Archive
Structured guides and deep dives across Python, AI/ML, automation, and modern infrastructure. From first principles to production.
AI & Machine Learning
Leverage pretrained models to build production-ready classifiers with limited data -- covering feature extraction vs fine-tuning strategies, learning rate scheduling, and domain adaptation techniques in PyTorch.
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MLOps & Infrastructure
You've got a massive machine learning project ahead. Maybe you're training a 7B parameter language model.
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Claude Code
You've probably experienced the friction: a ticket lands in Jira, you read it, context-switch to your IDE, create a branch, write code, push it, and open a PR.
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AI & Machine Learning
Understand how RNNs and LSTMs process sequential data, from the vanishing gradient problem to gated memory cells, with practical PyTorch implementations for classification, generation, and time series forecasting.
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MLOps & Infrastructure
You've got expensive GPUs sitting in your cluster, and they're only being used 30% of the time. Yeah, that hurts to think about.
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Claude Code
A deep-dive into building production-grade Model Context Protocol servers in Python and TypeScript, with OAuth authentication, testing harnesses, deployment options, and rate limiting patterns.
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AI & Machine Learning
Build and train convolutional neural networks for image classification in PyTorch, covering convolution mechanics, pooling, ResNet skip connections, data augmentation, and achieving 90%+ accuracy on CIFAR-10.
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MLOps & Infrastructure
Here's the problem: you've got a Kubernetes cluster with $500K worth of NVIDIA GPUs sitting idle while some jobs sit in the queue waiting for the perfect moment to run.
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Claude Code
Test coverage is one of those things every development team knows they should care about, but actually achieving 90%+ coverage across a real codebase? That's where things get messy.
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MLOps & Infrastructure
You've got a shiny GPU cluster, a pile of ML training jobs, and a growing team that can't keep stepping on each other's toes while provisioning resources.
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