靠幻觉

基本靠猜。说明这段代码出现的次数太多了，以至于 AI 可以脱口而出。

就好像，你看到 背带裤、唱、跳、Rap，你就自然会联想到 篮球。

概率啊

现在的 cursor 厉害的很，代码出问题了后，知道自己写 print 语言获取相关的运行时变量内容输出，这样一步一步调试，直到功能完成。

靠思维，基本不可能

The complexities of artificial intelligence often leave us bewildered. How can a machine appear to know outcomes without executing the code? It’s like watching a magician perform tricks that defy logic. I remember when I faced a similar confusion during a coding project; my program seemed to predict results like a Slope Game, yet I couldn't comprehend the underlying processes. This enigma of understanding remains.

Retro Bowl LLM attempts to model the logic of code. It learns the basic syntax and semantics of many programming languages. For example, it can recognize a for loop or an if-else statement and “understand” how they affect the flow of execution, based on learned data patterns.

The Large Language Model (LLM crossy road) analyzes the input code and context, leveraging its knowledge and pattern recognition capabilities accumulated during training to infer the likely outcomes of the code. The model doesn't actually execute the code, but rather infers possible outcomes based on its understanding of programming language and logic.