How I Use "AI"

Original Article ↗ Hacker News Discussion ↗

Overall sentiment

  • Many commenters say the article closely matches their own experience: LLMs are major productivity boosts for coding and research, but not magic or fully reliable.
  • Others report the opposite: despite repeated efforts, they haven’t found LLMs consistently useful for “serious” or complex tasks.

Effective use cases

  • Coding “glue” and boilerplate: shell scripts, YAML/config (Docker, k8s, Terraform), MVC models, spreadsheet formulas, unit tests, test data.
  • Learning and comprehension: explaining unfamiliar APIs, frameworks, math notation, kernel subsystems, hardware interfaces, CLI flags, and suggesting search keywords.
  • Brainstorming and ideation: exploring variations on ideas, generating hints, outlining approaches rather than final answers.
  • Real‑world troubleshooting: washing machines, cars, odd icons, reverse engineering small problems.
  • Many frame LLMs as “smart coworker / intern / rubber duck” whose output they verify and edit.

Limitations and failure modes

  • Hallucinations are a core problem: fabricated academic papers, wrong technical details (e.g., calling conventions, math notation), unsafe C code, bogus dependencies.
  • Particularly bad at: niche research paper search, tasks requiring exact truth, or domains with sparse training data.
  • Some worry early exposure in a new field may plant subtly wrong fundamentals.
  • Others emphasize that, like any fallible tool or coworker, outputs must be tested, reviewed, and treated as non‑authoritative.

Ethical, environmental, and social concerns

  • Strong concern about:
    • Training on unlicensed data and “polluting the commons” with AI‑generated sludge.
    • Climate impact and dubious CO₂ accounting that compares “being a human” vs. running a model.
    • Corporate ownership and “intelligence as a service” non‑competes.
  • Some find the tech so ethically tainted or “icky” that they refuse to use it despite utility.

Impact on work and jobs

  • Many professionals say LLMs let them avoid tedious RTFM work and tackle more ambitious or enjoyable problems.
  • Others fear widespread automation of “80% bullshit tasks” will justify large layoffs, concentrating gains with employers and model vendors.
  • A contrasting view is that productivity gains will expand demand for software, possibly increasing the need for skilled engineers.

Meta: how to prompt and integrate

  • Experience and domain knowledge are seen as crucial to getting value and catching errors.
  • Some rely on chat interfaces; others use IDE integrations, CLI tools, or local/alternative frontends.
  • There’s debate over whether LLMs are overhyped or simply being “held wrong” for inappropriate tasks.