This story was originally published on HackerNoon at: https://hackernoon.com/how-to-split-string-every-nth-character-in-python.
Learn 3 easy ways to split string every nth character in Python using list comprehensions, wrap function and regular expressions.
Check more stories related to programming at: https://hackernoon.com/c/programming. You can also check exclusive content about #python, #programming, #python-programming, #python-tutorials, #string-splitting, #python-strings, #python-string-splitting, #how-to-split-strings, and more.

This story was written by: @techosha. Learn more about this writer by checking @techosha's about page, and for more stories, please visit hackernoon.com.

Learn three different ways to split a string in Python at every Nth character. You can do this using list comprehensions, wrap() function or regular expressions.

This story was originally published on HackerNoon at: https://hackernoon.com/database-anonymization-the-basics.
Explore the database anonymization process from scratch. Who needs it, and what do we gain? What is anonymization in simple terms? An example of anonymization.
Check more stories related to programming at: https://hackernoon.com/c/programming. You can also check exclusive content about #postgresql, #database, #anonymization, #information-security, #data-masking, #staging-database, #data-testing, #greenmask-github, and more.

This story was written by: @woyten. Learn more about this writer by checking @woyten's about page, and for more stories, please visit hackernoon.com.

This series delves into database anonymization, discussing challenges with current software and the development of Greenmask, an open-source project. It covers terminology, processes, and practical applications of Greenmask. This first article introduces database anonymization, its importance, and provides a simple example. The context is illustrated through various industry scenarios, highlighting the necessity of anonymized data for testing and AI learning. Additionally, it differentiates between synthetic and anonymized data, and discusses Test Data Management (TDM) platforms.

This story was originally published on HackerNoon at: https://hackernoon.com/orca-2-enhancing-reasoning-in-smaller-language-models-example-from-benchmarks-and-output.
Orca 2 enhances small language models' reasoning by teaching diverse strategies for tasks, outperforming models up to 10x larger in complex benchmarks.
Check more stories related to programming at: https://hackernoon.com/c/programming. You can also check exclusive content about #language-models, #orca-2, #reasoning-techniques, #machine-learning, #small-models, #imitation-learning, #ai-benchmarks, #model-training, and more.

This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page, and for more stories, please visit hackernoon.com.

Teaching Orca 2 to be a Cautious Reasoner is based on the work of Arindam Mitra, Luciano Del Corro, Shweti Mahajan, Andres Codas, Guoqing Zheng, Corby Rosset, Hamed Khanpour, and Ahmed Awadall.