Gossip

gossip

Read Wednesday&#x27;s Scottish <strong>Gossip</strong> in full here.

'Kenneth Vargas finalising permanent Hearts switch' - gossip - BBC Sport

A TikTok user turned her storytime about divorcing a &#x27;pathological liar&#x27; into TikTok’s newest hit reality series.

'Who TF Did I Marry' Is the 50-Part Gossip Session Taking Over TikTok

Broadway’s biggest <strong>gossiper</strong> brings &quot;remote work&quot; to a whole new level.

Broadway Gossip Travels Long Distance

From Vietnamese Upbringing to Marketing to a Global Audience: a Candid Convo with Linh Dao Smooke

ভিয়েতনামী লালন-পালন থেকে বিপণন থেকে একটি বিশ্বব্যাপী দর্শক: লিন দাও স্মুকের সাথে একটি অকপট কনভো

Von der vietnamesischen Erziehung zum Marketing für ein globales Publikum: ein offenes Gespräch mit Linh Dao Smooke

De la educación vietnamita al marketing para una audiencia global: una conversación sincera con Linh Dao Smooke

De l'éducation vietnamienne au marketing auprès d'un public mondial : une conversation franche avec Linh Dao Smooke

वियतनामी परवरिश से लेकर वैश्विक दर्शकों के लिए मार्केटिंग तक: लिन्ह दाओ स्मूके के साथ एक स्पष्ट बातचीत

ベトナム育ちから世界のオーディエンスへのマーケティングまで：リン・ダオ・スムークとの率直な会話

베트남에서 자란 것부터 글로벌 청중을 대상으로 하는 마케팅까지: Linh Dao Smooke와의 솔직한 대화

Da educação vietnamita ao marketing para um público global: uma conversa franca com Linh Dao Smooke

От вьетнамского воспитания до маркетинга и глобальной аудитории: откровенная беседа с Линь Дао Смук

Vietnam'da Yetişme Döneminden Küresel Bir Kitleye Pazarlamaya: Linh Dao Smooke ile Samimi Bir Konuşma

Từ nền giáo dục Việt Nam đến tiếp thị đến đối tượng khán giả toàn cầu: Cuộc trò chuyện thẳng thắn với Linh Dao Smooke

从越南成长经历到面向全球受众的营销：与 Linh Dao Smooke 的坦诚对话

Key Takeaways Solana uses a peer-to-peer gossip protocol based on the Plumtree algorithm, allowing decentralized communication between nodes to propagate state changes efficiently across the network without relying on a central source.
Every 0.1 seconds, Solana nodes exchange "push" and "pull" messages, ensuring continuous and up-to-date information sharing to maintain rapid data dissemination and synchronization across the network.
The gossip protocol consists of four main message types—Push, Pull, Prune, and Ping/Pong—that help distribute data, request missing information, manage node connections, and check node vitality.
Gossip data is stored in a Cluster Replicated Data Store (CrdsTable), which is continuously updated with incremental updates to the network's state, ensuring data availability and synchronization among nodes.
Solana's archive system uses "warehouse nodes" to store transaction history, with recent data (1-2 epochs) retained by validators, while full transaction history is stored in Ledger Archives or Google Bigtable instances for easy access by RPCs and other participants. Solana uses a peer-to-peer gossip protocol based on the Plumtree algorithm, allowing decentralized communication between nodes to propagate state changes efficiently across the network without relying on a central source. Every 0.1 seconds, Solana nodes exchange "push" and "pull" messages, ensuring continuous and up-to-date information sharing to maintain rapid data dissemination and synchronization across the network. The gossip protocol consists of four main message types—Push, Pull, Prune, and Ping/Pong—that help distribute data, request missing information, manage node connections, and check node vitality. Gossip data is stored in a Cluster Replicated Data Store (CrdsTable), which is continuously updated with incremental updates to the network's state, ensuring data availability and synchronization among nodes. Solana's archive system uses "warehouse nodes" to store transaction history, with recent data (1-2 epochs) retained by validators, while full transaction history is stored in Ledger Archives or Google Bigtable instances for easy access by RPCs and other participants. Gossip Gossip protocol is a peer-to-peer communication method in distributed systems, where the nodes in the network exchange state information periodically about themselves and other nodes each node is connected to in a decentralized manner. This is similar to how rumors spread on social media. It implies that any slight state change will eventually be propagated through the network, and all nodes will have full information on that state change. On Solana, its implementation of the gossip protocol uses an informal communication method with a tree broadcast approach heavily modeled against the Plumtree algorithm. This makes the propagation of state changes without relying on a central head source. It acts as a control panel for Solana as it aids validators and RPC nodes to know which addresses and ports are open for communication. Solana Gossip works with other major components such as validators, turbine protocol, archive nodes, proof of history, etc. The Validators and RPCs exchange signed data objects every 0.1 seconds over UDP using gossip, guaranteeing information availability throughout the network. Every gossip message has to be at or below the maximum transmission unit (MTU) of 1280 bytes, known as the "packet struct" in the codebase. The gossip protocol serves as a central mechanism for nodes to efficiently manage cluster communication by continuously sharing signed data objects, such as contact information, ledger height, and votes. Every 0.1 seconds, nodes exchange "push" and "pull" messages—where push messages can be forwarded and both types may trigger responses—facilitating rapid data dissemination across the network. Operating over a well-known UDP/IP port or port range, the gossip protocol enables nodes to advertise their gossip endpoints (socket addresses) to one another after the cluster is bootstrapped. This decentralized, high-frequency communication graph ensures Solana’s nodes remain synchronized and resilient, supporting the blockchain’s scalability and performance. In Solana's gossip protocol, four message types facilitate cluster communication: Push messages, the most frequent, distribute data to a select group of "push peers." Pull messages, along with their Pull Responses, are sent periodically to retrieve any missing information by requesting and delivering unshared data. Prune messages enable nodes to trim down their active connections as needed. Lastly, Ping and Pong messages serve as vitality checks, where a ping prompts a pong reply to confirm a peer node’s operational status. Gossip data is stored in a Cluster Replicated Data Store (CrdsTable). This data structure is continuously updated with gossip protocol messages. These messages contain gossip records, which are essentially incremental updates to the CrdsTable. Archive Solana differentiates itself from other blockchains by not requiring the full transaction history to determine an account's current state. Its account model ensures that the state at any given slot is known, enabling validators to store the current state without needing to process all historical blocks. RPCs and validators typically don't retain the entire ledger history; instead, they store only 1 or 2 epochs (2-4 days) of transaction data, which is enough to validate the latest block. Archive data is managed by "warehouse nodes," run by professional RPC service providers, the Solana Foundation, and other ecosystem participants to ensure transaction history is accessible. These warehouse nodes often maintain either a Ledger Archive, which stores raw ledger and AccountsDB snapshots for replaying from scratch, or a Google Bigtable Instance, which stores block data from the genesis block onward in a format suitable for RPC requests. Conclusion In conclusion, Solana's gossip protocol and archive system enable efficient communication and data synchronization across the network. The gossip protocol ensures rapid state propagation, while the archive system stores transaction history for easy access by validators and RPCs. Together, these features support Solana's scalability, performance, and decentralization.

Solana’s Secret to Staying Fast? A Blockchain Gossip Network

Solanas Geheimnis, schnell zu bleiben? Ein Blockchain-Klatschnetzwerk

¿El secreto de Solana para mantenerse rápido? Una red de chismes sobre blockchain.

Solanina tajna da ostane brz? Blockchain mreža tračeva

Solana titka a gyors tartáshoz? Blockchain pletykahálózat

Solana の高速化の秘訣は？ブロックチェーンゴシップネットワーク

អាថ៌កំបាំង​របស់ Solana ក្នុងការ​រក្សា​ភាព​រហ័សរហួន? បណ្តាញនិយាយដើម Blockchain

Solanas noslēpums, kā palikt ātram? Blockchain tenku tīkls

Ny tsiambaratelon'i Solana amin'ny fijanonana haingana? Tambajotra Gossip Blockchain

Sirta Solana si ay u degdegaan? Shabakada xanta ee Blockchain

வேகமாக இருப்பதற்கு சோலானாவின் ரகசியம்? ஒரு பிளாக்செயின் கிசுகிசு நெட்வொர்க்

Xana Xihundla Xa Solana Xa Ku Tshama Hi Ku Tikhoma? Netiweke ya Vuhlekuri bya Blockchain

Секрет Солани залишатися швидким? Блокчейн-мережа пліток

Glossary INV (Inventory): A message sent to nodes in the network to depict the availability of data each node requires. GETDATA: This is a message sent to a node that queries for the data specified in the inv message. Spanning Tree: A spanning tree of a graph is a subset of the graph that connects all its vertices with the minimum number of edges without forming any cycles. Introduction Blockchain is gaining massive attention as support for cryptocurrencies due to the rapid growth of memecoins and the celebrated launch of cryptocurrencies. However, one key challenge in blockchain networks is the efficient propagation of messages between nodes, which leads to communication resource consumption. In traditional gossip protocol, a key problem plaguing it is the latency issue—a node must wait for the next cycle to transmit a message. The Plumtree algorithm is designed to reduce latency and redundancy in message transmission, particularly in peer-to-peer networks and distributed systems where reliable and fast message dissemination is important. It can achieve this by finding the shortest path to send the message to the next node in the spanning tree. By doing this, each node participates in forwarding messages without relying on a central source. This article delves into the implementation of the Plumtree algorithm in the Solana network and its role in increasing the speed of message propagation in the network. Furthermore, a comparison between Plumtree and GossipSub, a gossip-based epidemic broadcast algorithm used in Ethereum. Solana Broadcast In Solana, a gossip-based block propagation method is as follows: when a node receives a new block, it sends a BROADCAST message to other nodes in the network. This broadcast includes information about the new block, such as the block header, transaction data, state changes, and other metadata like compressed data, etc. Upon receiving the BROADCAST message, the receiving node checks if it already has the block in its ledger history. When the nodes check its history and have no record of that block, it sends a QUERY request back to the broadcasting node, querying for the block data. When the node receives the data of the queried block, it verifies it and adds it to the network. This ensures that the node does not share the block data with every other node in the network repeatedly. Plumtree Algorithm Origin The plum is a fruit of the Prunus species, Prunus domestica, a genus of trees that also includes peach, cherry, nectarine, almond, and apricot trees. Plums have a lot of health benefits. They contain a lot of nutrients, are rich in antioxidants, promote bone health, and help lower blood sugar. China and Romania are the two largest plum producers. The main sources of inspiration for the Plumtree algorithm are as follows: The flowering of the plum trees in the early spring.
The transformation of the pollinated flowers to plums.
The plums drop before maturity by 20–30% due to various reasons or diseases, such as diseases caused by plant viruses.
The plums have a shelf life of around 2–6 weeks, even when they are stored at 0 °C. The Plumtree algorithm implements these sources of inspiration as follows: the positions of the flowers and the positions of the plums are represented as matrices; the dropping of the plums before maturity is represented by a fruitiness threshold (FT), and the shelf life of the plums after they are collected is represented by a ripeness threshold (RT). The two thresholds lead to the development of three types of equations to update the positions of the flowers. How Plumtree Works Plumtree is a broadcast method that combines tree-based and gossip-based approaches to reduce redundancy while maintaining high message reliability. In a tree-based broadcast, each node transmits and improves efficiency by forwarding messages only in a tree-based manner. It also uses gossip-based links between nodes to properly handle inter-node failures. In a tree-based broadcast, a tree consisting of all participant nodes is constructed, and messages are transmitted only along the tree, which reduces the redundancy of messages. However, if the number of nodes increases or decreases or if a network failure occurs, the tree becomes incomplete, and a message may not reach all nodes. For instance, the block is broadcasted, and a spanning tree is constructed using the Plumtree algorithm. In subsequent broadcasts, the constructed tree is used. If the exchange of inv and getdata messages is omitted, the blocks are not sent directly to the child nodes of the tree and hence fail. In Gossip-based broadcast, when a node attempts to broadcast a message, the node randomly selects nodes to send the message. The node that receives the message for the first time repeats this process to send the message, which not only provides high scalability but also makes the system more resistant to network inadequacies and node failures. For instance, when a node broadcasts a message, it randomly chooses a node to send it to. The node that receives the message for the first time repeats this process. This property allows the system to respond flexibly to network failures and increases or decreases in the number of nodes. The following approaches are utilized in the Gossip-based broadcast to disseminate data: Eager push: In this approach, once a message is received, it is sent to the neighboring nodes immediately.
Lazy push: In this approach, when a node receives a message, it sends the message identifier to the neighboring nodes. If the node has not received the message, it makes a pull request to the neighboring nodes.
Pull: Once a node has not received a message, it makes a query to its neighboring nodes—pull on recently received messages to update its state with that particular message. Plumtree uses a combination of an Eager push mechanism for sending a small number of messages and a Lazy push mechanism for assisting transmission. Plumtree in Solana Network Solana utilizes a peer-to-peer communication approach combined with a Plumtree tree-inspired algorithm, allowing for efficient dissemination of data throughout the network of nodes without depending on a central source. This structure enables messages within the network to be disseminated hierarchically, with nodes directly interacting and making each node in the network operate autonomously in relaying messages to the subsequent node in the tree. A spanning tree structure is constructed using the Plumtree algorithm for the nodes in the Solana network, allowing for a more scalable approach to broadcasting information throughout the network. In basic terms, every node shares messages with its direct neighbors, who in turn, propagate the information across the spanning tree. Differences Between Plumtree Algorithm and GossipSub in Ethereum The differences between the Plumtree algorithm and GossipSub used for information broadcasting in Solana and Ethereum, respectively, include: Solana Tree-based PTA uses a tree-based broadcast approach where nodes are connected in a hierarchical manner where the parent node is connected to the child node. The focus is on building an optimal routing path over time through message duplication handling, while GossipSub utilizes topic-based P2P overlays, where nodes subscribe to specific topics related to validator duties and communication within the Ethereum network. These overlays self-optimize to find the shortest path for message dissemination. Solana Tree-based PTA uses epidemic broadcast but focuses on handling message redundancy by downgrading peers with suboptimal message paths, while in GossipSub, messages are disseminated via a random epidemic broadcast. Wrapping Up In this article, we examined the Plumtree algorithm in Solana's Gossip Protocol, highlighting its tree-based and gossip-based approach to efficient message dissemination. We also compared it with Ethereum’s GossipSub, noting its differences. Glossary INV (Inventory): A message sent to nodes in the network to depict the availability of data each node requires. INV (Inventory): GETDATA : This is a message sent to a node that queries for the data specified in the inv message. GETDATA inv inv Spanning Tree : A spanning tree of a graph is a subset of the graph that connects all its vertices with the minimum number of edges without forming any cycles. Spanning Tree spanning tree Introduction Blockchain is gaining massive attention as support for cryptocurrencies due to the rapid growth of memecoins and the celebrated launch of cryptocurrencies. However, one key challenge in blockchain networks is the efficient propagation of messages between nodes, which leads to communication resource consumption. In traditional gossip protocol, a key problem plaguing it is the latency issue—a node must wait for the next cycle to transmit a message. The Plumtree algorithm is designed to reduce latency and redundancy in message transmission, particularly in peer-to-peer networks and distributed systems where reliable and fast message dissemination is important. It can achieve this by finding the shortest path to send the message to the next node in the spanning tree . By doing this, each node participates in forwarding messages without relying on a central source. spanning tree spanning tree This article delves into the implementation of the Plumtree algorithm in the Solana network and its role in increasing the speed of message propagation in the network. Furthermore, a comparison between Plumtree and GossipSub, a gossip-based epidemic broadcast algorithm used in Ethereum. Solana Broadcast In Solana, a gossip-based block propagation method is as follows: when a node receives a new block, it sends a BROADCAST message to other nodes in the network. This broadcast includes information about the new block, such as the block header, transaction data, state changes, and other metadata like compressed data, etc. Upon receiving the BROADCAST message, the receiving node checks if it already has the block in its ledger history. When the nodes check its history and have no record of that block, it sends a QUERY request back to the broadcasting node, querying for the block data. BROADCAST BROADCAST QUERY When the node receives the data of the queried block, it verifies it and adds it to the network. This ensures that the node does not share the block data with every other node in the network repeatedly. Plumtree Algorithm Origin The plum is a fruit of the Prunus species, Prunus domestica , a genus of trees that also includes peach, cherry, nectarine, almond, and apricot trees. Plums have a lot of health benefits. They contain a lot of nutrients, are rich in antioxidants, promote bone health, and help lower blood sugar. China and Romania are the two largest plum producers. Prunus domestica The main sources of inspiration for the Plumtree algorithm are as follows: The flowering of the plum trees in the early spring. The transformation of the pollinated flowers to plums. The plums drop before maturity by 20–30% due to various reasons or diseases, such as diseases caused by plant viruses. The plums have a shelf life of around 2–6 weeks, even when they are stored at 0 °C. The flowering of the plum trees in the early spring. The transformation of the pollinated flowers to plums. The plums drop before maturity by 20–30% due to various reasons or diseases, such as diseases caused by plant viruses. The plums have a shelf life of around 2–6 weeks, even when they are stored at 0 °C. The Plumtree algorithm implements these sources of inspiration as follows: the positions of the flowers and the positions of the plums are represented as matrices; the dropping of the plums before maturity is represented by a fruitiness threshold (FT), and the shelf life of the plums after they are collected is represented by a ripeness threshold (RT). The two thresholds lead to the development of three types of equations to update the positions of the flowers. fruitiness threshold fruitiness threshold ripeness threshold ripeness threshold How Plumtree Works Plumtree is a broadcast method that combines tree-based and gossip-based approaches to reduce redundancy while maintaining high message reliability. In a tree-based broadcast, each node transmits and improves efficiency by forwarding messages only in a tree-based manner. It also uses gossip-based links between nodes to properly handle inter-node failures. In a tree-based broadcast, a tree consisting of all participant nodes is constructed, and messages are transmitted only along the tree, which reduces the redundancy of messages. However, if the number of nodes increases or decreases or if a network failure occurs, the tree becomes incomplete, and a message may not reach all nodes. For instance, the block is broadcasted, and a spanning tree is constructed using the Plumtree algorithm. In subsequent broadcasts, the constructed tree is used. If the exchange of inv and getdata messages is omitted, the blocks are not sent directly to the child nodes of the tree and hence fail. spanning tree spanning tree inv inv getdata getdata In Gossip-based broadcast, when a node attempts to broadcast a message, the node randomly selects nodes to send the message. The node that receives the message for the first time repeats this process to send the message, which not only provides high scalability but also makes the system more resistant to network inadequacies and node failures. For instance, when a node broadcasts a message, it randomly chooses a node to send it to. The node that receives the message for the first time repeats this process. This property allows the system to respond flexibly to network failures and increases or decreases in the number of nodes. The following approaches are utilized in the Gossip-based broadcast to disseminate data: Eager push: In this approach, once a message is received, it is sent to the neighboring nodes immediately. Lazy push: In this approach, when a node receives a message, it sends the message identifier to the neighboring nodes. If the node has not received the message, it makes a pull request to the neighboring nodes. Pull: Once a node has not received a message, it makes a query to its neighboring nodes—pull on recently received messages to update its state with that particular message. Eager push : In this approach, once a message is received, it is sent to the neighboring nodes immediately. Eager push Lazy push : In this approach, when a node receives a message, it sends the message identifier to the neighboring nodes. If the node has not received the message, it makes a pull request to the neighboring nodes. Lazy push Pull : Once a node has not received a message, it makes a query to its neighboring nodes— pull on recently received messages to update its state with that particular message. Pull pull pull Plumtree uses a combination of an Eager push mechanism for sending a small number of messages and a Lazy push mechanism for assisting transmission. Plumtree in Solana Network Solana utilizes a peer-to-peer communication approach combined with a Plumtree tree-inspired algorithm, allowing for efficient dissemination of data throughout the network of nodes without depending on a central source. This structure enables messages within the network to be disseminated hierarchically, with nodes directly interacting and making each node in the network operate autonomously in relaying messages to the subsequent node in the tree. A spanning tree structure is constructed using the Plumtree algorithm for the nodes in the Solana network, allowing for a more scalable approach to broadcasting information throughout the network. In basic terms, every node shares messages with its direct neighbors, who in turn, propagate the information across the spanning tree. Differences Between Plumtree Algorithm and GossipSub in Ethereum The differences between the Plumtree algorithm and GossipSub used for information broadcasting in Solana and Ethereum, respectively, include: Solana Tree-based PTA uses a tree-based broadcast approach where nodes are connected in a hierarchical manner where the parent node is connected to the child node. The focus is on building an optimal routing path over time through message duplication handling, while GossipSub utilizes topic-based P2P overlays, where nodes subscribe to specific topics related to validator duties and communication within the Ethereum network. These overlays self-optimize to find the shortest path for message dissemination. Solana Tree-based PTA uses a tree-based broadcast approach where nodes are connected in a hierarchical manner where the parent node is connected to the child node. The focus is on building an optimal routing path over time through message duplication handling, while GossipSub utilizes topic-based P2P overlays , where nodes subscribe to specific topics related to validator duties and communication within the Ethereum network. These overlays self-optimize to find the shortest path for message dissemination. tree-based broadcast topic-based P2P overlays Solana Tree-based PTA uses epidemic broadcast but focuses on handling message redundancy by downgrading peers with suboptimal message paths, while in GossipSub, messages are disseminated via a random epidemic broadcast. Solana Tree-based PTA uses epidemic broadcast but focuses on handling message redundancy by downgrading peers with suboptimal message paths, while in GossipSub, messages are disseminated via a random epidemic broadcast . epidemic broadcast random epidemic broadcast Wrapping Up In this article, we examined the Plumtree algorithm in Solana's Gossip Protocol, highlighting its tree-based and gossip-based approach to efficient message dissemination. We also compared it with Ethereum’s GossipSub, noting its differences.

The PlumTree Algorithm (PTA) in the Solana Gossip Protocol: Some Thoughts

Authors:
(1) Esteban Villa-Turek, Corresponding author;
(2) Rod Abhari, Collaborator;
(3) Erik C. Nisbet, Collaborator;
(4) Yu Xu, Collaborator;
(5) Ayse Deniz Lokmanoglu, Collaborator. Table of Links Abstract Transnational Network Dynamics of Problematic Information Diffusion Theoretical Framework Methodology Statistical Analysis and Results Discussion and Limitations Conclusions and Policy Implications, and References Statistical Analysis and Results After analyzing the main brokers in the network qualitatively, the study employed Exponential Random Graph Models (ERGMs) to statistically determine which nodal attributes are significant in explaining the emergence of network ties. ERGMs are particularly well suited for this task, as they allow researchers to statistically assess and predict the structural characteristics of ties in an observed network. This operates by simulating randomly-generated networks that are specified to structurally equate the observed network, allowing to conduct hypothesis tests on the observed network’s attributes (Robins & Lusher, 2013). In other words, ERGMs are useful to compare in this study the relative influence of transnational factors on tie formation in the networks to other non-transnational actor-level factors to contrast their predictive power around the emergence dynamics of the networks (Robins et al., 2007). In fact, ERGMs have been emphasized in the literature as an ideal method for social network analysis in communication studies, mainly because they allow researchers to employ structure, external relationships or attributes as predictors, all while accounting for the interdependent nature of network data (Shumate & Palazzolo, 2010). Therefore, the study estimates two ERGMs on the two networks using the ergm package version 3.9.4, as implemented for the R programing language (Handcock et al., 2018). To estimate ERGMs for both networks, the study makes use of endogenous attributes as mechanisms through which the network is structured, as well as of exogenous attributes of the nodes and of exogenous relations in the network (Contractor et al., 2006). In general, endogenous attributes correspond to those self-organizing factors in a network that are merely structural and do not depend on attributes of nodes or edges, like the number of edges or triangles. Conversely, exogenous attributes are the ones that help explain the formation of ties in a network based on nonstructural attributes, like attributes of nodes and edges, or other relationships between the nodes that exist in other networks, and that could help explain the emergence of ties in the observed network. We hypothesize, as mentioned above, that linguistic similarity, thematic similarity, and cultural and geographic proximity increase the likelihood of tie formation in both networks. If the hypotheses hold, it would be expected to see a higher likelihood in co-sharing activity in the network between groups that share the same language, the same themes, and that are culturally or geographically close to one another, as captured by the SCI (Bailey et al., 2018). Exogenous attributes are employed to estimate the influence of non-structural characteristics of the network on its emergence, like node characteristics or external network relationships. The exogenous nodal attributes to test for linguistic and thematic similarity are given by the hand-coded language and group category labels introduced above. The exogenous relations in the network to account for cultural similarity are modeled using the SCI, which is calculated by taking the number of Facebook connections between two geographic regions and dividing them by the product of Facebook users in both regions, which is then scaled to have a maximum value of 1,000,000,000 (maximum connectedness) and 1 (minimum connectedness), accounting for every possible country-country pair and without having any missing values (Facebook Data For Good Social Connectedness Index Methodology, 2021). This study employs the SCI to account for cultural similarity relations in social networks, especially those that emerge across national borders. As mentioned above, the SCI empirically shows that people across the world tend to form ties with those who are most similar to them, based for instance on sociodemographic characteristics like race, age, religion, education, but also on other “intangible aspects such as attitudes and beliefs (…)”, which can be understood, among others, as cultural in nature (Bailey et al., 2018). To reiterate, the SCI has proven useful to correlate transnational connectedness with historic intranational and international migratory patterns (Bailey et al., 2018), a relationship that might help account for the emergence of ties between seemingly distant (or close) countries in both networks under study. Finally, the nodal attribute to model geographic co-location is given by the hand-coded domain label discussed above. MPV ERGM Table 1 presents the estimated ERGM coefficients. Table 1 ERGM coefficients for the MPV network The interpretation of ERGM coefficients is similar to that of a logistic regression. In that sense, they allow us to assess whether each term increases or decreases the likelihood of tie formation in the networks. In terms of geographical co-location, the results do not indicate a statistically significant effect on the likelihood of tie formation. However, the same cannot be said of cultural similarity, which shows a positive and significant effect, indicating that cultural similarity between groups’ nationalities increases the likelihood of tie formation in the network, holding everything else constant. Regarding language similarity, our findings indicate that linguistic similarity decreases the likelihood of tie formation, holding everything else constant. In terms of thematic similarity, we estimated the likelihood of tie formation among all possible thematic pairs in the network. The results range from the intuitive increase in likelihood of tie formation in pairs like Conspiracy Theory-Conspiracy Theory groups, or Politics-Politics groups. Nevertheless, our estimates indicate that other possible thematic pairs that include Conspiracy Theory groups see an increase in likelihood of tie formation, such as in the cases of Conspiracy Theory groups paired with Community, Media, Politics or Religion & Spirituality groups, which seems to indicate a sort of spillover effect on the diffusion of problematic information from dedicated Facebook groups to non-dedicated conspiratorial groups. DR ERGM Table 2 presents the estimated ERGM coefficients. Table 2 ERGM coefficients for the DR network In the DR network data geographical co-location indicates a positive and statistically significant effect on the likelihood of tie formation, as well as cultural similarity, which shows a positive and significant effect. In this regard the estimates differ from the MPV network, which could be attributed to the higher population variance in terms of culture, language, geography, etc. In the same vein, it seems that language similarity, would increase the likelihood of tie formation in the network, but our estimate is not statistically significant. Finally, in terms of thematic similarity, the same trend operates on all possible thematic pairs in the network. In that sense, we see the same intuitive increase in likelihood effect in Conspiracy Theory-Conspiracy Theory, and Politics-Politics groups. More interestingly, our estimates for this network also indicate that other thematic pairs in which Conspiracy Theory groups are active see an increase in likelihood of tie formation. In other words, thematic pairs in which Conspiracy Theory groups are paired with Community, Media or Politics groups also see an increase in the likelihood of tie formation in the network (there were no Religion & Spirituality groups in the super-spreader core for the DR network). This finding, reiterating, would indicate a spillover effect on the diffusion of problematic information from dedicated groups to unassuming and vulnerable groups. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Esteban Villa-Turek, Corresponding author; (2) Rod Abhari, Collaborator; (3) Erik C. Nisbet, Collaborator; (4) Yu Xu, Collaborator; (5) Ayse Deniz Lokmanoglu, Collaborator. Authors: Authors: (1) Esteban Villa-Turek, Corresponding author; (2) Rod Abhari, Collaborator; (3) Erik C. Nisbet, Collaborator; (4) Yu Xu, Collaborator; (5) Ayse Deniz Lokmanoglu, Collaborator. Table of Links Abstract Abstract Transnational Network Dynamics of Problematic Information Diffusion Transnational Network Dynamics of Problematic Information Diffusion Theoretical Framework Theoretical Framework Methodology Methodology Statistical Analysis and Results Statistical Analysis and Results Discussion and Limitations Discussion and Limitations Conclusions and Policy Implications, and References Conclusions and Policy Implications, and References Statistical Analysis and Results After analyzing the main brokers in the network qualitatively, the study employed Exponential Random Graph Models (ERGMs) to statistically determine which nodal attributes are significant in explaining the emergence of network ties. ERGMs are particularly well suited for this task, as they allow researchers to statistically assess and predict the structural characteristics of ties in an observed network. This operates by simulating randomly-generated networks that are specified to structurally equate the observed network, allowing to conduct hypothesis tests on the observed network’s attributes (Robins & Lusher, 2013). In other words, ERGMs are useful to compare in this study the relative influence of transnational factors on tie formation in the networks to other non-transnational actor-level factors to contrast their predictive power around the emergence dynamics of the networks (Robins et al., 2007). In fact, ERGMs have been emphasized in the literature as an ideal method for social network analysis in communication studies, mainly because they allow researchers to employ structure, external relationships or attributes as predictors, all while accounting for the interdependent nature of network data (Shumate & Palazzolo, 2010). Therefore, the study estimates two ERGMs on the two networks using the ergm package version 3.9.4, as implemented for the R programing language (Handcock et al., 2018). To estimate ERGMs for both networks, the study makes use of endogenous attributes as mechanisms through which the network is structured, as well as of exogenous attributes of the nodes and of exogenous relations in the network (Contractor et al., 2006). In general, endogenous attributes correspond to those self-organizing factors in a network that are merely structural and do not depend on attributes of nodes or edges, like the number of edges or triangles. Conversely, exogenous attributes are the ones that help explain the formation of ties in a network based on nonstructural attributes, like attributes of nodes and edges, or other relationships between the nodes that exist in other networks, and that could help explain the emergence of ties in the observed network. We hypothesize, as mentioned above, that linguistic similarity, thematic similarity, and cultural and geographic proximity increase the likelihood of tie formation in both networks. If the hypotheses hold, it would be expected to see a higher likelihood in co-sharing activity in the network between groups that share the same language, the same themes, and that are culturally or geographically close to one another, as captured by the SCI (Bailey et al., 2018). Exogenous attributes are employed to estimate the influence of non-structural characteristics of the network on its emergence, like node characteristics or external network relationships. The exogenous nodal attributes to test for linguistic and thematic similarity are given by the hand-coded language and group category labels introduced above. The exogenous relations in the network to account for cultural similarity are modeled using the SCI, which is calculated by taking the number of Facebook connections between two geographic regions and dividing them by the product of Facebook users in both regions, which is then scaled to have a maximum value of 1,000,000,000 (maximum connectedness) and 1 (minimum connectedness), accounting for every possible country-country pair and without having any missing values (Facebook Data For Good Social Connectedness Index Methodology, 2021). This study employs the SCI to account for cultural similarity relations in social networks, especially those that emerge across national borders. As mentioned above, the SCI empirically shows that people across the world tend to form ties with those who are most similar to them, based for instance on sociodemographic characteristics like race, age, religion, education, but also on other “intangible aspects such as attitudes and beliefs (…)”, which can be understood, among others, as cultural in nature (Bailey et al., 2018). To reiterate, the SCI has proven useful to correlate transnational connectedness with historic intranational and international migratory patterns (Bailey et al., 2018), a relationship that might help account for the emergence of ties between seemingly distant (or close) countries in both networks under study. Finally, the nodal attribute to model geographic co-location is given by the hand-coded domain label discussed above. MPV ERGM MPV ERGM MPV ERGM Table 1 presents the estimated ERGM coefficients. Table 1 Table 1 ERGM coefficients for the MPV network ERGM coefficients for the MPV network The interpretation of ERGM coefficients is similar to that of a logistic regression. In that sense, they allow us to assess whether each term increases or decreases the likelihood of tie formation in the networks. In terms of geographical co-location, the results do not indicate a statistically significant effect on the likelihood of tie formation. However, the same cannot be said of cultural similarity, which shows a positive and significant effect, indicating that cultural similarity between groups’ nationalities increases the likelihood of tie formation in the network, holding everything else constant. Regarding language similarity, our findings indicate that linguistic similarity decreases the likelihood of tie formation, holding everything else constant. In terms of thematic similarity, we estimated the likelihood of tie formation among all possible thematic pairs in the network. The results range from the intuitive increase in likelihood of tie formation in pairs like Conspiracy Theory-Conspiracy Theory groups, or Politics-Politics groups. Nevertheless, our estimates indicate that other possible thematic pairs that include Conspiracy Theory groups see an increase in likelihood of tie formation, such as in the cases of Conspiracy Theory groups paired with Community, Media, Politics or Religion & Spirituality groups, which seems to indicate a sort of spillover effect on the diffusion of problematic information from dedicated Facebook groups to non-dedicated conspiratorial groups. DR ERGM DR ERGM Table 2 presents the estimated ERGM coefficients. Table 2 Table 2 ERGM coefficients for the DR network ERGM coefficients for the DR network In the DR network data geographical co-location indicates a positive and statistically significant effect on the likelihood of tie formation, as well as cultural similarity, which shows a positive and significant effect. In this regard the estimates differ from the MPV network, which could be attributed to the higher population variance in terms of culture, language, geography, etc. In the same vein, it seems that language similarity, would increase the likelihood of tie formation in the network, but our estimate is not statistically significant. Finally, in terms of thematic similarity, the same trend operates on all possible thematic pairs in the network. In that sense, we see the same intuitive increase in likelihood effect in Conspiracy Theory-Conspiracy Theory, and Politics-Politics groups. More interestingly, our estimates for this network also indicate that other thematic pairs in which Conspiracy Theory groups are active see an increase in likelihood of tie formation. In other words, thematic pairs in which Conspiracy Theory groups are paired with Community, Media or Politics groups also see an increase in the likelihood of tie formation in the network (there were no Religion & Spirituality groups in the super-spreader core for the DR network). This finding, reiterating, would indicate a spillover effect on the diffusion of problematic information from dedicated groups to unassuming and vulnerable groups. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Graphing the Gossip: Unraveling Social Media Conspiracies

HOLLINGFORD GOSSIPS.

Rainbow Valley by L. M. Montgomery, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . MORE GOSSIP CHAPTER XV. MORE GOSSIP On the evening after Mrs. Myra Murray of the over-harbour section had been buried Miss Cornelia and Mary Vance came up to Ingleside. There were several things concerning which Miss Cornelia wished to unburden her soul. The funeral had to be all talked over, of course. Susan and Miss Cornelia thrashed this out between them; Anne took no part or delight in such goulish conversations. She sat a little apart and watched the autumnal flame of dahlias in the garden, and the dreaming, glamorous harbour of the September sunset. Mary Vance sat beside her, knitting meekly. Mary’s heart was down in the Rainbow Valley, whence came sweet, distance-softened sounds of children’s laughter, but her fingers were under Miss Cornelia’s eye. She had to knit so many rounds of her stocking before she might go to the valley. Mary knit and held her tongue, but used her ears. “I never saw a nicer looking corpse,” said Miss Cornelia judicially. “Myra Murray was always a pretty woman—she was a Corey from Lowbridge and the Coreys were noted for their good looks.” “I said to the corpse as I passed it, ‘poor woman. I hope you are as happy as you look.’” sighed Susan. “She had not changed much. That dress she wore was the black satin she got for her daughter’s wedding fourteen years ago. Her Aunt told her then to keep it for her funeral, but Myra laughed and said, ‘I may wear it to my funeral, Aunty, but I will have a good time out of it first.’ And I may say she did. Myra Murray was not a woman to attend her own funeral before she died. Many a time afterwards when I saw her enjoying herself out in company I thought to myself, ‘You are a handsome woman, Myra Murray, and that dress becomes you, but it will likely be your shroud at last.’ And you see my words have come true, Mrs. Marshall Elliott.” Susan sighed again heavily. She was enjoying herself hugely. A funeral was really a delightful subject of conversation. “I always liked to meet Myra,” said Miss Cornelia. “She was always so gay and cheerful—she made you feel better just by her handshake. Myra always made the best of things.” “That is true,” asserted Susan. “Her sister-in-law told me that when the doctor told her at last that he could do nothing for her and she would never rise from that bed again, Myra said quite cheerfully, ‘Well, if that is so, I’m thankful the preserving is all done, and I will not have to face the fall house-cleaning. I always liked house-cleaning in spring,’ she says, ‘but I always hated it in the fall. I will get clear of it this year, thank goodness.’ There are people who would call that levity, Mrs. Marshall Elliott, and I think her sister-in-law was a little ashamed of it. She said perhaps her sickness had made Myra a little light-headed. But I said, ‘No, Mrs. Murray, do not worry over it. It was just Myra’s way of looking at the bright side.’” “Her sister Luella was just the opposite,” said Miss Cornelia. “There was no bright side for Luella—there was just black and shades of gray. For years she used always to be declaring she was going to die in a week or so. ‘I won’t be here to burden you long,’ she would tell her family with a groan. And if any of them ventured to talk about their little future plans she’d groan also and say, ‘Ah, I won’t be here then.’ When I went to see her I always agreed with her and it made her so mad that she was always quite a lot better for several days afterwards. She has better health now but no more cheerfulness. Myra was so different. She was always doing or saying something to make some one feel good. Perhaps the men they married had something to do with it. Luella’s man was a Tartar, believe me, while Jim Murray was decent, as men go. He looked heart-broken to-day. It isn’t often I feel sorry for a man at his wife’s funeral, but I did feel for Jim Murray.” “No wonder he looked sad. He will not get a wife like Myra again in a hurry,” said Susan. “Maybe he will not try, since his children are all grown up and Mirabel is able to keep house. But there is no predicting what a widower may or may not do and I, for one, will not try.” “We’ll miss Myra terrible in church,” said Miss Cornelia. “She was such a worker. Nothing ever stumped her. If she couldn’t get over a difficulty she’d get around it, and if she couldn’t get around it she’d pretend it wasn’t there—and generally it wasn’t. ‘I’ll keep a stiff upper lip to my journey’s end,’ said she to me once. Well, she has ended her journey.” “Do you think so?” asked Anne suddenly, coming back from dreamland. “I can’t picture her journey as being ended. Can you think of her sitting down and folding her hands—that eager, asking spirit of hers, with its fine adventurous outlook? No, I think in death she just opened a gate and went through—on—on—to new, shining adventures.” “Maybe—maybe,” assented Miss Cornelia. “Do you know, Anne dearie, I never was much taken with this everlasting rest doctrine myself—though I hope it isn’t heresy to say so. I want to bustle round in heaven the same as here. And I hope there’ll be a celestial substitute for pies and doughnuts—something that has to be MADE. Of course, one does get awful tired at times—and the older you are the tireder you get. But the very tiredest could get rested in something short of eternity, you’d think—except, perhaps, a lazy man.” “When I meet Myra Murray again,” said Anne, “I want to see her coming towards me, brisk and laughing, just as she always did here.” “Oh, Mrs. Dr. dear,” said Susan, in a shocked tone, “you surely do not think that Myra will be laughing in the world to come?” “Why not, Susan? Do you think we will be crying there?” “No, no, Mrs. Dr. dear, do not misunderstand me. I do not think we shall be either crying or laughing.” “What then?” “Well,” said Susan, driven to it, “it is my opinion, Mrs. Dr. dear, that we shall just look solemn and holy.” “And do you really think, Susan,” said Anne, looking solemn enough, “that either Myra Murray or I could look solemn and holy all the time—all the time, Susan?” “Well,” admitted Susan reluctantly, “I might go so far as to say that you both would have to smile now and again, but I can never admit that there will be laughing in heaven. The idea seems really irreverent, Mrs. Dr. dear.” “Well, to come back to earth,” said Miss Cornelia, “who can we get to take Myra’s class in Sunday School? Julia Clow has been teaching it since Myra took ill, but she’s going to town for the winter and we’ll have to get somebody else.” “I heard that Mrs. Laurie Jamieson wanted it,” said Anne. “The Jamiesons have come to church very regularly since they moved to the Glen from Lowbridge.” “New brooms!” said Miss Cornelia dubiously. “Wait till they’ve gone regularly for a year.” “You cannot depend on Mrs. Jamieson a bit, Mrs. Dr. dear,” said Susan solemnly. “She died once and when they were measuring her for her coffin, after laying her out just beautiful, did she not go and come back to life! Now, Mrs. Dr. dear, you know you cannot depend on a woman like that.” “She might turn Methodist at any moment,” said Miss Cornelia. “They tell me they went to the Methodist Church at Lowbridge quite as often as to the Presbyterian. I haven’t caught them at it here yet, but I would not approve of taking Mrs. Jamieson into the Sunday School. Yet we must not offend them. We are losing too many people, by death or bad temper. Mrs. Alec Davis has left the church, no one knows why. She told the managers that she would never pay another cent to Mr. Meredith’s salary. Of course, most people say that the children offended her, but somehow I don’t think so. I tried to pump Faith, but all I could get out of her was that Mrs. Davis had come, seemingly in high good humour, to see her father, and had left in an awful rage, calling them all ‘varmints!’” “Varmints, indeed!” said Susan furiously. “Does Mrs. Alec Davis forget that her uncle on her mother’s side was suspected of poisoning his wife? Not that it was ever proved, Mrs. Dr. dear, and it does not do to believe all you hear. But if I had an uncle whose wife died without any satisfactory reason, I would not go about the country calling innocent children varmints.” “The point is,” said Miss Cornelia, “that Mrs. Davis paid a large subscription, and how its loss is going to be made up is a problem. And if she turns the other Douglases against Mr. Meredith, as she will certainly try to do, he will just have to go.” “I do not think Mrs. Alec Davis is very well liked by the rest of the clan,” said Susan. “It is not likely she will be able to influence them.” “But those Douglases all hang together so. If you touch one, you touch all. We can’t do without them, so much is certain. They pay half the salary. They are not mean, whatever else may be said of them. Norman Douglas used to give a hundred a year long ago before he left.” “What did he leave for?” asked Anne. “He declared a member of the session cheated him in a cow deal. He hasn’t come to church for twenty years. His wife used to come regular while she was alive, poor thing, but he never would let her pay anything, except one red cent every Sunday. She felt dreadfully humiliated. I don’t know that he was any too good a husband to her, though she was never heard to complain. But she always had a cowed look. Norman Douglas didn’t get the woman he wanted thirty years ago and the Douglases never liked to put up with second best.” “Who was the woman he did want.” “Ellen West. They weren’t engaged exactly, I believe, but they went about together for two years. And then they just broke off—nobody ever knew why. Just some silly quarrel, I suppose. And Norman went and married Hester Reese before his temper had time to cool—married her just to spite Ellen, I haven’t a doubt. So like a man! Hester was a nice little thing, but she never had much spirit and he broke what little she had. She was too meek for Norman. He needed a woman who could stand up to him. Ellen would have kept him in fine order and he would have liked her all the better for it. He despised Hester, that is the truth, just because she always gave in to him. I used to hear him say many a time, long ago when he was a young fellow ‘Give me a spunky woman—spunk for me every time.’ And then he went and married a girl who couldn’t say boo to a goose—man-like. That family of Reeses were just vegetables. They went through the motions of living, but they didn’t live.” “Russell Reese used his first wife’s wedding-ring to marry his second,” said Susan reminiscently. “That was too economical in my opinion, Mrs. Dr. dear. And his brother John has his own tombstone put up in the over-harbour graveyard, with everything on it but the date of death, and he goes and looks at it every Sunday. Most folks would not consider that much fun, but it is plain he does. People do have such different ideas of enjoyment. As for Norman Douglas, he is a perfect heathen. When the last minister asked him why he never went to church he said ‘Too many ugly women there, parson—too many ugly women!’ I should like to go to such a man, Mrs. Dr. dear, and say to him solemnly, ‘There is a hell!’” “Oh, Norman doesn’t believe there is such a place,” said Miss Cornelia. “I hope he’ll find out his mistake when he comes to die. There, Mary, you’ve knit your three inches and you can go and play with the children for half an hour.” Mary needed no second bidding. She flew to Rainbow Valley with a heart as light as her heels, and in the course of conversation told Faith Meredith all about Mrs. Alec Davis. “And Mrs. Elliott says that she’ll turn all the Douglases against your father and then he’ll have to leave the Glen because his salary won’t be paid,” concluded Mary. “I don’t know what is to be done, honest to goodness. If only old Norman Douglas would come back to church and pay, it wouldn’t be so bad. But he won’t—and the Douglases will leave—and you all will have to go.” Faith carried a heavy heart to bed with her that night. The thought of leaving the Glen was unbearable. Nowhere else in the world were there such chums as the Blythes. Her little heart had been wrung when they had left Maywater—she had shed many bitter tears when she parted with Maywater chums and the old manse there where her mother had lived and died. She could not contemplate calmly the thought of such another and harder wrench. She couldn’t leave Glen St. Mary and dear Rainbow Valley and that delicious graveyard. “It’s awful to be minister’s family,” groaned Faith into her pillow. “Just as soon as you get fond of a place you are torn up by the roots. I’ll never, never, never marry a minister, no matter how nice he is.” Faith sat up in bed and looked out of the little vine-hung window. The night was very still, the silence broken only by Una’s soft breathing. Faith felt terribly alone in the world. She could see Glen St. Mary lying under the starry blue meadows of the autumn night. Over the valley a light shone from the girls’ room at Ingleside, and another from Walter’s room. Faith wondered if poor Walter had toothache again. Then she sighed, with a little passing sigh of envy of Nan and Di. They had a mother and a settled home—they were not at the mercy of people who got angry without any reason and called you a varmint. Away beyond the Glen, amid fields that were very quiet with sleep, another light was burning. Faith knew it shone in the house where Norman Douglas lived. He was reputed to sit up all hours of the night reading. Mary had said if he could only be induced to return to the church all would be well. And why not? Faith looked at a big, low star hanging over the tall, pointed spruce at the gate of the Methodist Church and had an inspiration. She knew what ought to be done and she, Faith Meredith, would do it. She would make everything right. With a sigh of satisfaction, she turned from the lonely, dark world and cuddled down beside Una. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. L. M. Montgomery (2004). Rainbow Valley. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/5343/pg5343-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

MORE GOSSIP

Rainbow Valley by L. M. Montgomery, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . SHEER GOSSIP CHAPTER II. SHEER GOSSIP “Where are the other children?” asked Miss Cornelia, when the first greetings—cordial on her side, rapturous on Anne’s, and dignified on Susan’s—were over. “Shirley is in bed and Jem and Walter and the twins are down in their beloved Rainbow Valley,” said Anne. “They just came home this afternoon, you know, and they could hardly wait until supper was over before rushing down to the valley. They love it above every spot on earth. Even the maple grove doesn’t rival it in their affections.” “I am afraid they love it too well,” said Susan gloomily. “Little Jem said once he would rather go to Rainbow Valley than to heaven when he died, and that was not a proper remark.” “I suppose they had a great time in Avonlea?” said Miss Cornelia. “Enormous. Marilla does spoil them terribly. Jem, in particular, can do no wrong in her eyes.” “Miss Cuthbert must be an old lady now,” said Miss Cornelia, getting out her knitting, so that she could hold her own with Susan. Miss Cornelia held that the woman whose hands were employed always had the advantage over the woman whose hands were not. “Marilla is eighty-five,” said Anne with a sigh. “Her hair is snow-white. But, strange to say, her eyesight is better than it was when she was sixty.” “Well, dearie, I’m real glad you’re all back. I’ve been dreadful lonesome. But we haven’t been dull in the Glen, believe me. There hasn’t been such an exciting spring in my time, as far as church matters go. We’ve got settled with a minister at last, Anne dearie.” “The Reverend John Knox Meredith, Mrs. Dr. dear,” said Susan, resolved not to let Miss Cornelia tell all the news. “Is he nice?” asked Anne interestedly. Miss Cornelia sighed and Susan groaned. “Yes, he’s nice enough if that were all,” said the former. “He is very nice—and very learned—and very spiritual. But, oh Anne dearie, he has no common sense! “How was it you called him, then?” “Well, there’s no doubt he is by far the best preacher we ever had in Glen St. Mary church,” said Miss Cornelia, veering a tack or two. “I suppose it is because he is so moony and absent-minded that he never got a town call. His trial sermon was simply wonderful, believe me. Every one went mad about it—and his looks.” “He is very comely, Mrs. Dr. dear, and when all is said and done, I do like to see a well-looking man in the pulpit,” broke in Susan, thinking it was time she asserted herself again. “Besides,” said Miss Cornelia, “we were anxious to get settled. And Mr. Meredith was the first candidate we were all agreed on. Somebody had some objection to all the others. There was some talk of calling Mr. Folsom. He was a good preacher, too, but somehow people didn’t care for his appearance. He was too dark and sleek.” “He looked exactly like a great black tomcat, that he did, Mrs. Dr. dear,” said Susan. “I never could abide such a man in the pulpit every Sunday.” “Then Mr. Rogers came and he was like a chip in porridge—neither harm nor good,” resumed Miss Cornelia. “But if he had preached like Peter and Paul it would have profited him nothing, for that was the day old Caleb Ramsay’s sheep strayed into church and gave a loud ‘ba-a-a’ just as he announced his text. Everybody laughed, and poor Rogers had no chance after that. Some thought we ought to call Mr. Stewart, because he was so well educated. He could read the New Testament in five languages.” “But I do not think he was any surer than other men of getting to heaven because of that,” interjected Susan. “Most of us didn’t like his delivery,” said Miss Cornelia, ignoring Susan. “He talked in grunts, so to speak. And Mr. Arnett couldn’t preach at all. And he picked about the worst candidating text there is in the Bible—‘Curse ye Meroz.’” “Whenever he got stuck for an idea, he would bang the Bible and shout very bitterly, ‘Curse ye Meroz.’ Poor Meroz got thoroughly cursed that day, whoever he was, Mrs. Dr. dear,” said Susan. “The minister who is candidating can’t be too careful what text he chooses,” said Miss Cornelia solemnly. “I believe Mr. Pierson would have got the call if he had picked a different text. But when he announced ‘I will lift my eyes to the hills’ he was done for. Every one grinned, for every one knew that those two Hill girls from the Harbour Head have been setting their caps for every single minister who came to the Glen for the last fifteen years. And Mr. Newman had too large a family.” “He stayed with my brother-in-law, James Clow,” said Susan. “‘How many children have you got?’ I asked him. ‘Nine boys and a sister for each of them,’ he said. ‘Eighteen!’ said I. ‘Dear me, what a family!’ And then he laughed and laughed. But I do not know why, Mrs. Dr. dear, and I am certain that eighteen children would be too many for any manse.” “He had only ten children, Susan,” explained Miss Cornelia, with contemptuous patience. “And ten good children would not be much worse for the manse and congregation than the four who are there now. Though I wouldn’t say, Anne dearie, that they are so bad, either. I like them—everybody likes them. It’s impossible to help liking them. They would be real nice little souls if there was anyone to look after their manners and teach them what is right and proper. For instance, at school the teacher says they are model children. But at home they simply run wild.” “What about Mrs. Meredith?” asked Anne. “There’s no Mrs. Meredith. That is just the trouble. Mr. Meredith is a widower. His wife died four years ago. If we had known that I don’t suppose we would have called him, for a widower is even worse in a congregation than a single man. But he was heard to speak of his children and we all supposed there was a mother, too. And when they came there was nobody but old Aunt Martha, as they call her. She’s a cousin of Mr. Meredith’s mother, I believe, and he took her in to save her from the poorhouse. She is seventy-five years old, half blind, and very deaf and very cranky.” “And a very poor cook, Mrs. Dr. dear.” “The worst possible manager for a manse,” said Miss Cornelia bitterly. “Mr. Meredith won’t get any other housekeeper because he says it would hurt Aunt Martha’s feelings. Anne dearie, believe me, the state of that manse is something terrible. Everything is thick with dust and nothing is ever in its place. And we had painted and papered it all so nice before they came.” “There are four children, you say?” asked Anne, beginning to mother them already in her heart. “Yes. They run up just like the steps of a stair. Gerald’s the oldest. He’s twelve and they call him Jerry. He’s a clever boy. Faith is eleven. She is a regular tomboy but pretty as a picture, I must say.” “She looks like an angel but she is a holy terror for mischief, Mrs. Dr. dear,” said Susan solemnly. “I was at the manse one night last week and Mrs. James Millison was there, too. She had brought them up a dozen eggs and a little pail of milk—a very little pail, Mrs. Dr. dear. Faith took them and whisked down the cellar with them. Near the bottom of the stairs she caught her toe and fell the rest of the way, milk and eggs and all. You can imagine the result, Mrs. Dr. dear. But that child came up laughing. ‘I don’t know whether I’m myself or a custard pie,’ she said. And Mrs. James Millison was very angry. She said she would never take another thing to the manse if it was to be wasted and destroyed in that fashion.” “Maria Millison never hurt herself taking things to the manse,” sniffed Miss Cornelia. “She just took them that night as an excuse for curiosity. But poor Faith is always getting into scrapes. She is so heedless and impulsive.” “Just like me. I’m going to like your Faith,” said Anne decidedly. “She is full of spunk—and I do like spunk, Mrs. Dr. dear,” admitted Susan. “There’s something taking about her,” conceded Miss Cornelia. “You never see her but she’s laughing, and somehow it always makes you want to laugh too. She can’t even keep a straight face in church. Una is ten—she’s a sweet little thing—not pretty, but sweet. And Thomas Carlyle is nine. They call him Carl, and he has a regular mania for collecting toads and bugs and frogs and bringing them into the house.” “I suppose he was responsible for the dead rat that was lying on a chair in the parlour the afternoon Mrs. Grant called. It gave her a turn,” said Susan, “and I do not wonder, for manse parlours are no places for dead rats. To be sure it may have been the cat who left it, there. He is as full of the old Nick as he can be stuffed, Mrs. Dr. dear. A manse cat should at least look respectable, in my opinion, whatever he really is. But I never saw such a rakish-looking beast. And he walks along the ridgepole of the manse almost every evening at sunset, Mrs. Dr. dear, and waves his tail, and that is not becoming.” “The worst of it is, they are never decently dressed,” sighed Miss Cornelia. “And since the snow went they go to school barefooted. Now, you know Anne dearie, that isn’t the right thing for manse children—especially when the Methodist minister’s little girl always wears such nice buttoned boots. And I do wish they wouldn’t play in the old Methodist graveyard.” “It’s very tempting, when it’s right beside the manse,” said Anne. “I’ve always thought graveyards must be delightful places to play in.” “Oh, no, you did not, Mrs. Dr. dear,” said loyal Susan, determined to protect Anne from herself. “You have too much good sense and decorum.” “Why did they ever build that manse beside the graveyard in the first place?” asked Anne. “Their lawn is so small there is no place for them to play except in the graveyard.” “It was a mistake,” admitted Miss Cornelia. “But they got the lot cheap. And no other manse children ever thought of playing there. Mr. Meredith shouldn’t allow it. But he has always got his nose buried in a book, when he is home. He reads and reads, or walks about in his study in a day-dream. So far he hasn’t forgotten to be in church on Sundays, but twice he has forgotten about the prayer-meeting and one of the elders had to go over to the manse and remind him. And he forgot about Fanny Cooper’s wedding. They rang him up on the ‘phone and then he rushed right over, just as he was, carpet slippers and all. One wouldn’t mind if the Methodists didn’t laugh so about it. But there’s one comfort—they can’t criticize his sermons. He wakes up when he’s in the pulpit, believe me. And the Methodist minister can’t preach at all—so they tell me. I have never heard him, thank goodness.” Miss Cornelia’s scorn of men had abated somewhat since her marriage, but her scorn of Methodists remained untinged of charity. Susan smiled slyly. “They do say, Mrs. Marshall Elliott, that the Methodists and Presbyterians are talking of uniting,” she said. “Well, all I hope is that I’ll be under the sod if that ever comes to pass,” retorted Miss Cornelia. “I shall never have truck or trade with Methodists, and Mr. Meredith will find that he’d better steer clear of them, too. He is entirely too sociable with them, believe me. Why, he went to the Jacob Drews’ silver-wedding supper and got into a nice scrape as a result.” “What was it?” “Mrs. Drew asked him to carve the roast goose—for Jacob Drew never did or could carve. Well, Mr. Meredith tackled it, and in the process he knocked it clean off the platter into Mrs. Reese’s lap, who was sitting next him. And he just said dreamily. ‘Mrs. Reese, will you kindly return me that goose?’ Mrs. Reese ‘returned’ it, as meek as Moses, but she must have been furious, for she had on her new silk dress. The worst of it is, she was a Methodist.” “But I think that is better than if she was a Presbyterian,” interjected Susan. “If she had been a Presbyterian she would mostly likely have left the church and we cannot afford to lose our members. And Mrs. Reese is not liked in her own church, because she gives herself such great airs, so that the Methodists would be rather pleased that Mr. Meredith spoiled her dress.” “The point is, he made himself ridiculous, and I, for one, do not like to see my minister made ridiculous in the eyes of the Methodists,” said Miss Cornelia stiffly. “If he had had a wife it would not have happened.” “I do not see if he had a dozen wives how they could have prevented Mrs. Drew from using up her tough old gander for the wedding-feast,” said Susan stubbornly. “They say that was her husband’s doing,” said Miss Cornelia. “Jacob Drew is a conceited, stingy, domineering creature.” “And they do say he and his wife detest each other—which does not seem to me the proper way for married folks to get along. But then, of course, I have had no experience along that line,” said Susan, tossing her head. “And I am not one to blame everything on the men. Mrs. Drew is mean enough herself. They say that the only thing she was ever known to give away was a crock of butter made out of cream a rat had fell into. She contributed it to a church social. Nobody found out about the rat until afterwards.” “Fortunately, all the people the Merediths have offended so far are Methodists,” said Miss Cornelia. “That Jerry went to the Methodist prayer-meeting one night about a fortnight ago and sat beside old William Marsh who got up as usual and testified with fearful groans. ‘Do you feel any better now?’ whispered Jerry when William sat down. Poor Jerry meant to be sympathetic, but Mr. Marsh thought he was impertinent and is furious at him. Of course, Jerry had no business to be in a Methodist prayer-meeting at all. But they go where they like.” “I hope they will not offend Mrs. Alec Davis of the Harbour Head,” said Susan. “She is a very touchy woman, I understand, but she is very well off and pays the most of any one to the salary. I have heard that she says the Merediths are the worst brought up children she ever saw.” “Every word you say convinces me more and more that the Merediths belong to the race that knows Joseph,” said Mistress Anne decidedly. “When all is said and done, they do,” admitted Miss Cornelia. “And that balances everything. Anyway, we’ve got them now and we must just do the best we can by them and stick up for them to the Methodists. Well, I suppose I must be getting down harbour. Marshall will soon be home—he went over-harbour to-day—and wanting his super, man-like. I’m sorry I haven’t seen the other children. And where’s the doctor?” “Up at the Harbour Head. We’ve only been home three days and in that time he has spent three hours in his own bed and eaten two meals in his own house.” “Well, everybody who has been sick for the last six weeks has been waiting for him to come home—and I don’t blame them. When that over-harbour doctor married the undertaker’s daughter at Lowbridge people felt suspicious of him. It didn’t look well. You and the doctor must come down soon and tell us all about your trip. I suppose you’ve had a splendid time.” “We had,” agreed Anne. “It was the fulfilment of years of dreams. The old world is very lovely and very wonderful. But we have come back very well satisfied with our own land. Canada is the finest country in the world, Miss Cornelia.” “Nobody ever doubted that,” said Miss Cornelia, complacently. “And old P.E.I. is the loveliest province in it and Four Winds the loveliest spot in P.E.I.,” laughed Anne, looking adoringly out over the sunset splendour of glen and harbour and gulf. She waved her hand at it. “I saw nothing more beautiful than that in Europe, Miss Cornelia. Must you go? The children will be sorry to have missed you.” “They must come and see me soon. Tell them the doughnut jar is always full.” “Oh, at supper they were planning a descent on you. They’ll go soon; but they must settle down to school again now. And the twins are going to take music lessons.” “Not from the Methodist minister’s wife, I hope?” said Miss Cornelia anxiously. “No—from Rosemary West. I was up last evening to arrange it with her. What a pretty girl she is!” “Rosemary holds her own well. She isn’t as young as she once was.” “I thought her very charming. I’ve never had any real acquaintance with her, you know. Their house is so out of the way, and I’ve seldom ever seen her except at church.” “People always have liked Rosemary West, though they don’t understand her,” said Miss Cornelia, quite unconscious of the high tribute she was paying to Rosemary’s charm. “Ellen has always kept her down, so to speak. She has tyrannized over her, and yet she has always indulged her in a good many ways. Rosemary was engaged once, you know—to young Martin Crawford. His ship was wrecked on the Magdalens and all the crew were drowned. Rosemary was just a child—only seventeen. But she was never the same afterwards. She and Ellen have stayed very close at home since their mother’s death. They don’t often get to their own church at Lowbridge and I understand Ellen doesn’t approve of going too often to a Presbyterian church. To the Methodist she never goes, I’ll say that much for her. That family of Wests have always been strong Episcopalians. Rosemary and Ellen are pretty well off. Rosemary doesn’t really need to give music lessons. She does it because she likes to. They are distantly related to Leslie, you know. Are the Fords coming to the harbour this summer?” “No. They are going on a trip to Japan and will probably be away for a year. Owen’s new novel is to have a Japanese setting. This will be the first summer that the dear old House of Dreams will be empty since we left it.” “I should think Owen Ford might find enough to write about in Canada without dragging his wife and his innocent children off to a heathen country like Japan,” grumbled Miss Cornelia. “The Life Book was the best book he’s ever written and he got the material for that right here in Four Winds.” “Captain Jim gave him the most of that, you know. And he collected it all over the world. But Owen’s books are all delightful, I think.” “Oh, they’re well enough as far as they go. I make it a point to read every one he writes, though I’ve always held, Anne dearie, that reading novels is a sinful waste of time. I shall write and tell him my opinion of this Japanese business, believe me. Does he want Kenneth and Persis to be converted into pagans?” With which unanswerable conundrum Miss Cornelia took her departure. Susan proceeded to put Rilla in bed and Anne sat on the veranda steps under the early stars and dreamed her incorrigible dreams and learned all over again for the hundredth happy time what a moonrise splendour and sheen could be on Four Winds Harbour. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. L. M. Montgomery (2004). Rainbow Valley. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/5343/pg5343-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

SHEER GOSSIP

HackerNoon Reporter: Please tell us briefly about your  background. Hi, my background is in neuroscience from Johns Hopkins University. I had a career that focused on areas of neuroscience, clinical research, and computational neuroscience before I moved over into startup after founding my own company. I also had a tangent career with acting and was a main cast member of the television show Gossip Girl. What's your startup called? And in a sentence or two, what does it do? We are called MyMarkit. We are the world’s first fintech blockchain marketplace, and it begets a whole new way to shop. Our marketplace is built ground up around ledgers that are mined from transactions between users for products in a free market (marketplace), which also creates a truly digital currency for the first time backed to the commodities. Our tech automates traditional overhead fulfillment/COGS and yields 70-80% gross margins at scale. It also means that buyers/vendors keep 100% of what they sell on our platform, while buyers can get the lowest prices possible anywhere. Our automation tech also allows for 100% infallible authentication and data analytics/tracking for the first time for the lifetime of the product. We are starting in the resale luxury market space before moving on to retail verticals. Welcome to a new way to shop! What is the origin story? I came across the idea for a lossless transaction after working on fully sustainable frameworks for a non-profit. I thought, why is it that the moment I purchase something, I will never get anywhere near the price that I just paid for it? Is this item no longer worth anything and the currency I just used to purchase it is worth everything? I realized, that’s not the case. The currency represents the value of the product purchased. From there, we looked at the fundamentals of blockchain and crypto in order to understand currency at its inception (whether it’s fiat, digital, or otherwise). We realized that a free market can translate to a marketplace where transactions between users can mine a currency that represents and is backed to the commodity to begin with. And thus, a lossless transaction and a true digital currency was born. It’s like having real world NFTs for physical goods for the first time. What do you love about your team, and why are you the ones to solve this problem? Our team is made of experts in every area of commerce, blockchain, marketplaces, fintech, and crypto/digital currency. We are a total of over 45 people strong, and we’ve worked over years to build our tech stack from the ground up to scale. I think the most interesting thing is that we were not all so myopic in thinking about how digital currency/blockchain is currently utilized. We had to go to its roots to find a way to use it that neutralized all currencies and brought it into the real world with true backing/worth. If you weren’t building your startup, what would you be doing? I would probably be in some other area of creation or helping others to create. The common theme of my life is to push boundaries and to do things that seem impossible in order to come up with solutions that should exist… but just don’t yet. At the moment, how do you measure success? What are your core metrics? Our success is measured by the market demand and the users. We know our product is something that consumers have asked for but have no alternatives currently to use. We lose so much money in how we purchase. We have so much volatility in how we use currency. We wanted to marry fintech, payment gateways, marketplaces, and blockchain all under one roof. And the most important metric is how happy our customers are with what we’ve created. They say that this is something that should have existed long ago. And for the first time, they don’t have to pay fees or lose out on commissions for selling their stuff. Buyers are so happy that they can get all of the cost savings by getting the lowest price possible on our platform. That is the most successful and most rewarding feedback. What’s most exciting about your traction to date? We have been in stealth mode to build out our technology, so we are just coming out of stealth. The pre-launch user sign up was over our expectations. We’ve onboarded executives from some of the biggest competitors in this space. We’ve had a phenomenal requests to white label our technology and to provide our tech solutions for other ecommerce or marketplace companies. We’ve also had developments in the financial services space to roll out digital wallets, debut our coin, and other exciting offerings. What technologies are you currently most excited about, and most worried about? And why? I’m most excited to see the future developments of digital currencies from countries. I think it’s a game-changing addition to the crypto world in that more regulations will soon arise, for better or worse. I am excited to see how blockchain solutions will be used for everyday use cases instead of more SAAS-focused enterprise functions. Overall, the future of blockchain with regards to financial services and various related spaces will be the one to look out for. What drew you to get published on HackerNoon? What do you like most about our platform? We were nominated for Startup Of The Year, so we wanted to participate in the nominations. We are just going out for press now with our launch, so we appreciate that there is a community of tech-related news sourced by amazing writers to find the latest innovations for readers. What advice would you give to the 21-year-old version of yourself? Get over the concept of fear because on the other side of it is freedom. My biggest regrets were from doing things the expected way because certain ideas or innovations were too “ambitious” for the current market. I believe that’s how dynamic change happens - we have to get out of the mindset of what’s done already to do things that are truly novel and pioneering. What is something surprising you've learned this year that your contemporaries would benefit from knowing? LVMH just paved the way for retail brands to adopt blockchain authentication technology. So, it’s a matter of time before we will be using more variations of blockchain in everyday use cases. MyMarkit Inc. is nominated as a . Go vote! startup of the year in Playa Vista

From Gossip Girl Cast To Building A Blockchain Marketplace: Nan Zhang, MyMarkit Inc. CEO

Please tell us briefly about your  background. HackerNoon Reporter: My background is in augmented reality. I’m passionate about products where users can share what they experience, and how they feel - this is something I was working on before I started Gossip, which is also augmented reality, just in a different way. Most of the time the content we are listening to augments our perception of reality. If we are listening to a funny story, we might leave a bigger tip to the waiter or grant an extra smile. What's your startup called? And in a sentence or two, what does it do? It’s called ! Gossip Our product allows users to create and share short voice stories (30s or less) that expire every 7 days - because who wants to listen to the old stuff? What is the origin story? Back in January 2020 I was on treadmill listening to a podcast with Brian Norgard when the idea of Gossip came to my mind. Sharing emotions is something that makes every product truly great, and back then sharing emotions and experiences through voice was non existent. Back then we had Apple Music, Spotify and podcasting - none of those gave the opportunity to build relationships to the users. What do you love about your team, and why are you the ones to solve this problem? We strongly believe in voice being one of the most important parts of human communication. Gossip team is still very small - just 5 amazing people, and all of us are very passionate about the product we are building. Our mission at Gossip is to give people a fast, fun and engaging way to share their stories, ideas and emotions thru voice. If you weren’t building your startup, what would you be doing? Exploring different areas and reading about exciting things. I am a very curious person, so I am sure I would come up with something! At the moment, how do you measure success? What are your core metrics? Our current goal is to understand what drives stickiness and engagement. We are looking at what content our gossipers are posting and how much engagement it drives. What’s most exciting about your traction to date? Gossip is a product built for creators - it’s a new way for the followers and creators to connect. We’ve done several collaborations with TikTok creators and it’s amazing to see how excited the followers are when talking to the creators and hearing them reply. What technologies are you currently most excited about, and most worried about? And why? We are very excited about voice lenses, something we are working on at Gossip. Voice lenses is an upcoming feature in our product: using lenses, gossipers will be able to talk like their favourite character. What advice would you give to the 21-year-old version of yourself? Read a lot, eat less and healthier, drink more water, exercise more. What is something surprising you've learned this year that your contemporaries would benefit from knowing? Do nice things to others and expect nothing in return - a lot of good things will follow. Gossip was nominated as in Startups of the Year hosted by HackerNoon! one of the best startups in San Francisco

"The content we listen to augments our perception of reality", said Gleb Braverman, Gossip Inc. CEO

Recently whilst struggling to fall asleep, I had a bizarre idea; how important is gossip to our species and our evolution? Anthropologists have long debated the value of story-telling and collective belief in success of the human race - surely gossip is a big part of this? Celestus: The Gossip Town As such, i've decided to build a game of sorts, in this game/simulation there are residents who partake in the occasional gossip. They are just rules based bots who go about their day. Now enter the : AI entities optimised to keep gossip going, spread it as far and wide as possible or gain status by generating unique rumours. Digital GossipMonger Image: The Sim comes with interactive terminal and generates Json,CSV and Parquet reports. To get the ball rolling I had to think high level and develop a set of rules, from which I could build on. The environment has a time engine. Gossip can be created Gossip can be or (in terms of impact to target) positive negative Gossip can be about one or multiple targets. Even about no targets. Gossip has an . associated risk Gossip has a /popularity (value decreases to 0) shelf-life Gossips will be associated to citizens who spread or create it. Citizens can only create or share rumours when near other people and die Citizens age Citizens (normally) want more status points Creating gossips gain you status points or removes some and finally... Citizens get old and die  😭 These rules were only the foundations that allowed me to build all the functions, thanks to the help of sociologists friends and some research i've been able to embed all these rules and a into playbook templates, each of which map to a given psychology/sociology framework. lot more This means we can run the simulation over and over again, to see how the world evolves under different academic assumptions. The key is not to make any assumptions myself, but to code as much flexibility to iterate through all the possible sociological frameworks to compare, validate and learn. Integrating AI models and make sure they behave one of the key reasons for building two engines in one project [Data Oriented System (DOS) and Game Like System (GLS)] , is so that the AI models can integrate through the terminal DOS interface - this allows them to operate at CPU speed. Of course doesn't let us humans get in the middle of the training process (other than setting breakpoints at certain iterations in the loops to allow value tweaking). Once the models are trained, , they can be deployed on the game platform and run at human speeds so I can interact with them and see how things evolve at a more manageable pace. (Rnn, Genetic, Random Forrest etc) A big challenge to all this isn't the models themselves, but ensuring they behave and don't have access to data that would allow them to cheat. For example let's say there is a rumour about , three other people know the rumour, let's call them Sarah, Bob and Dave. Jim doesn't know who started the rumour, but me (Adam, a real person) has to track the original creator of the rumour. I track it in a , Sarah Bob and Dave are rules based bots so they only access the interfaces I let them including their internal references. If however , even if I restrict his access to the gossip Database he might be able to infer the original gossip source via relationships in the data, especially if it's leaky and designed poorly. Jim gossip Database Jim was an AI It's all about Status....and other things You might be thinking, Or if you are more experienced in AI litterature you might ask what is it then that drives these agents? 'how are you managing and defining value?'. The answer isn't straight forward, so here is a list: Lets try various value permutations , see what sticks and resembles real life. (in addition to framework permutations) Stacked Categorical Coding:  In real life we have many motivators, money, health, popularity. I've encoded this into a single value - . I'm open to adding in more mechanisms, but I spent a lot of time calculating the value contributions, in this case I think merged encoding is efficient and simple. status AI optimisers are being trained on various catagories and results documented, at the moment the main ones are personal status, spread count,  gossip longevity and unsupervised exploration. But why tho? At the risk of sounding wishy washy, it's fair to say we have already transitioned from to . humans can hack machines machines can hack humans Social media news feeds, click-bait marketing and soon (I think) GPT3 powered telephone manipulation of unsuspecting victims have direct impact on our well being. More importantly they take advantage of the evolutionary blind spots in our reasoning and logic. Usually this is done via an subtle manipulation of our internalised social framework (i.e. adverts disguised as news articles about someone you respect). A near future scenario could include overseas call center bots impersonating the voice of a close relative asking for money. Even if this is blown out of proportion, by simulating societal mechanisms, producing synthetic and hybrid data it might be possible to shine a spotlight on our specific social mechanisms that get abused and possibly even develop counter-measures. If that's all sounds a bit pie in the sky, then what I can say is, By having zoom calls with my friends with sociology, psychology and marketing backgrounds i've been able to brainstorm, refine ideas and help bring together different disciplines to hopefully synthesize something of value. it's fun! So far the entire project has been coded up by me - but it's all open source, please reach out to me if you want to be part of this. Alternatively if you want to build on top of the platform you can fork the repo and reach out to me for Dev notes. https://github.com/murchie85/gossipSimulator Thanks for reading!

Gossip Simulation Implemented In Python

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Methodology 2 Methodology 3 Hardware 3 Hardware 4 Software 4 Software 5 Network 5 Network 6 Consensus 6 Consensus 7 Cryptocurrency Economics 7 Cryptocurrency Economics 8 Client API 8 Client API 9 Governance 9 Governance 10 Geography 10 Geography 11 Case Studies 11 Case Studies 12 Discussion and References 12 Discussion and References A. Decentralization and Policymaking A. Decentralization and Policymaking B. Software Testing B. Software Testing C. Brief Evaluations per Layer C. Brief Evaluations per Layer D. Measuring decentralization D. Measuring decentralization E. Fault Tolerance and Decentralization E. Fault Tolerance and Decentralization 5 Network Blockchain nodes communicate over a peer-to-peer (P2P) network, the decentralization properties of which have been of great interest to researchers [134,4,82,76]. Systems often implement a message diffusion mechanism [78] via a gossip protocol that avoids full graph connectivity [54]. Users typically use the Internet to access the P2P overlay, though some efforts try to introduce an independent infrastructure [47]. Here we explore networking aspects which present single points of failure, in terms of topology and bootstrapping. A notable research question that arises organically from our analysis, and touches upon both following subsections, is creating a P2P network that is both permissionless and Byzantine resilient, e.g., as explored in [49,124]. Topology. The first networking aspect of interest is the network’s topology. Evaluating a real-world network’s clustering properties is a well-known problem, traditionally done by generating random graphs and comparing the expected with the observed values [63,135,1]. In blockchain systems, every node maintains a list of peer connections. Crucially, message provenance is not typically provided, so no party can know the network origin of an incoming message. Therefore, the resource of interest are “bridges” (single nodes or small cluster of nodes) between the network graph’s components and the relevant parties are the bridges’ owners or operators. Here, a component is a single node or a cluster of tightly interconnected nodes. Topology Topology A distributed network, in the tradition of Baran [15], is key in maintaining safety and liveness. Under the CAP theorem [27], any networked system can satisfy at most two of the following properties: i) a consistent data copy; ii) data availability; iii) network partition tolerance. Ledger systems are no exception. Each node needs to maintain at least one connection to an honest party to receive all messages and avoid eclipse attacks [87]. If some parties cannot communicate with the rest of the network, either they halt and any transactions they submit are dropped (violating liveness) or they produce separate ledger versions (violating safety).[9] By preventing communication between a node and the rest of the network, an attacker reduces the honest computational power and isolates that node, making a 51% or a double spending attack (against the isolated node) easier to deploy. Additionally, an adversary can violate liveness by blocking the node’s transactions from reaching the rest of the network. Third, an adversary that controls all of a node’s connections can link transactions to the specific node, correlate the user’s addresses, and associate them with real-world information, such as an IP address, thus compromising privacy[10]. This also holds on a macroscopic level, i.e., a node that acts as a central communication hub between two clusters can obtain information and even deanonymize some participants (violating privacy). Node Bootstrapping and Peer Discovery. Joining a ledger’s network and synchronizing with it is the so-called “bootstrapping” process. All real-world ledgers rely on an initial (trusted) setup, the first (“genesis”) block.[11] Obtaining the correct genesis block is done in an out-of-band, typically secure mechanism, since it is often well-known and easy to validate from various sources. Node Bootstrapping and Peer Discovery. Node Bootstrapping and Peer Discovery. Initially, the node needs to connect to some peers and receive all available chains. Therefore, the resource here is bootstrapping nodes and the relevant parties are these nodes’ operators. Then, using the ledger protocol’s chain resolution mechanism, it decides which chain to adopt. There are two points of interest here. First, it should be guaranteed that the node connects to at least one honest peer, to retrieve all available information and avoid getting eclipsed. As explained above, if nodes gets eclipsed, the system’s liveness, safety and privacy properties may get compromised. However, connecting to honest peers is not straightforward, given that the node has no knowledge about the network participants, a standard problem in P2P networks [57]. Blockchain systems predominantly use either hardcoded peer lists or DNS seeding [120], although both techniques are censorship-prone. Notably, [120] showed that more secure alternatives, such as ZMap [61], cannot be feasibly used in existing blockchain systems. Second, even if the node connects to some honest peers, catching up by using only genesis (“bootstrapping from genesis”) is not always feasible. Especially in PoS systems, an attacker can effortlessly assemble an arbitrarily-long, seemingly correct chain (violating safety) [81,31,118]. To counter such attacks, some ledgers employ checkpoints [107,52,50], which are often issued centrally and are either hardcoded or received from the peers. Other solutions do exist, e.g., analyzing block density and relying on key erasures [12] or using VDFs [24,53], but enforcing and/or relaxing such assumptions still poses an interesting research problem. Authors:
(1) Christina Ovezik, University of Edinburgh (c.ovezik@ed.ac.uk);
(2) Dimitris Karakostas, University of Edinburgh (dkarakos@ed.ac.uk);
(3) Aggelos Kiayias, University of Edinburgh and IOG (akiayias@ed.ac.uk). Authors: Authors: (1) Christina Ovezik, University of Edinburgh (c.ovezik@ed.ac.uk); (2) Dimitris Karakostas, University of Edinburgh (dkarakos@ed.ac.uk); (3) Aggelos Kiayias, University of Edinburgh and IOG (akiayias@ed.ac.uk). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv [9] In longest-chain protocols, like Bitcoin, miners keep producing blocks in isolation, ending up with different ledger versions. In BFT-style protocols, like Algorand, the ledger cannot be updated if a large number of block producers become unreachable and thus do not adopt new transactions. [10] Note that protecting privacy in the network layer can be a particularly challenging problem, even in the setting where no single adversary controls all of a node’s connections. [11] Some proposals rely on computational assumptions instead of a trusted setup [80], but their real-world performance and applicability is still untested.

Eclipse Attacks, Network Clusters & DNS Seeding

Ethereum Block Building: The Hidden Economy Behind Every Transaction

Ethereum-Blockaufbau: Die verborgene Wirtschaft hinter jeder Transaktion

Construcción de bloques de Ethereum: la economía oculta detrás de cada transacción

Ethereum Block Building: piilotettu talous jokaisen transaktion takana

Ethereum Block Building: l'economia nascosta dietro ogni transazione

イーサリアムブロック構築: すべてのトランザクションの背後にある隠れた経済

Ethereum bloku ēka: slēptā ekonomika aiz katra darījuma

Kago ya Ethereum Block: Ekonomi ye e Pategilego ka Morago ga Kgwebišano ye Nngwe le E Nngwe

Ethereum Block Building: A economia oculta por trás de cada transação

Ethereum Block Building: Den dolda ekonomin bakom varje transaktion

Ethereum Block Building: Ang Nakatagong Ekonomiya sa Likod ng Bawat Transaksyon

Ethereum Block Building: Ikhonomi Leyi Fihliweke Endzhaku Ka Ntirhisano wun’wana Na wun’wana

Xây dựng khối Ethereum: Nền kinh tế ẩn sau mỗi giao dịch

What Is the Solana Gulf Stream? How It Differs From Traditional Mempool Architectures

Solana Gulf Stream nədir? Ənənəvi Mempool Memarlıqlarından Necə Fərqlidir

¿Qué es la Corriente del Golfo de Solana? ¿En qué se diferencia de las arquitecturas tradicionales de Mempool?

Cos'è la Corrente del Golfo di Solana? In che modo differisce dalle architetture tradizionali Mempool

Solana Gulf Stream とは何か? 従来の Mempool アーキテクチャとの違い

Солана шығанағы ағыны дегеніміз не? Оның дәстүрлі Mempool архитектураларынан айырмашылығы

Kas ir Solanas Golfa straume? Kā tas atšķiras no tradicionālās Mempool arhitektūras

Czym jest Solana Gulf Stream? Czym różni się od tradycyjnych architektur Mempool

Что такое Solana Gulf Stream? Чем он отличается от традиционных архитектур Mempool

Umugezi wa Solana ni iki? Nigute Itandukana na Gakondo ya Mempool yubatswe

Çfarë është Rryma e Gjirit Solana? Si ndryshon nga arkitekturat tradicionale të Mempool

Що таке Гольфстрім Солана? Чим вона відрізняється від традиційної архітектури Mempool

Solana Gulf Stream 是什么？它与传统 Mempool 架构有何不同

In traditional blockchains, block propagation occurs through a peer-to-peer network in which new blocks and transactions are broadcast to all propagating nodes. This process usually happens sequentially or is flooded. However, this method is not scalable, as it becomes difficult to manage as the network grows due to the sheer number of communications. Let’s consider a network of 35,000 validators. The leader needs to transmit a 128 MB block (about 500,000 transactions at 250 bytes per transaction) to all 35,000 validators. The traditional block propagation implementation would require the leader to have a unique connection to each validator and transmit the full 128 MB 35,000 times. This would result in a total data transmission of 4.69 TB, which is far beyond typical bandwidth capabilities and would be infeasible to accommodate with so many connections. To solve the issue of slow block dissemination, Solana introduced Turbine, a multi-layer block propagation mechanism used by clusters to broadcast ledger entries to all nodes. Broadly speaking, Turbine breaks blocks into smaller blocks and disseminates them through an order of nodes, reducing the load on an individual node. What is Turbine? Turbine's architecture was heavily inspired by BitTorrent's. Both rely on data chunking—breaking large data into smaller pieces—and utilize the peer-to-peer network to disseminate data. Turbine is optimized for streaming. It transmits data using UDP only, which provides latency benefits. It implements a random path per packet through the network as leaders (block producers) stream their data. This high speed of block propagation allows Solana to maintain its high throughput. More so, Turbine addresses the issue of data availability, ensuring that all nodes can access the required data to validate transactions efficiently. This is done without requiring an enormous amount of bandwidth, which is a common bottleneck in other blockchain networks. The block propagation process of Turbine Before a block is propagated, the leader builds and orders the block given the incoming stream of transactions. After the block is built, it is ready to be sent via Turbine to the rest of the network. This process is referred to as block propagation. Messages are then passed between validators, and these messages are encapsulated within the block data to satisfy either the commitment status “confirmed” or "finalized.". block propagation block propagation While leaders build and propose entire blocks, the actual data is sent as shreds (partial blocks) to other validators in the network. Shreds are the atomic units sent between validators. This process of shredding and propagation ensures a swift and efficient distribution of block data across Solana, maintaining high throughput and network security. Note: A confirmed block has received a supermajority of ledger votes, whereas a finalized block has been confirmed and has 31+ confirmed blocks built atop the target block. Note: A confirmed block has received a supermajority of ledger votes, whereas a finalized block has been confirmed and has 31+ confirmed blocks built atop the target block. Note: A confirmed block has received a supermajority of ledger votes, whereas a finalized block has been confirmed and has 31+ confirmed blocks built atop the target block. Erasure Coding Before being sent to the Turbine Tree, shreds are encoded with Reed-Solomon erasure coding, a data protection method that uses specific types of error-correcting code. This method is designed to protect against data loss in case of failures or errors. It divides data into smaller blocks and encodes each block with extra information. Turbine relies heavily on the retransmission of packets between validators who could decide to rebroadcast incorrect data or incomplete data. Due to retransmission, any network-wide packet loss is compounded, and the probability of the packet failing to reach its destination increases on each hop. For instance, if a leader transmits 20% of the block’s packets as erasure codes, then the network can drop any 20% of the packets without losing the block. Leaders can dynamically adjust this number (FEC rate) based on network conditions by taking into account variables such as recently observed network-wide packet loss and tree depth. In Solana, data shreds are partial blocks from the original block built by the leader, and the recovery shreds are the Reed-Solomon-generated erasure-coded blocks. Blocks on Solana typically leverage an FEC of 32:32 (32 out of 64 packets can be lost with no need to re-transmit). Turbine Tree Solana uses a Turbine Tree, a structured network topology, to facilitate the efficient propagation of shreds among validators. When shreds are properly encoded into their respective shred groups, they are ready to be disseminated through the Turbine Tree to inform other validators in the network of the most updated state. The Turbine Tree, being known to all, ensures each validator knows exactly where they are responsible for relaying that shred. The Turbine Tree is typically a 2- or 3-hop tree (depending on the number of active validators), given the current DATA_PLANE_FANOUT value of 200. Furthermore, nodes can resort to gossip and repair if they don't receive enough shreds or if the loss rate exceeds the FEC rate. Under the current implementation, a node lacking enough shreds to reconstruct the block sends a request to the leader for re-transmission. Under deterministic Turbine, any node that receives the full block can send the repair shreds that the requesting node needs, thus pushing data transmission further down to the areas of the tree that are requesting data. Conclusion In this report, we examined Solana's Turbine block propagation mechanism, which breaks large blocks into smaller shreds to efficiently distribute data across the network. We highlighted how erasure coding ensures data integrity and how the Turbine Tree facilitates swift transmission among validators.

Solana’s Block Propagation Problem Was Huge—Here’s How They Fixed It

كانت مشكلة انتشار الكتل في Solana هائلة - وإليك كيفية إصلاحها

El problema de propagación de bloques de Solana era grave: así lo solucionaron

בעיית התפשטות הבלוק של סולאנה הייתה ענקית - הנה איך הם תיקנו את זה

Solana のブロック伝播問題は深刻だったが、どのように解決されたのか

სოლანას ბლოკის გამრავლების პრობლემა უზარმაზარი იყო - აი, როგორ მოაგვარეს ისინი

Bothata bja Solana bja go Phatlalatša Diboloko e be e le bjo Bogolo—Here’s How They Fixed it

Solanaq Bloquekuna mast’arikuynin sasachakuyninqa hatunmi karqan —Kaypin imaynatachus allicharqanku

Dambudziko raSolana Rokuparadzira Dambudziko Raive Hombe-Heino Magadzirirwo Avakarigadzirisa

Solana'nın Blok Yayılım Sorunu Çok Büyüktü - İşte Bunu Nasıl Düzelttikleri

Проблема Солани з розповсюдженням блоків була величезною — ось як вони її виправили

سولانا کے بلاک پروپیگیشن کا مسئلہ بہت بڑا تھا - یہ ہے کہ انہوں نے اسے کیسے ٹھیک کیا

Vấn đề lan truyền khối của Solana rất lớn—Đây là cách họ khắc phục

https://open.spotify.com/episode/76y8RrdWwdnlhyNsFFbNFx?si=GniO7qcNQWWE-yDpoQyNYw&embedable=true https://open.spotify.com/episode/76y8RrdWwdnlhyNsFFbNFx?si=GniO7qcNQWWE-yDpoQyNYw&embedable=true Weaponized Apathy Your brain is being systematically hijacked. Full stop. Your brain is being systematically hijacked. Full stop. Every notification, every headline, every social validation marker—they're all targeting the same neural pathway that evolution designed for genuine survival threats. The amygdala doesn't distinguish between a saber-toothed tiger and a negative comment on your post. It's the same biochemical cascade. This isn't philosophy. This is neuroscience. And it's time we applied it ruthlessly. Most people waste their limited cognitive resources on things that don't matter. They drain their mental energy worrying about what someone said about them online, political arguments that change nothing, minor inconveniences that are forgotten in a week, and manufactured outrage designed to hijack attention. Meanwhile, they ignore the things that actually move the needle in their lives. It's time to take back control. It's time to take back control. The Neurobiology of Not Giving a Damn Your average human has approximately 70,000 thoughts per day. Research indicates that 80% are negative and 95% are repetitive. This isn't random. Your default mode network—the brain circuitry active when you're not focused on a specific task—is constantly scanning for threats. It's literally wired to give a fuck about everything, especially potential social rejection. default mode network Weaponized apathy isn't about becoming emotionally dead. It's about neural optimization—ruthlessly eliminating emotional expenditure on stimuli that deliver zero evolutionary advantage. ruthlessly eliminating emotional expenditure on stimuli that deliver zero evolutionary advantage The modern world is bombarding you with stimuli your brain was never designed to handle. Your ancestors never had to process 24/7 news cycles of global catastrophes. They never had access to the opinions of thousands of strangers. They weren't constantly comparing themselves to curated highlight reels. They didn't have unlimited access to information without context. Your brain processes this onslaught as if each piece of information represents an immediate threat. This leaves you perpetually stuck in a low-grade fight-or-flight response, pumping out cortisol and adrenaline in response to things you can't control. Let me tell you something nobody else will: You are not morally obligated to care about everything. You are not morally obligated to care about everything. In fact, caring about everything is the fastest path to achieving nothing. The Attention Death Spiral Here's what they won't tell you: Your ability to care is not unlimited. It's a finite neurological resource. Your ability to care is not unlimited. finite neurological resource Every ounce of emotional bandwidth you waste on Instagram likes, political arguments with strangers, office gossip, and perceived slights is bandwidth stolen from the neural pathways that could be dedicated to your actual goals. Every time you allow your nervous system to activate over something inconsequential, you're literally restructuring your brain to become more reactive and less effective. This is the attention death spiral: You engage with emotional triggers. Your brain gets a hit of stress hormones. You become more sensitized to similar triggers. Your attention becomes increasingly fragmented. Your ability to focus on high-value activities diminishes. You become less effective at everything that matters. Repeat until your life consists entirely of reacting to bullshit. Breaking free from this spiral isn't just nice to have. It's the foundational skill of effective entrepreneurs in the digital age. Radical Neuroplasticity Protocol The solution isn't gentle mindfulness. It's aggressive neural rewiring. It's aggressive neural rewiring. Your brain's default settings are optimized for survival in conditions that no longer exist. It's time to update the operating system. Step 1: The Neurochemical Reset Step 1: The Neurochemical Reset Your brain has been conditioned to react emotionally to things that don't matter. It's time to break that pattern. When you notice an emotional trigger, observe it without engaging. This isn't about suppression – it's about rewiring. observe it without engaging Think of it like this: When your brain signals "This deserves your attention," simply respond: "This offers no real advantage. Not worth my bandwidth." "This offers no real advantage. Not worth my bandwidth." Start small: Read headlines without clicking the bait. Scroll past outrage-inducing posts without commenting. Notice criticism without feeling compelled to defend yourself. With practice, your automatic emotional reactions will weaken and eventually disappear. Step 2: Dopamine Redirection Step 2: Dopamine Redirection Social media platforms have hacked your brain's reward system. They've trained you to chase likes, comments, and arguments for cheap dopamine hits. Time to take your brain chemistry back. Create new reward pathways around selective apathy: Feel a sense of victory when you choose not to engage. Enjoy the peace of mind that comes from ignoring manufactured drama. Immediately do something you genuinely enjoy after successfully disengaging. For example, when you resist jumping into an online argument, go make your favorite coffee or take a quick walk. Your brain will start associating selective attention with pleasure instead of FOMO. Step 3: The Evolutionary Audit Step 3: The Evolutionary Audit Our brains evolved to manage about 150 relationships and respond to immediate physical threats. Not infinite social drama and global catastrophes. When something demands your attention, run it through this filter: Does it directly impact my physical safety or health? Does it affect my ability to advance my goals or status? Does it significantly impact the people I genuinely care about? If you answer "no" to all three, your concern is an evolutionary mismatch. Let it go. I use this daily. When I catch my mind spinning on something, I ask myself if it's truly relevant to my survival, goals, or inner circle. If not, I consciously redirect to something that is. The Strategic Advantage of Selective Concern Those who can choose what they care about operate on an entirely different level from those who can't. While others are depleting their cortisol reserves over social media debates, burning cognitive resources on office politics, and wasting neural real estate on celebrity opinions, you'll be preserving your executive function capabilities and directing neurological resources toward high-value targets. Your competitors cannot defeat what they cannot emotionally manipulate. Your competitors cannot defeat what they cannot emotionally manipulate. Consider Steve Jobs. Consider Steve Jobs. In his biography, Walter Isaacson details how Jobs practiced "extreme focus" - not just in product design, but in emotional investment. Jobs famously wore the same outfit daily to eliminate decision fatigue and maintained a ruthless approach to saying "no" to projects, features, and concerns that didn't align with his core vision. This wasn't just about productivity - it was about preserving cognitive and emotional bandwidth for what actually mattered. While competitors spread themselves thin trying to please everyone, Jobs concentrated Apple's resources on creating breakthrough products that redefined entire industries. As he said: "People think focus means saying yes to the thing you've got to focus on. But that's not what it means at all. It means saying no to the hundred other good ideas that there are. You have to pick carefully." "People think focus means saying yes to the thing you've got to focus on. But that's not what it means at all. It means saying no to the hundred other good ideas that there are. You have to pick carefully." The same principle applies to your emotional investments. Applied Neurological Freedom Let's get practical. Let's get practical. I used to waste mental energy on everything. Industry gossip, social media arguments, and news cycles that changed daily – all living rent-free in my head while my actual goals collected dust. I had to develop practical filters or watch my ambitions die a slow death by a thousand distractions. Here are three approaches that actually work: 1. The 72-Hour Test 1. The 72-Hour Test When something triggers an emotional response, ask yourself: "Will this matter 72 hours from now?" "Will this matter 72 hours from now?" That's it. Simple but ruthless. Most concerns fail this test instantly. Arguments online, minor criticisms, small rejections – they feel world-ending at the moment but evaporate within days. Elon Musk does something similar. When asked how he handles criticism, he said he filters by asking if it will matter in a week or a month. His answer: "Almost never." This isn't about ignoring legitimate problems. It's about not treating every input as equally deserving of your limited mental bandwidth. 2. Impact-to-Effort Ratio 2. Impact-to-Effort Ratio For everything demanding your attention, quickly evaluate: Potential impact ÷ Energy required Potential impact ÷ Energy required This cuts through emotional noise and forces you to be rational about where your attention goes. A project that might transform your business but requires focused work: high priority. A random comment questioning your approach that would take hours to address: low priority. The math doesn't lie. When you start calculating this consciously, you'll be shocked at what you've been prioritizing. 3. The Intervention Question 3. The Intervention Question For any concern, ask: "Can I directly affect this situation?" "Can I directly affect this situation?" If you can't meaningfully intervene, it doesn't deserve your sustained attention. Global news cycles, other people's opinions, problems from five years ago – none of these pass this filter. This isn't callousness. It's acknowledging reality. Your emotional investment should align with your ability to influence outcomes. Strategic Focus vs. Reactive Attention Here's what I've observed after years of working with entrepreneurs: Those who can selectively deploy their attention consistently outperform those who can't. Not because they work harder. Because they work cleaner. Look at any high performer you respect. They're not reacting to everything. They're proactively focused on what moves their needle. Bill Gates doesn't split his foundation's resources across thousands of issues. He chooses specific problems where focused attention can create leverage. Elite athletes don't waste mental energy worrying about the crowd or their last mistakes during critical moments. They channel 100% of their focus to execution. This isn't dismissing real concerns. It's strategic allocation of a finite resource – your attention. When I work with entrepreneurs who feel scattered, we conduct a simple exercise: Track exactly where your mental energy goes for three days. Note what you get in return from each investment. Identify what high-value opportunities are being neglected. The pattern is consistent: most are investing heavily in low-return mental activities while neglecting the few that would create real leverage. The Last Fuck You'll Ever Need to Give Here's the cognitive trap: Here's the cognitive trap: You've been conditioned to believe that caring about everything makes you a better person. The truth? Indiscriminate concern is not a virtue. It's neurological self-sabotage. Indiscriminate concern is not a virtue. It's neurological self-sabotage. The people who achieve extraordinary outcomes aren't caring more—they're caring with laser precision. They've mastered the ultimate psychological skill: the ability to choose, without guilt or hesitation, exactly where their finite neurological resources will be deployed. They understand that every unnecessary fuck given is a strategic opportunity lost. In my own work, I've seen this pattern repeatedly. The entrepreneurs who break through aren't the ones who care about everything - they're the ones who ruthlessly prioritize their emotional bandwidth for what matters most. This isn't about becoming cold. It's about becoming calibrated. It's about becoming calibrated. This isn't philosophy. It's neurology. Your brain has limited bandwidth. Deploy it wisely. Your brain has limited bandwidth. Deploy it wisely. Neurological Sovereignty What we're really talking about is neurological sovereignty – reclaiming your brain from those who profit from hijacking it. This isn't just self-help. It's revolution. It's revolution. Your attention is the most valuable commodity on the planet right now. Companies worth billions exist solely to capture and monetize it. Naval Ravikant nails it: "The modern devil is cheap dopamine." "The modern devil is cheap dopamine." Your brain's reward systems have been weaponized against you. You've been programmed to seek emotional engagement with things that don't matter. Taking back control isn't easy. Your brain will resist because it's addicted. Society will resist because your independence threatens the status quo. You'll face accusations of being disconnected, cold, or uncaring. But remember: But remember: You can't set yourself on fire to keep everyone else warm. You can't set yourself on fire to keep everyone else warm. You can't contribute anything meaningful if your brain is scattered across a thousand meaningless concerns. You can't achieve your vision if you're constantly reacting instead of creating. The path forward isn't complicated, but it requires discipline: Choose ruthlessly what gets access to your attention. Practice daily disengagement from low-value concerns. Redirect your mental energy toward what actually moves the needle. Make this your default operating system. Your neurology is too valuable to waste on things that drain without giving back. Your attention is too precious to surrender to those who profit from your anxiety. Your potential is too important to sacrifice on the altar of indiscriminate concern. It's time to take back control. It's time to take back control. It's time for neurological sovereignty. It's time for neurological sovereignty. – Scott

Neurological Sovereignty: The Neurobiology of Not Giving a Damn

Have a "Signature Something" and 26 Other Lifehacks for Being Eccentrically Charming

The Role of Volunteer Computing in Decentralized FaaS for Web3 Applications

Lunar New Year Travel is a Weird Experiment to See How Much Humans Will Endure for Love

চন্দ্র নববর্ষ ভ্রমণ হল একটি অদ্ভুত পরীক্ষা যা দেখার জন্য মানুষ কতটা ভালবাসার জন্য সহ্য করবে

Putovanje po lunarnoj novoj godini je čudan eksperiment da vidimo koliko će ljudi izdržati zbog ljubavi

Reisen zum chinesischen Neujahr sind ein seltsames Experiment, um herauszufinden, wie viel Menschen für die Liebe ertragen können

El viaje del Año Nuevo Lunar es un experimento extraño para ver cuánto aguantan los humanos por amor

Ilargi Urte Berriko bidaiak Esperimentu bitxi bat da gizakiek maitasunagatik zenbat jasango duten ikusteko

سفر سال نو قمری آزمایشی عجیب است برای اینکه ببینیم انسان ها چقدر برای عشق تحمل خواهند کرد

Voyager à l'occasion du Nouvel An lunaire est une expérience étrange pour voir jusqu'où les humains peuvent aller par amour

चंद्र नववर्ष यात्रा एक अजीब प्रयोग है, जिससे पता चलता है कि मनुष्य प्रेम के लिए कितना कुछ सह सकता है

旧正月の旅行は、人間が愛のためにどれだけ耐えられるかを見る奇妙な実験である

음력설 여행은 인간이 사랑을 위해 얼마나 오래 버틸 수 있는지 알아보는 이상한 실험이다

Reizen tijdens het Chinese Nieuwjaar is een vreemd experiment om te zien hoeveel mensen zullen doorstaan voor de liefde

A viagem do Ano Novo Lunar é um experimento estranho para ver o quanto os humanos suportarão por amor

Killa musuq wata puriyqa huk extraño experimentom qawanapaq maykamam runakuna munakuywan aguantanqaku

Путешествие на Лунный Новый год — это странный эксперимент, чтобы увидеть, на что способны люди ради любви

Ukwezi kwumwaka mushya ni ukwezi kudasanzwe kugirango turebe uko abantu bazihanganira urukundo

Kufamba Kwegore Idzva Kwemwedzi Chiyedzo Chisinganzwisisike Kuona Kuti Vanhu Vachatsungirira Zvakadii Nerudo

ወርሓዊ ሓድሽ ዓመት ጉዕዞ ደቂ ሰባት ንፍቕሪ ክሳብ ክንደይ ክጻወሩ ምዃኖም ንምርኣይ ዝግበር ጓና ፈተነ እዩ።

Ay Yeni Yılı Seyahati, İnsanların Aşk İçin Ne Kadar Dayanabileceğini Görmek İçin Garip Bir Deneyimdir

Du lịch Tết Nguyên Đán là một thử nghiệm kỳ lạ để xem con người sẽ chịu đựng được bao nhiêu vì tình yêu

农历新年旅行是一次奇怪的实验，看看人类能为爱情忍受多少

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