Ultimate Command Cheat Sheets
Your comprehensive resource for command-line tools, cybersecurity utilities, and development frameworks.
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cpupower - CPU Frequency & Governor Control Cheatsheetcpupower inspects and controls CPU frequency scaling and idle states on Linux — view current speeds, set governors like performance or powersave, pin frequencies, and tune C-states to trade power for latency and throughput.TuneD - System Performance Tuning Profiles CheatsheetTuneD is a daemon that applies system performance tuning profiles on Linux — switch between throughput, latency, powersave, and virtualization profiles that adjust CPU governors, kernel parameters, disk schedulers, and more with one command.earlyoom - Early Out-of-Memory Daemon Cheatsheetearlyoom is a lightweight userspace daemon that prevents Linux from freezing under memory pressure — it monitors free RAM and swap, and kills the biggest memory hog before the kernel OOM killer causes a system-wide hang.numactl - NUMA CPU & Memory Binding Cheatsheetnumactl runs processes with a specified NUMA memory-placement and CPU-binding policy — pin workloads to specific NUMA nodes to avoid slow cross-node memory access, and inspect NUMA topology on multi-socket servers.DevPod - Open-Source Dev Environments as Code CheatsheetDevPod is a client-only, open-source tool that spins up reproducible dev environments from a devcontainer.json on any backend — local Docker, a remote machine, Kubernetes, or the cloud — working with any IDE, like self-hosted Codespaces.devenv - Declarative Nix Developer Environments Cheatsheetdevenv builds fast, declarative, reproducible developer environments using Nix directly — define packages, languages, services, processes, scripts, and git hooks in a single file, with more power than JSON-based tools at the cost of Nix syntax.Devbox - Reproducible Dev Environments CheatsheetDevbox creates isolated, reproducible development environments powered by Nix — add packages with a simple CLI and JSON config, no Docker or VM required, and get the same toolchain on every machine and in CI.BestRAG - Hybrid RAG Storage & Retrieval CheatsheetBestRAG is a Python library for hybrid retrieval-augmented generation — it combines dense, sparse, and late-interaction embeddings over Qdrant so you store documents once and search with fused vector + keyword relevance for better RAG.
Latest Blog Posts
Reproducible Dev Environments in 2026: Devbox, devenv, DevPod, and Dev Containers"Works on my machine" is a solvable problem. This guide covers the 2026 landscape of reproducible development environments — Nix-based tools like Devbox and devenv, container-based Dev Containers and DevPod — and how to pick the right approach for your team.Reverse Engineering WebAssembly in 2026: Reading the Web's New BinaryWebAssembly now runs everywhere — browsers, edge, plugins, and malware — and analysts increasingly need to read it. This guide covers wasm reverse engineering in 2026: the format, the toolchain (WABT, diswasm, Binaryen, wasm-tools), and a practical analysis workflow.Code-First AI Agents in 2026: Why Agents Are Learning to Write CodeJSON tool calls are giving way to agents that act by writing Python. This guide explains the code-first agent paradigm — why it reduces LLM calls and improves performance — through smolagents and Pydantic AI, plus the sandboxing that makes it safe.Open-Source Web App Scanning in 2026: ZAP, Nuclei, Wapiti, and the DAST StackNo single scanner finds every web vulnerability. This guide explains dynamic application security testing in 2026 and how the open-source tools — ZAP, Nuclei, Wapiti, Nikto — fit together into a layered DAST pipeline that plugs into CI/CD.AI Agent Memory in 2026: Knowledge Graphs, Temporal Facts, and OS-Style PagingContext windows are not memory. This guide surveys the 2026 agent-memory landscape — Mem0, Cognee, Graphiti/Zep, and Letta/MemGPT — explaining vector, graph, and temporal approaches, and how to choose a memory layer that actually persists what matters.The State of LLM Inference Engines in 2026: vLLM, llama.cpp, Aphrodite, LMDeployThere is no single best way to run an LLM anymore — there is a small set of mature engines, each with a personality. A practical guide to the 2026 inference landscape: throughput serving, local/edge inference, quantization breadth, and how to choose.Document Parsing for RAG in 2026: Why Ingestion Decides Retrieval QualityEvery RAG system is only as good as the documents it ingested. This guide covers the 2026 document-parsing and chunking stack — Docling, Marker, Unstructured, and chunking toolkits — and why getting ingestion right matters more than your choice of vector database.eBPF Runtime Security in 2026: Falco vs Tetragon vs TraceeUserspace security agents are giving way to eBPF, which watches the kernel itself with under 1% overhead. A deep comparison of the three open-source eBPF runtime security tools — Falco, Tetragon, and Tracee — covering detection, enforcement, forensics, and how to choose.