The Grug Brained Developer (2022)

Original Article ↗ Hacker News Discussion ↗

Reception & style of the essay

  • Many commenters call this one of their favorite programming essays and use it for onboarding or personal “complexity discipline.”
  • The caveman (“grug”) voice is divisive: some find it charming, memorable, and a useful way to slow down and think; others find it tiring, gimmicky, or hard to skim and prefer translated/“normal English” versions.
  • A minority see the tone as flirting with anti‑intellectualism or “us vs them” (“big brain vs grug”), though defenders argue it’s written by someone capable of sophisticated work who has learned to prefer simplicity.

Complexity, simplicity & experience

  • Core theme widely endorsed: unnecessary complexity is the main long‑term cost in software. People report repeatedly simplifying designs and getting better results and happier users.
  • Skeptics say “avoid complexity” is tautological like “avoid unnecessary work”; the hard part is knowing what is necessary or premature, which only experience and concrete techniques teach.
  • Several argue not all complexity is bad: complex domains require complex systems; the real enemy is complicated or entangled designs, not rich but well‑organized ones.
  • DRY vs duplication is debated: over‑abstraction is a common source of “complexity demons.” Many promote SPOT (Single Point Of Truth), the rule of three before abstracting, and “duplication is cheaper than the wrong abstraction.”

Languages, tools & web tech (C++, Rust, GC, HTMX)

  • Choice of C++ vs Rust vs others is framed less as purity and more as hiring and risk: organizations often pick languages where they can hire “20 devs tomorrow,” even if newer languages are technically nicer.
  • Some praise garbage‑collected languages (Java, etc.) as a huge simplifier for composition and reuse; others note GC is a non‑starter in hard real‑time/embedded contexts where tight memory and timing guarantees dominate.
  • Rust is viewed by some as a better‑designed C++, but the borrow checker and async lifetimes are seen as painful where a GC might fit better; others argue proper Rust data‑structure design pays off, but async is still rough.
  • HTMX and “HTML over the wire” get strong support as aligning with grug principles for many business apps: less SPA/micro‑frontend machinery, more server‑centric simplicity. Others see it as just trading one kind of complexity for another.

Patterns & design (Visitor, tagged unions, factoring)

  • The essay’s blunt dismissal of the Visitor pattern (“bad”) triggered a long thread.
  • Critics of Visitor say: in languages with tagged unions and pattern matching (Rust enums, modern Java, ML‑family), it’s usually clearer to encode operations directly on the AST or use straightforward recursive functions.
  • Defenders argue Visitor (or “walkers”) can still be useful to centralize tree‑traversal logic and separate traversal from node processing, especially in languages lacking closures or algebraic data types.
  • Some suggest many classic OO patterns (Visitor included) exist to paper over language limitations; in languages with first‑class functions and good pattern matching, they “disappear” into more natural constructs.
  • “Factoring vs refactoring”: several note that many teams only talk about re‑factoring, and never learn initial factoring as a deliberate skill. Good factoring is described as emergent from working code and narrow interfaces, not big upfront designs.

Microservices, architecture & cloud incentives

  • The microservices section of the essay resonates strongly: many anecdotes of tiny systems (single forms, low load) built as sprawling microservice meshes with shared DBs, queues, API gateways, custom observability, etc.
  • Common critique: teams use microservices as the only way they know to decompose systems, or to create jobs for “architects,” leading to over‑engineering and poor performance on trivial workloads.
  • Several argue that in practice “a service is a database”: if many “services” share one DB or schema, they are effectively one highly coupled system; atomicity and rollback boundaries define real service borders.
  • Others counter that network boundaries can be a valuable factoring tool when languages and developers lack modular discipline; the network forces small APIs, data‑only contracts, and backward compatibility.
  • Organizational factors (Conway’s Law, siloed teams, blame‑shifting) are cited as major drivers: microservices often primarily decompose people and responsibility, with technical architecture following.
  • A “cloud conspiracy” view appears: vendors benefit from architectures that require orchestration, managed databases/queues, multiple environments, and heavy networking—raising cost and lock‑in compared to simpler monoliths or bare‑metal deployments.

Debuggers, print statements & observability

  • The essay’s pro‑debugger stance sparked one of the longest subthreads.
  • A significant group barely use interactive debuggers, preferring print/logging for speed, history, and applicability in distributed/microservice production environments where stepping is hard.
  • Debugger advocates argue that conditional breakpoints, watch expressions, and “just my code” views are superpowers, especially for understanding unfamiliar code and complex state; print‑only debugging is seen as self‑limiting.
  • Many point out practical barriers: fragile debugger setups in large polyglot systems, container meshes that are hard to attach to, async/await and microservices complicating call stacks and timing, and weak debugger tooling in some languages.
  • A middle position emerges: both logs and debuggers are essential. Logs support post‑hoc reasoning and production triage; debuggers excel at inspecting narrow local behavior. Several emphasize investing in good logging, tracing, and local dev environments to reduce overall complexity.

Overall takeaway

  • Across topics—languages, patterns, microservices, tooling—commenters largely accept the essay’s central claim: complexity is the main hidden tax.
  • Disagreements center on where complexity is truly necessary, how much can be offloaded to tools or architecture, and how to teach concrete heuristics rather than slogans.