Face and basic landmarks detection using mediapipe models with efficiency and very good accuracy and draw on image or save detected faces using opencv in python.
Read more.
Deep LearningNuMind recently released NuExtract 3, a highly efficient 4B-parameter vision-language model. Explore how to replace legacy OCR pipelines with this advanced VLM to extract high-fidelity Markdown and structured JSON from complex documents.
Deep LearningThriftAttention leverages selective mixed precision to run long-context LLMs in FP4. Discover how this breakthrough reduces memory bandwidth without sacrificing model accuracy.
Deep LearningMicrosoft Research has introduced SkillOpt, a framework that allows autonomous AI agents to build, optimize, and manage their own tools. Discover how this executive strategy moves us from static prompt engineering to dynamic agent evolution.
Deep LearningNVIDIA recently introduced Gated DeltaNet-2 to revolutionize linear attention by decoupling the erase and write mechanisms. We explore how this independent channel-wise gating allows the 1.3B parameter model to surpass Mamba-2 and Mamba-3 in long-context retrieval.
Deep LearningStandard reinforcement learning treats language model outputs as a single pass or fail grade. Discover how DelTA introduces token-level credit assignment to isolate and reward correct reasoning steps without expensive human annotations.
LLMUIUC researchers recently unveiled Spreadsheet-RL, a novel reinforcement learning framework that teaches AI agents to execute complex tabular tasks. Discover how this breakthrough overcomes the spatial reasoning limits of language models to automate multi-step spreadsheet manipulation.
LLMHeadroom is a new context optimization library that compresses tool outputs and RAG chunks before they reach your LLM. Learn how to integrate this lightweight tool to drastically reduce token usage without sacrificing agent accuracy.
LLMDiscover how the trending TradingAgents framework uses multiple specialized LLMs to simulate a complete hedge fund. We explore the architecture code and metrics behind multi-agent financial systems.
Deep LearningStanford researchers have introduced Item Response Scaling Laws to predict large language model performance using classical psychometrics. This framework reduces the computational demand of scaling predictions by up to 99 percent.
