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.
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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.
LLMGoogle's new native any-to-any AI model effectively collapses the traditional generative stack. Explore the architectural shift from stitched pipelines to unified latent spaces and what it means for developers.
Deep LearningByteDance's new Lance model is revolutionizing AI by unifying image understanding, generation, and editing into a single dual-stream architecture. Discover how collaborative multi-task training is ending the era of fragmented machine learning pipelines.
Deep LearningDive deep into the architecture of LatentSync and learn how to build a production-ready lip-syncing pipeline. We explore audio conditioning, temporal consistency, and end-to-end implementation using Latent Diffusion Models.
LLMGoogle's mid-2026 release of Gemini 3.5 Flash marks a turning point for autonomous AI development. By drastically lowering inference costs while mastering complex agentic coding tasks, it paves the way for scalable multi-agent systems.
LLMDiscover how to leverage the newly released 9-billion parameter Qwopus3.5-9B-Coder model for local agentic workflows. We build an end-to-end autonomous coding assistant capable of complex tool calling entirely on consumer hardware.
