Haskell's lazy evaluation means the order of operations is not guaranteed, making side effects like `print` statements unpredictable. This forced the language to be pure by default. Conversely, OCaml's strict, predictable evaluation order made it easy to incorporate I/O and side effects, allowing it to be impure by default.

According to Boris Churney, the specific programming language a developer uses is becoming less important because the AI model doesn't care. While features like type-checking currently help guide the model, future AIs will be sophisticated enough to write perfect code directly in low-level languages like assembly, abstracting away language choice entirely.

The dream of hardware optimized for functional programming (e.g., dataflow or SK combinator machines) proved to be a mistake. These machines were essentially hardware-based interpreters. The better approach is to build a sophisticated compiler that translates functional code into efficient instructions for general-purpose CPUs.

A trace compiler like LuaJIT identifies and records frequently executed code paths, or "traces," often inlining function calls. It then compiles these specific paths based on assumptions (e.g., a variable is an integer). The major complexity is reverting to the interpreter when an assumption fails.

Lua's speed advantage over languages like Python isn't just from targeted optimization, but also from its small size. This minimalism allows the entire virtual machine to fit within a CPU's cache, leading to significant, natural performance gains that larger runtimes cannot achieve.

The creator of Lua clarifies that a scripting language's defining feature is its role in a "dual-language architecture," coordinating components written in another language (like Bash coordinating C programs). This distinguishes it from the broader category of dynamic languages like JavaScript.

Most compilers use complex, untyped intermediate representations. GHC desugars Haskell into a tiny, statically-typed language called Core. This allows a type-checker to run after each optimization pass, immediately catching bugs in the compiler that would otherwise manifest as cryptic runtime segfaults in the final compiled program.

Lazy evaluation allows programmers to modularly separate producer and consumer logic (e.g., an infinite data generator and a selective consumer) that would have to be merged in a strict language. For example, one can generate an infinite chess game tree and have a separate function explore only the necessary branches.

Data science often requires prototyping in a slow, high-level language (like R) before rewriting critical parts in a fast, low-level one (like C). The Julia language was designed to eliminate this by offering high-level syntax that compiles to fast, efficient code, closing the performance gap.