Under the Hood: Understanding tools like n8n and building Custom Nodes for Enterprise AI Workflows

Under the Hood: Understanding tools like n8n and building Custom Nodes for Enterprise AI Workflows

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Author(s): Samvardhan Singh Originally published on Towards AI. Learn how to build a Custom Node for Automated AI Infrastructure Monitoring and Neural Network Training Pipeline Diagnostics If you’re not a medium member, access this article for free here. image by authorThis article explores n8n, a powerful open-source workflow automation platform, detailing its architecture and capabilities for building custom nodes to enhance automated AI infrastructure monitoring and neural network training diagnostics. The article emphasizes n8n’s extensibility and modular development […]

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KV Cache Optimization via Multi-Head Latent Attention

KV Cache Optimization via Multi-Head Latent Attention

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Home Table of Contents KV Cache Optimization via Multi-Head Latent Attention Recap of KV Cache The Need for KV Cache Optimization Multi-Head Latent Attention (MLA) Low-Rank KV Projection Up-Projection Decoupled Rotary Position Embeddings (RoPE) RoPE in Standard MHA Challenges in MLA: The Need for Decoupling PyTorch Implementation of Multi-Head Latent Attention Multi-Head Latent Attention Toy Transformer and Inference Experiments and Analysis Summary Citation Information KV Cache Optimization via Multi-Head Latent Attention Transformer-based language models have long relied on […]

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Advancing science and math with GPT-5.2

Advancing science and math with GPT-5.2

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GPT-5.2 is OpenAI’s strongest model yet for math and science, setting new state-of-the-art results on benchmarks like GPQA Diamond and FrontierMath. This post shows how those gains translate into real research progress, including solving an open theoretical problem and generating reliable mathematical proofs.

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The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel

The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel

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DBSCAN shows how far we can go with a very simple idea: count how many neighbors live close to each point. It finds clusters and marks anomalies without any probabilistic model, and it works beautifully in Excel. But because it relies on one fixed radius, HDBSCAN is needed to make the method robust on real data. The post The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel appeared first on Towards Data Science.

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