Can a Local LLM Run My AI Assistant?

From Local LLM to Tool-Using Agent

Using Gemma 4, Ollama, OpenAI Agents SDK, and Tavily MCP to build a lightweight research agent The post From Local LLM to Tool-Using Agent appeared first on Towards Data Science .

Tool Calling, Explained: How AI Agents Decide What to Do Next

Understanding ow LLMs interact with the world around them, from returning data to taking action The post Tool Calling, Explained: How AI Agents Decide What to Do Next appeared first on Towards Data Science .

Build an LLM Agent That Can Write and Run Code

A hands-on walkthrough of code execution with the OpenAI Agents SDK and Docker The post Build an LLM Agent That Can Write and Run Code appeared first on Towards Data Science .

Can an LLM Forget the Right Things?

The article discusses a specialized LLM inference runtime designed for real-time applications, such as a 33 ms robot control cycle. Unlike typical runtimes that ignore physical deadlines, this system refuses new requests when the deadline is at risk, evicts key‑value cache entries based on meaning rather than age, and is implemented entirely in hand‑written CUDA without relying on cuBLAS or libtorch.

Introducing Agents.js: Give tools to your LLMs using JavaScript

LLMs help robots understand vague instructions and focus on key details

To help robots do chores in places like homes and factories, a new approach from MIT uses one language model to clarify users’ instructions, then another to ignore irrelevant info.

LLM-Powered AI Agent Systems and Their Applications in Industry

arXiv:2505. 16120v3 Announce Type: replace Abstract: The emergence of Large Language Models (LLMs) has reshaped agent systems.

The all new le Chat: Your AI assistant for life and work

Running Codex as a Headless Agent

Turning Codex from an interactive assistant into a programmable automation component The post Running Codex as a Headless Agent appeared first on Towards Data Science .

You Probably Don’t Need an Agent Framework

Most LLM applications need a clear workflow, not an autonomous agent. Here's how to build one in plain Python.

Note on 18th September 2026

Simon Willison reflects on his current disinterest in large language models (LLMs), comparing it to a geneticist dismissing the newly opened Jurassic Park. He emphasizes that this stance feels odd given the excitement surrounding LLMs. The note highlights his personal stance on AI and generative‑AI topics.

Echoverse: Deep, evolving environments for computer-use agents

Computer-use AI agents struggle with multi-step workflows like email and customer support. Echoverse trains agents in realistic environments rather than simply providing more training tasks, helping them improve as the tasks, tests, and environments evolve.