IMPORTANT POINTS:
- A group of six engineers drew up a plan to get Terra Classic back on track after it collapsed in May 2022.
- The objective is the reactivation of the Terra Classic blockchain ecosystem. For this to happen, the community must first approve it.
- “We are all passionate LUNC holders and community members committed to reviving and decentralizing the blockchain, as we believe that is where the true payoff for all lies,” the engineers stated.
Six engineers who call themselves the “Six Samurai” have come up with a strategy to revive the Terra Classic blockchain ecosystem. The goal is to eliminate the trace of its founder Do Know and restore LUNC to glory.
“We are all passionate LUNC holders and community members committed to reviving and decentralizing the blockchain as we believe that is where the real payoff lies for everyone”Six Samurai.
“We have assembled this team of seasoned industry veterans, with the goal of bringing value to the Terra Classic blockchain so that we can help push this chain where it deserves to go,” the team added, noting:
“We also believe in open source, decentralization, and definition above all else.”
Engineers’ 3-step plan to bring Terra Classic back to life
- Include Terra Classic in the Keplr web interface and in Mintscan to increase its presence in the wider Cosmos.
- Perform a Parity Upgrade Impact Analysis to identify and fix broken DApps and smart contracts.
- Complete the transition to a new Oracle Feeder, which the active set has not yet fully executed.
The strategy also includes increasing the participation of validators in the testnets and improving the documentation for L1, L2 and validators, understanding that it is a vital step to simplify the access of newcomers to the chain.
Finally, the idea is to optimize the synchronization time of nodes through network updates, establishment of a TerraUSD testnet (USTC) for the evaluation of financial services, the creation of a performance-generating application for holders of tokens and start of a developer rewards program based on user participation.
