Search Results for "scratch"
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Showing 805 open source projects for "scratch"
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Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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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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LLMs-from-scratch
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
LLMs-from-scratch is an educational codebase that walks through implementing modern large-language-model components step by step. It emphasizes building blocks—tokenization, embeddings, attention, feed-forward layers, normalization, and training loops—so learners understand not just how to use a model but how it works internally. The repository favors clear Python and NumPy or PyTorch implementations that can be run and modified without heavyweight frameworks obscuring the logic. ... -
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LLM From Scratch
Build and train a GPT-style language model
LLM From Scratch is a hands-on educational workshop project that teaches developers how to build and train a GPT-style language model entirely from scratch using PyTorch. Instead of relying on high-level abstractions or prebuilt frameworks, the project walks users through implementing every core component manually, including tokenization, transformer architecture, training loops, and autoregressive text generation. -
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RAG from Scratch
Demystify RAG by building it from scratch
RAG From Scratch is an educational open-source project designed to teach developers how retrieval-augmented generation systems work by building them step by step. Instead of relying on complex frameworks or cloud services, the repository demonstrates the entire RAG pipeline using transparent and minimal implementations. The project walks through key concepts such as generating embeddings, building vector databases, retrieving relevant documents, and integrating the retrieved context into language model prompts. ... -
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Train LLM From Scratch
A straightforward method for training your LLM
Train LLM From Scratch is an educational PyTorch project that shows how to build and train a transformer-based language model from the ground up. It is based on the architecture described in Attention Is All You Need and is designed to make the training pipeline understandable rather than hidden behind a large framework. The repository walks through the process from downloading data to generating text with a trained model. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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AI Engineering from Scratch
Learn it. Build it. Ship it for others
AI Engineering from Scratch is a comprehensive open-source curriculum designed to teach artificial intelligence by building every component from first principles rather than relying on prebuilt frameworks. The project is structured into more than 20 phases and hundreds of lessons, covering topics that range from foundational mathematics to advanced systems such as large language models, retrieval pipelines, and multi-agent architectures. -
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AI Agents From Scratch
Demystify AI agents by building them yourself. Local LLMs
AI Agents from Scratch is an educational repository designed to teach developers how to build autonomous AI agents using large language models and modern AI frameworks. The project walks through the process of constructing agents step by step, beginning with simple prompt-based interactions and gradually introducing more advanced capabilities such as planning, tool use, and memory. -
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llms-from-scratch-cn
Build a large language model from 0 only with Python foundation
llms-from-scratch-cn is an educational open-source project designed to teach developers how to build large language models step by step using practical code and conceptual explanations. The repository provides a hands-on learning path that begins with the fundamentals of natural language processing and gradually progresses toward implementing full GPT-style architectures from the ground up. -
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All RL Algorithms from Scratch
Implementation of all RL algorithms in a simpler way
All RL Algorithms from Scratch is an educational reinforcement learning repository built around readable Python and Jupyter Notebook implementations. Its goal is to help learners understand how major reinforcement learning algorithms work under the hood instead of hiding the logic behind large frameworks. The project includes notebooks for value-based methods, policy-gradient methods, actor-critic algorithms, model-based learning, multi-agent reinforcement learning, planning, and hierarchical approaches. ... -
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undici
An HTTP/1.1 client, written from scratch for Node.js
An HTTP/1.1 client, written from scratch for Node.js. This section documents our most commonly used API methods. Additional APIs are documented in their own files within the docs folder and are accessible via the navigation list on the left side of the docs site. Garbage collection in Node is less aggressive and deterministic (due to the lack of clear idle periods that browsers have through the rendering refresh rate) which means that leaving the release of connection resources to the garbage collector can lead to excessive connection usage, reduced performance (due to less connection re-use), and even stalls or deadlocks when running out of connections. -
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Pony OS
My Little Unix: Kernels are Magic
ponyos is a lightweight, UNIX-like operating system developed as a personal hobby project by the creator of ToaruOS. It features a monolithic kernel written in C and aims for a POSIX-compliant environment with a built-from-scratch userland. ponyos includes a graphical interface, dynamic linker, shared libraries, and a package manager. It’s designed for fun, experimentation, and as a platform to learn and play with operating system internals, with surprisingly advanced features given its hobbyist origin. -
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Bun
Incredibly fast JavaScript runtime, bundler, test runner
...Bun is an all-in-one JavaScript runtime & toolkit designed for speed, complete with a bundler, test runner, and Node. js-compatible package manager. Bun is a new JavaScript runtime built from scratch to serve the modern JavaScript ecosystem. Bun starts fast and runs fast. It extends JavaScriptCore, the performance-minded JS engine built for Safari. As computing moves to the edge, this is critical. Bun provides a minimal set of highly optimized APIs for performing common tasks, like starting an HTTP server and writing files. ... -
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Budibase
Create business apps and automate workflows in minutes
...Budibase pulls data from multiple sources, including MongoDB, CouchDB, PostgreSQL, MariaDB, MySQL, Airtable, S3, DynamoDB, or a REST API. And unlike other platforms, with Budibase you can start from scratch and create business apps with no data sources. Request new data sources. -
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YOLOv9
Learning What You Want to Learn Using Programmable Gradient Info
...It is a modern object detection repository focused on improving how deep networks preserve useful information during training. The project introduces Programmable Gradient Information and the GELAN architecture to improve gradient flow, parameter efficiency, and train-from-scratch performance. It provides scripts and model assets for training, testing, and running inference on detection tasks. YOLOv9 is designed for real-time detection scenarios where both accuracy and efficiency matter. It is especially relevant for researchers and engineers comparing next-generation YOLO architectures or building production computer vision systems. -
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MiniMind
Train a 26M-parameter GPT from scratch in just 2h
minimind is a framework that enables users to train a 26-million-parameter GPT (Generative Pre-trained Transformer) model from scratch in approximately two hours. It provides a streamlined process for data preparation, model training, and evaluation, making it accessible for individuals and organizations to develop their own language models without extensive computational resources. -
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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 quickly or reproduce the released model setup more closely. ... -
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Minosoft
An open source Minecraft reimplementation written from scratch
Minosoft is an open-source reimplementation of the Minecraft client written primarily in Kotlin and Java. Rather than modifying the official client, the project attempts to recreate the functionality of the game from scratch while maintaining compatibility with Minecraft servers. The goal of Minosoft is to provide a more flexible and stable architecture that developers can experiment with while exploring alternative implementations of the Minecraft client. Because it is written with modern programming tools and languages, the project aims to improve reliability and extendability compared with traditional client software. ... -
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Zed
High-performance, multiplayer code editor from the creators of Atom
Zed is a next-generation code editor designed for high-performance collaboration with humans and AI. Written from scratch in Rust to efficiently leverage multiple CPU cores and your GPU. Integrate upcoming LLMs into your workflow to generate, transform, and analyze code. Chat with teammates, write notes together, and share your screen and project. Multibuffers compose excerpts from across the codebase in one editable surface. Evaluate code inline via Jupyter runtimes and collaboratively edit notebooks. ... -
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CopilotKit
Build in-app AI chatbots, and AI-powered Text areas
...The AI chatbot can talk to your app frontend & backend, and to 3rd party services (Salesforce, Dropbox, etc.) via plugins. Autocompletion + AI editing + generate from scratch. Indexed on your users' content. -
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WooCommerce
An open source eCommerce plugin for WordPress
...Create products, process orders, and keep an eye on key stats in real-time. WooCommerce is developer friendly, too. Built with a REST API, WooCommerce is scalable and can integrate with virtually any service. Design a complex store from scratch, extend a store for a client, or simply add a single product to a WordPress site. Your store, your way. -
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GPT All Star
AI-powered code generation tool for scratch development of web apps
AI-powered code generation tool for scratch development of web applications with a team collaboration of autonomous AI agents. This is a research project, and its primary value is to explore the possibility of autonomous AI agents. -
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Happy-LLM
Large Language Model Principles and Practice Tutorial from Scratch
Happy-LLM is an open-source educational project created by the Datawhale AI community that provides a structured and comprehensive tutorial for understanding and building large language models from scratch. The project guides learners through the entire conceptual and practical pipeline of modern LLM development, starting with foundational natural language processing concepts and gradually progressing to advanced architectures and training techniques. It explains the Transformer architecture, pre-training paradigms, and model scaling strategies while also providing hands-on coding examples so readers can implement and experiment with their own models. ... -
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os-tutorial
How to create an OS from scratch
os-tutorial is an open source educational project by cfenollosa that teaches the basics of building an operating system from scratch. The repository provides step-by-step lessons starting with bootloaders and moving through kernel development, interrupts, memory management, and system calls. Each tutorial is accompanied by clear explanations, code examples, and references to deepen understanding. The project uses x86 assembly and C to illustrate concepts, making it accessible to students and hobbyists interested in low-level programming. ... -
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QtScrcpy
Android real-time display control software
...If you are interested in it and want to learn how it works but do not know how to get started, you can choose to purchase my recorded video lessons. It details the development architecture and the development process of the entire software, and help you develop QtScrcpy from scratch. -
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Hermes Agent
The agent that grows with you
...Rather than functioning as a stateless chatbot, it maintains long-term memory across sessions and can generate searchable “Skill Documents” that capture how it solved complex tasks so it doesn’t start from scratch each time. The agent interfaces with messaging platforms like Telegram, Discord, Slack, and WhatsApp through a single gateway process, and also offers an interactive terminal user interface with history, autocomplete, and streamable tool output. It supports scheduled automation in natural language, allowing users to set up recurring tasks such as daily briefings or system audits that it runs unattended. -
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Karpathy-Inspired Claude Code Guidelines
A single CLAUDE.md file to improve Claude Code behavior
...The project organizes a progressive path through exercises, notebooks, code examples, and practical mini-projects that echo Karpathy’s approach to “learning by doing,” where students build core concepts from first principles rather than consuming superficial abstractions. It covers topics like implementing backpropagation from scratch, understanding convolutional and recurrent networks, building simple training loops, and exploring real datasets with hands-on code. This collection makes abstract theoretical ideas concrete by walking learners through real code and tangible outcomes, helping demystify parts of machine learning that often feel opaque in purely textbook settings.