XtremePython 2023
2023-12-05..2023-12-06
-- online --
[XtremePython 2021, XtremePython 2023, XtremePython 2024 Online Conference]
| title | author | length |
|---|---|---|
| Beyond the Basics: Hacking and Securing Machine Learning Systems and Environments | Joshua Arvin Lat | 37m |
Shows that loading untrusted model files can run code and deliver a reverse shell by embedding a pickle payload that triggers on load (examples use torch.load… | ||
| Taking Asynchronous Python to the Next Level Using Trio | Dmitrii Ivashchenko | 37m |
Practical tour of Trio that focuses on concrete APIs and behaviors: structured concurrency with nurseries and start_soon that guarantee children finish before… | ||
| Making your network ready for Cyber Forensics Analysis | Nishant Krishna | 37m |
Shows a small Python toolkit that runs three quick checks — auditctl checks for kernel audit rules, verifies syslog/CIS logging, and inspects crontab plus new… | ||
| Boost The Performance with Codon | Ikko Ashimine | 37m |
Codon compiles Python ahead-of-time to native machine code and yields 10 to 100x speedups on numeric and recursive workloads in the demos (primes, Fibonacci… | ||
| Kill All Mutants! (Intro to Mutation Testing) | Dave Aronson | 21m |
Mutation testing mutates functions into many mutants and runs the test suite against each one, measuring killed vs survived to surface weak tests or redundant… | ||
| Accelerating Scientific Computing in Python with Numba: A Real-World Example | Aleksei Babenkov | 37m |
Shows a real pipeline: compare C (clang -O3) assembly to Python compiled with Numba JIT via LLVM IR and inspect_asm, then solve a 10M-point irregular time… | ||
| Creating Interactive GenAI applications powered by Streamlit, open source LLMs | Shivay Lamba | 40m |
Shows a working Streamlit app that pairs the Pieces Python SDK and Pieces OS via WebSocket to call cloud and local LLMs (GPT‑3.5, GPT‑4, Llama 2, Mistral)… | ||
| Cache me if you can: Boosting performance with Data Caching in Python | Ria Bhatia | 17m |
Shows concrete wins: a simple dict cache makes repeated API fetches drop from 1.14s to effectively 0s per request, and Python's @lru_cache cuts fib(40) from… | ||
| nothing matches | ||