Prompt engineering is hard. If you’re from a programming background you may find it very odd that all of a sudden you’re trying to get a computer to do something by bribing it (“I’ll give you a 25% tip”), encouring it (“You’re a leading expert on how to prompt”) and plain just nagging it (“Do not”).

Let’s be honest, prompt engineering can feel like a dark art. You spend hours tweaking words, adding clauses, and praying to the AI gods for a decent output. It’s tedious, time-consuming, and often feels more like trial-and-error than actual engineering. If you’re tired of wrestling with prompts, I have good news: DSPy is here to change the game.

Well, it depends! Let’s start with the models.

It feels like a new model is released pretty much every month claiming to be “best in class” and having superior results to competitor models.

Can Large Language Models (LLMs) Write Compilable Code?

Large language models (LLMs) have demonstrated impressive capabilities in generating human-like text, translating languages, and even writing different kinds of creative content. But can these powerful AI tools also write code that’s actually compilable and functional? The answer, in short, is a qualified yes, but with important caveats.

Python is getting ready to lose its GIL

The Python Global Interpreter Lock (GIL) is a mechanism that prevents multiple threads from executing Python code at the same time. This has been a source of frustration for some Python users, as it can limit the performance of applications that need to use multiple cores.

PEP 703 proposes a solution to this problem by making the Python interpreter thread-safe and removing the GIL. This would allow multiple threads to execute Python code at the same time, which would improve performance for some applications.