Terra Classic Engineers Working on Major Revival Plan with ‘Six Samurai’ Proposal

The ‘Six Samurai’ proposal presents roadmap to important components such as the migration from Columbus-5 to Columbus-6 and an upgrade to the Cosmos SDK.

Six engineers working on the Terra Classic ecosystem have proposed a ‘Six Samurai’ plan for the complete revival of the blockchain ecosystem. This development comes as some community members of Terra have been looking to break away entirely from the shadows of disgraced founder Do Kwon and bring new life to the LUNC ecosystem.

Terra Classic, the original network developed by Terraform Labs, has remained as an autonomous blockchain instead of transitioning to Terra 2.0, which emerged as a separate version following Terra’s downfall. As of Monday, June 26, the LUNC tokens on Terra Classic are currently valued at $580 million.

Terra Classic engineers co-led by “Solid Snake” and “Bilbo Baggins” submitted a governance proposal to the Terra Classic community with a $116,000 three-month spend. If approved by the community, these engineers would work part-time on the project.

The proposal represents an ambitious roadmap put forward by passionate LUNC holders. The roadmap includes important components such as the migration from Columbus-5 to Columbus-6 and an upgrade to the Cosmos SDK. Additionally, the proposal outlines plans to list Terra Classic on Keplr’s Web Interface, which offers analytic visualizations, as well as on Mintscan, a block explorer for Cosmos. In the proposal, the Samurai Six engineers noted:

“LUNC has limitless upside potential, and we want to help realize it by leveraging our skills to bring value to the blockchain and all its investors in order to accomplish a true revival of the ecosystem. We’re here to provide value to the ecosystem on its road to revival, and we see ourselves as contributors to the Terra Classic blockchain, both as developers and long-time community members/investors.”

Six Samurai Propose a Plan for Testnet for TerraUSD (USTC)

Some additional tasks that the proposal includes are upgrading the network in order to reduce the syncing times between the nodes. It also includes building a testnet for the TerraUSD (USTC) stablecoin for testing financial services.

Furthermore, there’s a plan for building an application for generating yield to token holders along with a plan to reward developers for the user activity that their applications generate.

The Terra Luna Classic community is currently voting on a proposal called “USTC / Stable Algo Quant Team.” The proposal aims to incrementally adjust and simulate the TerraClassicUSD (USTC) repeg. Initial voting shows support from the community and the Quant team developers who will be working on setting the USTC repeg to $1. The Joint L1 Task Force (L1TF) developer team will also assist the Quant team in this repeg process.

A team member named RedlineDrifter shared on Twitter that Proposal 11597 is up for voting. This proposal is crucial for the Terra Luna Classic chain, and other community members are also spreading awareness about the USTC repeg proposal.

According to the proposal, the Quant team requires a payment of $20,000 in LUNC for the first month. The Quant team consists of members like RedlineDrifter, Faffy, Alex, Bilbo Baggins, and Kyjack. Additionally, Terra Classic’s core developer and professor Edward Kim will volunteer to contribute to the team by assisting in AI research related to backtesting and USTC repeg.

The Quant team’s objective is to improve an existing tool, conduct a thorough analysis, and provide valuable insights into the strengths and weaknesses of the algorithm.


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Bhushan Akolkar

Bhushan is a FinTech enthusiast and holds a good flair in understanding financial markets. His interest in economics and finance draw his attention towards the new emerging Blockchain Technology and Cryptocurrency markets. He is continuously in a learning process and keeps himself motivated by sharing his acquired knowledge. In free time he reads thriller fictions novels and sometimes explore his culinary skills.

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