In Python 3.9 and later versions, you can use the generic syntax for all standard collections currently available in the typing module, which makes your code cleaner and more readable.
In Python 3.10 and later versions, you can use the | operator for the union syntax, which further simplifies your code.
Manually searching for recipes and compiling information can be time-consuming.
recipe-scrapers automates this process by quickly extracting data from multiple sources and presenting it in a consolidated format.
Index slicing is not feasible for extremely large data sets as it requires the entire list to reside in memory.
itertools.islice() offers a more efficient approach by enabling the processing of only a portion of the data stream at a time, without the need to load the entire dataset into memory.
Large Language Models (LLMs) are powerful at producing human-like text, but their outputs lack structure, which can limit their usefulness in many practical applications that require organized data.
Mirascope offers a solution by enabling the extraction of structured information from LLM outputs reliably.
The following code uses Mirascope to extract meeting details such as topic, date, time, and participants.
Neural forecasting methods can enhance forecasting accuracy, but they are often difficult to use and computationally expensive.
NeuralForecast provides a simple way to use efficient models, using familiar scikit-learn syntax. The models available in NeuralForecast range from classic networks like RNN to the latest transformers.
Manually reviewing code changes in pull requests (PRs) can be time-consuming and error-prone, especially in large projects or teams. Sourcery can streamline this process by automatically handling the review process.
After submitting a PR, Sourcery quickly reviews the code, checking for bugs and code quality, allowing developers to focus on more complex tasks.