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Applied AI

10 Ways to Be Data Illiterate (and How to Avoid Them)

Data literacy is an essential skill in today's data-driven world. As AI engineers, understanding how to properly handle, analyze, and interpret data can make the difference between success and failure in our projects. In this post, we will explore ten common pitfalls that lead to data illiteracy and provide actionable strategies to avoid them. By becoming aware of these mistakes and learning how to address them, you can enhance your data literacy and ensure your work is both accurate and impactful. Let's dive in and discover how to navigate the complexities of data with confidence and competence.

Data Flywheel Go Brrr: Using Your Users to Build Better Products

You need to be taking advantage of your users wherever possible. It’s become a bit of a cliche that customers are your most important stakeholders. In the past, this meant that customers bought the product that the company sold and thus kept it solvent. However, as AI seemingly conquers everything, businesses must find replicable processes to create products that meet their users’ needs and are flexible enough to be continually improved and updated over time. This means your users are your most important asset in improving your product. Take advantage of that and use your users to build a better product!

Unraveling the History of Technological Skepticism

Technological advancements have always been met with a mix of skepticism and fear. From the telephone disrupting face-to-face communication to calculators diminishing mental arithmetic skills, each new technology has faced resistance. Even the written word was once believed to weaken human memory.

Technology Perceived Threat
Telephone Disrupting face-to-face communication
Calculators Diminishing mental arithmetic skills
Typewriter Degrading writing quality
Printing Press Threatening manual script work
Written Word Weakening human memory

A feat of strength MVP for AI Apps

A minimum viable product (MVP) is a version of a product with just enough features to be usable by early customers, who can then provide feedback for future product development.

Today I want to focus on what that looks like for shipping AI applications. To do that, we only need to understand 4 things.

  1. What does 80% actually mean?

  2. What segments can we serve well?

  3. Can we double down?

  4. Can we educate the user about the segments we don’t serve well?

The Pareto principle, also known as the 80/20 rule, still applies but in a different way than you might think.

Kojima's Philosophy in LLMs: From Sticks to Ropes

Hideo Kojima's unique perspective on game design, emphasizing empowerment over guidance, offers a striking parallel to the evolving world of Large Language Models (LLMs). Kojima advocates for giving players a rope, not a stick, signifying support that encourages exploration and personal growth. This concept, when applied to LLMs, raises a critical question: Are we merely using these models as tools for straightforward tasks, or are we empowering users to think critically and creatively?

Good LLM Observability is just plain observability

In this post, I aim to demystify the concept of LLM observability. I'll illustrate how everyday tools employed in system monitoring and debugging can be effectively harnessed to enhance AI agents. Using Open Telemetry, we'll delve into creating comprehensive telemetry for intricate agent actions, spanning from question answering to autonomous decision-making.

If you want to learn about my consulting practice check out my expert calls page. If you're interested in working together please reach out to me via email

What is Open Telemetry?

Essentially, Open Telemetry comprises a suite of APIs, tools, and SDKs that facilitate the creation, collection, and exportation of telemetry data (such as metrics, logs, and traces). This data is crucial for analyzing and understanding the performance and behavior of software applications.