196 related articles

Cosmonapse is an open-source multi-agent framework that replaces central orchestrators with peer nodes, using typed signals and an event bus to fully decouple tool calls, memory, and HITL. Supports Python and TypeScript under Apache 2.0.

A deep dive into AI testing workflows: API capture, test case generation, Agent orchestration, and automated execution. Learn the two core challenges — incomplete information and mandatory human review.

GPT 5.6's Sol, Terra, and Luna tested across 108 engineering checks. Backend scores tie between Sol and Terra — the real gap is in frontend and packaging delivery.

A deep dive into embedding a coding agent in Slack: core architecture, permission design, async task handling, and RAG context management for AI-powered dev workflows.

GPT-Live hands-on: Voice chat now powered by GPT-5.5 Thinking, full-duplex architecture, real-time search, visual cards & tool calling. Full review inside.

OpenAI merges Codex into ChatGPT and launches GPT-5.6. Discover how proactive task planning, cross-device coordination, and autonomous agents are redefining AI assistants.

An orchestration Agent looped for hours, firing thousands of LLM calls and burning weeks of budget. Learn the root causes and practical defenses: circuit breakers, tiered budgets, and iteration limits.

OpenAI merges ChatGPT and Codex into a super app and releases three new GPT-5.6 models: Sol, Terra, and Luna. A deep dive into four hands-on workflows—Computer Use, Loops, and multi-threading—for the AI agent era.

Mock testing can't cover the real side effects of high-risk, irreversible AI Agent actions. Learn sandbox environments, shadow mode, dry run, HITL, and more.

A deep dive into state machine-based voice AI agent architecture, comparing Pipecat Flows and Vapi Squad, and exploring the latency vs. accuracy trade-offs in agentic handoffs.

A comprehensive analysis of ASP.NET Core's architecture and technical strengths: cross-platform deployment, high-performance Kestrel server, modular middleware, built-in DI, and modern web capabilities like Blazor, gRPC, and SignalR.

NASA's JPL open-sourced the F´ (F Prime) flight software framework: C++-based, component-driven, and validated in real space missions. Ideal for CubeSats, drones, and embedded systems, it has over 11,000 GitHub Stars.

An in-depth look at the core advantages of the Cypress front-end automated testing framework: fast execution, a simple easy-to-use API, and stable reliable results. Covers architecture, Selenium comparison, and component testing.

A systematic AI Agent development learning path covering fundamentals, prompt engineering, tool calling, multi-agent collaboration, and hands-on practice with LangChain, CrewAI, and Dify.

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.

BrowserWing is an open-source browser action recorder (1300+ GitHub stars) with 78 built-in scripts for GitHub, Bilibili, Zhihu, and more. Record once, run repeatedly, and let AI Agents reliably execute fixed web workflows.

A developer stress-tested GPT-5.6 for six weeks across 67 projects, burning $180K-$240K in inference. Real cases of task persistence, Rust rewrites, autonomous browser control — plus honest frontend and 3D shortfalls.

Master LangGraph core concepts: nodes, edges, and routing functions. Learn StateGraph, MemorySaver, and ToolNode through a weather-query Hello World example, and understand how LangGraph relates to LangChain and powers Agent workflows.

How can CS students who dislike competitive programming systematically pivot to AI/ML? This guide covers skill priorities (Python/SQL/ML/deployment), portfolio strategy, Kaggle tips, and real paths to landing AI/ML internships.

How can new graduates transition from software engineer to platform engineer? This article breaks down the path of joining as a Grad SWE first, then transferring internally, analyzes C# vs Python trade-offs, and offers a 14-month prep plan for AI/ML infrastructure.