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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Machine LearningOn May 7, 2026, researchers uncovered a massive typosquatting campaign on Hugging Face mimicking an OpenAI repository. The malicious payload deployed a Rust-based infostealer on thousands of Windows machines, highlighting critical vulnerabilities in the AI software supply chain.
LLMDiscover how Lossless Context Management introduces deterministic memory to LLMs. This new architecture eliminates the hallucinations and forgotten instructions that plague long-horizon AI agents.
LLMMassive context windows are inherently lossy and prone to hallucination. Discover how Lossless Context Management provides a deterministic memory architecture that radically improves coding agents and long-horizon tasks.
LLMZyphra recently launched ZAYA1-8B, a highly efficient Mixture of Experts reasoning model optimized for math and code. More importantly, it marks a historic milestone as the first large-scale MoE trained entirely on AMD Instinct MI300X hardware, challenging the long-standing NVIDIA monopoly.
PythonExplore the architecture and capabilities of pi-mono. This trending open-source toolkit unifies LLM APIs, CLI tools, and interchangeable frontends to streamline AI agent development.
Deep LearningResearchers at UPenn have introduced mollifier layers to neural networks, mathematically smoothing noisy data to solve complex inverse problems. Discover how this elegant technique stabilizes physics-informed deep learning across fluid dynamics, materials science, and genetics.
LLMMoonshot AI has released Kimi K2.6, a 1-trillion-parameter MoE model capable of orchestrating 100 parallel sub-agents. We dive deep into its native vision encoder, Parallel-Agent Reinforcement Learning, and the hardware realities of deploying it.
Machine LearningMIT CSAIL's new Federated Tiny Training Engine tackles the biggest bottlenecks in edge ML by cutting memory overhead by 80% and communication bandwidth by 69%. This deep-dive explores how sparse updates and aggressive quantization make private, on-device training a reality for microcontrollers.
Deep LearningResearchers have cracked one of the most notoriously unstable problems in mathematics by introducing Mollifier Layers to neural networks. This mathematically refined architecture smooths noisy data to reverse-engineer hidden causes in fields ranging from weather prediction to genetics.
