The hottest Taxonomy Substack posts right now

The term 'data product' has become so broad that it lacks credibility and value.
Data professionals can learn a lot from actual product management and strategy.
Creating a taxonomy based on intention and proximity to the customer can improve the understanding and management of data products.

Creating a standard system for evaluating prompts is important because prompts can vary in how they're used and understood. This makes it hard to measure their effectiveness.
The TELeR taxonomy helps to categorize prompts so that they can be better compared and understood. It focuses on aspects like clarity and the level of detail in prompts.
Using clear goals, examples, and context in prompts can lead to better responses from language models. This helps the models to understand exactly what is being asked.

Categories can be re-interpreted to suit different needs in different contexts.
The book 'Why Fish Don't Exist' explores the flaws of categorization and how it can define us.
Giving up the idea of 'fish' as a category can lead to insights and a better understanding of complexity.

A left tech policy should aim to reduce the central role of markets in people's lives through decommodification, providing resources as a right and enabling democratic decision-making.
When considering tech platforms, it's essential to move beyond the generic term 'platforms' and analyze size, function, and type of power they have to guide regulation effectively.
Methods of decommodification and democratization for digital infrastructures can include public ownership, cooperative ownership, non-ownership, or abolition, accompanied by legislative regulations on data usage and algorithms.

Time in the microbial world operates differently than human time - it can be glacial or lightning fast, impacting how we interact with and perceive these organisms.
Microbes have a complex system of classification based on their abundance and genetic diversity, leading to the concept of 'ecotypes.'
Microbial time, diverging from human time, affects how we approach issues like pandemics or environmental solutions that involve manipulating microbial behavior.

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Large Language Models (LLMs) should be evaluated based on their knowledge, alignment, and safety. This helps ensure they meet necessary standards.
Evaluation has become more complex as LLMs can do higher-level tasks, rather than just basic language checks like syntax and vocabulary.
Creating a clear taxonomy for LLM evaluation helps guide researchers and companies in assessing these models effectively.