#python #agents #gcp #gemini #genai_agents #generative_ai #llmops #mlops #observability

You can quickly create and deploy AI agents using the Agent Starter Pack, a Python package with ready-made templates and full infrastructure on Google Cloud. It handles everything except your agent’s logic, including deployment, monitoring, security, and CI/CD pipelines. You can start a project in just one minute, customize agents for tasks like document search or real-time chat, and extend them as needed. This saves you time and effort by providing production-ready tools and integration with Google Cloud services, letting you focus on building smart AI agents without worrying about backend setup or deployment details.

https://github.com/GoogleCloudPlatform/agent-starter-pack

#python #dictionary_attack #password #password_strength #weak_passwords #wordlist #wordlist_generator

**CUPP** is a free Python 3 tool that creates custom password wordlists from personal details like names, birthdays, pet names, or nicknames, using interactive questions or existing dictionaries. Run it with options like `-i` for profiling or `-l` to download huge wordlists. This helps you in legal penetration tests or investigations by generating targeted lists for efficient brute-force or dictionary attacks, cracking weak passwords faster than generic ones.

https://github.com/Mebus/cupp

#python #datascience #formula1 #motorsport

FastF1 is a Python package that lets you easily access and analyze Formula 1 data like results, schedules, timing, telemetry, and more. It uses Pandas DataFrames with custom F1 tools, Matplotlib for charts, and caching for fast scripts—install via pip install fastf1. You benefit by quickly pulling historical and live F1 stats to build insights, visualizations, or apps without hassle.

https://github.com/theOehrly/Fast-F1

#python #help_wanted #looking_for_contributors

This M3U playlist gives you a single, regularly updated file of free, legal TV channels worldwide (grouped by country and marked for HD, geo-blocking, or YouTube live) so you can add it to an IPTV player and watch many working streams without hunting links; it focuses on quality (only free, mainstream channels, one URL per channel) and lets you contribute fixes or channel changes via GitHub pull requests, which helps you get reliable channels and keeps the list current for smoother viewing.

https://github.com/Free-TV/IPTV

#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

#python

**ty** is a super-fast Python type checker and language server built in Rust by Astral (makers of uv and Ruff). It's 10-100x faster than mypy or Pyright, with rich error messages, IDE features like auto-complete and hover help, and support for big projects or partial typing. Try it via `uvx ty check`. This helps you catch bugs early, code faster with real-time feedback, and boost productivity in editors like VS Code.

https://github.com/astral-sh/ty

#rich_text_format #lcd_display #python #serial_communication #smart_display #smart_screen #system_monitor #system_monitoring #turing_smart_screen #xuanfang

**turing-smart-screen-python** is free open-source Python software (3.9+) for small USB-C IPS smart screens like Turing 3.5"/5", XuanFang, and others on Windows, Linux, Raspberry Pi, or macOS. Use it as a standalone system monitor showing CPU/GPU usage, temps, memory, and custom data via easy themes (with editor and community shares), or integrate into your Python projects to display text, images, progress bars, brightness, rotation, and RGB LEDs. It auto-detects ports with a simple GUI wizard—no coding needed. You benefit by turning your screen into a customizable HW dashboard or app display affordably, cross-platform, without vendor limits.

https://github.com/mathoudebine/turing-smart-screen-python

#python #large_language_models #llm #penetration_testing #python

PentestGPT is a free, open-source AI tool that automates penetration testing like solving CTF challenges in web, crypto, and more. Install easily with Docker, add your API key (Anthropic, OpenAI, or local LLMs), then run pentestgpt --target [IP] for interactive guidance on scans, exploits, and reports. New v1.0 adds autonomous agents and session saving. It boosts your speed and accuracy in ethical hacking, helping beginners learn steps fast and pros tackle complex targets efficiently.

https://github.com/GreyDGL/PentestGPT

#python

Mini-SGLang is a compact, easy-to-read inference framework (~5,000 Python lines) that runs and serves large language models with high speed using optimizations like radix cache, chunked prefill, overlap scheduling, tensor parallelism, and FlashAttention/FlashInfer kernels. It’s CUDA-dependent, quick to install from source, and can launch an OpenAI-compatible API or interactive shell for single- or multi‑GPU serving, letting you test or deploy models (e.g., Qwen, Llama) with low latency and scalable throughput. Benefit: you get a transparent, modifiable engine to deploy fast, efficient LLM inference for development, benchmarking, or production use.

https://github.com/sgl-project/mini-sglang

#python #ai #bug_detection #code_audit #code_quality #code_review #developer_tools #devsecops #google_gemini #llm #react #sast #security_scanner #supabase #typescript #vite #vulnerability_scanner #xai

**DeepAudit** is an AI-powered code audit tool using multi-agent collaboration to deeply scan projects for vulnerabilities like SQL injection, XSS, and path traversal. Import code from GitHub/GitLab or paste snippets; agents plan, analyze with RAG knowledge, and verify issues via secure Docker sandbox PoCs, generating PDF reports with fix suggestions. Deploy easily with one Docker command, supports local Ollama models for privacy, and cuts traditional tools' high false positives. **You benefit** by automating secure audits like a pro hacker—saving time, reducing errors, ensuring real exploits are caught, and speeding safe releases without manual hassle.

https://github.com/lintsinghua/DeepAudit