🔥 rasbt/LLMs-from-scratch is trending — and it deserves your attention.
🔗 https://github.com/rasbt/LLMs-from-scratch
📝 Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
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The rasbt/LLMs-from-scratch GitHub repository is an open-source project that allows users to build a large language model (LLM) from scratch. This repository is the official code repository for the book "Build a Large Language Model (From Scratch)".
It provides a step-by-step guide on how to develop, pretrain, and finetune a
The project includes a range of features, such as code for loading the weights of larger pretrained models for finetuning, and a
The repository is suitable for users with a strong foundation in
The project's code is well-structured, with each chapter of the book including several exercises and solutions.
In short, this project is perfect for anyone looking to dive into the world of LLMs and build their own model from scratch - and with this repository, you can literally build a large language model from scratch, one line of code at a time!
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/rasbt/LLMs-from-scratch
📝 Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
──────────────────────────────
The rasbt/LLMs-from-scratch GitHub repository is an open-source project that allows users to build a large language model (LLM) from scratch. This repository is the official code repository for the book "Build a Large Language Model (From Scratch)".
It provides a step-by-step guide on how to develop, pretrain, and finetune a
GPT-like LLM. The code is designed to run on conventional laptops and automatically utilizes GPUs if available. The project includes a range of features, such as code for loading the weights of larger pretrained models for finetuning, and a
troubleshooting guide for common issues. The repository is suitable for users with a strong foundation in
Python programming and some experience with deep neural networks. The project's code is well-structured, with each chapter of the book including several exercises and solutions.
In short, this project is perfect for anyone looking to dive into the world of LLMs and build their own model from scratch - and with this repository, you can literally build a large language model from scratch, one line of code at a time!
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🧠 Channel: https://t.iss.one/GithubRe
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📌 Spotted on GitHub Trending: ton-blockchain/acton — let's break it down.
🔗 https://github.com/ton-blockchain/acton
📝 Toolchain for TON smart contract development and beyond
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Introducing Acton, an all-in-one TON smart contract development toolkit written in Rust. It combines project scaffolding, build, testing, scripting, wallet and network operations, verification, linting, formatting, debugging, and low-level VM tooling in one CLI.
Key features include a single CLI for the full contract lifecycle, native speed, and a Tolk-first workflow with built-in wrappers, testing utilities, and scripts. Acton is also ready for dApp development with project templates and automatically generated TypeScript wrappers.
To get started with Acton, you can install it using the latest public installer with the following
Technical highlights include a fast test runner with fork mode, gas snapshots, coverage, mutation, and fuzzing testing. Acton also has a browser test UI for failed tests, traces, logs, and coverage inspection.
Acton is suitable for developers looking to build and deploy TON smart contracts. With its comprehensive set of features and ease of use, Acton is a great choice for anyone looking to get started with TON development.
In short, Acton is the ultimate toolkit for TON smart contract development - build, test, and deploy with ease.
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/ton-blockchain/acton
📝 Toolchain for TON smart contract development and beyond
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Introducing Acton, an all-in-one TON smart contract development toolkit written in Rust. It combines project scaffolding, build, testing, scripting, wallet and network operations, verification, linting, formatting, debugging, and low-level VM tooling in one CLI.
Key features include a single CLI for the full contract lifecycle, native speed, and a Tolk-first workflow with built-in wrappers, testing utilities, and scripts. Acton is also ready for dApp development with project templates and automatically generated TypeScript wrappers.
To get started with Acton, you can install it using the latest public installer with the following
curl command: curl -LsSf https://github.com/ton-blockchain/acton/releases/latest/download/acton-installer.sh | sh. After installation, make sure acton is on your PATH and verify the installation with acton --version.Technical highlights include a fast test runner with fork mode, gas snapshots, coverage, mutation, and fuzzing testing. Acton also has a browser test UI for failed tests, traces, logs, and coverage inspection.
Acton is suitable for developers looking to build and deploy TON smart contracts. With its comprehensive set of features and ease of use, Acton is a great choice for anyone looking to get started with TON development.
In short, Acton is the ultimate toolkit for TON smart contract development - build, test, and deploy with ease.
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🧠 Channel: https://t.iss.one/GithubRe
Github Top Repositories
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💡 trycua/cua just hit the trending charts — here's why it matters.
🔗 https://github.com/trycua/cua
📝 Open-source infrastructure for Computer-Use Agents. Sandboxes, SDKs, and benchmarks to train and evaluate AI agents that can control full desktops (macOS, Linux, Windows).
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The trycua/cua GitHub repository is a game-changer for anyone interested in building, benchmarking, and deploying agents that interact with computers. At its core, Cua provides a versatile platform for creating autonomous agents that can perform tasks on various operating systems, including macOS, Linux, Windows, and Android.
One of the key features is the
To get started, users can install
The technical highlights of Cua include its support for multiple platforms, near-native performance on Apple Silicon, and a wide range of tools and libraries for building and deploying agents. The project is well-documented, with extensive guides, examples, and API references available.
The target audience for Cua includes developers, researchers, and anyone interested in building autonomous agents for computer-use tasks. With its open-source license and active community, Cua is an exciting project that has the potential to revolutionize the way we interact with computers.
In a nutshell, Cua is a powerful platform for building autonomous agents that can interact with computers in a variety of ways, and its potential impact on the field of AI and computer science is enormous: Cua is not just a tool, it's a new paradigm for human-computer interaction.
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/trycua/cua
📝 Open-source infrastructure for Computer-Use Agents. Sandboxes, SDKs, and benchmarks to train and evaluate AI agents that can control full desktops (macOS, Linux, Windows).
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The trycua/cua GitHub repository is a game-changer for anyone interested in building, benchmarking, and deploying agents that interact with computers. At its core, Cua provides a versatile platform for creating autonomous agents that can perform tasks on various operating systems, including macOS, Linux, Windows, and Android.
One of the key features is the
cua-driver, which allows agents to interact with native macOS apps in the background, enabling tasks like clicking, typing, and verifying without interrupting the user. The cua package provides a unified API for building sandboxes on any OS or container image, making it easy to develop and deploy agents across different environments.To get started, users can install
cua using pip install cua and explore the various tools and libraries, including cuabot for co-op computer-use, cua-bench for benchmarks and RL environments, and lume for macOS virtualization.The technical highlights of Cua include its support for multiple platforms, near-native performance on Apple Silicon, and a wide range of tools and libraries for building and deploying agents. The project is well-documented, with extensive guides, examples, and API references available.
The target audience for Cua includes developers, researchers, and anyone interested in building autonomous agents for computer-use tasks. With its open-source license and active community, Cua is an exciting project that has the potential to revolutionize the way we interact with computers.
In a nutshell, Cua is a powerful platform for building autonomous agents that can interact with computers in a variety of ways, and its potential impact on the field of AI and computer science is enormous: Cua is not just a tool, it's a new paradigm for human-computer interaction.
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🧠 Channel: https://t.iss.one/GithubRe
Github Top Repositories
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🔍 Deep-diving into github/spec-kit — fresh off the trending list.
🔗 https://github.com/github/spec-kit
📝 💫 Toolkit to help you get started with Spec-Driven Development
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The Spec Kit is an open-source toolkit that enables developers to focus on product scenarios and predictable outcomes, rather than writing every piece of code from scratch. It flips the script on traditional software development by making
To get started with Spec Kit, you can
Spec Kit also offers a range of community extensions that can be used to extend its functionality, including
The target audience for Spec Kit appears to be developers and development teams who want to streamline their software development process and focus on creating high-quality products.
One-liner takeaway: With Spec Kit, developers can build high-quality software faster by focusing on product scenarios and executable specifications, rather than writing every piece of code from scratch.
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🧠 Channel: https://t.iss.one/GithubRe
🔗 https://github.com/github/spec-kit
📝 💫 Toolkit to help you get started with Spec-Driven Development
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The Spec Kit is an open-source toolkit that enables developers to focus on product scenarios and predictable outcomes, rather than writing every piece of code from scratch. It flips the script on traditional software development by making
specifications executable, directly generating working implementations. To get started with Spec Kit, you can
install the Specify CLI using uv tool install or pipx install. Then, initialize a new project with specify init, establish project principles with /speckit.constitution, and create a spec with /speckit.specify. Spec Kit also offers a range of community extensions that can be used to extend its functionality, including
Agent Assign, AI-Driven Engineering (AIDE), and API Evolve. These extensions can be browsed and searched on the Community Extensions website.The target audience for Spec Kit appears to be developers and development teams who want to streamline their software development process and focus on creating high-quality products.
One-liner takeaway: With Spec Kit, developers can build high-quality software faster by focusing on product scenarios and executable specifications, rather than writing every piece of code from scratch.
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🧠 Channel: https://t.iss.one/GithubRe