Tell HN: GPT copilots aren’t that great for programming
AI coding assistants like GitHub Copilot and ChatGPT are widely seen as helpful for boilerplate, autocomplete, and learning unfamiliar tools or frameworks, but far less reliable for complex or large-scale programming tasks. Many programmers report modest productivity gains offset by issues like hallucinated APIs, subtle bugs, limited understanding of project context, and concerns over code quality and plagiarism. There is broad agreement that these tools work best as aids for experienced developers and routine tasks, with hopes that deeper codebase integration and stronger correctness guarantees could unlock more transformative benefits in the future.
Overall sentiment on GPT copilots for programming
- Many find copilots and chat-based LLMs underwhelming for complex or novel programming tasks.
- Widely seen as “autocomplete on steroids” rather than true problem solvers or replacements for experienced developers.
- Opinions range from “life-changing” to “simply not useful,” depending heavily on use case, expectations, and user skill.
Where they work well
- Repetitive / boilerplate code:
- Autocompleting obvious patterns, mapping similar data structures, scaffolding simple functions, basic scripts, and unit tests.
- Generating simple SQL queries, shell commands, Docker/Nginx configs, or one-off scripts and data wrangling.
- Learning and exploration:
- Quick “how do I do X in Y language/library?” questions.
- Getting idiomatic examples, disambiguating documentation, or summarizing new frameworks/tech.
- Acting as a non-judgmental “rubber duck” for architectural discussions or unfamiliar domains.
- Admin / infra tasks:
- Linux troubleshooting from logs, basic cloud/devops config, or explaining APIs/tools.
Where they fail or are risky
- Complex, large-scale, or highly novel code:
- Poor at deep reasoning, architecture, or intricate algorithms; often produces compiling-but-wrong or non-compiling code.
- Struggles with complex SQL, dependency injection edge cases, specialized libraries, or advanced ML/compilers.
- Hallucinations and false authority:
- Makes up APIs, methods, or even entire libraries; sometimes confidently wrong about language or DB features.
- Back-and-forth corrections often degrade quality; restarting sessions is a common workaround.
- Code quality and maintainability:
- Risk of subtle bugs, security issues, and repetitive non-DRY code.
- Some teams report no productivity gain and worse code; others see modest boosts (5–40%) but stress careful review.
Impact by experience level
- Experienced devs:
- Most value comes when they already know what they want and can review output; they use it for grunt work and research.
- Some feel significantly more productive; others see only marginal gains.
- Beginners / non-devs:
- Can ship small projects or scripts they otherwise couldn’t, but may learn less fundamentals and struggle to detect errors.
- Several argue copilots may actually be harmful for true beginners who cannot validate results.
Models, quality, and ecosystem concerns
- Some report GPT‑4 outperforming GPT‑4 Turbo and 3.5, and note perceived quality regressions over time.
- Context window limits and lack of deep project understanding are recurring pain points; tools promising whole-codebase context are seen as promising next steps.
- Ethical and legal worries about training data, plagiarism, and corporate overreliance on AI, including potential impacts on hiring and morale.