Convert Figma logo to code with AI

rome logotools

Unified developer tools for JavaScript, TypeScript, and the web

23,768
663
23,768
94
View on GitHubView on NPM

Top Related Projects

meta-llama's avatar

llama

55,392

Inference code for Llama models

huggingface's avatar

transformers

131,798

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

microsoft's avatar

DeepSpeed

34,658

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

pytorch's avatar

pytorch

82,049

Tensors and Dynamic neural networks in Python with strong GPU acceleration

google's avatar

jax

29,761

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Quick Overview

The rome/tools repository is a collection of tools and utilities developed by the Rome team. It includes a linter, a formatter, and a bundler, all designed to improve the developer experience for JavaScript and TypeScript projects.

Pros

  • Comprehensive Toolset: The repository provides a suite of tools that cover various aspects of the development workflow, including linting, formatting, and bundling.
  • Opinionated Defaults: The tools come with sensible default configurations, making it easy to get started without extensive setup.
  • Actively Maintained: The project is actively maintained by the Rome team, ensuring regular updates and bug fixes.
  • Cross-Platform Compatibility: The tools are designed to work seamlessly across different operating systems, including Windows, macOS, and Linux.

Cons

  • Steep Learning Curve: The tools may have a steeper learning curve compared to more established alternatives, especially for developers who are new to the Rome ecosystem.
  • Limited Ecosystem Integration: The Rome tools may not integrate as seamlessly with existing toolchains and workflows as some other popular options.
  • Potential Performance Overhead: Depending on the size and complexity of the project, the Rome tools may have a higher performance overhead compared to some lighter-weight alternatives.
  • Adoption Challenges: As a relatively new set of tools, the Rome ecosystem may face adoption challenges compared to more established options in the JavaScript/TypeScript community.

Code Examples

The rome/tools repository provides several tools, including a linter, a formatter, and a bundler. Here are a few examples of how to use these tools:

Linting

// Lint a single file
rome check src/index.js

// Lint an entire directory
rome check src/

The rome check command runs the Rome linter on the specified file or directory, checking for any code style or syntax issues.

Formatting

// Format a single file
rome format src/index.js

// Format an entire directory
rome format src/

The rome format command runs the Rome formatter on the specified file or directory, automatically fixing any code formatting issues.

Bundling

// Bundle a project
rome bundle src/index.js --out-file dist/bundle.js

The rome bundle command bundles the specified entry point file, along with its dependencies, into a single output file.

Getting Started

To get started with the rome/tools repository, follow these steps:

  1. Install the Rome tools by running the following command:

    npm install -g @rome/tools

  2. Initialize a new Rome configuration file in your project directory:

    rome init

    This will create a .rome.json file with the default configuration.

  3. Run the linter on your project:

    rome check .

    This will check your codebase for any linting issues.

  4. Format your code using the Rome formatter:

    rome format .

    This will automatically fix any code formatting issues.

  5. Bundle your project using the Rome bundler:

    rome bundle src/index.js --out-file dist/bundle.js

    This will create a bundled version of your project in the dist directory.

  6. Customize the Rome configuration by editing the .rome.json file to fit your project's needs.

That's it! You're now ready to use the Rome tools to improve your JavaScript and TypeScript development workflow.

Competitor Comparisons

meta-llama logo

llama

55,392

Inference code for Llama models

Pros of Llama

  • Focuses on large language models, offering cutting-edge AI capabilities
  • Provides pre-trained models and fine-tuning scripts for various NLP tasks
  • Supports multiple programming languages and frameworks

Cons of Llama

  • Limited to language model tasks, less versatile for general development
  • Requires significant computational resources for training and inference
  • May have more complex setup and usage compared to Rome

Code Comparison

Llama (Python):

from llama import Llama

model = Llama.load("path/to/model")
output = model.generate("Hello, how are you?")
print(output)

Rome (JavaScript):

import { lint } from "@rometools/js-api";

const diagnostics = await lint("code.js");
console.log(diagnostics);

Summary

Llama is specialized for large language models and AI tasks, offering powerful NLP capabilities but requiring more resources. Rome is a broader development toolset, focusing on linting, formatting, and code quality across multiple languages. Llama is ideal for AI researchers and developers working on natural language processing, while Rome is better suited for general-purpose development and maintaining code quality across projects.

huggingface logo

transformers

131,798

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Pros of Transformers

  • Extensive library of pre-trained models for various NLP tasks
  • Active community with frequent updates and contributions
  • Comprehensive documentation and tutorials

Cons of Transformers

  • Larger codebase and potentially steeper learning curve
  • Focused primarily on NLP, less versatile for other domains
  • Higher computational requirements for some models

Code Comparison

Transformers:

from transformers import pipeline

classifier = pipeline("sentiment-analysis")
result = classifier("I love this product!")[0]
print(f"Label: {result['label']}, Score: {result['score']:.4f}")

Tools:

use rome_js_parser::parse_module;
use rome_js_formatter::format;

let source_code = "function greet() { console.log('Hello, world!'); }";
let parsed = parse_module(source_code).unwrap();
let formatted = format(&parsed).unwrap();
println!("{}", formatted);

Summary

Transformers excels in NLP tasks with its vast model library and active community, while Tools focuses on developer tooling and code analysis. Transformers may require more resources and has a steeper learning curve, whereas Tools offers a more lightweight solution for specific development tasks. The code examples demonstrate their different use cases: Transformers for NLP tasks and Tools for code parsing and formatting.

microsoft logo

DeepSpeed

34,658

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Pros of DeepSpeed

  • Highly optimized for large-scale distributed training of deep learning models
  • Offers advanced features like ZeRO optimizer and pipeline parallelism
  • Extensive documentation and integration with popular frameworks like PyTorch

Cons of DeepSpeed

  • Steeper learning curve due to its focus on advanced optimization techniques
  • May be overkill for smaller projects or simpler machine learning tasks
  • Requires more setup and configuration compared to Rome

Code Comparison

DeepSpeed:

import deepspeed
model_engine, optimizer, _, _ = deepspeed.initialize(args=args,
                                                     model=model,
                                                     model_parameters=params)

Rome:

import { lint } from "@rometools/js-api";
const diagnostics = await lint(source);

Summary

DeepSpeed is a powerful tool for optimizing large-scale deep learning models, offering advanced features and extensive documentation. However, it may be more complex for simpler projects. Rome, on the other hand, is a lightweight linter and formatter focused on JavaScript and TypeScript development, providing a simpler setup and usage for web development tasks.

pytorch logo

pytorch

82,049

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Pros of PyTorch

  • Extensive ecosystem and community support
  • Comprehensive documentation and tutorials
  • Wide range of pre-trained models and datasets

Cons of PyTorch

  • Steeper learning curve for beginners
  • Larger codebase and installation size
  • More complex setup for certain use cases

Code Comparison

PyTorch:

import torch

x = torch.tensor([1, 2, 3])
y = torch.tensor([4, 5, 6])
z = torch.add(x, y)

Rome:

import { add } from "@rometools/js-api";

const x = [1, 2, 3];
const y = [4, 5, 6];
const z = add(x, y);

Summary

PyTorch is a powerful deep learning framework with a vast ecosystem, while Rome is a lightweight toolchain for JavaScript and TypeScript development. PyTorch offers more extensive machine learning capabilities, but Rome provides a simpler and more focused toolset for web development. The choice between them depends on the specific project requirements and the developer's expertise in their respective domains.

google logo

jax

29,761

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Pros of JAX

  • Designed for high-performance numerical computing and machine learning
  • Supports automatic differentiation and GPU/TPU acceleration
  • Integrates well with NumPy and offers a familiar API for Python developers

Cons of JAX

  • Steeper learning curve for developers not familiar with functional programming concepts
  • Limited ecosystem compared to more established machine learning frameworks
  • May require more boilerplate code for certain operations

Code Comparison

JAX example:

import jax.numpy as jnp
from jax import grad, jit

def f(x):
    return jnp.sum(jnp.sin(x))

grad_f = jit(grad(f))

Rome example:

import { lint } from "@rome/tools";

const diagnostics = await lint(files, {
  formatter: {
    enabled: true,
  },
});

Summary

JAX is focused on numerical computing and machine learning, offering high performance and GPU acceleration. Rome is a toolchain for JavaScript and TypeScript development, providing linting, formatting, and other developer tools. While they serve different purposes, both aim to improve developer productivity in their respective domains.

Convert Figma logo designs to code with AI

Visual Copilot

Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.

Try Visual Copilot

README

[!IMPORTANT]

Welcome to Biome, the community successor of Rome!

[!WARNING]

Rome won't be maintained anymore by the same people that maintained it so far. Biome will provide new features and fixes.

Rome's logo depicting an ancient Roman arch with the word Rome to its side

MIT licensed Discord chat CI on main npm version VSCode version

Rome is a formatter, linter, bundler, and more for JavaScript, TypeScript, JSON, HTML, Markdown, and CSS.

Rome is designed to replace Babel, ESLint, webpack, Prettier, Jest, and others.

Rome unifies functionality that has previously been separate tools. Building upon a shared base allows us to provide a cohesive experience for processing code, displaying errors, parallelizing work, caching, and configuration.

Rome has strong conventions and aims to have minimal configuration. Read more about our project philosophy.

Rome is written in Rust.

Rome has first-class IDE support, with a sophisticated parser that represents the source text in full fidelity and top-notch error recovery.

Rome is MIT licensed and moderated under the Contributor Covenant Code of Conduct.

Documentation

Check out our homepage to learn more about Rome, or directly head to the Getting Started guide if you want to start using Rome.

Technical documentation

Browse Rome's internal Rust API Documentation if you're interested to learn more about how Rome works.