KasarLabs / barknet

https://kasar.io
MIT License
13 stars 0 forks source link

Barknet: Bitcoin Sovereign Rollup

A sovereign rollup based on the Madara engine and powered by Starknet. Proudly developed by Kasar Labs in collaboration with Taproot Wizards.

Quick Links

Getting Started

For detailed guidelines on setting up and utilizing Barknet, please refer to the official documentation provided in the repository.

Contribute

📣 Building App Chains

Do NOT fork this repo and build your app chain on top unless completely necessary. By adding changes using forking, you will have to periodically rebase (and solve conflicts) to remain updated with the latest version of Madara.

One of the main features of Madara is to allow users to start their app chains that support Cairo contracts and Starknet like blocks. Hence, to make it easy for users to build a custom app chain, we have created an app-chain-template which imports Madara as a pallet. This removes all the boilerplate code and allows you to focus on code only relevant to your app chain. Moreover, updating Madara is as simple as updating the pallet version.

License

Barknet is licensed under the Apache 2.0 license.


Let's scale Bitcoin togethers! 🚀 Get started with our comprehensive documentation, which covers everything from project structure and architecture to benchmarking and running Madara:

🏗️ Build & Run

Want to dive straight in? Check out our Getting Started Guide for instructions on how to build and run Madara on your local machine.

Benchmarking

Benchmarking is an essential process in our project development lifecycle, as it helps us to track the performance evolution of Madara over time. It provides us with valuable insights into how well Madara handles transaction throughput, and whether any recent changes have impacted performance.

You can follow the evolution of Madara's performance by visiting our Benchmark Page.

However, it's important to understand that the absolute numbers presented on this page should not be taken as the reference or target numbers for a production environment. The benchmarks are run on a self-hosted GitHub runner, which may not represent the most powerful machine configurations in real-world production scenarios.

Therefore, these numbers primarily serve as a tool to track the relative performance changes over time. They allow us to quickly identify and address any performance regressions, and continuously optimize the system's performance.

In other words, while the absolute throughput numbers may not be reflective of a production environment, the relative changes and trends over time are what we focus on. This way, we can ensure that Madara is always improving, and that we maintain a high standard of performance as the project evolves.

One can use flamegraph-rs to generate flamegraphs and look for the performance bottlenecks of the system by running the following :

./target/release/madara setup
flamegraph --root --open  -- ./target/release/madara run --dev

In parallel to that, run some transactions against your node (you can use Gomu Gomu no Gatling benchmarker). Once you stop the node, the flamegraph will open in your browser.

🌐 Connect to the dev webapp

Once your Madara node is up and running, you can connect to our Dev Frontend App to interact with your chain. Connect here!

🤝 Contribute

We're always looking for passionate developers to join our community and contribute to Madara. Check out our contributing guide for more information on how to get started.

📖 License

This project is licensed under the MIT license.

See LICENSE for more information.

Happy coding! 🎉

Contributors ✨

Thanks goes to these wonderful people (emoji key):

Abdel @ StarkWare
Abdel @ StarkWare

💻
Timothée Delabrouille
Timothée Delabrouille

💻
0xevolve
0xevolve

💻
Lucas @ StarkWare
Lucas @ StarkWare

💻
Davide Silva
Davide Silva

💻
Finiam
Finiam

💻
Resende
Resende

💻
drspacemn
drspacemn

💻
Tarrence van As
Tarrence van As

💻
Siyuan Han
Siyuan Han

📖
Zé Diogo
Zé Diogo

💻
Matthias Monnier
Matthias Monnier

💻
glihm
glihm

💻
Antoine
Antoine

💻
Clément Walter
Clément Walter

💻
Elias Tazartes
Elias Tazartes

💻
Jonathan LEI
Jonathan LEI

💻
greged93
greged93

💻
Santiago Galván (Dub)
Santiago Galván (Dub)

💻
ftupas
ftupas

💻
Paul-Henry Kajfasz
Paul-Henry Kajfasz

💻
chirag-bgh
chirag-bgh

💻
danilowhk
danilowhk

💻
Harsh Bajpai
Harsh Bajpai

💻
amanusk
amanusk

💻
Damián Piñones
Damián Piñones

💻
marioiordanov
marioiordanov

💻
Daniel Bejarano
Daniel Bejarano

💻
sparqet
sparqet

💻
Robin Straub
Robin Straub

💻
tedison
tedison

💻
lanaivina
lanaivina

💻
Oak
Oak

💻
Pia
Pia

💻
apoorvsadana
apoorvsadana

💻
Francesco Ceccon
Francesco Ceccon

💻
ptisserand
ptisserand

💻
Zizou
Zizou

💻
V.O.T
V.O.T

💻
Abishek Bashyal
Abishek Bashyal

💻
Ammar Arif
Ammar Arif

💻
lambda-0x
lambda-0x

💻
exp_table
exp_table

💻
Pilou
Pilou

💻
hithem
hithem

💻
Chris Lexmond
Chris Lexmond

💻
Tidus91
Tidus91

💻
Veronika S
Veronika S

💻
Asten
Asten

💻
ben2077
ben2077

💻
Michael Zaikin
Michael Zaikin

💻
João Pereira
João Pereira

📖
kasteph
kasteph

💻
Ayush Tomar
Ayush Tomar

💻
tchataigner
tchataigner

💻

This project follows the all-contributors specification. Contributions of any kind welcome!