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Publicaciones del Canal
| 2 | How I Found a $90,000 Remote Code Execution Vulnerability in Google
A leaked Flask session signing secret let an attacker forge session cookies containing malicious pickled data, turning OAuth state handling into unauthenticated remote code execution on several Google developer sites. The writeup shows how combiningpicklewith client-controlled session data can turn a secret leak into code execution, and why untrusted data should never cross a pickle dese...
https://simonkoeck.com/writeups/google-devsite-flask-session-pickle-rce | 50 |
| 3 | Python 3.15's best new features
Trey Hunner's opinion on which Python 3.15 features will be most exciting for the average Python user.
https://www.pythonmorsels.com/python315/ | 62 |
| 4 | BOOTH
A lightweight, zero-dependency Python checkpoint layer for structured LLM output reliability.
https://github.com/Vedantgitbot/booth | 76 |
| 5 | Python vs Go: A Practical Comparison for Developers
Python offers a huge ecosystem, especially for AI, data, and scripting, while Go trades some flexibility for faster execution, simpler deployment, and built-in concurrency. This post compares where each language fits best today, including performance, typing, tooling, developer adoption, and why many teams end up using both rather than choosing one outright.
https://blog.jetbrains.com/go/2026/10/13/python-vs-go/ | 79 |
| 6 | Python Hub Weekly Digest for 2026-10-25
https://pythonhub.dev/digest/2026-10-25/ | 81 |
| 7 | Quicksand
Quicksand is an async Python API for launching, controlling, snapshotting, and rolling back QEMU virtual machines to provide isolated sandboxes for AI agents without requiring Docker or root privileges.
https://github.com/microsoft/quicksand | 75 |
| 8 | loopinfo: let your for loop know if it is on the first or last item
Python loops do not know where they are. loopinfo adds loop.first, loop.last, loop.index, loop.count, previous, next and more.
https://pypi.org/project/loopinfo/ | 81 |
| 9 | Speculative Decoding: The Art of Guessing Well
Speculative decoding lets smaller models guess several tokens ahead while the larger model verifies them in parallel, reducing LLM inference latency without changing the final sampling distribution. From the acceptance math to Gumbel coupling, EAGLE-3, and multi-token prediction, the piece shows how increasingly sophisticated drafters make those guesses accurate enough to deliver real sp...
https://astralord.github.io/posts/speculative-decoding-the-art-of-guessing-well/ | 90 |
| 10 | Building a small Django app with help from AI
This tutorial demonstrates how to build a basic Django idea-voting application using AI as an interactive assistant rather than an autonomous developer. It emphasizes using targeted prompts to generate boilerplate, explain error messages, write tests, and catch edge cases like database race conditions while keeping architectural decisions in the developer’s hands.
https://codewithstein.com/building-a-small-django-app-with-help-from-ai/ | 88 |
| 11 | python-date-parser
A Rust-backed Python library for fast fuzzy parsing of heterogeneous date strings, with bulk-list and Polars APIs that avoid Python-level iteration and can outperform pure-Python alternatives by orders of magnitude.
https://github.com/SEIU-Tech/python-date-parser | 91 |
| 12 | Lifeguard
Lifeguard is a static analyzer to detect Lazy Imports incompatibilities and ease the adoption overhead for Lazy Imports in Python.
https://github.com/Facebook/lifeguard | 83 |
| 13 | CacheCanary
CacheCanary is a Python CLI and GitHub Action that detects why Claude prompt caching silently stops working on Amazon Bedrock, checks saved requests in CI, and analyzes Bedrock logs to show cache hit rates and the cost of misses.
https://github.com/Haarris/cachecanary | 86 |
| 14 | LongLive
Long video generation and world-action modeling research from NVIDIA. Each project has its own directory, code, documentation and model weights.
https://github.com/NVlabs/LongLive | 87 |
| 15 | fastapi-stream-lease
Distributed stream and SSE concurrency lease manager for FastAPI and Starlette backed by atomic Redis Lua scripts.
https://github.com/agustin18/fastapi-stream-lease | 82 |
| 16 | why .parquet dominates modern data engineering
This video explains why the Parquet file format has become a dominant standard in modern data engineering by using columnar storage to improve speed and reduce file size compared to traditional CSV formats. It details key technical features like compression, encoding, and predicate pushdown that allow for significantly more efficient data retrieval and storage for large-scale analysis.
https://www.youtube.com/watch?v=6Qkzfwk0W58 | 81 |
| 17 | Parsing JSON at tens of gigabytes per second… in Python
Free-threaded Python 3.14 can push JSON parsing into the tens of gigabytes per second when fastsimdjson spreads work across many cores and avoids eagerly materializing Python objects. On a 64-core machine, Lemire reports throughput around 66 GB/s, showing how dramatically multithreaded parsing can scale when Python’s GIL is no longer the limiting factor.
https://lemire.me/blog/2026/10/12/parsing-json-at-tens-of-gigabytes-per-second-in-python/ | 84 |
| 18 | A Clean Way to Implement Undo in Python
This video explains how to implement the Memento design pattern in Python to handle state restoration and undo functionality without breaking encapsulation. It demonstrates a clean, modern approach using frozen data classes, protocols, and generics to manage state snapshots independently of application logic.
https://www.youtube.com/watch?v=ZXAt8AP3DnY | 86 |
| 19 | AirLLM
AirLLM is a Python library for running very large language models on consumer GPUs with limited memory by loading model layers sequentially instead of keeping the entire model in VRAM.
https://github.com/lyogavin/airllm | 90 |
| 20 | Python 3.14 vs Python 3.15 – performance testing
Performance benchmarks on AMD and Intel systems reveal that Python 3.15 is overall ~1.45× faster than Python 3.14. While massive speedups in Base32, Ascii85, and Base85 C-rewrite encoding operations heavily skew the average, most other workloads show a more modest performance improvement of around 1.20×.
https://en.lewoniewski.info/2026/python-3-14-vs-python-3-15-performance-testing/ | 92 |
