Practical software engineering guidance from Jeff Bailey on tools like jq, SSH, and Git, plus architecture diagrams, book summaries, and fundamentals.
Learn SQL: Queries, Joins, Aggregates, and Query Plans
Learn SQL with sqlite3: filter, join, group, and rank rows, read a query in execution order, and use EXPLAIN to find out why a query is slow.
Learn Agile: Core Practices and Hard Truths for Agilists
Learn agile: the manifesto values, Scrum and Kanban basics, retrospectives, and the trust-first habits that keep Scrum Masters and coaches useful.
Learn Platform Engineering: Golden Paths, Platform as a Product, and Team Topologies
Learn platform engineering fast: what an internal developer platform is, platform-as-a-product thinking, golden paths, cognitive load, and why platforms fail.
Learn AWS Lambda: Handlers, Cold Starts, and the AWS CLI
Learn AWS Lambda: create, invoke, and delete a Python function with the AWS CLI, and understand cold starts, invocation types, concurrency, and cost.
SPDX vs CycloneDX: Which SBOM Standard to Use
SPDX vs CycloneDX compared on standards status, German BSI rules, VEX, license data, tool defaults, and version churn, with real Syft output for each format.
Learn fd: Find Files Fast with Patterns, Filters, and -x
Learn fd, the fast find alternative: search by name, regex, and glob, filter by type and depth, respect .gitignore, and run commands on results.
What Is Graph Engineering?
Graph engineering designs an AI agent’s control flow as nodes, edges, and shared state, so loops become visible, pausable, resumable, and testable.
Learn Zsh: Globbing, Expansion, Completion, History, and a Command Reference
Learn zsh fast: glob qualifiers, parameter expansion, arrays, completion, history, options, key bindings, and .zshrc, plus a verified command reference.
Learn Nushell: Tables, Pipelines, Data Files, and a Command Reference
Learn Nushell fast: tables and pipelines, JSON, CSV, and TOML files, records, lists, custom commands, and config, plus a searchable command reference.
What Are AI Evals?
AI evals explained: how datasets, graders, and scores tell you whether a language model feature works, and why vibe checks and benchmarks fall short.