#python#asr#deeplearning#generative_ai#large_language_models#machine_translation#multimodal#neural_networks#speaker_diariazation#speaker_recognition#speech_synthesis#speech_translation#tts
NVIDIA NeMo is a powerful, easy-to-use platform for building, customizing, and deploying generative AI models like large language models (LLMs), vision language models, and speech AI. It lets you quickly train and fine-tune models using pre-built code and checkpoints, supports the latest model architectures, and works on cloud, data center, or edge environments. NeMo 2.0 is even more flexible and scalable, with Python-based configuration and modular design, making it simple to experiment and scale up. The main benefit is that you can create advanced AI applications faster, with less effort, and at lower cost, while getting high performance and easy deployment options[1][2][3].
https://github.com/NVIDIA/NeMo
#typescript#ai#chatgpt#docsgpt#hacktoberfest#information_retrieval#language_model#llm#machine_learning#natural_language_processing#python#pytorch#rag#react#semantic_search#transformers#web_app
DocsGPT is an open-source AI tool that helps you quickly find accurate answers from many types of documents and web sources without errors. It supports formats like PDF, DOCX, images, and integrates with websites, APIs, and chat platforms like Discord and Telegram. You can deploy it privately for security, customize it to fit your brand, and connect it to tools for advanced actions. This means you save time searching for information, get reliable answers with sources, and improve productivity whether you’re a developer, support team, or business user. It’s easy to set up and scales well for many users[2][3][4].
https://github.com/arc53/DocsGPT
https://docs.pipenv.org/
#Pipenv — the officially recommended #Python#packaging tool from Python.org, free (as in freedom).
Pipenv is a tool that aims to bring the best of all packaging worlds (#bundler, #composer, #npm, #cargo, #yarn, etc.) to the Python world. #Windows is a first–class citizen, in our world.
It automatically creates and manages a #virtualenv for your projects, as well as adds/removes #packages from your #Pipfile as you install/uninstall packages. It also generates the ever–important Pipfile.lock, which is used to produce deterministic builds.
http://www.jaggedverge.com/2017/11/how-a-web-page-request-makes-it-down-to-the-metal/
How a web page request makes it down to the metal
by : Janis Posted in : Tutorials, work-in-progess Tags : #NGINX, #Python No Comments
The other day I was interested in how many steps occur between sending a #POST or #GET#request from a website to the actual processing that happens on the CPU of the #server. I figured that I knew bits and pieces of the puzzle but I wanted to see the complete path from the highest levels of abstraction all the way to the lowest without missing anything too big in-between. It turns out that in a modern web system there are a lot of steps. I have been really fascinated by this much like the explorer that wants to find a path from one known place to another. If you are interested in better understanding how your computer works you might find walking along this path with your tech stack helpful.
Frontend
prelude: GET request
Browser page #rendering
POST request
sidenote: #CSRF#token
Network stack
sidenote: The Internet
#TCP
sidenote: more comprehensive treatment of network stack
Backend
Handling web request
#WSGI
#Django
Django URL routing
Django views
Python implementations
#CPython
CPython bytecode
CPython bytecode execution details
Machine Code
CPython to machine code
Machine code execution
Hardware implementation details
Microcode
Processor #pipeline
Silicon implementation of addition
Silicon adder unit
AND gate
Transistor
#python#artificial_intelligence#cloud_ml#computer_systems#courseware#deep_learning#edge_machine_learning#embedded_ml#machine_learning#machine_learning_systems#mobile_ml#textbook#tinyml
You can learn how to build real-world AI systems from start to finish with an open-source textbook originally from Harvard University. It teaches you not just how to train AI models but how to design scalable systems, manage data pipelines, deploy models in production, monitor them continuously, and optimize for devices like phones or IoT gadgets. This helps you become an engineer who can create efficient, reliable, and sustainable AI systems that work well in practice. The book offers hands-on labs, community support, and free online access, making it easier to gain practical skills in machine learning systems engineering.
https://github.com/harvard-edge/cs249r_book
#cplusplus#apple_silicon#bsd#c_plus_plus#cmake#floss#game#gplv2#json#linux#lua#macos_app#python#strategy#windows
Widelands is a free, open-source real-time strategy game like Settlers II, where you lead a small clan to build roads, gather resources like wood and gold, manage four unique tribes, trade, or fight in single-player campaigns and multiplayer. Download it easily for Windows, Mac, or Linux, or compile from source with simple scripts and tools like CMake on various systems. This lets you enjoy deep, replayable empire-building fun at no cost, anytime with friends or AI.
https://github.com/widelands/widelands
#python#data_analysis#dingtalk_robot#docker#feishu_robot#hot_news#mail#mcp#mcp_server#news#ntfy#python#telegram_bot#trending_topics#wechat_robot
TrendRadar is a lightweight, easy-to-deploy tool that gathers trending topics from 11+ major platforms like Zhihu, Douyin, and Baidu in just 30 seconds. It lets you set custom keywords to filter only news you care about, eliminating information overload. The tool offers three smart notification modes—daily summaries, current rankings, or incremental alerts—and supports multiple channels including WeChat Work, Feishu, DingTalk, Telegram, and email. You can customize how trends are ranked using a personalized algorithm that weighs ranking position, frequency, and hotness. With GitHub Pages for web reports, Docker support, and AI-powered analysis through MCP protocol, TrendRadar transforms scattered platform algorithms into one unified, user-controlled news feed tailored to your interests.
https://github.com/sansan0/TrendRadar
#python#agent#agentic#agentic_ai#agents#agents_sdk#ai#ai_agents#aiagentframework#genai#genai_chatbot#llm#llms#multi_agent#multi_agent_systems#multi_agents#multi_agents_collaboration
The Agent Development Kit (ADK) is an open-source Python toolkit that helps you easily build, test, and deploy smart AI agents, from simple helpers to complex multi-agent systems. It lets you write agent logic in Python, use many built-in or custom tools, and organize multiple agents to work together. You can deploy agents anywhere, including Google Cloud, and evaluate their performance with built-in tools. ADK supports flexible workflows and works with various AI models, not just Google’s. This means you get full control and flexibility to create powerful AI applications that fit your needs, speeding up development and making it easier to manage AI projects.
https://github.com/google/adk-python
#python#cli#cti#cybersecurity#forensics#hacktoberfest#information_gathering#infosec#linux#osint#pentesting#python#python3#reconnaissance#redteam#sherlock#tools
Sherlock is a powerful tool that helps you find social media accounts by username across over 400 networks. It's easy to use and works on many operating systems like macOS, Linux, and Windows. You can install it using methods like `pipx` or Docker, and then simply type the username you want to search for. Sherlock will show you where that username is used on different social media platforms. This tool is useful for gathering information quickly and can be run locally or even online through services like Apify. It saves time and effort in finding accounts across many platforms.
https://github.com/sherlock-project/sherlock
#javascript#ai#algorithm#artificial_intelligence#chatgpt#claude#cursor#deep_learning#deepseek#gemini#generative_ai#gpt#llm#mcp#openai#python#rag#vibe_coding#vibecoding#vue#vuepress
鱼皮的 AI知识库 offers a free Vibe Coding tutorial for beginners, teaching AI-powered programming with natural language prompts to build and monetize apps fast—no coding skills needed. It covers tools, projects, tips, and paths like making your first work in 10 minutes, plus AI guides on DeepSeek, Cursor, and more. You benefit by quickly creating profitable products, breaking tech barriers, and enjoying AI perks to improve life and work. Start at ai.codefather.cn/vibe.
https://github.com/liyupi/ai-guide
#python#ai#faiss#gpt_oss#langchain#llama_index#llm#localstorage#offline_first#ollama#privacy#python#rag#retrieval_augmented_generation#vector_database#vector_search#vectors
LEANN is a tiny, powerful vector database that lets you turn your laptop into a personal AI assistant capable of searching millions of documents using 97% less storage than traditional systems without losing accuracy. It works by storing a compact graph and computing embeddings only when needed, saving huge space and keeping your data private on your device. You can search your files, emails, browser history, chat logs, live data from platforms like Slack and Twitter, and even codebases—all locally without cloud costs. This means fast, private, and efficient AI-powered search and retrieval on your own laptop.
https://github.com/yichuan-w/LEANN
#rust#ai#ai_engineering#anthropic#artificial_intelligence#deep_learning#genai#generative_ai#gpt#large_language_models#llama#llm#llmops#llms#machine_learning#ml#ml_engineering#mlops#openai#python#rust
TensorZero is a free, open-source tool that helps you build and improve large language model (LLM) applications by using real-world data and feedback. It gives you one simple API to connect with all major LLM providers, collects data from your app’s use, and lets you easily test and improve prompts, models, and strategies. You can see how your LLMs perform, compare different options, and make them smarter, faster, and cheaper over time—all while keeping your data private and under your control. This means you get better results with less effort and cost, and your apps keep improving as you use them[1][2][3].
https://github.com/tensorzero/tensorzero