Ethereum has just given a major upgrade to the network by introducing “Shapella”. This update was implemented at epoch 194,048 and allows users to withdraw their staked ETH from the beacon chain. This is the most significant upgrade since The Merge in September 2022.
#VitalikButerin reveal #EthereumNext major hard fork and scaling plans https://t.co/vVCuw33QZ5
— Kyptos (@azcoinnews) April 14, 2023
During a recent Ethereum Foundation livestream, Ethereum founder Vitalik Buterin said that the development team is now prioritizing scaling features to expand the network. He noted that the Shapella upgrade is the final step in the transition to proof-of-stake (PoS), and ETH withdrawal support is critical.
Buterin also warned that any on-chain activity could cost each ethereum transaction hundreds of dollars in fees if the scaling process fails to live up to expectations. Therefore, the focus must be on scaling, and the upcoming EIP-4844 hard fork (proto-danksharding) promises to eliminate “call data” and turn it into “blobs”, reducing storage costs on the network during major operations.
Buterin believes that proto-danksharding can bring 10 times efficiency to the scaling process, and also supports full sharding deployment. He has been impressed with the development and efforts of the Layer 2 team, as more and more zkEVM projects continue to launch along with the development of Arbitrum and Optimism.
In terms of long-term stability, Buterin explained that the network is still in an unstable upgrade phase and will only stabilize once the scaling roadmap is complete. Scaling solutions and the upcoming hard fork need to be implemented carefully and at a slower pace.
According to the new development roadmap announced at the EthCC conference in July 2022, the transformation of Ethereum requires some speed adjustments. At some point, the rate of change on Ethereum will need to slow down, and security and stability will become priorities instead of rapid growth.
Buterin explores interesting questions
Buterin explores an interesting question: How does the time it takes to get from point A to point B scale with distance in the real world? He uses the GeoLife dataset to randomly select points where people actually stop, and uses an API to understand transit times between points.
Buterin found that travel times increased more slowly, and the farther they traveled, the more likely they were to use faster modes of transportation, but at some fixed cost. Research has shown that for distances less than 500 km, a suitable power rule given by linear regression is travel_time = 965.8020738916074 * distance^0.61385563661612214 (time in seconds and distance in kilometers).
To collect data over longer distances, Buterin manually collected travel-time data for 16 pairs of points more than 500 kilometers apart.
After getting the results, he found that ChatGPT 3.5 was too good at teaching him some libraries and APIs that he hadn’t heard of before but others used, despite running into some issues and bugs and having to manually fix some code.
Buterin’s research provides valuable insights into the relationship between travel time and distance, and demonstrates the potential for using AI to extract meaningful insights from datasets.
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board take
according to Kyptos
