Search Results for "scratch"
Sort By:
261 projects for "scratch" with 1 filter applied:
-
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
$300 in Free Credit Towards Top Cloud ServicesStart your project in minutes. After credits run out, 20+ products include free monthly usage. Only pay when you're ready to scale.
-
1
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. ... -
2
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. -
3
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. -
4
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. -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
-
5
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. -
6
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. ... -
7
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. -
8
SimpleLLM
950 line, minimal, extensible LLM inference engine built from scratch
SimpleLLM is a minimal, extensible large language model inference engine implemented in roughly 950 lines of code, built from scratch to serve both as a learning tool and a research platform for novel inference techniques. It provides the core components of an LLM runtime—such as tokenization, batching, and asynchronous execution—without the abstraction overhead of more complex engines, making it easier for developers and researchers to understand and modify. -
9
S&box
s&box is a modern game engine, built on Valve's Source 2
...The platform emphasizes multiplayer and community experiences, giving creators direct control over networking, physics, rendering, and audio without needing to build those systems from scratch. With real-time recompilation and fast iteration loops, developers can see changes instantly, speeding up the creative process dramatically compared to traditional engines. The sbox ecosystem fosters collaboration, sharing, and extension through packages and community content, enabling a vibrant marketplace of user-created mods and assets. -
Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
-
10
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. ... -
11
DeepSeek Coder
DeepSeek Coder: Let the Code Write Itself
DeepSeek-Coder is a series of code-specialized language models designed to generate, complete, and infill code (and mixed code + natural language) with high fluency in both English and Chinese. The models are trained from scratch on a massive corpus (~2 trillion tokens), of which about 87% is code and 13% is natural language. This dataset covers project-level code structure (not just line-by-line snippets), using a large context window (e.g. 16K) and a secondary fill-in-the-blank objective to encourage better contextual completions and infilling. Multiple sizes of the model are offered (e.g. 1B, 5.7B, 6.7B, 33B) so users can trade off inference cost vs capability. ... -
12
Hello JavaScript
Course repository designed to teach JavaScript from scratch in Spanish
hello-javascript is a comprehensive course repository designed to teach JavaScript from scratch in Spanish, pairing source code with hours of video content and hundreds of exercises. It covers the JavaScript language core, from variables and control flow to functions, objects, arrays, and modern ECMAScript features. The project also touches the browser and Node.js ecosystems so learners can understand both client and server contexts. -
13
Deep Learning Is Nothing
Deep learning concepts in an approachable style
Deep-Learning-Is-Nothing presents deep learning concepts in an approachable, from-scratch style that demystifies the stack behind modern models. It typically begins with linear algebra, calculus, and optimization refreshers before moving to perceptrons, multilayer networks, and gradient-based training. Implementations favor small, readable examples—often NumPy first—to show how forward and backward passes work without depending solely on high-level frameworks. -
14
Google Authenticator PAM Library
Example PAM module demonstrating two-factor authentication
...Because verification happens locally, codes work offline and do not depend on any Google service, which suits high-security and air-gapped environments. Administrators can tune skew tolerance, rate limiting, emergency scratch codes, and which PAM control flags enforce 2FA for specific users or groups. The project’s design favors simplicity and auditability, keeping the runtime small and the configuration explicit so it’s easy to review in production change control. -
15
LTX-2
Python inference and LoRA trainer package for the LTX-2 audio–video
...It is architected to give developers low-level control over rendering pipelines, GPU resource management, shader orchestration, and cross-platform abstractions so they can craft visually compelling experiences without starting from scratch. Beyond basic rendering scaffolding, LTX-2 includes optimized math libraries, resource loaders, utilities for texture and buffer handling, and integration points for native event loops and input systems. The framework targets both interactive graphical applications and media-rich experiences, making it a solid foundation for games, creative tools, or visualization systems that demand both performance and flexibility. ... -
16
Machine learning algorithms
Minimal and clean examples of machine learning algorithms
Machine learning algorithms is an open-source repository that provides minimal and clean implementations of machine learning algorithms written primarily in Python. The project focuses on demonstrating how fundamental machine learning methods work internally by implementing them from scratch rather than relying on high-level libraries. This approach allows learners to study the mathematical and algorithmic details behind widely used models in a transparent and readable way. The repository includes implementations of both supervised and unsupervised learning techniques, along with dimensionality reduction and clustering methods. ... -
17
Turbo Laravel
Conventions to make the most out of Hotwire in Laravel
...In order to help you to get a better understanding of the many sides of Hotwire, we offer a free Bootcamp inspired by the official Laravel Bootcamp. In the Turbo Laravel Bootcamp, you’ll get a hands-on introduction to Hotwire and Turbo Laravel building a web application from scratch and then building the hybrid native app for it. -
18
Yandex Music API
Non-official Python library for works with API service Index
...In addition to implementing a clean API, this library has a number of — high-level wrapping classes in order to make the development of customers and scripts simple and understandable. All documentation was written from scratch based on logical analysis during reverse development (reverse engineering) API. -
19
LLM101n
LLM101n: Let's build a Storyteller
LLM101n is an educational repository that walks you through building and understanding large language models from first principles. It emphasizes intuition and hands-on implementation, guiding you from tokenization and embeddings to attention, transformer blocks, and sampling. The materials favor compact, readable code and incremental steps, so learners can verify each concept before moving on. You’ll see how data pipelines, batching, masking, and positional encodings fit together to train a... -
20
TurboQuant PyTorch
From-scratch PyTorch implementation of Google's TurboQuant
TurboQuant PyTorch is a specialized deep learning optimization framework designed to accelerate neural network inference and training through advanced quantization techniques within the PyTorch ecosystem. The project focuses on reducing the computational and memory footprint of models by converting floating-point representations into lower-precision formats while preserving performance. It provides tools for experimenting with different quantization strategies, enabling developers to balance... -
21
GenericAgent
Self-evolving autonomous agent framework
...The project also focuses on extensibility, allowing developers to plug in custom tools or APIs and tailor agent behavior to specific use cases. By abstracting common agent patterns, it reduces the overhead of building agent systems from scratch. Overall, GenericAgent provides a foundation for scalable and reusable AI agent development. -
22
Coding Interview University
A complete computer science study plan to become a software engineer
...It outlines how to choose one primary programming language, recommends foundational books, and explains an effective daily routine of studying concepts then implementing them from scratch. The plan includes extensive topic coverage from algorithms and data structures to systems fundamentals, plus optional advanced areas for deeper growth. -
23
Neuron AI
The PHP Agentic Framework to build production-ready AI driven apps
...It is designed for developers who want to create systems such as RAG pipelines, multi-agent workflows, and business process automations without having to hand-build every integration from scratch. The framework provides an Agent class that can be extended to inherit core capabilities like memory, tools, function calling, and retrieval-augmented generation. Its design is modular, so developers can swap model providers with minimal changes to their application code, which makes it practical for teams that need flexibility across vendors. ... -
24
Dependabot
Dependabot's core logic for creating update PR's
Dependabot-Core is the library at the heart of Dependabot security/version updates. Use it to generate automated pull requests updating dependencies for projects written in Ruby, JavaScript, Python, PHP, Dart, Elixir, Elm, Go, Rust, Java and .NET. It can also update git submodules, Docker files, and Terraform files. Check for the latest version of a dependency that's resolvable given a project's other dependencies. Generate updated manifest and lockfiles for a new dependency version.... -
25
TorchCode
Practice implementing softmax, attention, GPT-2 and more
TorchCode is an interactive learning and practice platform designed to help developers master PyTorch by implementing core machine learning operations and architectures from scratch. It is structured similarly to competitive programming platforms like LeetCode but focuses specifically on tensor operations and deep learning concepts. The platform provides a collection of curated problems that cover fundamental topics such as activation functions, normalization layers, attention mechanisms, and full transformer architectures. ...
