from scattered scripts to a maintainable automated testing framework.
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·3 min
Benchmarking Popular Claude Skill Libraries: A 5-Step Core Pattern for AI Agent Development
A deep-dive evaluation of Addy Osmani, Matt Pocock, and Gary Tan's skill libraries, distilling a 5-step Research→Prototype→Plan→Build→Test agent dev loop and why the best skill system is always your own.
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·4 min
AI Weekly: Top Coding Model Returns, Google's New Flash Checkpoint, and the Video Generation Race
This week in AI: Anthropic's flagship coding model returns globally with new safety classifiers, Google tests a new Gemini Flash checkpoint, video generation heats up, and Figure AI robots enter BMW factories.
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·4 min
Getting Started with Software Testing: A Complete Guide to Core Concepts and Classification Systems
A comprehensive guide to software testing fundamentals covering definitions, purposes, classification by phase, technique, and method, plus core concepts like smoke testing and regression testing.
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·3 min
Token Doomsday: The Industry Truth Behind AI Coding's Spiraling Costs
GitHub Copilot shifts from flat-rate to per-token billing, sending dev costs from $29/mo to $1,000+. Uber burns its annual AI budget in months. A deep dive into Token Doomsday.
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·3 min
Kimi K2.7-Code vs K2.6 Real-World Comparison: Who Wins Across Five Hardcore Challenges?
Real-world comparison of Kimi K2.7-Code vs K2.6 across five hardcore challenges: particle effects, rigid body physics, soft body physics, UI design, and code review — with quality, Token, and cost data.
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