performance optimisation

Practical, bash-first guide to harden Redis for AI workloads: install and baseline, benchmark with redis-benchmark, choose persistence (ephemeral cache vs durable AOF/Streams), set maxmemory/eviction/TTLs, enable defrag, tune Linux and network (somaxconn, THP, FDs), use pipelining/ pooling, and model patterns (thundering-herd-safe inference cache, Redis Stack vectors) to cut tail latency and cloud spend.

Turn slow Bash scripts into fast, intelligent pipelines by measuring first (hyperfine, GNU time, strace), cutting fork/subshell overhead with builtins and vectorized tools, parallelizing safely with xargs/GNU parallel, and auto-tuning -P via a simple feedback loop; add guardrails with ShellCheck and dash. Includes copy-paste snippets, benchmarks, and case studies with big CI/ETL wins.

A Bash-friendly, distro-agnostic guide to boosting AI training/inference on Linux by tuning the OS: install key tools, profile first, right-size and pin OpenMP/BLAS threads, bind CPU and memory with numactl, set performance governors/tuned profiles, refine NUMA and HugePages, use fast BLAS and clean I/O, then verify with perf/time; includes a quick-start script and troubleshooting to apply changes safely and repeatably.

Practical guide to speeding up Bash at scale by pairing classic UNIX tools with AI: profile first, trace bottlenecks, feed data to an LLM for targeted, minimal-diff refactors, then benchmark and test to lock in wins. Covers setup (hyperfine, strace, jq, ShellCheck, shfmt, rg/fd, GNU parallel, pv), common fork-reducing rewrites, example pipelines, prompts, and portability tips for safe, reproducible performance gains.

Practical guide to using AI as a Bash performance co-pilot: install a cross-distro toolbox, baseline with time/hyperfine/strace, generate an AI context pack from traces, ask for vectorized/parallel replacements for loop-heavy code, auto-benchmark and verify outputs, then lock in gains with ShellCheck/shfmt/Bats. Includes prompts, safe filename handling, common wins (rg/fd/awk/parallel), and a real dedupe case showing 5-10x speedups.