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Source channel @githubtrending · Post #15340 · Dec 17
#python#gym#gym_environment#reinforcement_learning#reinforcement_learning_agent#reinforcement_learning_environments#rl_environment#rl_training NeMo Gym helps you build and run reinforcement‑learning training environments for large language models, letting you develop, test, and collect verified rollouts separately from the training loop and integrate with your preferred RL framework and model endpoints (OpenAI, vLLM, etc.). It includes ready resource servers, datasets, and patterns for multi‑step, multi‑turn, and tool‑using scenarios, runs on a typical dev machine (no GPU required), and is early-stage with evolving APIs and docs. Benefit: you can generate high‑quality, verifiable training data faster and plug it into existing training pipelines to improve model behavior. https://github.com/NVIDIA-NeMo/Gym
Search: #cybersec
@hackspace · Post #1492 · 12/07/2025, 09:54 PM
Simple liner for CVE-2025-55182 React2Shell: subfinder -dL wildcards.txt -all -recursive > subs.txt Nuclei -t CVE-2025-55182.yaml -l final.txt Add FOFA, Shodan,Zoomeye filters : vul.cve="CVE-2025-55182" , asn="REDACTED" && (app="Next.js" || app="React.js") #infosec#cybersec
@libreware · Post #1153 · 07/05/2023, 04:07 PM
Snappy: A tool to detect rogue WiFi access points on open networks – Cybersecurity researchers have released a new tool called 'Snappy' that can help detect fake or rogue WiFi access points that attempts to steal data from unsuspecting people. Attackers can create fake access points in supermarkets, coffee shops, and malls that impersonate real ones already established at the location. This is done to trick users into connecting to the rogue access points and relay sensitive data through the attackers' devices. #Cybersec#Python #Wifi#RogueAccessPoints