Articles

Retirement and hibernation offer a chance to slow down, relax, and enjoy the simple things in life. From wearing pajamas to engaging in winter activities, learn how to make the most of your downtime.

Discover how to optimize your Docker image size and reduce it from 1.2 GB to a mere 80 MB. Learn practical tips and best practices for containerization.

Learn how to deploy a MERN Stack notes app on AWS, understanding the building blocks behind popular deployment platforms like Vercel and Netlify.

Product experimentation teams often struggle with causal inference on LLM-based features due to the opt-in trap, where users who click to opt in differ systematically from those who don't. Propensity score methods can help reweight comparison groups to approximate a randomized experiment.

When measuring model quality, standard regression approaches can be flawed due to confounding variables. This tutorial shows how to use instrumental variables to extract a clean causal estimate from LLM routing decisions in Python.

Product teams can use counterfactual estimation to answer retrospective questions about the effects of AI prompt engineering, despite the lack of random treatment assignment in logged production data.

Standard product experiments answer a binary question about average efficacy, but uplift modeling turns that binary into a nuanced spectrum, estimating a conditional average treatment effect for each user based on their specific features.

Discover how to avoid p-hacking and stop early in product experiments using the mixture Sequential Probability Ratio Test (mSPRT) and sequential testing. This tutorial provides a step-by-step guide on implementing mSPRT in Python and applying it to a synthetic LLM product dataset.

Collaborator contamination occurs when users in a control group are influenced by treated users in a collaborative workspace, leading to biased results in product experiments. Cluster randomization is a technique that can help overcome this issue by flipping the coin at the workspace level.

The Global Rollout Problem occurs when a team ships a model upgrade to the entire user base simultaneously, eliminating the control group. Synthetic control is a solution that builds a weighted combination of untreated units to estimate causal effects.

The Riemann Principle, a fundamental concept in mathematics, has far-reaching implications for the development of artificial intelligence. Its application in AI safety is crucial for mitigating risks and ensuring the reliability of complex systems.

Chain-of-thought debugging is a revolutionary approach to debugging that involves allowing AI to think through bugs step-by-step. In this article, we'll delve into the world of DeepSeek-R1 and learn when to let AI take the reins.