/Data

Introducing Impressions At Netflix

- Tulika Bhatt tl;dr: “Capturing these moments and turning them into a personalized journey is no simple feat. It requires a state-of-the-art system that can track and process these impressions while maintaining a detailed history of each profile’s exposure. This nuanced integration of data and technology empowers us to offer bespoke content recommendations.”

featured in #591

Dump The Golden Dataset: Switch To Random Sampling

- Nishant Shukla tl;dr: Golden Datasets have long been a reliable method for measuring AI prompt performance. But as AI innovation moves fast, companies need a more agile, flexible, and cost-effective solution to stay ahead of their competition. Enter random sampling of AI prompt performance—a cutting-edge approach that adapts to real-world data and drives scalable performance for QA Wolf customers. Stay ahead of the curve—watch the webinar now.

featured in #574

Dump The Golden Dataset: Switch To Random Sampling

- Nishant Shukla tl;dr: Golden Datasets have long been a reliable method for measuring AI prompt performance. But as AI innovation moves fast, companies need a more agile, flexible, and cost-effective solution to stay ahead of their competition. Enter random sampling of AI prompt performance—a cutting-edge approach that adapts to real-world data and drives scalable performance for QA Wolf customers. Stay ahead of the curve—watch the webinar now.

featured in #573

Designing Data Products

- Kiran Prakash tl;dr: “Working backwards from the end goal is a core principle of software development, and we’ve found it to be highly effective in modelling data products. In this article we'll explore a step-by-step, methodical approach to identifying data products that avoids overdesign while providing just enough clarity for teams to begin implementation.”

featured in #572

Dump The Golden Dataset: Switch To Random Sampling

- Nishant Shukla tl;dr: Golden Datasets have long been a reliable method for measuring AI prompt performance. But as AI innovation moves fast, companies need a more agile, flexible, and cost-effective solution to stay ahead of their competition. Enter random sampling of AI prompt performance—a cutting-edge approach that adapts to real-world data and drives scalable performance for QA Wolf customers. Stay ahead of the curve—watch the webinar now.

featured in #570

Dump The Golden Dataset: Switch To Random Sampling

- Nishant Shukla tl;dr: Golden Datasets have long been a reliable method for measuring AI prompt performance. But as AI innovation moves fast, companies need a more agile, flexible, and cost-effective solution to stay ahead of their competition. Enter random sampling of AI prompt performance—a cutting-edge approach that adapts to real-world data and drives scalable performance for QA Wolf customers. Stay ahead of the curve—watch the webinar now.

featured in #568

Dump The Golden Dataset: Switch To Random Sampling

- Nishant Shukla tl;dr: Golden Datasets have long been a reliable method for measuring AI prompt performance. But as AI innovation moves fast, companies need a more agile, flexible, and cost-effective solution to stay ahead of their competition. Enter random sampling of AI prompt performance—a cutting-edge approach that adapts to real-world data and drives scalable performance for QA Wolf customers. Stay ahead of the curve—watch the webinar now.

featured in #566

Control Data Access with Targeted Row-Level Security

tl;dr: Integrate Clerk with Neon Authorize to enforce Row-Level Security (RLS) in Postgres using JWTs. This setup enhances security by securing database queries based on user identity. For team leads, it simplifies security management and reduces risk, allowing teams to focus on development.

featured in #566

Why Software Only Moves Forward

tl;dr: “Data is the biggest reason software only moves forward. Once you save state, your code will need to understand that state forever. This is double true for state that leaves your system and becomes distributed. Billing state, emails, and async jobs are a common early introduction to these issues.”

featured in #565

Use Data That Looks Like Data

- Thorsten Ball tl;dr: “Time for me to pass on something I've been practicing for years but haven't found written down somewhere. It's a simple thing. A practical thing. Forged in the trenches. It won't win any contests in which the audience gasps and says "oh, now that is clever." But it’s easy. Simple even. And it can save a lot of time and tears and, at the end of the day, isn't that some of the best stuff? When debugging or testing your program, do not use data that looks like a variable or type name.”

featured in #557