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Testing Vision Language Models (VLM) on edge devices. Check how small VLMs perform on our custom Raspberry Pi Cluster and Jetson Nanos.
Video Anomaly Detection (VAD) is one of the most challenging problems in computer vision. It involves identifying rare, abnormal events in videos – such as burglary, fighting, or accidents –
The rapid growth of video content has created a need for advanced systems to process and understand this complex data. Video understanding is a critical field in AI, where the
Learn how Video-RAG boosts training-free and low-compute long-video understanding by pairing OCR, ASR, and open-vocabulary detection with any long-video LVLMs.
What if object detection wasn't just about drawing boxes, but about having a conversation with an image? Dive deep into the world of Vision Language Models (VLMs) and see how
Welcome back to our LangGraph series! In our previous post, we explored the fundamental concepts of LangGraph by building a Visual Web Browser Agent that could navigate, see, scroll, and summarize
In the groundbreaking 2017 paper “Attention Is All You Need”, Vaswani et al. introduced Sinusoidal Position Embeddings to help Transformers encode positional information, without recurrence or convolution. This elegant, non-learned
Self-attention, the beating heart of Transformer architectures, treats its input as an unordered set. That mathematical elegance is also a curse: without extra signals, the model has no idea which
SimLingo is a remarkable model that combines autonomous driving, language understanding, and instruction-aware control—all in one unified, camera-only framework. It not only delivered top rankings on CARLA Leaderboard 2.0 and
What if a radiologist facing a complex scan in the middle of the night could ask an AI assistant for a second opinion, right from their local workstation? This isn't
In the evolving landscape of open-source language models, SmolLM3 emerges as a breakthrough: a 3 billion-parameter, decoder-only transformer that rivals larger 4 billion-parameter peers on many benchmarks, while natively supporting
Developing intelligent agents, using LLMs like GPT-4o, Gemini, etc., that can perform tasks requiring multiple steps, adapt to changing information, and make decisions is a core challenge in AI development.
