GitGuardian detects secrets—like passwords and API keys—in code, but the sheer volume can overwhelm users. Sifting through them to find the most critical ones is like searching for a needle in a haystack. In this talk, we’ll dive into how we leveraged LLMs at key stages to prioritize secrets efficiently and at scale.

Large Language Models (LLMs) are powerful but often computationally expensive, making deployment challenging. In this talk, we’ll explore how to quantize an LLM in just three lines of code using Pruna AI’s frictionless solution. I’ll introduce our data-free vector quantization approach, which optimizes CUDA kernels to enable efficient inference—all in under five minutes. Whether you're working on edge AI, server-side deployments, or simply curious about making LLMs more efficient, this session will give you a hands-on glimpse into state-of-the-art quantization techniques.