Search Results for "path-setting" - Page 2
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Showing 149 open source projects for "path-setting"
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CS-Ebook
Curated list of classic, high-quality computer science books
...CS-Ebook is actively maintained and updated to reflect relevant and modern resources while preserving foundational texts. Its organized structure allows users to navigate topics efficiently and follow a progressive learning path. Contributions are encouraged, ensuring the list evolves with community input and continues to highlight valuable resources. -
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Arize Phoenix
Uncover insights, surface problems, monitor, and fine tune your LLM
...Deep Learning Models (CV, LLM, and Generative) are an amazing technology that will power many of future ML use cases. A large set of these technologies are being deployed into businesses (the real world) in what we consider a production setting. -
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TencentDB Agent Memory
TencentDB Agent Memory delivers fully local long-term memory for AI
...For personalization, it organizes memory from raw conversations into atoms, scenarios, and persona-level knowledge. The design keeps high-level memory inspectable while preserving a drill-down path back to raw evidence. It is built for OpenClaw and Hermes-style agent workflows that need lower token usage, better continuity, and no external API dependency. -
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JDA
Java wrapper for the popular chat & VOIP service
...Note that JDA is not a good tool to build a custom discord client as it loads all servers/guilds on startup, unlike a client which does this via lazy loading instead. If you need a bot, use a bot account from the Application Dashboard. Creating the JDA Object is done via the JDABuilder class. After setting the token and other options via setters, the JDA Object is then created by calling the build() method. When build() returns, JDA might not have finished starting up. However, you can use await ready() on the JDA object to ensure that the entire cache is loaded before proceeding. -
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ElevenLabs Python
The official Python SDK for the ElevenLabs API
...It exposes ElevenLabs’ main models such as Eleven Multilingual v2, Eleven Flash v2.5, and Eleven Turbo v2.5, each targeting different trade-offs between latency, cost, and quality. The SDK is designed for quick setup: after installing the package and setting an API key, you can generate speech in multiple languages and play or process the resulting audio bytes. It includes helper utilities (like play and stream) so you can either play audio locally or integrate it into your own playback or networking pipeline. -
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Kener
Kener is a Modern Self hosted Status Page, batteries included
Kener: Open-source Node.js status page tool, designed to make service monitoring and incident handling a breeze. It offers a sleek and user-friendly interface that simplifies tracking service outages and improves how we communicate during incidents. And the best part? Kener integrates seamlessly with GitHub, making incident management a team effort—making it easier for us to track and fix issues together in a collaborative and friendly environment. -
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Weights and Biases
Tool for visualizing and tracking your machine learning experiments
...Set wandb.config once at the beginning of your script to save your hyperparameters, input settings (like dataset name or model type), and any other independent variables for your experiments. This is useful for analyzing your experiments and reproducing your work in the future. Setting configs also allows you to visualize the relationships between features of your model architecture or data pipeline and model performance. -
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mcpo
A simple, secure MCP-to-OpenAPI proxy server
...The project emphasizes “dead-simple” setup and pairs with Open WebUI documentation that shows end-to-end integration. It supports running multiple tools and makes them discoverable to clients that expect Swagger/JSON schemas. In practice, mcpo shortens the path from a local MCP tool to a shareable, network-accessible microservice. -
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SAM 3D Body
Code for running inference with the SAM 3D Body Model 3DB
...The repository provides Python code to run inference, utilities to download checkpoints from Hugging Face, and demo scripts that turn images into 3D meshes and visualizations. There are Jupyter notebooks that walk you through setting up the model, running it on example images, and visualizing outputs in 3D, making it approachable even if you are not a 3D expert. -
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NextJS Ollama LLM UI
Fully-featured web interface for Ollama LLMs
NextJS Ollama LLM UI is a web-based frontend interface built with Next.js to make interacting with Ollama-hosted large language models easy and fast. Its goal is to remove the complexity of setting up and managing UI components for local or offline LLM usage by providing a straightforward chat experience with support for responsive layouts, light and dark themes, and local chat history storage in the browser. The interface stores conversations in local storage, so no separate backend database is required, making it ideal for hobbyists, experimenters, and developers who want a simple, web-accessible portal to their models. ... -
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handson-ml3
Fundamentals of Machine Learning and Deep Learning
...The author includes solutions for exercises and sets up an environment specification so you can reproduce results. Because the discipline of ML evolves rapidly, this repo serves both as a learning path and a reference library you can revisit as models. -
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Node.js Telegram Bot API
Telegram Bot API for NodeJS
...Bot must be enabled on inline mode for receive some messages.T elegram only supports HTTPS connections to WebHooks. Therefore, in order to set a WebHook, you will need a SSL certificate. The library makes it easy to get started sending files. By default, you may provide a file-path and the library will handle reading it for you. -
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RWARE
MuA multi-agent reinforcement learning environment
robotic-warehouse is a simulation environment and framework for robotic warehouse automation, enabling research and development of AI and robotic agents to manage warehouse logistics, such as item picking and transport. -
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MLJAR Studio
Python package for AutoML on Tabular Data with Feature Engineering
We are working on new way for visual programming. We developed a desktop application called MLJAR Studio. It is a notebook-based development environment with interactive code recipes and a managed Python environment. All running locally on your machine. We are waiting for your feedback. The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data,... -
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Gitingest
Create prompt-friendly codebase digests from any Git repository URL
...This makes it easier to provide meaningful code context when working with AI systems that require compact, readable inputs. Developers can generate these digests from either a local directory or a remote repository by supplying a repository path or URL. The generated output is optimized for prompt usage, helping AI models understand codebases more effectively without requiring manual file aggregation. In addition to producing the code digest, Gitingest also calculates statistics about the extracted content such as repository structure, total size of the extract, and token count. ... -
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Granite TSFM
Foundation Models for Time Series
granite-tsfm collects public notebooks, utilities, and serving components for IBM’s Time Series Foundation Models (TSFM), giving practitioners a practical path from data prep to inference for forecasting and anomaly-detection use cases. The repository focuses on end-to-end workflows: loading data, building datasets, fine-tuning forecasters, running evaluations, and serving models. It documents the currently supported Python versions and points users to where the core TSFM models are hosted and how to wire up service components. ... -
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eve
The Framework for Building Agents
eve is Vercel’s open-source framework for building, running, and scaling durable backend AI agents. It uses a filesystem-first model where an agent is defined as a directory of files, making the project structure itself the agent contract. The framework is designed to include production concerns from the start instead of leaving developers to assemble durability, sandboxing, approvals, and deployment separately. Eve supports durable execution, sandboxed compute, human-in-the-loop approvals,... -
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Core AI Models
Model export recipes, Python primitives, and Swift runtime utilities
...The repository also contains agent skills that guide coding assistants through Core AI workflows, model authoring, and compression exploration. It is useful for developers who want a curated path from model preparation to local app integration on Apple hardware. -
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dive-into-llms
"Dive into LLMs" series of practical programming tutorials
The dive-into-llms project is an educational resource designed to provide a comprehensive introduction to large language models and their underlying concepts. It combines theoretical explanations with practical examples, guiding users through topics such as model architecture, training processes, and inference techniques. The repository is structured as a learning pathway, making it accessible to both beginners and intermediate practitioners interested in understanding how LLMs work. It... -
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Map-Anything
MapAnything: Universal Feed-Forward Metric 3D Reconstruction
...The model flexibly accepts different input combinations (images, intrinsics, poses, sparse or dense depth) and produces a rich set of outputs including per-pixel 3D points, camera intrinsics, camera poses, ray directions, confidence maps, and validity masks. Its inference path is fully feed-forward with optional mixed-precision and memory-efficient modes, making it practical to scale to long image sequences while keeping latency predictable. -
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dstack
Open-source tool designed to enhance the efficiency of workloads
dstack is an open-source tool designed to enhance the efficiency of running ML workloads in any cloud (AWS, GCP, Azure, Lambda, etc). It streamlines development and deployment, reduces cloud costs, and frees users from vendor lock-in. -
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DeepSeekMath-V2
Towards self-verifiable mathematical reasoning
...Under the hood, Math-V2 uses a massive Mixture-of-Experts (MoE) architecture (activated parameter count reportedly in the hundreds of billions) derived from DeepSeek’s experimental base architecture. For math problems, it employs a generator-verifier loop: it first generates a candidate proof (or solution path), then runs a verifier that assesses correctness and completeness. -
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AI-Job-Notes
AI algorithm position job search strategy
...It assembles study paths, checklists, and interview prep materials, but also covers job-search mechanics—portfolio building, resume patterns, and communication tips. The emphasis is on doing: practicing with project ideas, setting up reproducible experiments, and showcasing results that convey impact. It ties technical study (ML/DL fundamentals) to real hiring signals like problem-solving, code quality, and experiment logging. The repository’s structure encourages progressive preparation—from fundamentals to mock interviews and post-interview retrospectives. ... -
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TimesFM
Pretrained time-series foundation model developed by Google Research
TimesFM is a pretrained time-series foundation model from Google Research built for forecasting tasks, designed to generalize across many domains without requiring extensive per-dataset retraining. It provides a decoder-only model approach to forecasting, aiming for strong performance even in zero-shot or low-data settings where traditional models often struggle. The project includes code and an inference API intended to make it practical to run forecasts programmatically, with options to... -
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Color Thief
Grab the color palette from an image using just Javascript
...Color is returned as an array of three integers representing red, green, and blue values. When called in the browser, the image argument expects an HTML image element, not a URL. When run in Node, this argument expects a path to the image. quality is an optional argument that must be an Integer of value 1 or greater, and defaults to 10. The number determines how many pixels are skipped before the next one is sampled. We rarely need to sample every single pixel in the image to get good results. The bigger the number, the faster a value will be returned. ...