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.
Goose is a free, open‑source AI agent that runs locally and can autonomously plan, code, test, debug, and execute full development workflows—making it especially useful for data scientists who need to automate repetitive, multi‑step tasks. It supports any LLM, interfaces with file systems and APIs, and can extend its capabilities via the Model Context Protocol (MCP) to connect with databases, Git, Slack, and more.
- Autonomous task execution from high‑level instructions.
- Local execution preserves data privacy and control.
- LLM‑agnostic: works with GPT‑4, Claude, or local models.
- Two interfaces: desktop GUI and CLI.
- Extensible through MCP for external tools and services.
- Ideal for rapid prototyping, data pipeline automation, MLOps, and environment setup.
- Autonomous task execution from high‑level instructions.
- Local execution preserves data privacy and control.
- LLM‑agnostic: works with GPT‑4, Claude, or local models.
- Two interfaces: desktop GUI and CLI.
- Extensible through MCP for external tools and services.
- Ideal for rapid prototyping, data pipeline automation, MLOps, and environment setup.
This article is a year-end recap from Towards Data Science (TDS) highlighting the most popular articles published in 2025. The year was heavily focused on AI Agents and their development, with significant interest in related frameworks like MCP and contextual engineering. Beyond agents, Python remained a crucial skill for data professionals, and there was a strong emphasis on career development within the field. The recap also touches on the evolution of RAG (Retrieval-Augmented Generation) into more sophisticated context-aware systems and the importance of optimizing LLM (Large Language Model) costs. TDS also celebrated its growth as an independent publication and its Author Payment
Simon Willison’s annual review of the major trends, breakthroughs, and cultural moments in the large language model ecosystem in 2025, covering reasoning models, coding agents, CLI tools, Chinese open‑weight models, image editing, academic competition wins, and the rise of AI‑enabled browsers.
The article discusses four open-source AI research agents that serve as cost-effective alternatives to OpenAI’s Deep Research AI Agent. These alternatives offer robust search capabilities, AI-powered extraction, and reasoning features, allowing researchers to automate and optimize their workflows without incurring high costs.
Exploring secure environments for testing and running AI agent code, including options like Docker, online IDEs, and dedicated platforms.
This tutorial demonstrates how to build a powerful document search engine using Hugging Face embeddings, Chroma DB, and Langchain for semantic search capabilities.
In this tutorial, we build a hierarchical planner agent using an open-source instruct model. We design a structured multi-agent architecture comprising a planner agent, an executor agent, and an aggregator agent, where each component plays a specialized role in solving complex tasks. We use the planner agent to decompose high-level goals into actionable steps, the executor agent to execute those steps using reasoning or Python tool execution, and the aggregator agent to synthesize results into a coherent final response. By integrating tool usage, structured planning, and iterative execution, we create a fully autonomous agent system that demonstrates how modern AI agents reason, plan, and act in a scalable and modular manner.
This article explores the Model Context Protocol (MCP), an open protocol designed to standardize AI interaction with tools and data, addressing the fragmentation in AI agent ecosystems. It details current use cases, future possibilities, and challenges in adopting MCP.
Understanding the architectural trade-offs between autonomous agents and orchestrated workflows — because someone needs to make this decision, and it might as well be you
