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Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and…

Systematic methodology and concrete tool recipes for debugging Unix, Python, and PyTorch programs - crashes, hangs, segfaults, wrong output, CUDA OOM, NaN/Inf, slowness, and multi-node/multi-GPU issues. Use when a program crashes, hangs, deadlocks, segfaults,…

Practical, example-driven recipes for the Python standard library and everyday idioms - string/text wrangling, formatting, regex, numbers, the core data structures (list/tuple/set/dict) and comprehensions/itertools, functions, classes, dates/times,…