1373 related articles

Systematically learn the OpenCode AI programming tool: covering both desktop and WSL installation, core commands, model and rule configuration, MCP integration, and Agent Skills.

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.

OpenAI releases the GPT-5.6 model family, focusing on real-world applications: from automating greenhouses and empowering small entrepreneurs, to Codex 5.6 helping a mathematician disprove a three-year problem. A deep dive into GPT-5.6's multi-agent architecture and end-to-end execution.

Databricks tested leading coding agents on a production codebase of millions of lines. Key findings: token price misleads cost estimates, open-source GLM 5.2 handles hard tasks, and harness design determines real-world performance.

OpenAI launches GPT-5.6 with three tiers — Sol, Terra, Luna. Terra cuts price in half; Luna inputs at $1/M tokens. ChatGPT Work agent automates tasks end-to-end; Codex merges into a unified platform.

Local LLM tool Ollama closes a $65M Series B, bringing total funding to $88M. With 9M developers and 85% of Fortune 500 having deployed internally, this deep dive explores why enterprises embrace local LLMs: compliance, Agent cost savings, and open-source ecosystem.

How can experienced Java and backend developers pivot to AI? This deep-dive explains why the Agent direction is the best fit — skills transfer well, market demand is high, and the path from "using frameworks" to "understanding source code" is clear.

AI Agents are reshaping software development with 42.8% market CAGR. Learn the difference between Agents and traditional AI, plus a complete LangChain-based curriculum to launch your career in intelligent agent development.

GPT-5.6 is officially released, merging ChatGPT and Codex into one app and launching the three-tier Sol, Terra, and Luna models. A detailed breakdown of 16 hands-on tests plus Worker mode and Codex dev upgrades.

Why do beginners struggle with AI Agent development? This article breaks down a concise tutorial approach: real-world examples, core logic focus, and practical mindset-building to help you get started fast.

Learn how to pick the best LLM, RAG, and AI Agent courses. Discover 4 key criteria for hands-on AI learning and top resources for developers.

Learn how to connect SSMS SQL projects to an Azure DevOps CI/CD pipeline—covering YAML build config, SQL code quality analysis, managed identity authentication, and dynamic firewall rules for secure Azure SQL deployment.

An AI research engineer with 3 years of experience sent 50 applications to FAANG with zero replies. This article breaks down the hidden barriers of top-tech AI roles, the truth about LinkedIn ghost jobs, and the MLE vs. Research Engineer divide.

GPT-5.6 is now officially available to all users, launching the three-tier Sol, Terra, and Luna models with four-agent parallelism. An in-depth look at the official benchmarks, API pricing, safety, and Ultra mode.

GPT-5.6 (Sol, Terra, Luna) hands-on testing: a Hokkaido farmer controls a greenhouse with AI, a NYC small business builds custom software, and a Polish mathematician breaks a 3-year problem. A deep dive into end-to-end autonomous execution.

A clear, in-depth guide to how AI Agents work: the paradigm shift from traditional programs, the perception-decision-action loop, and the four pillars—LLMs, tool calling, memory, and RAG.

A complete AI Agent learning roadmap covering BDI theory, core components (Perception/Planning/Execution), AutoGen multi-agent frameworks, and DeepSeek RAG projects for beginners.

OpenAI's GPT-5.6 preview introduces So, Terra, and Luna. All three score perfect marks on long-horizon agentic tasks, with Terra priced 50% below GPT-5.5.

A collection of 28 fully reproducible enterprise-grade AI Agent projects covering code debugging, financial analysis, customer service, and multi-agent collaboration—deployable even for beginners.

A complete AI Agent learning roadmap covering agent principles, prompt engineering, RAG, multi-agent systems, and hands-on projects — from zero to real-world deployment.