An "oh fuck" moment in time

Perceived Capabilities and Limits

• Many see LLMs as excellent “translators”: between languages (e.g., Rust→Haskell, SQL→Jooq) or formats (YAML↔JSON, code scaffolding).
• Others report that large, ambitious tasks (e.g., fully porting complex libraries like megaparsec or entire apps) quickly degrade into wrong, shallow, or partial implementations.
• Some argue LLMs aren’t “inventing” but recombining existing patterns; translation/wrapping C APIs is seen as commodity work, not true novelty.

Coding Assistants in Practice

• Popular use cases: boilerplate generation, refactors/renames, API lookups, unit-test scaffolding, state-machine skeletons, simple scripts, and “smarter grep/RTFM” for docs.
• IDE-integrated tools (Windsurf, Cursor, Cody, Copilot-like systems) are praised for autocomplete and local edits, but criticized for:
  • Deleting or duplicating code unexpectedly.
  • Getting stuck in fix loops.
  • Producing placeholders instead of full implementations.
  • Poor handling of project structure and long-lived maintainable design.

Reasoning, Hallucinations, and Trust

• Users note recurring hallucinations: invented sentences in translations, wrong logic-puzzle answers, fabricated links, incorrect descriptions of protocols/APIs.
• “Confidently wrong” answers erode trust, especially when domain knowledge is required to even detect errors.
• Some insist logic puzzles are a poor benchmark; others argue basic reasoning is prerequisite if we expect reliable code.

Impact on Developers and Skills

• One camp: assistants are a “massive force multiplier”; engineers who ignore them “won’t make it,” analogous to refusing modern tools.
• Counterpoint: fundamentals matter more; assistants are easy to pick up later, while overreliance can atrophy reasoning skills and produce graduates who “can’t program without ChatGPT.”
• Debate over future roles: developers shifting toward project management, code review, maintenance of AI-generated “spaghetti,” vs. pessimistic views that entire occupations may become obsolete.

Originality, Intelligence, and “Mechanical Turk”

• Some compare LLMs to a scaled-up Mechanical Turk: impressive output but entirely dependent on human training data, thus not genuine intelligence.
• Others respond that humans also “stand on the shoulders of giants,” and that high performance on many benchmarks suggests something more than simple lookup.

Copyright and Licensing

• Concern that AI-generated ports/wrappers may embed open-source code without attribution, complicating clean-room and licensing.
• Suggestions appear that AI-generated output should be public domain, but others note this doesn’t resolve underlying infringement of training data.