239 related articles

In-depth comparison of LangSmith, Langfuse, PromptLayer, Helicone, and Orq.ai across Prompt management, Evals, and observability to help teams choose the best unified LLM Ops platform.

In-depth analysis of Claude Opus, Gemini Pro, and ChatGPT: the real competitive landscape among top AI models, limitations of community benchmarks, and scientific methods for model selection.

API Mock is fast but misses bugs; Sandbox is realistic but costly. This article analyzes their core differences and provides a layered testing strategy for building reliable Agent test systems.

Learn how GitHub Agentic Workflows automate cross-repo documentation updates. See the Aspire team's AI-driven approach: event triggers, smart drafting, and SME review to keep docs in sync with code.

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.

An in-depth look at AI testing challenges. Learn to write reusable Skill packs and master Agent testing and LLM evaluation—covering the SKILL.md six-dimensional rule, skill-creator, EvalScope, and dataset selection.

Cursor's swarm experiment had AI teams implement a SQLite engine in Rust from scratch using only 835 pages of docs. All-frontier models cost $10K+, hybrid role division just $1.3K—a 7.9x gap.

Coze is ByteDance's homegrown agent-building platform. This article covers getting started with Coze, its comparison with Dify, skill system, workflow orchestration, and multi-agent collaboration.

Coze is ByteDance's homegrown agent-building platform. This article explains getting started with Coze, comparison with Dify, its skill system, workflow orchestration, and multi-agent collaboration.

Notion co-founder Simon Last shares Notion's journey from note-taking tool to AI agent workspace: from first tasting GPT-4 to personal and custom agents.

A Google DeepMind engineer reveals that over 50,000 AI agent skills come with almost no evals. Learn how to write skill evaluations, from description tuning to test design.

A Google DeepMind engineer reveals that over 50,000 AI agent skills come with almost no evals. This guide covers skill descriptions, test design, eval harnesses, and retirement strategies.

Agent Device is an open-source tool from CallStack that lets AI truly "understand" interfaces via structured accessibility snapshots, closing the loop of open, observe, operate, and verify across iOS, Android, and React Native.

How Pinterest engineers built Medic for Apache Spark — a multi-agent auto-diagnosis tool — covering the evolution from a single ReAct agent, observability, log denoising, and end-to-end testing.

In-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing and the transition path for test engineers.

An in-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing methods and the transition path for test engineers.
Alibaba Open-Sources Code Review Tool …
Alibaba open-sources code review tool open-code-review, using a hybrid architecture of deterministic rule pipelines and LLM Agents. Supports line-level comments, OpenAI/Anthropic APIs, battle-tested at Alibaba scale, written in Go, fully free and open-source.

jlens-gguf is an open-source tool bringing Anthropic's Jacobian Lens interpretability method to GGUF and llama.cpp, enabling internal observation, real-time steering, and abliteration for both dense and MoE models.

Can beginners really earn over 10,000 yuan in their first month with AI coding gigs? This article breaks down the four-week AI coding learning path week by week and objectively assesses the real monetization barriers.

Claude Code isn't just a chat AI—it can directly read projects, modify code, and run commands. This article compares Claude Code with ordinary AI across five dimensions: interaction, context, execution, memory, and tool calling.