ethereum merge
The Merge of Ethereum’s network is a significant accomplishment in the history of open-source software and Web3. For the most part, this transition from the more energy-intensive proof-of-work consensus mechanism to the less energy-intensive proof-of-stake has been celebrated for its increased sustainability.
But while the story of the day is about energy conservation, the Merge skirts a looming problem for Ethereum. Network updates after the Merge are intended to finally begin improving Ethereum’s scalability – but these proposals threaten the viability and sustainability of a healthy Web3 ecosystem.
“Atomic composability,” which made decentralized finance (DeFi) on Ethereum possible to begin with, becomes compromised. And when you break atomic composability, you hinder the very thing that makes an ecosystem truly sustainable long-term.
Atomic composability is a technical term for saying that any application on a network can frictionlessly interact with any other application. Ethereum is going to consciously break composability by segregating parts of its network from each other in the implementation of sharding or layer 2 systems.
To make this concept more concrete, let’s consider the health of a rainforest or a desert.
Without pollinators, one-third of all our edible fruits and vegetables could not grow, according to experts.
You’ve most likely heard about the declining bee population, but the story repeats itself over and over and over again all over the world. The destruction of agave plants for tequila production jeopardizes the survival of the bats that pollinate the most important cactus in the desert, putting the entire ecosystem at risk. Whaling ultimately leads to the destruction of fish stocks.
Mots-clés : cybersécurité, sécurité informatique, protection des données, menaces cybernétiques, veille cyber, analyse de vulnérabilités, sécurité des réseaux, cyberattaques, conformité RGPD, NIS2, DORA, PCIDSS, DEVSECOPS, eSANTE, intelligence artificielle, IA en cybersécurité, apprentissage automatique, deep learning, algorithmes de sécurité, détection des anomalies, systèmes intelligents, automatisation de la sécurité, IA pour la prévention des cyberattaques.
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