This article provides a hands-on coding guide to explore nanobot, a lightweight personal AI agent framework. It details recreating core subsystems like the agent loop, tool execution, memory persistence, skills loading, session management, subagent spawning, and cron scheduling. The tutorial uses OpenAI’s gpt-4o-mini and demonstrates building a multi-step research pipeline capable of file operations, long-term memory storage, and concurrent background tasks. The goal is to understand not just how to *use* nanobot, but how to *extend* it with custom tools and architectures.
This article details a tutorial on building cybersecurity AI agents using the CAI framework. It guides readers through setting up the environment with Colab, loading API keys, and creating base agents. The tutorial progresses to advanced capabilities, including custom function tools, multi-agent handoffs, agent orchestration, input guardrails, and dynamic tools.
It demonstrates how CAI transforms Python functions and agent definitions into flexible cybersecurity workflows capable of reasoning, delegating, validating, and responding in a structured way. The article also showcases CTF-style pipelines, multi-turn context handling, and streaming responses, offering a comprehensive overview of CAI's potential for security applications.
It demonstrates how CAI transforms Python functions and agent definitions into flexible cybersecurity workflows capable of reasoning, delegating, validating, and responding in a structured way. The article also showcases CTF-style pipelines, multi-turn context handling, and streaming responses, offering a comprehensive overview of CAI's potential for security applications.
This document details the features, best practices, and migration guidance for GPT-5, OpenAI's most intelligent model. It covers new API features like minimal reasoning effort, verbosity control, custom tools, and allowed tools, along with prompting guidance and migration strategies from older models and APIs.
A detailed blog post discussing OpenAI's newly released open-weight GPT models, including performance benchmarks, initial testing on various hardware (Mac laptops, Cerebras), and comparisons to other open-source models. It covers aspects like reasoning capabilities, tool calling, and the new OpenAI Harmony prompt format.
An overview of the top AI-powered coding tools available in 2025, including Cursor, Claude Code, Windsurf, VSCode, and others, highlighting their features and popularity among developers.
| **Tool** | **Developer** | **Key Features** | **Notes** |
|-------------------|----------------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------|
| **Cursor** | Anysphere | AI-powered autocomplete, code rewriting, agent mode, integrates with Web resources | Most popular AI-focused IDE, fork of VSCode |
| **Claude Code** | Anthropic | Works in MacOS/Linux terminal, command-line interface, high control over workflow | Preferred by developers who like command-line tools |
| **Windsurf** | Codeium (acquired by OpenAI)| AI autocorrect, agentic AI, Cascade interface, works in other IDEs (e.g., Jetbrains) | Rebranded from Codeium, acquired by OpenAI for $3 billion |
| **VSCode** | Microsoft | Extensions support, GitHub Copilot (agent mode in preview), slow AI integration | Popular IDE, but slower to adopt AI features compared to startups |
| **Vibe Coding Tools** | Various (e.g., Lovable, Replit, Bolt, Firebase) | Chat interface, no software installation, browser-based, minimal coding required | Designed for "vibe coding," where users describe what they want to build |
| **Other Tools** | OpenAI, Mistral, Apple, etc. | Codex (OpenAI), Mistral Code, Apple’s AI tools, Zed, Devin (AI software engineer) | Rapidly evolving, many new tools emerging frequently |
| **Tool** | **Developer** | **Key Features** | **Notes** |
|-------------------|----------------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------|
| **Cursor** | Anysphere | AI-powered autocomplete, code rewriting, agent mode, integrates with Web resources | Most popular AI-focused IDE, fork of VSCode |
| **Claude Code** | Anthropic | Works in MacOS/Linux terminal, command-line interface, high control over workflow | Preferred by developers who like command-line tools |
| **Windsurf** | Codeium (acquired by OpenAI)| AI autocorrect, agentic AI, Cascade interface, works in other IDEs (e.g., Jetbrains) | Rebranded from Codeium, acquired by OpenAI for $3 billion |
| **VSCode** | Microsoft | Extensions support, GitHub Copilot (agent mode in preview), slow AI integration | Popular IDE, but slower to adopt AI features compared to startups |
| **Vibe Coding Tools** | Various (e.g., Lovable, Replit, Bolt, Firebase) | Chat interface, no software installation, browser-based, minimal coding required | Designed for "vibe coding," where users describe what they want to build |
| **Other Tools** | OpenAI, Mistral, Apple, etc. | Codex (OpenAI), Mistral Code, Apple’s AI tools, Zed, Devin (AI software engineer) | Rapidly evolving, many new tools emerging frequently |
LLM 0.26 introduces tool support, allowing LLMs to access and utilize Python functions as tools. The article details how to install, configure, and use these tools with various LLMs like OpenAI, Anthropic, Gemini, and Ollama models, including examples with plugins and ad-hoc functions. It also discusses the implications for building 'agents' and future development plans.
This article details the Model Context Protocol (MCP), a new approach to integrating Large Language Models (LLMs) like Azure OpenAI with tools. MCP focuses on structured data exchange to improve reliability, observability, and functionality, moving beyond simple text-in, text-out interactions. It aims to standardize how LLMs interact with tools, enhancing their ability to utilize those tools effectively.
