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FFmpeg Cheat Sheet
A categorized collection of FFmpeg commands for video automation
FFmpeg Cheat Sheet is a curated collection of commonly used FFmpeg commands and examples designed to help users quickly understand and apply multimedia processing tasks. It serves as a reference guide for operations such as transcoding, trimming, resizing, and streaming. The repository organizes commands into categories, making it easier to find solutions for specific use cases. It is particularly useful for beginners who want to learn FFmpeg syntax and workflows without reading extensive documentation. ... -
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nesa
Run AI models end-to-end encrypted
nesa is an open-source initiative focused on building decentralized AI infrastructure that enables secure, verifiable, and privacy-preserving machine learning and inference across distributed environments. The project aims to address key challenges in modern AI systems, such as data privacy, trust, and centralization, by leveraging cryptographic techniques and decentralized architectures. NESA allows developers to run AI computations in a way that ensures data integrity and confidentiality,... -
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Best-of Web Development with Python
A ranked list of awesome python libraries for web development
This curated list contains 570 awesome open-source projects with a total of 2.4M stars grouped into 26 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from Github and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! A ranked list of awesome python libraries for web... -
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Vidi2
Large Multimodal Models for Video Understanding and Editing
...The system is built with open-source release in mind, giving developers access to model code, inference scripts, and evaluation pipelines so they can reproduce research results or integrate Vidi into their own video-processing workflows. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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BeeAI Framework
Build production-ready AI agents in both Python and Typescript
...BeeAI also provides orchestration tools for designing dynamic workflows, enabling multiple agents to coordinate tasks through structured execution flows, retries, and parallel processing. -
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Text2Code for Jupyter notebook
A proof-of-concept jupyter extension which converts english queries
...When a user enters a textual command, the extension interprets the request and generates a corresponding Python code snippet that can be inserted into the notebook and executed automatically. The system uses natural language processing techniques to identify the intent of the query, extract relevant variables, and map the request to predefined code templates. Technologies such as sentence embeddings and named entity recognition are used to interpret user instructions and construct appropriate code outputs. -
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OpenVINO Notebooks
Jupyter notebook tutorials for OpenVINO
openvino_notebooks is a collection of interactive Jupyter notebooks designed to demonstrate how to build, optimize, and deploy artificial intelligence applications using the OpenVINO toolkit. The repository provides practical tutorials that guide developers through various AI workflows including computer vision, natural language processing, and generative AI tasks. Each notebook demonstrates how to run pre-trained models, optimize inference performance, and deploy models across hardware such as CPUs, GPUs, and specialized accelerators. The tutorials also illustrate how OpenVINO integrates with models from frameworks like PyTorch, TensorFlow, and ONNX to accelerate inference workloads. ... -
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xLSTM
Neural Network architecture based on ideas of the original LSTM
xLSTM is an open-source machine learning architecture that reimagines the classic Long Short-Term Memory (LSTM) network for modern large-scale language modeling and sequence processing tasks. The project introduces a new recurrent neural network design that incorporates exponential gating mechanisms and enhanced memory structures to overcome limitations of traditional LSTM models. By introducing innovations such as matrix-based memory and improved normalization techniques, xLSTM improves the ability of recurrent networks to capture long-range dependencies in sequential data. ... -
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TigerBot
TigerBot: A multi-language multi-task LLM
TigerBot is an open-source family of large language models designed to support multilingual and multi-task natural language processing applications. The project focuses on building high-performance models capable of handling both English and Chinese tasks while maintaining strong reasoning and conversational abilities. TigerBot models are based on modern transformer architectures and are trained on large datasets that cover multiple domains and languages. The project provides both base models and chat-optimized variants that can be used for dialogue systems, question answering, and general language understanding tasks. ... -
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Fulling
Full-stack Engineer Agent. Built with Next.js, Claude, shadcn/ui
...Developers can connect external services by simply providing API credentials, allowing the AI system to automatically integrate features such as authentication or payment processing. -
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Transformer Explainer
Learn How LLM Transformer Models Work with Interactive Visualization
Transformer Explainer is an interactive visualization tool created to help users understand how transformer-based language models operate internally. The platform runs a lightweight GPT-2 model directly in the user’s browser and allows users to experiment with text prompts while observing the model’s internal operations. Through visual diagrams and interactive interfaces, the tool reveals how tokens are processed through layers such as embeddings, attention mechanisms, and feed-forward... -
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DreamCraft3D
Official implementation of DreamCraft3D
...The name suggests a “dream crafting” metaphor—users probably supply textual or image prompts and generate 3D assets (point clouds, meshes, scenes). The repository includes model code, inference scripts, sample prompts, and possibly dataset preparation pipelines. It may integrate rendering or post-processing modules (e.g. mesh smoothing, texturing) to make the outputs more output-ready. Because 3D generation is hardware‐intensive, the repository likely also includes optimizations like quantization, pruning, or inference accelerations (e.g. using FlashMLA or DeepEP) to make the generation pipeline faster or more efficient. DreamCraft3D may also support style or attribute control (e.g. ... -
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amrlib
A python library that makes AMR parsing, generation and visualization
A python library that makes AMR parsing, generation and visualization simple. amrlib is a python module designed to make processing for Abstract Meaning Representation (AMR) simple by providing the following functions. Sentence to Graph (StoG) parsing to create AMR graphs from English sentences. Graph to Sentence (GtoS) generation for turning AMR graphs into English sentences. A QT-based GUI to facilitate the conversion of sentences to graphs and back to sentences. -
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LittleJS
The Tiny JavaScript Game Engine That Can!
LittleJS is a super lightweight 2D JavaScript game engine with fast WebGL rendering. It is designed to be small, simple, and easy to use for various applications, from game jams to commercial releases. This engine has everything necessary to make high-quality games, including fast rendering, physics, particles, sound effects, music, keyboard/mouse/gamepad input handling, update/render loop, and debug tools. It is recommended that you start by copying the LittleJS Starter Project This file is... -
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Scalene
High-performance CPU, GPU, and memory profiler for Python
...It runs orders of magnitude faster than other profilers while delivering far more detailed information. Once Scalene has profiled your program, it will launch a web browser with an interactive user interface (all processing is done locally). Hover over bars to see breakdowns of CPU and memory consumption, and click on underlined column headers to sort the columns. Scalene is fast. It uses sampling instead of instrumentation or relying on Python's tracing facilities. Its overhead is typically no more than 10-20% (and often less). Scalene performs profiling at the line level and per function, pointing to the functions and the specific lines of code responsible for the execution time in your program. ... -
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ffmpeg_develop_doc
2023, the latest audio and video learning materials, projects
ffmpeg_develop_doc is a curated repository that aggregates a comprehensive collection of learning resources related to FFmpeg and multimedia development. It includes command references, technical articles, academic papers, tutorials, and example projects covering audio and video processing concepts. The repository is structured as a knowledge base, offering materials on encoding, decoding, streaming protocols, and real-time media systems. It also contains interview preparation resources and practical case studies, making it useful for both learning and professional development. In addition to documentation, it links to open-source projects and implementation examples that demonstrate real-world usage of FFmpeg. ... -
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pg_textsearch
PostgreSQL extension for BM25 relevance-ranked full-text search
...The extension integrates with PostgreSQL’s native indexing mechanisms, allowing for scalable and performant search operations across large datasets. It also supports advanced query features such as ranking, filtering, and linguistic processing. By embedding search capabilities within the database, it simplifies architecture and reduces operational complexity. The project is particularly useful for applications that require fast and accurate text retrieval. Overall, pg_textsearch extends PostgreSQL into a more powerful platform for text-based data exploration. -
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KACTL
KTH algorithm competition template library
KACTL (the KTH Algorithmic Contest Template Library) is an extensively curated and high-performance C++ algorithms library created by the competitive programming team at the Royal Institute of Technology (KTH) to serve as a trusted, battle-tested codebase for algorithmic contests, programming competitions, and general algorithm development. The repository aggregates dozens of concise implementations of essential data structures, numerical methods, graph algorithms, string processing tools, computational geometry routines, and optimization techniques, all designed with speed, correctness, and compactness in mind. Instead of reinventing algorithms on the fly during contests like ACM-ICPC or Codeforces rounds, competitors can import exactly the component they need — whether a segment tree with lazy propagation, a minimum cost flow solver, or a fast Fourier transform — and focus their energy on problem logic and strategy. -
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Kimi-Audio
Audio foundation model excelling in audio understanding
Kimi-Audio is an ambitious open-source audio foundation model designed to unify a wide array of audio processing tasks — from speech recognition and audio understanding to generative conversation and sound event classification — within a single cohesive architecture. Instead of fragmenting work across specialized models, Kimi-Audio handles automatic speech recognition (ASR), audio question answering, automatic audio captioning, speech emotion recognition, and audio-to-text chat in one system, enabling developers to build rich, multimodal audio applications without stitching together disparate components. ... -
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FlowGram
Extensible workflow development framework
...Instead of shipping as a ready-made product, it provides the building blocks — a canvas for wiring together nodes, a form engine for configuring node parameters, a variable-scope and type-inference engine, and a set of “materials” (pre-built node types such as code execution, conditional logic, LLM calls, etc.) that can be composed into larger workflows. This makes FlowGram highly flexible: you can prototype data-processing pipelines, AI-agent flows, automation scripts, or even business process automation without writing all the plumbing yourself. The framework supports both free-layout canvases (for free-form graphs) and fixed-layout canvases (for more structured flowcharts, including loops, branches, compound nodes), giving you visual freedom depending on your use-case. -
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Orpheus TTS
Towards Human-Sounding Speech
...It is designed to produce human-like speech with natural intonation, emotion, and rhythm, targeting quality comparable to or better than many closed-source systems. The project ships both pretrained and finetuned English models, as well as a family of multilingual models released as a research preview, and includes data-processing scripts so users can train or finetune their own variants. Inference is provided through a Python package that uses vLLM under the hood for high-throughput, low-latency generation, including streaming examples that show how to generate audio chunks in real time. The maintainers provide Colab notebooks, a standardized prompting format, and one-click deployment via Baseten for production-grade, FP8/FP16 optimized inference with ~200 ms streaming latency. -
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Supertonic
Lightning-fast, on-device TTS, running natively via ONNX
...Supertonic is designed to handle real-world text gracefully, including numbers, dates, currency symbols, abbreviations, and technical units, without requiring heavy pre-processing or custom text normalization. The repository provides complete reference implementations across many programming ecosystems—Python, Node.js, browser (WebGPU/WASM), Java, C++, C#, Go, Swift, iOS, Rust, and Flutter. -
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WorkerVless2sub
Automate batch replacement to generate subscription generators
WorkerVless2sub is a dedicated subscription generator focused on providing “preferred line” subscriptions by processing VMess, VLESS, Trojan nodes, filtering and replacing automatically via a Cloudflare Worker script. The idea is you supply node lists (or use existing APIs/CSV sources) and the worker filters them by criteria like speed or reliability and then emits a subscription link for end-users. It supports deployment on Cloudflare Pages or Workers, offers configuration via variables like HOST, UUID, PATH, ADD, ADDAPI etc., and has an Apache-2.0 license. ... -
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fastdup
An unsupervised and free tool for image and video dataset analysis
fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale. -
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Adafruit Unified Sensor Driver
Common sensor library
Many small embedded systems exist to collect data from sensors, analyze the data, and either take appropriate action or send that sensor data to another system for processing. One of the many challenges of embedded systems design is the fact that parts you used today may be out of production tomorrow, or system requirements may change and you may need to choose a different sensor down the road. Creating new drivers is a relatively easy task, but integrating them into existing systems is both error-prone and time-consuming since sensors rarely use the exact same units of measurement. ...
