As AI models grow in complexity and hardware evolves to meet the demand, the software layer connecting the two must also adapt. We recently sat down with Stephen Jones, a Distinguished Engineer at NVIDIA and one of the original architects…
Jina AI has released Jina-VLM, a 2.4B parameter vision language model that targets multilingual visual question answering and document understanding on constrained hardware. The model couples a SigLIP2 vision encoder with a Qwen3 language backbone and uses an attention pooling…
Mistral AI has introduced Devstral 2, a next generation coding model family for software engineering agents, together with Mistral Vibe CLI, an open source command line coding assistant that runs inside the terminal or IDEs that support the Agent Communication…
Zhipu AI has open sourced the GLM-4.6V series as a pair of vision language models that treat images, video and tools as first class inputs for agents, not as afterthoughts bolted on top of text. Model lineup and context length…
In this article, you will learn how to design, prompt, and validate large language model outputs as strict JSON so they can be parsed and used reliably in production systems. Topics we will cover include: Why JSON-style prompting constrains the…
In this article, you will learn a practical, repeatable way to choose the right AI agent framework and orchestration pattern for your specific problem, your team, and your production needs. Topics we will cover include: A three-question decision framework to…
The new LiteRT NeuroPilot Accelerator from Google and MediaTek is a concrete step toward running real generative models on phones, laptops, and IoT hardware without shipping every request to a data center. It takes the existing LiteRT runtime and wires…
In this tutorial, we explore how an intelligent agent can gradually form procedural memory by learning reusable skills directly from its interactions with an environment. We design a minimal yet powerful framework in which skills behave like neural modules: they…
In this article, you will learn how to turn a raw time series into a supervised learning dataset and use decision tree-based models to forecast future values. Topics we will cover include: Engineering lag features and rolling statistics from a…
BERT is an early transformer-based model for NLP tasks that’s small and fast enough to train on a home computer. Like all deep learning models, it requires a tokenizer to convert text into integer tokens. This article shows how to…
