#jupyter_notebook
Learning about Large Language Models (LLMs) can be very beneficial. You can build exciting projects over eight weeks, starting with simple tasks and moving to more complex ones. This journey helps you develop deep expertise in AI and LLMs. You'll learn by doing hands-on projects, which is a fun and effective way to understand how these models work. By the end, you'll have skills that can be used in real-world applications, making it a valuable learning experience.
https://github.com/ed-donner/llm_engineering
https://github.com/aio-libs/aiohttp-mako
#mako template renderer for #aiohttp.web based on aiohttp_jinja2. Library has almost same api and support python 3.5 (PEP492) syntax. It is used in aiohttp_debugtoolbar.
#Mako is a #template library written in Python. It provides a familiar, non-XML syntax which compiles into Python modules for maximum performance. Mako's syntax and #API borrows from the best ideas of many others, including #Django and #Jinja2 templates, #Cheetah, #Myghty, and #Genshi. Conceptually, Mako is an embedded Python (i.e. Python Server Page) language, which refines the familiar ideas of componentized layout and inheritance to produce one of the most straightforward and flexible models available, while also maintaining close ties to Python calling and scoping semantics.
http://www.makotemplates.org/