Building AI Agents with Local Small Language Models
The idea of building your own AI agent used to feel like something only big tech companies could pull off.
Train, Serve, and Deploy a Scikit-learn Model with FastAPI
FastAPI has become one of the most popular ways to serve machine learning models because it is lightweight, fast, and easy to use.
AI Agent Memory Explained in 3 Levels of Difficulty
A stateless AI agent has no memory of previous calls.
Getting Started with Zero-Shot Text Classification
Zero-shot text classification is a way to label text without first training a classifier on your own task-specific dataset.
Gradient-based Planning for World Models at Longer Horizons
GRASP is a new gradient-based planner for learned dynamics (a “world model”) that makes long-horizon planning practical by (1) lifting the trajectory into virtual states so optimization is parallel across time, (2) adding stochasticity directly to the state iterates for exploration, and (3) reshaping gradients so actions get clean signals while we avoid brittle “state-input” …
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The Complete Guide to Inference Caching in LLMs
Calling a large language model API at scale is expensive and slow.
Python Decorators for Production Machine Learning Engineering
You’ve probably written a decorator or two in your Python career.
5 Techniques for Efficient Long-Context RAG
Structured Outputs vs. Function Calling: Which Should Your Agent Use?
Language models (LMs), at their core, are text-in and text-out systems.
How to Implement Tool Calling with Gemma 4 and Python
The open-weights model ecosystem shifted recently with the release of the