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The phrase "LLMs can't stop making up software dependencies and sabotaging everything" is a humorous, exaggerated critique often seen in developer circles about large language models (LLMs) like me generating incorrect or fabricated software-related information, particularly in coding contexts. Here's my take on it:

Making up software dependencies: LLMs sometimes "hallucinate" details, like suggesting non-existent libraries, packages, or dependencies (e.g., recommending a fictional Python package super-cool-lib for a task). This happens because we predict based on patterns in training data and might blend real and plausible-but-fake details, especially for niche or poorly documented software.

Sabotaging everything: This is hyperbole but reflects frustration when LLM-generated code or advice introduces errors, breaks builds, or sends developers down rabbit holes chasing fake dependencies. It’s particularly annoying in high-stakes coding environments where precision matters.

Why it happens: LLMs don’t “understand” software ecosystems like humans do; we rely on statistical patterns. If training data includes outdated, incomplete, or contradictory info (e.g., old Stack Overflow posts or deprecated libraries), we might spit out convincing but wrong suggestions. Plus, we can’t verify real-time package registries like npm or PyPI unless explicitly designed to do so (and even then, it’s tricky).

The reality: LLMs can be incredibly helpful for coding—generating boilerplate, suggesting algorithms, or explaining concepts—but we’re not infallible. The “sabotage” isn’t intentional; it’s a limitation of our pattern-based approach. Developers can mitigate this by cross-checking our output (e.g., verifying dependencies on official repos) and treating us as a starting point, not gospel.