What I have found gripping

• Talking through a different way to solve a coding problem leads to new insights

• One can use Minitest to benchmark Ruby methods

• Learning from folks from other contexts and in more senior roles allows one to make new mental models for new technical concepts. Thankful to everyone I spoke to this week

What I have read

• Algorithm (Techtarget), Alexander S. Gillis

• Mental Models: An Interdisciplinary Synthesis of Theory and Methods, Natalie A. Jones et al.

• Prototype-based programming, MDN Web Docs Glossary

• Deconstruct #02, Afsa Akbar

• 5 Ally Actions - Jun 21, 2024, Better Allies

What I have watched

Harvard Professor Explains Algorithms in 5 Levels of Difficulty, WIRED

Featured quote

Algorithms work by following a set of instructions or rules to complete a task or solve a problem. They can be expressed as natural languages, programming languages, pseudocode, flowcharts and control tables. Natural language expressions are rare, as they are more ambiguous. Programming languages are normally used for expressing algorithms executed by a computer. — Alexander S. Gillis

Further reading and resources

In English

• Mastering Bias and Variance in Machine Learning Models | ML Optimization, Diarra Bell (IBM)

In French

• Les algorithmes : des boîtes noires, vraiment ? David Monniaux, Chercheur en informatique, Centre national de la recherche scientifique (CNRS), Université Grenoble Alpes (UGA)