Show HN: LLM Rescuer – Fixing the billion dollar mistake in Ruby
This experimental Ruby gem offers a satirical solution to Tony Hoare's 'billion-dollar mistake,' using large language models to dynamically guess intended values when nil objects are invoked. It cunningly monkey-patches NilClass to magically prevent NoMethodErrors, hilariously illustrating the unpredictable and costly nature of throwing AI at fundamental programming problems. The project's self-aware humor and deep dive into the absurdity of AI hype resonate strongly with HN's audience.
The Lowdown
LLM Rescuer is an experimental Ruby gem that proposes a tongue-in-cheek, AI-driven solution to Tony Hoare's "billion-dollar mistake" – the null reference. Instead of crashing when a method is called on a nil object, this gem employs a Large Language Model (LLM) to guess the developer's intent and provide a plausible return value, allowing the program to continue execution.
- The gem directly addresses Tony Hoare's famous regret regarding the invention of the null reference, framing the LLM solution as potentially an even more expensive "trillion-dollar mistake."
- It operates by controversially monkey-patching
NilClassin Ruby, interceptingNoMethodErrors that occur when methods are called onnil. - Upon interception, an LLM (e.g., GPT-5) analyzes the surrounding code context to infer what the developer likely intended.
- The LLM then generates a "guessed" return value, which can range from sensible defaults (like "John Doe" for a user's name or
0.0for a shopping cart total) to entirely unpredictable and humorous outcomes. - The project explicitly warns that it is not for production use due to its experimental nature, unpredictable behavior, and significant potential costs associated with LLM API token consumption.
- Humorous "What Could Go Wrong?" scenarios are presented, suggesting that nil objects might gain sentience, users could be named "ChatGPT's Best Friend," or APIs might respond with interpretive dance descriptions.
- A "Cost Analysis" section estimates the high API expenses, contrasting the free (but crashing) traditional nil handling with the potentially massive bills from LLM Rescuer.
Ultimately, LLM Rescuer serves as a brilliant piece of satire, highlighting the modern temptation to use AI as a magic bullet for all problems, even those that demand more fundamental code robustness, all while underscoring the inherent unpredictability and cost implications of such approaches.