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
featured in #557
How We Built Ngrok's Data Platform
- Christian Hollinger tl;dr: “How we built it, what we learned, as well as some selective deep dives I found interesting enough to be worth sharing in more detail, since they’ll bridge the gap between what people usually understand by the term “data engineering” and how we run data here at ngrok. Some of this might even be useful for your own data platform endeavors, whether your team is big or small.”featured in #556
Introducing Netflix’s Key-Value Data Abstraction Layer
tl;dr: “In this post, we dive deep into how Netflix’s KV abstraction works, the architectural principles guiding its design, the challenges we faced in scaling diverse use cases, and the technical innovations that have allowed us to achieve the performance and reliability required by Netflix’s global operations.”featured in #552
Building And Scaling Notion’s Data Lake
tl;dr: “In the past three years Notion’s data has expanded 10x due to user and content growth, with a doubling rate of 6-12 months. Managing this rapid growth while meeting the ever-increasing data demands of critical product and analytics use cases, especially our recent Notion AI features, meant building and scaling Notion’s data lake. Here’s how we did it.”featured in #533
featured in #516
Data Fetching Patterns In Single-Page Applications
- Juntao Qiu tl;dr: “When a single-page application needs to fetch data from a remote source, it needs to do so while remaining responsive and providing feedback to the user during an often slow query. Five patterns help with this. Asynchronous State Handler wraps these queries with meta-queries for the state of the query. Parallel Data Fetching minimizes wait time. Fallback Markup specifies fallback displays in markup. Code Splitting loads only code that's needed. Prefetching gathers data before it may needed to reduce latency when it is.”featured in #515