Search Results for "path-setting" - Page 3
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Showing 189 open source projects for "path-setting"
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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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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All-in-RAG
Big Model Application Development Practice 1
All-in-RAG is an open-source educational project designed to teach developers how to build applications using retrieval-augmented generation techniques. The repository provides a structured learning path that covers both theoretical foundations and practical implementation steps for RAG systems. It explains the full development pipeline required to create knowledge-aware AI assistants, including data preparation, document indexing, vector embedding generation, and retrieval strategies. The project also explores advanced topics such as hybrid retrieval methods, query optimization, and evaluation techniques for improving system accuracy. ... -
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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. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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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. ... -
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OpenHome Abilities
Open-source abilities for OpenHome agents
...The system is meant to support a wide range of voice-driven actions, from API calls and media playback to quiz flows, device control, and multi-turn conversations, so it functions as a practical extension framework rather than a narrow template library. The repository includes official abilities maintained by the OpenHome team as well as community-contributed ones, creating both a stable baseline and a path for outside developers to publish their own work. -
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Claude Code CLI
A comprehensive guide for setting up Claude Code CLI
Claude Code CLI is a structured resource designed to help developers use AI-assisted tools effectively in iOS application development. It provides guidance on how to integrate AI workflows into the process of building mobile applications using Swift and Apple’s development ecosystem. The project covers best practices for structuring prompts, managing code generation, and refining outputs to match platform-specific requirements. It emphasizes real-world development scenarios, helping users... -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
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DeployStack
Centralized credential vault, governance, and token optimization
...It provides a structured way to compose resources such as cloud networking, compute, and managed services into coherent deployment blueprints that can be versioned and reused across projects. By abstracting common deployment patterns and capturing them as templates, Deploystack reduces duplication of effort that typically occurs when setting up stacks for different applications or environments. The project emphasizes repeatability and clarity, enabling teams to follow best practices for scalability, security, and operational reliability without hand-crafting deployment scripts for every new service. It supports integration with popular cloud providers and infrastructure tooling, streamlining workflows that span local development through staging and production environments. -
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AlphaTree
DNN && GAN && NLP && BIG DATA
...It presents diagrams and documentation describing the evolution of models such as LeNet, AlexNet, VGG, ResNet, DenseNet, and Inception networks. The repository organizes these architectures into a structured learning path that helps learners understand how deep learning models improved over time through changes in depth, architectural complexity, and training techniques. In addition to neural networks used for image classification, the project also references broader AI fields such as generative adversarial networks, natural language processing, and graph neural networks. -
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ML Retreat
Machine Learning Journal for Intermediate to Advanced Topics
ML Retreat is an open-source learning repository that serves as a structured journal documenting advanced topics in machine learning and artificial intelligence. The project compiles detailed notes, technical explanations, and curated resources that guide readers through complex concepts across modern AI research. Rather than functioning as a traditional tutorial series, the repository is organized as a learning journey that progressively explores increasingly advanced subjects. Topics... -
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TorchDistill
A coding-free framework built on PyTorch
...Even when you need to extract intermediate representations in teacher/student models, you will NOT need to reimplement the models, which often change the interface of the forward, but instead specify the module path(s) in the yaml file. In addition to knowledge distillation, this framework helps you design and perform general deep learning experiments (WITHOUT coding) for reproducible deep learning studies. i.e., it enables you to train models without teachers simply by excluding teacher entries from a declarative yaml config file. -
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Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
...Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before they pass into a neural network (if you use augmentation). The general recommendation is to use suitable augs for your data and as many as possible, then after some time of training disable the most destructive (for image) augs. You can turn on automatic mixed precision with one flag --amp. ... -
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files-to-prompt
Concatenate a directory full of files into a single prompt
files-to-prompt is a Python command-line tool that takes one or more files or entire directories and concatenates their contents into a single, LLM-friendly prompt. It walks the directory tree, outputting each file preceded by its relative path and a separator, so a model can understand which content came from where. The tool is aimed at workflows where you want to ask an LLM questions about a whole codebase, documentation set, or notes folder without manually copying files together. It includes rich filtering controls, letting you limit by extension, include or skip hidden files, and ignore paths that match glob patterns or .gitignore rules. ... -
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Sandbox Agent
Run Coding Agents in Sandboxes
...The project focuses on enabling more reliable and auditable agent behavior by separating execution from the host environment, which is especially important for applications involving automation, code generation, or system-level operations. Developers can use Sandbox Agent to simulate real-world workflows, debug agent decisions, and evaluate outcomes in a contained setting before deploying to production. It also supports extensibility, allowing integration with custom tools, APIs, and workflows tailored to specific use cases. -
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DeepSeek Engineer v2
A powerful coding assistant application
...Version 2.0 focuses on native function calling instead of rigid structured JSON responses. The assistant can read files, read multiple files, create files, create multiple files, and edit specific snippets when needed. It includes safeguards such as path validation, directory traversal protection, file size limits, and binary file exclusion. Overall, it is designed for developers who want a conversational coding tool that can inspect, modify, and reason about project files from the command line. -
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HiDream-I1
Open-source image generative foundation model
...It supports direct Python inference scripts, an interactive Gradio demo, and integration through the Hugging Face Diffusers library. The model uses a Llama 3.1 text encoder path and requires the proper Hugging Face access setup for automatic downloads. It is useful for researchers, developers, and creative AI builders who want an open text-to-image model with strong benchmark performance and multiple deployment options. -
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Keep Codex Fast
A backup-first Codex skill for keeping local Codex state fast
...When applied manually, it backs up first, archives old sessions, rotates large logs, moves stale worktrees, and prunes dead references instead of deleting important state. It also includes an optional repair path for oversized thread title and preview metadata. Its main value is helping users preserve continuity through handoff documents while reducing local drag in Codex. -
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MiniMind-O
A 0.1B Omni model trained from scratch
MiniMind-O is an educational open-source project for building a small end-to-end Omni model from scratch. It extends the MiniMind family by exploring a model that can handle text, audio, and image inputs while producing text and streaming speech outputs. The project is designed to make multimodal AI training more accessible by keeping the model size small enough for ordinary personal hardware. It includes both mini and full training data paths, allowing learners to run a complete workflow... -
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DATA SCIENCE ROADMAP
Data Science Roadmap from A to Z
DATA SCIENCE ROADMAP is an educational repository designed to guide learners through the process of becoming proficient in data science and machine learning. The project presents a structured roadmap that outlines the knowledge and skills required for different stages of a data science career. Topics typically include programming with Python, statistics, mathematics, machine learning algorithms, data visualization, and big data technologies. The roadmap also includes links to courses,... -
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Hello-Agents
Building an Intelligent Agent from Scratch
...It walks users through core concepts such as ReAct-style reasoning, tool usage, memory handling, and multi-step task execution, enabling hands-on experimentation with modern LLM-powered agent systems. The repository is structured as a progressive learning path, combining theory, exercises, and runnable code so users can incrementally build more capable agents. Its goal is to demystify agent engineering and help developers move from simple prompt scripts to robust autonomous systems. -
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Magika
Fast and accurate AI powered file content types detection
...Magika ships as a command-line tool and a library, providing drop-in detection that improves on traditional “magic number” and heuristic approaches, especially for ambiguous or short files. The project documentation highlights how the model is trained and optimized, and how its inference path enables millisecond-level classification. It also emphasizes reproducibility and developer ergonomics with clear install and usage instructions for common platforms. A public site complements the repo with background, examples, and guidance for integrating Magika into existing workflows. -
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CutLER
Code release for Cut and Learn for Unsupervised Object Detection
...The codebase provides training and inference scripts, model configs, and references to benchmarking results that report large gains over prior unsupervised baselines. It’s intended for researchers exploring self-supervised and unsupervised recognition, offering a practical path to scale beyond costly labeled corpora. The README links papers and gives a high-level overview of components and expected outputs, with pointers to demos and assets. The repository is actively starred and structured as a typical research release with license, contribution guidelines, and security policy. -
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AutoAgent
AutoAgent: Fully-Automated and Zero-Code LLM Agent Framework
...It also describes resource orchestration and iterative self-improvement behaviors, including controlled code generation for building tools and agent capabilities when needed. The project is designed to work with multiple LLM providers and model endpoints, allowing users to choose different backends by setting environment variables and model identifiers.