Run a Local LLM with OpenClaw on Your Mac Mini

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 .

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 .

Introducing AnyLanguageModel: One API for Local and Remote LLMs on Apple Platforms

How To Build Your Own LLM Runtime From Scratch

If you have ever wanted to actually build an LLM inference runtime yourself — pack your own weights, own every barrier, capture your own CUDA graphs — this is what that journey looks like on an H100. A step-by-step tour of a small runtime called annotated-llm-runtime, and the three bugs that produced most of the annotations.

Introducing OpenAI o3 and o4-mini

Our smartest and most capable models to date with full tool access

Adding MCP Tools to Reachy Mini

We got local models to triage the OpenClaw repo for FREE!*

How to Orchestrate a Fleet of OpenClaw Bots

Learn how to run OpenClaw bots for increased productivity The post How to Orchestrate a Fleet of OpenClaw Bots appeared first on Towards Data Science .

Stop Choosing Between Local and Cloud LLMs: A Field Guide to Hybrid Patterns

A hands-on walkthrough of a hybrid local-cloud workflow using Gemma 4 and GPT-5. 4, with reasoning and structured outputs The post Stop Choosing Between Local and Cloud LLMs: A Field Guide to Hybrid Patterns 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.

Flama: a Python framework for development and deployment of production-ready APIs, machine learning, and LLM services

Flama is an open‑source Python framework that unifies the development and deployment of production‑ready web APIs, machine‑learning services, and large‑language‑model (LLM) applications. Built on ASGI, it offers an async‑first, type‑driven programming model with seven subsystems—including dependency injection, a pluggable schema layer, automatic CRUD generation, a portable binary model format, a multi‑backend LLM server, a Rust‑accelerated core, and a Model Context Protocol module. The framework also provides built‑in JWT authentication, pagination, background tasks, WebSocket and streaming support, OpenAPI generation, and a CLI for running, packaging, and inspecting models.

Llama 2 on Amazon SageMaker a Benchmark