Archive
Structured guides and deep dives across Python, AI/ML, automation, and modern infrastructure. From first principles to production.
MLOps & Infrastructure
Squeeze maximum performance from your GPU training with mixed precision, gradient checkpointing, distributed data parallelism, and torch.compile -- practical techniques that deliver 2-4x speedups on existing hardware.
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MLOps & Infrastructure
You've built an amazing ML model. Now comes the hard part - deploying it at scale without losing your mind to manual infrastructure management.
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Claude Code
You've got worktrees set up, but now you're managing them manually. You create a worktree with a cryptic name like wt-a3f9e2, forget what it's for, switch to it three months later expecting it to...
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AI & Machine Learning
Explore the transformer architecture that revolutionized NLP, understand BERT vs GPT, and learn to fine-tune pretrained models for text classification using the Hugging Face ecosystem.
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n8n & Workflow Automation
Running LLMs in n8n without a cost strategy is how you wake up to a five-figure cloud bill. This guide covers a layered approach - from upstream filtering to intelligent model routing - that can reduce your AI spend by 5x to 50x.
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MLOps & Infrastructure
Your GPU bill just landed. That $15,000 monthly charge for your fine-tuning cluster - half those GPUs are sitting idle.
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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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