Search Results for "ace-step"
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Showing 19 open source projects for "ace-step"
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mimic
Intercept any app, then call it from Python like a library
...Generated clients are ordinary source files built on mimic.App, so developers can inspect and modify them directly. The system can preserve request templates and model multi-step API flows in which one call supplies data for another. Its command-line workflow checks prerequisites, records traffic, lists captured hosts, and prepares reusable sessions. The current documentation focuses on routing iPhone traffic through a Mac-hosted proxy. Mimic is intended for applications and accounts the user is authorized to inspect and automate. -
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nature-skills
Skill that conforms to the academic expression and scientific research
...It appears to focus on modularity and interoperability, allowing skills to be combined, extended, or integrated into broader agent ecosystems. The repository is designed to support experimentation with multi-step reasoning, task automation, and contextual tool orchestration for agentic AI systems. By standardizing reusable capabilities, the project helps developers build more maintainable and extensible AI workflows. Overall, nature-skills contributes to the growing ecosystem of modular AI agent tooling and structured automation frameworks. -
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Antigravity Awesome Skills
The Ultimate Collection of 700+ Agentic Skills for Claude Code
...The project includes skill definitions, example prompts, and usage patterns that highlight how modular abilities can be assembled into functioning assistants. Because it aims to reduce cognitive overhead, many skills show how to structure intents, handle context, and orchestrate multi-step reasoning without deep technical complexity. It also serves as inspiration for users looking to prototype new use cases — from conversational helpers that answer questions to workflow automators that trigger actions. -
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Courses (Anthropic)
Anthropic's educational courses
Anthropic’s courses repository is a growing collection of self-paced learning materials that teach practical AI skills using Claude and the Anthropic API. It’s organized as a sequence of hands-on courses—starting with API fundamentals and prompt engineering—so learners build capability step by step rather than in isolation. Each course mixes short readings with runnable notebooks and exercises, guiding you through concepts like model parameters, streaming, multimodal prompts, structured outputs, and evaluation. Assignments emphasize realistic tasks such as building small utilities, testing prompts against edge cases, and measuring quality so you learn to ship things that work. ... -
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Goutoujunshi
A Codex dating strategist who first captures emotions
Goutoujunshi is a Codex relationship-advice skill that combines emotional support, structured relationship analysis, and concrete next-step planning. It separates known facts, assumptions, and unknowns before evaluating attraction, reciprocity, risk, opportunity cost, and longer-term options. The skill can analyze user summaries, exported conversations, and chat screenshots while keeping track of who said what. Its knowledge base draws from relationship psychology, personality research, communication, sexuality, family studies, law, sociology, philosophy, and breakup recovery. ... -
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Lightly
A python library for self-supervised learning on images
...That's why - together with our community - we want to popularize the use of self-supervised methods to understand and curate raw image data. Our solution can be applied before any data annotation step and the learned representations can be used to visualize and analyze datasets. This allows selecting the best core set of samples for model training through advanced filtering. We provide PyTorch, PyTorch Lightning and PyTorch Lightning distributed examples for each of the models to kickstart your project. Lightly requires Python 3.6+ but we recommend using Python 3.7+. ... -
7
Boltons
250+ constructs, recipes, and snippets which extend the Python library
...In many cases the respective boltons module will describe 3rd-party alternatives worth investigating when use cases outgrow boltons. If you’ve found a natural “next-step” library worth mentioning, consider filing an issue! boltons has a minimalist architecture, remain as consistent, and self-contained as possible, with an eye toward maintaining its range of use cases and usage patterns as wide as possible. The boltons package depends on no packages, making it easy for inclusion into a project. -
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GenAI Processors
GenAI Processors is a lightweight Python library
...Its central abstraction is the Processor, a unit of work that consumes an asynchronous stream of parts (text, images, audio, JSON) and produces another stream, making it natural to chain operations and keep everything streaming end-to-end. Processors can be composed sequentially (to build multi-step flows) or in parallel (to fan-out work and merge results), which makes sophisticated agent behaviors easy to express with simple operators. The library offers built-in processors for classic turn-based Gemini calls as well as Live API streaming, so you can mix “batch” and real-time interactions in the same graph. It leans on Python’s asyncio to coordinate concurrency, handle network I/O, and juggle background compute threads without blocking. -
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DeepPavlov
A library for deep learning end-to-end dialog systems and chatbots
...They provide quick and convenient introduction on how to use DeepPavlov with complete, end-to-end examples. No installation needed. Guides explain the concepts and components of DeepPavlov. Follow step-by-step instructions to install, configure and extend DeepPavlov framework for your use case. DeepPavlov is an open-source framework for chatbots and virtual assistants development. It has comprehensive and flexible tools that let developers and NLP researchers create production-ready conversational skills and complex multi-skill conversational assistants. ... -
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Prompt Engineering Interactive Tutorial
Anthropic's Interactive Prompt Engineering Tutorial
...The course leans heavily on realistic failure modes (ambiguity, hallucination, brittle instructions) and shows how to iteratively debug prompts the way you would debug code. Lessons include building prompts from scratch for common tasks like extraction, classification, transformation, and step-by-step reasoning, with checkpoints that let you compare your outputs against solid baselines. You’ll also practice advanced patterns such as tool use, constrained generation, and response validation so outputs are trustworthy and machine-consumable. -
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Neural Network Visualization
Project for processing neural networks and rendering to gain insights
nn_vis is a minimalist visualization tool for neural networks written in Python using OpenGL and Pygame. It provides an interactive, graphical representation of how data flows through neural network layers, offering a unique educational experience for those new to deep learning or looking to explain it visually. By animating input, weights, activations, and outputs, the tool demystifies neural network operations and helps users intuitively grasp complex concepts. Its lightweight codebase is... -
12
quantitative
Quantized transactions python3
The “quantitative” repository by Jack-Cherish is a tutorial-style codebase for quantitative trading written in Python — essentially a learning resource that guides users through building algorithmic trading strategies step by step. It’s organized as a sequence of lessons (lesson1, lesson2, etc.), making it approachable for learners who want to understand both theory and practice in quantitative finance. The repo is evidently tied to a popular video series (on Bilibili) that reportedly drew substantial attention, suggesting the material is meant to be both educational and hands-on. ... -
13
YOLOV4 Pytorch
This is a source code for YoloV4-pytorch that can be used to train you
...It supports VOC-style datasets and includes scripts for prediction, mAP evaluation, FPS testing, video prediction, batch prediction, and heatmap generation. The project added multi-GPU training, seed settings for reproducible results, adaptive learning rate behavior based on batch size, and both step and cosine learning rate schedules. It also supports Adam and SGD optimizer choices, image cropping, adjustable parameters, and extensive code comments. It is a useful educational and applied repository for users who want to understand or customize YOLOv4 in PyTorch. -
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YOLOV3 Pytorch
This is a source code for yolo3-pytorch
...It includes utilities for annotation conversion, anchor generation, image prediction, video prediction, batch prediction, FPS measurement, heatmap output, and mAP evaluation. The project added multi-GPU training, target count statistics, learning rate scheduling with step and cosine options, and optimizer selection between Adam and SGD. It also includes adaptive learning rate adjustment based on batch size, image cropping, many configurable parameters, and expanded comments for easier study. It is well suited for learners and developers who want a hands-on YOLOv3 codebase in PyTorch. -
15
Faster-Rcnn
This is a pytorch implementation library of faster-rcnn
...It supports backbone options through pretrained VGG and ResNet weights, making it useful for comparing feature extractors. The project also includes learning rate scheduling through step and cosine methods, optimizer choices between Adam and SGD, adaptive learning rate behavior based on batch size, image cropping, FPS testing, video prediction, and batch prediction. It is a practical reference for users who want a more classical two-stage detector workflow in PyTorch. -
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DeepLabv3 Plus
Encoder-Decoder with Atrous Separable Convolution
...It provides pretrained weight workflows for MobileNetV2 and Xception backbones and notes that the correct backbone should be selected during training and prediction. The project also supports multi-GPU training, multiple backbones, learning rate schedules with step and cosine options, optimizer selection, and adaptive learning rate behavior based on batch size. It is useful for users who want a stronger semantic segmentation baseline than U-Net for scene-level segmentation tasks. -
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TensorFlow Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
TensorFlow Examples is a comprehensive repository of example implementations, tutorials, and reference code intended to help newcomers and intermediate learners dive into TensorFlow quickly. It contains both Jupyter notebooks and raw source code, covering a broad range of tasks: from basic machine-learning and neural-network models to more advanced use cases, using both TensorFlow v1 and v2 APIs. For clarity and educational value, each example is accompanied by explanatory comments or... -
18
ChainerRL
ChainerRL is a deep reinforcement learning library
...With this visualization tool, the behavior of ChainerRL agents can be easily inspected from a browser UI. Environments that support the subset of OpenAI Gym's interface (reset and step methods) can be used. -
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning
...It aims to be a practical map of the ecosystem, showing hands-on examples with libraries such as NumPy, pandas, matplotlib, scikit-learn, and others. Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. Advanced sections touch on neural networks and distributed computing topics, helping you bridge from basics to production-adjacent workflows. The collection is suitable for self-paced study, quick reference, or as teaching materials in workshops. By combining narrative explanations with executable code, it shortens the path from theory to working prototypes.
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