Estimated reading time: 4 minutes Table of contents What Is Context Engineering? Taxonomy of Context Engineering Key Insights and Research Gaps Applications and Impact Future Directions The paper “A Survey of Context Engineering for Large Language Models” establishes Context Engineering as a formal discipline that goes far beyond prompt engineering, providing a unified, systematic framework…
In this advanced Roboflow Supervision tutorial, we build a complete object detection pipeline with the Supervision library. We begin by setting up real-time object tracking using ByteTracker, adding detection smoothing, and defining polygon zones to monitor specific regions in a video stream. As we process the frames, we annotate them with bounding boxes, object IDs,…
Training large-scale transformers stably has been a longstanding challenge in deep learning, particularly as models grow in size and expressivity. MIT researchers tackle a persistent problem at its root: the unstable growth of activations and loss spikes caused by unconstrained weight and activation norms. Their solution is to enforce provable Lipschitz bounds on the transformer…
In this tutorial, we explore how to use the SHAP-IQ package to uncover and visualize feature interactions in machine learning models using Shapley Interaction Indices (SII), building on the foundation of traditional Shapley values. Shapley values are great for explaining individual feature contributions in AI models but fail to capture feature interactions. Shapley interactions go…
The generative AI landscape is dominated by massive language models, often designed for the vast capacities of cloud data centers. These models, while powerful, make it difficult or impossible for everyday users to deploy advanced AI privately and efficiently on local devices like laptops, smartphones, or embedded systems. Instead of compressing cloud-scale models for the…
Large language models (LLMs) specialized for coding are now integral to software development, driving productivity through code generation, bug fixing, documentation, and refactoring. The fierce competition among commercial and open-source models has led to rapid advancement as well as a proliferation of benchmarks designed to objectively measure coding performance and developer utility. Here’s a detailed,…
As we move through 2025, artificial intelligence (AI) is fundamentally reshaping secure browsing and Virtual Private Network (VPN) technologies. The explosion of sophisticated cyber threats, sharpened by the capabilities of AI and quantum computing, is forcing rapid innovation in privacy protection, user trust, and online security infrastructure. The Data Privacy Wakeup Call AI-Related Privacy Breaches:…
Introduction: Why Startups Are Looking at Vibe Coding Startups are under pressure to build, iterate, and deploy faster than ever. With limited engineering resources, many are exploring AI-driven development environments—collectively referred to as “Vibe Coding”—as a shortcut to launch minimum viable products (MVPs) quickly. These platforms promise seamless code generation from natural language prompts, AI-powered…
This tutorial demonstrates how to implement the Self-Refine technique using Large Language Models (LLMs) with Mirascope, a powerful framework for building structured prompt workflows. Self-Refine is a prompt engineering strategy where the model evaluates its own output, generates feedback, and iteratively improves its response based on that feedback. This refinement loop can be repeated multiple…
In today’s rapidly evolving AI landscape, many founders and observers find themselves preoccupied with the idea that successful startups must build foundational technology from scratch. Nowhere is this narrative more prevalent than among those launching so-called “LLM wrappers” — companies whose core offering builds on top of large language models (LLMs) like GPT or Claude….