Even

An Antipattern describes a common response to a problem that is ineffective and creates negative consequences. Alternatively, Design Patterns are common approaches to common problems which have been formalized and are generally considered a good practice. Antipatterns are the opposite and are undesirable. The Chatty Input / Output (I/O) Anti pattern occurs when the cumulative effect of a large number of Input and Output requests has a significant negative impact on the performance and responsiveness of a service. Network calls and other I/O operations are inherently slow compared to compute tasks. Each I/O request typically has significant overhead, and the cumulative effect of numerous I/O operations can slow down the service. Potential Sources Application components that can be the source of chatty I/O include: Databases: Can become chatty when a single use case or business transaction results in multiple queries to the same set of data.
APIs: Requiring multiple network calls for a single user operation will slow down an application as each call contains data overhead (i.e. sender information, headers, authentication).
File Operations: An application that continually reads and writes small amounts of information to a file will generate significant I/O overhead. Small write requests can also lead to file fragmentation, slowing subsequent I/O operations still further. Examples of Chatty I/O The following examples demonstrate the Antipattern. Reading and writing individual records to a database as distinct requests The following example reads from a database of products. There are three tables, Product, ProductSubcategory, and ProductPriceListHistory. The code retrieves all of the products in a subcategory, along with the pricing information, by executing a series of queries: Query the subcategory from the ProductSubcategory table.
Find all products in that subcategory by querying the Product table.
For each product, query the pricing data from the ProductListPriceHistory table. The application uses Entity Framework, a layer of software that enables developers to work with data using objects without focusing on the underlying database where this data is stored. The N+1 Problem The example above shows the problem explicitly, but sometimes an Object-Relational Mapping (ORM) can mask the problem, if it implicitly fetches child records one at a time. This is known as the "N+1 problem". The N+1 query problem happens when the data access framework executed N additional SQL statements to fetch the same data that could have been retrieved when executing the primary SQL query. The larger the value of N, the more queries will be executed and the larger the performance impact. Implementing a single logical operation as a series of HTTP requests This often happens when developers try to follow an object-oriented paradigm and treat remote objects as if they were local objects in memory. This can result in too many network round trips. For example, the following web API exposes the individual properties of User objects through individual HTTP GET methods. While there's nothing technically wrong with this approach, most clients will probably need to get several properties for each User, resulting in client code like the following. Reading and writing to a file on disk File I/O involves opening a file and moving to the appropriate point before reading or writing data. When the operation is complete, the file might be closed to save operating system resources. An application that continually reads and writes small amounts of information to a file will generate significant I/O overhead. Small write requests can also lead to file fragmentation, slowing subsequent I/O operations still further. The following example uses a FileStream to write a Customer object to a file. Creating the FileStream opens the file, and disposing it closes the file. (The using statement automatically disposes the FileStream object.) If the application calls this method repeatedly as new customers are added, the I/O overhead can accumulate quickly. How to Fix the Problem Some of the remedies for fixing this Antipattern include: Fetch data from a database as a single query, instead of several smaller queries.
Follow REST design principles for web APIs.
Buffer data in memory for file operations and then write data once Database: Fetch Data in a Single Query Fetch data from a database as a single query, instead of several smaller queries. Here's a revised version of the code that retrieves product information. API: Use REST Principles Follow REST design principles for web APIs. Here's a revised version of the web API from the earlier example. Instead of separate GET methods for each property, there is a single GET method that returns the User. This results in a larger response body per request, but each client is likely to make fewer API calls. File: Buffer Data in Memory For file I/O, consider buffering data in memory and then writing the buffered data to a file as a single operation. This approach reduces the overhead from repeatedly opening and closing the file and helps to reduce fragmentation of the file on disk. Service Granularity The Service Granularity Principle is a software design concern that specifies the scope of business functionality and the structure of the message payload in a service operation. Achieving optimal service granularity can be difficult. Too granular and the service can become chatty while too course can be inefficient by retrieving more data than is required. The Use Case for the service must be matched with the proper service granularity to ensure an efficient design which avoids the Chatty IO AntiPattern. Detecting the Problem Symptoms of chatty I/O include high latency and low throughput. End users are likely to report extended response times or failures caused by services timing out, due to increased contention for I/O resources. Process Monitoring Perform process monitoring of the production system to identify operations with poor response times.
Perform load testing of each operation identified in the previous step.
During the load tests, gather telemetry data about the data access requests made by each operation.
Gather detailed statistics for each request sent to a data store.
Profile the application in the test environment to detect possible, I/O bottlenecks Look for any of these symptoms: large number of small I/O requests made to the same file.
large number of small network requests made by an application instance to the same service.
large number of small requests made by an application instance to the same data store.
Applications and services becoming I/O bound. Monitor the Application Using App Dynamics Using an Application Performance Monitoring (APM) package such as App Dynamics to capture and analyze key metrics that can identify chatty I/O. The key performance metrics will vary depending on the I/O workload. For example, database, file or API. Summary Chatty I/O is an Anti pattern that can occur within several parts of an application or service including the database, file operations and inter-process communications with APIs. The cumulative effect of a large number of I/O requests can have a significant impact on performance and responsiveness. Several monitoring techniques can be used to find the root cause of the chatty component. Software designs should consider the Service Granularity Principle. That is, scoping data retrieval to match the Use Case so the appropriate amount of data is retrieved in each request. Thanks & Happy reading! An Antipattern describes a common response to a problem that is ineffective and creates negative consequences. Alternatively, Design Patterns are common approaches to common problems which have been formalized and are generally considered a good practice. Antipatterns are the opposite and are undesirable . undesirable The Chatty Input / Output (I/O) Anti pattern occurs when the cumulative effect of a large number of Input and Output requests has a significant negative impact on the performance and responsiveness of a service. Network calls and other I/O operations are inherently slow compared to compute tasks. Each I/O request typically has significant overhead, and the cumulative effect of numerous I/O operations can slow down the service. Potential Sources Application components that can be the source of chatty I/O include: Databases: Can become chatty when a single use case or business transaction results in multiple queries to the same set of data. APIs: Requiring multiple network calls for a single user operation will slow down an application as each call contains data overhead (i.e. sender information, headers, authentication). File Operations: An application that continually reads and writes small amounts of information to a file will generate significant I/O overhead. Small write requests can also lead to file fragmentation, slowing subsequent I/O operations still further. Databases: Can become chatty when a single use case or business transaction results in multiple queries to the same set of data. Databases: Databases: APIs: Requiring multiple network calls for a single user operation will slow down an application as each call contains data overhead (i.e. sender information, headers, authentication). APIs: APIs: File Operations: An application that continually reads and writes small amounts of information to a file will generate significant I/O overhead. Small write requests can also lead to file fragmentation, slowing subsequent I/O operations still further. File Operations: File Operations: Examples of Chatty I/O The following examples demonstrate the Antipattern. Reading and writing individual records to a database as distinct requests The following example reads from a database of products. There are three tables, Product, ProductSubcategory, and ProductPriceListHistory. The code retrieves all of the products in a subcategory, along with the pricing information, by executing a series of queries: Query the subcategory from the ProductSubcategory table. Find all products in that subcategory by querying the Product table. For each product, query the pricing data from the ProductListPriceHistory table. Query the subcategory from the ProductSubcategory table. Find all products in that subcategory by querying the Product table. For each product, query the pricing data from the ProductListPriceHistory table. The application uses Entity Framework, a layer of software that enables developers to work with data using objects without focusing on the underlying database where this data is stored. The N+1 Problem The example above shows the problem explicitly, but sometimes an Object-Relational Mapping (ORM) can mask the problem, if it implicitly fetches child records one at a time. This is known as the "N+1 problem". The N+1 query problem happens when the data access framework executed N additional SQL statements to fetch the same data that could have been retrieved when executing the primary SQL query. The larger the value of N, the more queries will be executed and the larger the performance impact. Implementing a single logical operation as a series of HTTP requests This often happens when developers try to follow an object-oriented paradigm and treat remote objects as if they were local objects in memory. This can result in too many network round trips. For example, the following web API exposes the individual properties of User objects through individual HTTP GET methods. While there's nothing technically wrong with this approach, most clients will probably need to get several properties for each User, resulting in client code like the following. Reading and writing to a file on disk File I/O involves opening a file and moving to the appropriate point before reading or writing data. When the operation is complete, the file might be closed to save operating system resources. An application that continually reads and writes small amounts of information to a file will generate significant I/O overhead. Small write requests can also lead to file fragmentation, slowing subsequent I/O operations still further. The following example uses a FileStream to write a Customer object to a file. Creating the FileStream opens the file, and disposing it closes the file. (The using statement automatically disposes the FileStream object.) If the application calls this method repeatedly as new customers are added, the I/O overhead can accumulate quickly. How to Fix the Problem Some of the remedies for fixing this Antipattern include: Fetch data from a database as a single query, instead of several smaller queries. Follow REST design principles for web APIs. Buffer data in memory for file operations and then write data once Fetch data from a database as a single query, instead of several smaller queries. Follow REST design principles for web APIs. Buffer data in memory for file operations and then write data once Database: Fetch Data in a Single Query Fetch data from a database as a single query, instead of several smaller queries. Here's a revised version of the code that retrieves product information. API: Use REST Principles Follow REST design principles for web APIs. Here's a revised version of the web API from the earlier example. Instead of separate GET methods for each property, there is a single GET method that returns the User. This results in a larger response body per request, but each client is likely to make fewer API calls. File: Buffer Data in Memory For file I/O, consider buffering data in memory and then writing the buffered data to a file as a single operation. This approach reduces the overhead from repeatedly opening and closing the file and helps to reduce fragmentation of the file on disk. Service Granularity The Service Granularity Principle is a software design concern that specifies the scope of business functionality and the structure of the message payload in a service operation. Achieving optimal service granularity can be difficult. Too granular and the service can become chatty while too course can be inefficient by retrieving more data than is required. The Use Case for the service must be matched with the proper service granularity to ensure an efficient design which avoids the Chatty IO AntiPattern. Detecting the Problem Symptoms of chatty I/O include high latency and low throughput. End users are likely to report extended response times or failures caused by services timing out, due to increased contention for I/O resources. Process Monitoring Perform process monitoring of the production system to identify operations with poor response times. Perform load testing of each operation identified in the previous step. During the load tests, gather telemetry data about the data access requests made by each operation. Gather detailed statistics for each request sent to a data store. Profile the application in the test environment to detect possible, I/O bottlenecks Perform process monitoring of the production system to identify operations with poor response times. Perform load testing of each operation identified in the previous step. During the load tests, gather telemetry data about the data access requests made by each operation. Gather detailed statistics for each request sent to a data store. Profile the application in the test environment to detect possible, I/O bottlenecks Look for any of these symptoms: large number of small I/O requests made to the same file. large number of small network requests made by an application instance to the same service. large number of small requests made by an application instance to the same data store. Applications and services becoming I/O bound. large number of small I/O requests made to the same file. large number of small network requests made by an application instance to the same service. large number of small requests made by an application instance to the same data store. Applications and services becoming I/O bound. Monitor the Application Using App Dynamics Using an Application Performance Monitoring (APM) package such as App Dynamics to capture and analyze key metrics that can identify chatty I/O. The key performance metrics will vary depending on the I/O workload. For example, database, file or API. Summary Chatty I/O is an Anti pattern that can occur within several parts of an application or service including the database, file operations and inter-process communications with APIs. The cumulative effect of a large number of I/O requests can have a significant impact on performance and responsiveness. Several monitoring techniques can be used to find the root cause of the chatty component. Software designs should consider the Service Granularity Principle. That is, scoping data retrieval to match the Use Case so the appropriate amount of data is retrieved in each request. Thanks & Happy reading!

Chatty I/O Is Killing Your App's Performance Without You Even Realizing It

This is Part 8 of a 11-part series based on the research paper “Reinforcement Learning In Agent-based Market Simulation: Unveiling Realistic Stylized Facts And Behavior”. Use the table of links below to navigate to the next part. Table of Links Part 1: Abstract & Introduction Part 2: Important Concepts Part 3: System Description Part 4: Agents & Simulation Details Part 5: Experiment Design Part 6: Continual Learning Part 7: Experiment Results Part 8: Market and Agent Responsiveness to External Events Part 9: Conclusion & References Part 10: Additional Simulation Results Part 11: Simulation Configuration 5.2 Market and Agent Responsiveness to External Events In the first experiment, we introduce a rule-based agent which performs a sequence of “flash sales”. During a flash sale event, this agent continuously sends sell orders (300 lots each) every second for a duration of 5 seconds. Then, the agent remains inactive for 400 seconds to allow the market to recover. The goal is to study how the market moves during and after the flash sale events and how the agents’ behavior changes after training with these events. Figure 7 depicts the price movement after the start of flash sale events. To create these plots we use the average price of multiple simulations normalized by the price at which flash sale started. The untrained group shows a significant and permanent price drop in contrast to the testing and continual training groups. In the testing group, prices recover to pre-flash-sale levels. However, the continual training group shows more realistic trajectories, with prices dropping and recovering to levels lower than the pre-sale prices. This price pattern aligns with descriptions of permanent and temporary price impact in [24]. We are also interested in how RL agents’ policy functions are affected when trained with flash sale events. Recall the action symmetric tweak and asymmetric tweak parameters control the prices of the limit orders sent by MM’s (see (1) and illustration in Figure 1). Intuitively, a larger symmetric tweak makes the agent place orders away from the best bid and ask (more conservatively). A positive asymmetric tweak indicates that the MM expects future higher prices than the current one, while a negative value indicates the MM expects the market to go down. When a flash sale event happens, market sell orders take liquidity from the bid side of the LOB and this results in an imbalanced LOB. In contrast, a balanced LOB means the volumes on the buy and sell sides are comparable. To determine the MM agents’ reaction we collect the states with balanced and imbalanced LOBs, and feed these states to the different agents’ policy functions. Figure 8 shows the action symmetric tweak values in the top row and the asymmetric tweak in the bottom row. We can see that when the order book is balanced the three groups output a similar distribution of actions (Figure 8b and 8d). However, when fed imbalanced LOBs (Figures 8a and 8c), the agents in the continual training group output higher symmetric price tweaks and lower asymmetric price tweaks, compared with the agents in the two other groups. Thus when a flash sale event is detected, the MM agents in the continual learning group tend to place orders deeper into the LOB and move the expected price equilibrium downward. This behavior aligns with the findings in literature [25, 26, 27]. These results show that the agents can adapt to different market conditions through continual learning. In this experiment, we only use continual learning Group A MM agents, as they need to adapt to changing market conditions. The LT agents are also from Group A but their reward function is changed through the evolution of the target buy/sell parameters. Specifically, Figure 9 shows the price process resulting from the activity of these informed LT agents. The four phases separated by dashed red lines are: Sell (0.3/0.4), Buy (0.4/0.35), Balanced buy and sell (0.4/0.4), and last Buy (0.4/0.3), the numbers in parenthesis indicate (buy fraction/sell fraction). We expect to see the price movement aligning with the target buy/sell parameters. Additionally, similar to the previous study we collect states in the first and the last phases (i.e., steps 0-10,000 when the price goes down and steps 30,000-36,000 when the price goes up). We feed these states to the MM’s policy function at the beginning of the day (Before) and at the end of the trading day (After). Figure 10 shows the distributions of the outputs in action symmetric and asymmetric tweak. In both scenarios when the price is going down and when the price is going up, the action symmetric tweak (top row) gets larger, thus the MM agent becomes more conservative and tends to enlarge the spread. This aligns with the findings in the previous experiment. We can also see that the distribution of price asymmetric tweak becomes negative when prices are going down and becomes positive when prices are going up. This means the learned agents change their expectations of future prices along with the observed market direction. In contrast, the asymmetric tweak average is close to zero for agents that are not continuously trained. This helps explain why the price tends to come back to pre-flash-sale values for the agents in Group B in Figure 7. Authors:
(1) Zhiyuan Yao, Stevens Institute of Technology, Hoboken, New Jersey, USA (zyao9@stevens.edu);
(2) Zheng Li, Stevens Institute of Technology, Hoboken, New Jersey, USA (zli149@stevens.edu);
(3) Matthew Thomas, Stevens Institute of Technology, Hoboken, New Jersey, USA (mthomas3@stevens.edu);
(4) Ionut Florescu, Stevens Institute of Technology, Hoboken, New Jersey, USA (ifloresc@stevens.edu). This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This is Part 8 of a 11-part series based on the research paper “Reinforcement Learning In Agent-based Market Simulation: Unveiling Realistic Stylized Facts And Behavior”. Use the table of links below to navigate to the next part. This is Part 8 of a 11-part series based on the research paper “ Reinforcement Learning In Agent-based Market Simulation: Unveiling Realistic Stylized Facts And Behavior ”. Use the table of links below to navigate to the next part. Part 8 Reinforcement Learning In Agent-based Market Simulation: Unveiling Realistic Stylized Facts And Behavior Table of Links Part 1: Abstract & Introduction Part 1: Abstract & Introduction Part 2: Important Concepts Part 2: Important Concepts Part 3: System Description Part 3: System Description Part 4: Agents & Simulation Details Part 4: Agents & Simulation Details Part 5: Experiment Design Part 5: Experiment Design Part 6: Continual Learning Part 6: Continual Learning Part 7: Experiment Results Part 7: Experiment Results Part 8: Market and Agent Responsiveness to External Events Part 8: Market and Agent Responsiveness to External Events Part 9: Conclusion & References Part 9: Conclusion & References Part 10: Additional Simulation Results Part 10: Additional Simulation Results Part 11: Simulation Configuration Part 11: Simulation Configuration 5.2 Market and Agent Responsiveness to External Events In the first experiment, we introduce a rule-based agent which performs a sequence of “flash sales”. During a flash sale event, this agent continuously sends sell orders (300 lots each) every second for a duration of 5 seconds. Then, the agent remains inactive for 400 seconds to allow the market to recover. The goal is to study how the market moves during and after the flash sale events and how the agents’ behavior changes after training with these events. Figure 7 depicts the price movement after the start of flash sale events. To create these plots we use the average price of multiple simulations normalized by the price at which flash sale started. The untrained group shows a significant and permanent price drop in contrast to the testing and continual training groups. In the testing group, prices recover to pre-flash-sale levels. However, the continual training group shows more realistic trajectories, with prices dropping and recovering to levels lower than the pre-sale prices. This price pattern aligns with descriptions of permanent and temporary price impact in [24]. We are also interested in how RL agents’ policy functions are affected when trained with flash sale events. Recall the action symmetric tweak and asymmetric tweak parameters control the prices of the limit orders sent by MM’s (see (1) and illustration in Figure 1). Intuitively, a larger symmetric tweak makes the agent place orders away from the best bid and ask (more conservatively). A positive asymmetric tweak indicates that the MM expects future higher prices than the current one, while a negative value indicates the MM expects the market to go down. When a flash sale event happens, market sell orders take liquidity from the bid side of the LOB and this results in an imbalanced LOB. In contrast, a balanced LOB means the volumes on the buy and sell sides are comparable. To determine the MM agents’ reaction we collect the states with balanced and imbalanced LOBs, and feed these states to the different agents’ policy functions. Figure 8 shows the action symmetric tweak values in the top row and the asymmetric tweak in the bottom row. We can see that when the order book is balanced the three groups output a similar distribution of actions (Figure 8b and 8d). However, when fed imbalanced LOBs (Figures 8a and 8c), the agents in the continual training group output higher symmetric price tweaks and lower asymmetric price tweaks, compared with the agents in the two other groups. Thus when a flash sale event is detected, the MM agents in the continual learning group tend to place orders deeper into the LOB and move the expected price equilibrium downward. This behavior aligns with the findings in literature [25, 26, 27]. These results show that the agents can adapt to different market conditions through continual learning. In this experiment, we only use continual learning Group A MM agents, as they need to adapt to changing market conditions. The LT agents are also from Group A but their reward function is changed through the evolution of the target buy/sell parameters. Specifically, Figure 9 shows the price process resulting from the activity of these informed LT agents. The four phases separated by dashed red lines are: Sell (0.3/0.4), Buy (0.4/0.35), Balanced buy and sell (0.4/0.4), and last Buy (0.4/0.3), the numbers in parenthesis indicate (buy fraction/sell fraction). We expect to see the price movement aligning with the target buy/sell parameters. Additionally, similar to the previous study we collect states in the first and the last phases (i.e., steps 0-10,000 when the price goes down and steps 30,000-36,000 when the price goes up). We feed these states to the MM’s policy function at the beginning of the day (Before) and at the end of the trading day (After). Figure 10 shows the distributions of the outputs in action symmetric and asymmetric tweak. In both scenarios when the price is going down and when the price is going up, the action symmetric tweak (top row) gets larger, thus the MM agent becomes more conservative and tends to enlarge the spread. This aligns with the findings in the previous experiment. We can also see that the distribution of price asymmetric tweak becomes negative when prices are going down and becomes positive when prices are going up. This means the learned agents change their expectations of future prices along with the observed market direction. In contrast, the asymmetric tweak average is close to zero for agents that are not continuously trained. This helps explain why the price tends to come back to pre-flash-sale values for the agents in Group B in Figure 7. Authors: (1) Zhiyuan Yao, Stevens Institute of Technology, Hoboken, New Jersey, USA (zyao9@stevens.edu); (2) Zheng Li, Stevens Institute of Technology, Hoboken, New Jersey, USA (zli149@stevens.edu); (3) Matthew Thomas, Stevens Institute of Technology, Hoboken, New Jersey, USA (mthomas3@stevens.edu); (4) Ionut Florescu, Stevens Institute of Technology, Hoboken, New Jersey, USA (ifloresc@stevens.edu). Authors: Authors: (1) Zhiyuan Yao, Stevens Institute of Technology, Hoboken, New Jersey, USA (zyao9@stevens.edu); (2) Zheng Li, Stevens Institute of Technology, Hoboken, New Jersey, USA (zli149@stevens.edu); (3) Matthew Thomas, Stevens Institute of Technology, Hoboken, New Jersey, USA (mthomas3@stevens.edu); (4) Ionut Florescu, Stevens Institute of Technology, Hoboken, New Jersey, USA (ifloresc@stevens.edu). This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

RL Agents Adapt to Flash Sale Events & Imbalanced Limit Order Books (LOBs)

LONDON, UK, December 31st, 2024/Chainwire/--FLock.io, the private AI training platform, has announced the official launch of its mainnet on Base. This launch coincides with the Token Generation Event (TGE) for its native token, FLOCK, which will serve as the utility token of the FLock ecosystem by incentivizing and rewarding community participation. The FLOCK token will debut on Bybit, offering participants in the incentivized testnet an airdrop of tokens at today’s TGE. Users have an opportunity to earn FLOCK by engaging in decentralized AI training through the platform, which is accessible via train.flock.io. These tokens will play a role in enabling collaboration among data providers, training nodes, and compute providers within FLock.io’s ecosystem. "The mainnet launch and TGE are huge steps forward for FLock.io," said Jiahao Sun, CEO and Co-Founder of FLock.io. "With the power of Base, we’re giving builders, users, anyone, the tools to create and collaborate on privacy-first AI models. And the FLOCK token is what ties it all together to make this ecosystem thrive." The mainnet launch is the culmination of a multi-year effort to build a decentralized, community-governed AI platform that prioritizes privacy and security in model development. Since its founding in 2022, FLock.io has raised a $6 million seed round in March 2024 and launched an incentivized testnet on Base Sepolia in May 2024. This progress attracted significant investment, with FLock.io securing a strategic funding round in December 2024, led by DCG. FLock.io’s platform has gained traction, with nearly 20,000 AI models created to date, with the ecosystem supported by over 600 training nodes, 1,000 validators, 63,000 delegators, and 700,000 end users of models. The platform’s products include AI Arena, a competitive AI development environment; FL Alliance, a federated learning collaboration platform; and the AI Marketplace, which enables users to interact with and deploy trained models. FLock.io’s platform ensures data ownership while facilitating community engagement through on-chain rewards to promote transparency and active participation. With over 30 advanced models, FLock.io has processed over 2.5 million model and validation submissions. FLock.io’s business partners include Morpheus, IOnet, and Aptos. As the only AI infrastructure project to receive the Ethereum Foundation Academic Grant in 2024, FLock.io represents a shift toward privacy-preserving, community-driven AI development, reducing dependence on centralized systems and empowering its global network of users. The mainnet launch and TGE pave the way for FLock.io’s next phase of privacy-focused, community-driven AI development on Base. Users can now find FLOCK on Bybit and CoinGecko exchanges. To learn more, users can visit flock.io. About FLock.io FLock.io is a community-driven platform facilitating the creation of private, on-chain AI models. By combining federated learning with blockchain technology, FLock.io offers a secure and collaborative environment for model training, ensuring data privacy and transparency. FLock.io’s ecosystem supports a diverse range of participants, including data providers, task creators, and AI developers, incentivizing engagement through its native FLOCK token. Users can learn more at FLock.io. Contact FLock.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here LONDON, UK, December 31st, 2024/Chainwire/--FLock.io, the private AI training platform, has announced the official launch of its mainnet on Base. This launch coincides with the Token Generation Event (TGE) for its native token, FLOCK, which will serve as the utility token of the FLock ecosystem by incentivizing and rewarding community participation. The FLOCK token will debut on Bybit, offering participants in the incentivized testnet an airdrop of tokens at today’s TGE. Users have an opportunity to earn FLOCK by engaging in decentralized AI training through the platform, which is accessible via train.flock.io . train.flock.io train.flock.io These tokens will play a role in enabling collaboration among data providers, training nodes, and compute providers within FLock.io’s ecosystem. "The mainnet launch and TGE are huge steps forward for FLock.io," said Jiahao Sun, CEO and Co-Founder of FLock.io. "With the power of Base, we’re giving builders, users, anyone, the tools to create and collaborate on privacy-first AI models. And the FLOCK token is what ties it all together to make this ecosystem thrive." "The mainnet launch and TGE are huge steps forward for FLock.io," said Jiahao Sun, CEO and Co-Founder of FLock.io. "With the power of Base, we’re giving builders, users, anyone, the tools to create and collaborate on privacy-first AI models. And the FLOCK token is what ties it all together to make this ecosystem thrive." The mainnet launch is the culmination of a multi-year effort to build a decentralized, community-governed AI platform that prioritizes privacy and security in model development. Since its founding in 2022, FLock.io has raised a $6 million seed round in March 2024 and launched an incentivized testnet on Base Sepolia in May 2024. This progress attracted significant investment, with FLock.io securing a strategic funding round in December 2024, led by DCG. FLock.io’s platform has gained traction, with nearly 20,000 AI models created to date, with the ecosystem supported by over 600 training nodes, 1,000 validators, 63,000 delegators, and 700,000 end users of models. The platform’s products include AI Arena, a competitive AI development environment; FL Alliance, a federated learning collaboration platform; and the AI Marketplace, which enables users to interact with and deploy trained models. FLock.io’s platform ensures data ownership while facilitating community engagement through on-chain rewards to promote transparency and active participation. With over 30 advanced models, FLock.io has processed over 2.5 million model and validation submissions. FLock.io’s business partners include Morpheus, IOnet, and Aptos. As the only AI infrastructure project to receive the Ethereum Foundation Academic Grant in 2024, FLock.io represents a shift toward privacy-preserving, community-driven AI development, reducing dependence on centralized systems and empowering its global network of users. The mainnet launch and TGE pave the way for FLock.io’s next phase of privacy-focused, community-driven AI development on Base. Users can now find FLOCK on Bybit and CoinGecko exchanges. To learn more, users can visit flock.io . flock.io flock.io About FLock.io FLock.io is a community-driven platform facilitating the creation of private, on-chain AI models. By combining federated learning with blockchain technology, FLock.io offers a secure and collaborative environment for model training, ensuring data privacy and transparency. FLock.io FLock.io FLock.io’s ecosystem supports a diverse range of participants, including data providers, task creators, and AI developers, incentivizing engagement through its native FLOCK token. Users can learn more at FLock.io . FLock.io FLock.io Contact FLock.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

FLock.io Announces Mainnet Launch And Token Generation Event On Base

أعلنت FLock.io عن إطلاق الشبكة الرئيسية وحدث توليد الرموز على القاعدة

FLock.io Əsas Şəbəkə Başlatması və Token Yaradılması Tədbirini Elan edir

Το FLock.io ανακοινώνει το Mainnet Launch And Token Generation Event On Base

FLock.io anuncia el lanzamiento de la red principal y el evento de generación de tokens en Base

FLock.io-k Mainnet-en abiaraztearen eta token-sorkuntzaren gertaera iragartzen du oinarrian

FLock.io Mengumumkan Peluncuran Mainnet dan Acara Pembuatan Token di Pangkalan

FLock.ioがBaseでのメインネットのローンチとトークン生成イベントを発表

FLock.io ປະກາດການເປີດຕົວ Mainnet ແລະເຫດການການສ້າງ Token ໃນຖານ

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Inanunsyo ng FLock.io ang Mainnet Launch At Token Generation Event sa Base

If You're So Smart, Why Aren't You Happy? The true test of intelligence is getting what you want out of life. It’s not about IQ scores, academic accolades, or your ability to solve puzzles; it’s about consistently aligning your behavior with your goals. Intelligence is a behavior, and understanding this can transform your life. Introduction: Behaviour is Everything Every circumstance in your life—success, failure, fear, shyness—is tied to behavior. This means you should abandon limiting beliefs like: - "That's just how I am." - "I was born this way." - "It's because of my parents." - "I’m an introvert." - "I'm a hot-tempered person." None of these define you. Instead, every action or result is the outcome of specific behaviors. Let’s break it down: - Poverty is a behavior. - Shyness is a behavior. - Honesty is a behavior. - Patience, focus, intelligence, discipline, anger, resilience—all behaviors. If everything is behavior, then the key to transformation lies in toggling it. Intelligence Words shape our understanding of behaviors, but dictionary definitions often fall short of practical use. They don't define most words based on "what to do". This makes things vague sometimes. If you simply define things into operations and steps you can take, things become clear and progress becomes easy. For example: - "Difficult" is redefined as a knowledge gap. If something feels hard, it’s because you lack the necessary knowledge. Fill that gap, and it becomes easy. This approach turns abstract concepts into actionable insights, helping you adjust your behavior to achieve desired results. Learning and Intelligence: Key Operational Definitions 1. Learning: Operational Definition: Same condition, new behavior.


Example: If I show you a red card and slap you, and next time you see the red card, you duck, you’ve learned. If not, you haven’t. 2. Intelligence: Operational Definition: Rate of learning.
Example: If it takes 10 slaps for you to learn to duck, and someone else learns after 4, they are more intelligent. The most intelligent person learns before the first slap. Intelligence is not fixed. You can increase it by improving your rate of learning. How to Get Smarter Two Ways to Boost Intelligence: 1. Improve Foresight: Enhance your perception and mental processing speed to anticipate consequences faster. This reduces the number of iterations required to learn. This is done with systems, mental models and leverage. 2. Increase Iterations: If you’re slower to learn, compensate by increasing the frequency of practice. For example, if it takes you 20 tries to master something, do 100 iterations while others do 10. Outpace them through sheer effort. If you can do both, you will be unstoppable. Both methods train your mental “muscle,” increasing your ability to adapt and grow. Practical Exercise: Ask yourself: "What do I have to do to [insert goal here]?" If you can’t break it down into immediate actions, it’s due to a knowledge gap. Start learning to fill that gap, and the path will become clear. Difficult Tasks and the Knowledge Gap Everything difficult can be made easier by acquiring knowledge. The formula is simple: 1. Learn: Change your behavior to match the situation. 2. Define the Goal: Break it down into actionable steps. If this is difficult, it means you need more knowledge. 3. Act: Apply the steps relentlessly until the difficult becomes easy. Turning Goals Into Actionable Behaviors Example 1: Patience - Goal: Get a nice car. - Actionable Behavior: Practice patience. - Operational Definition: Patience is doing something else in the meantime that doesn’t detract from the goal. - Action: Find tasks that build towards your goal while you wait. Example 2: Commitment - Goal: Become the best at what you do. - Actionable Behavior: Commitment - Operational Definition: Commitment is the elimination of alternatives. - Action: Eliminate distractions and focus solely on your craft. In the real world, you're going to break things down until it gets to the level you understand. After breaking down commitment, you may need to break down elimination, and other things under that. Worthwhile goals like becoming a billionaire will have a large number of things to break down a large number of times. Thoughts Are Useless Without Action Your thoughts serve no purpose unless they sharpen your actions. Instead of ruminating, ask: - "What specific actions can I take to move closer to my goal?" knowledge alone isn’t power; power comes from action. Using Anti-Goals: List behaviours that lead to failure and commit to doing the opposite. For instance: - Instead of trying to "be successful," avoid actions that cause failure. Success will naturally follow. Exercise: Flip the Script 1. Write down 5 behaviours that hinder your progress. 2. Flip them into actionable anti-goals. 3. Break each anti-goal into steps you can act on today. Closing Thoughts The true measure of intelligence is your ability to adjust behavior, learn quickly, and act consistently in order to get what you want. The faster you iterate and the more you refine your foresight, the closer you get to achieving your goals. So, if you’re so smart, why aren’t you happy? Happiness is a behaviour. Big love, Praise J.J. PS: I've compiled a directory of over 120 operational definitions to help you with everyday challenges. It's completely FREE, no daily limits, no email opt ins, no BS. You will find more information, the url, and much more in this article: https://hackernoon.com/dont-let-dictionaries-keep-you-stuck-understand-and-conquer-the-world-with-redefyne Enjoy! PSS: Subscribe to my newsletter for more insights: crive.substack.com If You're So Smart, Why Aren't You Happy? If You're So Smart, Why Aren't You Happy? The true test of intelligence is getting what you want out of life. It’s not about IQ scores, academic accolades, or your ability to solve puzzles; it’s about consistently aligning your behavior with your goals. Intelligence is a behavior, and understanding this can transform your life. Introduction: Behaviour is Everything Every circumstance in your life—success, failure, fear, shyness—is tied to behavior. This means you should abandon limiting beliefs like: - "That's just how I am." - "I was born this way." - "It's because of my parents." - "I’m an introvert." - "I'm a hot-tempered person." None of these define you. Instead, every action or result is the outcome of specific behaviors. Let’s break it down: - Poverty is a behavior. - Shyness is a behavior. - Honesty is a behavior. - Patience, focus, intelligence, discipline, anger, resilience—all behaviors. If everything is behavior, then the key to transformation lies in toggling it. Intelligence Words shape our understanding of behaviors, but dictionary definitions often fall short of practical use. They don't define most words based on "what to do". This makes things vague sometimes. If you simply define things into operations and steps you can take, things become clear and progress becomes easy. For example: - "Difficult" is redefined as a knowledge gap. If something feels hard, it’s because you lack the necessary knowledge. Fill that gap, and it becomes easy. This approach turns abstract concepts into actionable insights, helping you adjust your behavior to achieve desired results. Learning and Intelligence: Key Operational Definitions 1. Learning : Learning Operational Definition: Same condition, new behavior. Example: If I show you a red card and slap you, and next time you see the red card, you duck, you’ve learned. If not, you haven’t. Operational Definition: Same condition, new behavior. Operational Definition: Same condition, new behavior. Example: If I show you a red card and slap you, and next time you see the red card, you duck, you’ve learned. If not, you haven’t. Example: If I show you a red card and slap you, and next time you see the red card, you duck, you’ve learned. If not, you haven’t. 2. Intelligence : Intelligence Operational Definition: Rate of learning. Example: If it takes 10 slaps for you to learn to duck, and someone else learns after 4, they are more intelligent. The most intelligent person learns before the first slap. Operational Definition: Rate of learning. Example: If it takes 10 slaps for you to learn to duck, and someone else learns after 4, they are more intelligent. The most intelligent person learns before the first slap. Intelligence is not fixed. You can increase it by improving your rate of learning. How to Get Smarter Two Ways to Boost Intelligence: Two Ways to Boost Intelligence: 1. Improve Foresight: Improve Foresight: Enhance your perception and mental processing speed to anticipate consequences faster. This reduces the number of iterations required to learn. This is done with systems, mental models and leverage. Enhance your perception and mental processing speed to anticipate consequences faster. This reduces the number of iterations required to learn. This is done with systems, mental models and leverage. Enhance your perception and mental processing speed to anticipate consequences faster. This reduces the number of iterations required to learn. This is done with systems, mental models and leverage. 2. Increase Iterations: Increase Iterations: If you’re slower to learn, compensate by increasing the frequency of practice. For example, if it takes you 20 tries to master something, do 100 iterations while others do 10. Outpace them through sheer effort. If you’re slower to learn, compensate by increasing the frequency of practice. For example, if it takes you 20 tries to master something, do 100 iterations while others do 10. Outpace them through sheer effort. If you’re slower to learn, compensate by increasing the frequency of practice. For example, if it takes you 20 tries to master something, do 100 iterations while others do 10. Outpace them through sheer effort. If you can do both, you will be unstoppable. Both methods train your mental “muscle,” increasing your ability to adapt and grow. Practical Exercise: Practical Exercise: Ask yourself: "What do I have to do to [insert goal here]?" If you can’t break it down into immediate actions, it’s due to a knowledge gap. Start learning to fill that gap, and the path will become clear. Difficult Tasks and the Knowledge Gap Difficult Tasks and the Knowledge Gap Everything difficult can be made easier by acquiring knowledge. The formula is simple: 1. Learn: Change your behavior to match the situation. 2. Define the Goal: Break it down into actionable steps. If this is difficult, it means you need more knowledge. 3. Act: Apply the steps relentlessly until the difficult becomes easy. Turning Goals Into Actionable Behaviors Example 1: Patience - Goal: Get a nice car. - Actionable Behavior: Practice patience. - Operational Definition: Patience is doing something else in the meantime that doesn’t detract from the goal. - Action: Find tasks that build towards your goal while you wait. Example 2: Commitment - Goal: Become the best at what you do. - Actionable Behavior: Commitment - Operational Definition: Commitment is the elimination of alternatives. - Action: Eliminate distractions and focus solely on your craft. In the real world, you're going to break things down until it gets to the level you understand. After breaking down commitment, you may need to break down elimination, and other things under that. Worthwhile goals like becoming a billionaire will have a large number of things to break down a large number of times. Thoughts Are Useless Without Action Thoughts Are Useless Without Action Your thoughts serve no purpose unless they sharpen your actions. Instead of ruminating, ask: - "What specific actions can I take to move closer to my goal?" knowledge alone isn’t power; power comes from action. Using Anti-Goals: List behaviours that lead to failure and commit to doing the opposite. For instance: - Instead of trying to "be successful," avoid actions that cause failure. Success will naturally follow. Exercise: Flip the Script 1. Write down 5 behaviours that hinder your progress. 2. Flip them into actionable anti-goals. 3. Break each anti-goal into steps you can act on today. Closing Thoughts The true measure of intelligence is your ability to adjust behavior, learn quickly, and act consistently in order to get what you want. The faster you iterate and the more you refine your foresight, the closer you get to achieving your goals. So, if you’re so smart, why aren’t you happy? Happiness is a behaviour. Big love, Praise J.J. PS: I've compiled a directory of over 120 operational definitions to help you with everyday challenges. It's completely FREE, no daily limits, no email opt ins, no BS. You will find more information, the url, and much more in this article: https://hackernoon.com/dont-let-dictionaries-keep-you-stuck-understand-and-conquer-the-world-with-redefyne Enjoy! PSS: Subscribe to my newsletter for more insights: crive.substack.com

How to Increase Your Intelligence (even if You're Not Genetically Gifted)

Jak zvýšit svou inteligenci (i když nejste geneticky nadáni)

Cómo aumentar tu inteligencia (aunque no tengas dotes genéticas)

Hogyan lehet növelni intelligenciáját (még akkor is, ha nem vagy genetikailag tehetséges)

Ինչպես բարձրացնել ձեր ինտելեկտը (նույնիսկ եթե դուք գենետիկորեն օժտված չեք)

知能を高める方法（遺伝的に恵まれていなくても）

Kā palielināt savu intelektu (pat ja neesat ģenētiski apdāvināts)

Ahoana no hampitomboana ny faharanitan-tsainao (na dia tsy manana talenta ara-jeografika aza ianao)

Оюун ухаанаа хэрхэн нэмэгдүүлэх вэ (та генетикийн авъяасгүй байсан ч)

Tsela ya go Oketša Bohlale bja Gago (le ge o se na Mpho ya Leabela)

Jinsi ya Kuongeza Akili Yako (hata kama Huna Vipawa vya Kinasaba)

Чӣ тавр интеллекти худро зиёд кардан мумкин аст (ҳатто агар шумо аз ҷиҳати генетикӣ боистеъдод набошед)

Aql-idrokingizni qanday oshirish mumkin (hatto siz genetik qobiliyatga ega bo'lmasangiz ham)

“The best founders turn obstacles into opportunities and setbacks into setups for success.” 1. HackerNoon: What is your company in 2–5 words? Patrick Thean: Strategy alignment and execution. 2. Why is now the time for your company to exist? With a wave of high-profile CEOs stepping down – Peloton, Chevron, and others – it’s clear that today’s corporate leaders are under extraordinary pressure. Economic uncertainty, rising inflation, and the growing emphasis on ESG priorities have created an environment where the usual playbook no longer works. In this climate, companies must operate with sharper focus, tighter alignment, and better execution to navigate these challenges and thrive. Our approach helps organizations do that, equipping teams with the clarity and tools they need to succeed under today’s demanding conditions. 3. What do you love about your team, and why are you the ones to solve this problem? I love my team’s relentless drive to make things simpler and more effective for leaders and their teams. They bring deep experience, a strong sense of accountability, and a genuine passion for helping others succeed. Our ability to translate complex problems into practical, actionable solutions that deliver results sets us apart. We’ve lived through the same challenges our clients face – misalignment, unclear priorities, and execution gaps – and we’ve built a proven system that works because we’ve tested it in real-world scenarios. It’s not just about the tools we provide; it’s about the mindset and discipline we help instill to create lasting change. 4. If you weren’t building your startup, what would you be doing? If I weren’t building my company, I’d dedicate more of my time and energy to supporting causes I deeply care about, like Samaritan’s Feet. This incredible organization aimed to put 10 million pairs of shoes on the feet of 10 million children in 10 years, and their mission aligns with my passion for creating opportunities and hope for underserved communities. Whether through fundraising, hands-on involvement in shoe distribution events, or helping spread their message, I’d focus on expanding the impact of initiatives like this to transform lives and give kids a chance to dream bigger. 5. At the moment, how do you measure success? What are your metrics? Success is measured by our tangible impact on the leaders and teams we support. Our Rhythm Systems software helps organizations track success by focusing on clear priorities, creating alignment, and executing with discipline. The platform enables leaders to measure what matters most – achieving strategic goals, improving team accountability, or hitting key performance metrics. We look at client goal achievement rates, the consistency of execution rhythms, and the alignment of leadership teams. Beyond the data, success is also about the stories we hear – how our system has transformed how leaders manage their teams, navigate challenges, and achieve results faster and more effectively. When our clients win, that’s the ultimate measure of our success. 6. In a few sentences, what do you offer to whom? We help CEOs and leadership teams of growth-oriented mid-market companies achieve their goals faster and with less friction. We provide the tools, methodology, and coaching to clarify strategic priorities, align teams, and drive accountability with measurable KPIs. Our approach ensures leaders focus on what matters most, execute effectively, and make better decisions using real-time data. 7. What’s most exciting about your traction to date? What excites me most is seeing our system's real-world impact on our clients. Companies like MobilityWorks, which has achieved record revenue and profits, and AvidXchange, which has scaled from $40M to $100M in just two years, prove that our approach works. It’s not just about the numbers – it’s hearing from CEOs and teams who say our tools and methodology have transformed how they operate, helping them focus, align, and win. Watching our clients break through their ceilings and accomplish things they once thought impossible keeps us motivated daily. 8. Where do you think your growth will be next year? Next year, our growth continues to help CEOs and their leadership teams navigate the complexity of scaling their businesses. With the increasing challenges of hybrid work, economic uncertainty, and rapidly changing markets, more leaders seek practical tools to stay aligned and execute effectively. Rhythm Systems is positioned to meet that need with our platform, simplifying strategic planning and execution by investing in enhancements like AI-driven insights for better decision-making. Additionally, we’ll focus on expanding our reach through referrals and partnerships and sharing more success stories from clients who’ve used our system to achieve record results. It’s exciting as more companies realize the value of working with clarity, focus, and accountability. 9. Tell us about your first paying customer and revenue expectations over the next year. From start-up to IPO: AvidXchange became our first paying customer 16 years ago. They were growing dramatically and faced internal challenges as headcount quickly grew from 50 to 1,600 employees. Keeping teams aligned to meet key deliverables as the organization transformed was critical – but hard. Product launches, acquisitions, R&D, and customer success required seamless collaboration among multiple departments and teams. The risk of missing handoffs and falling short of customer commitments was greater than ever. How Rhythm Helped: Closing the communication gap around goals. Michael Praeger, CEO and Co-Founder of AvidXchange, committed to his team’s success through vertical and horizontal alignment. Rhythm launched a company-wide training initiative, focusing on how to use the software and methodology to promote transparency around goals. Rhythm coaches pinpointed areas of misalignment, which helped teams make crucial planning and execution adjustments to bridge the communication gap and pivot quickly when necessary. Key Learnings: Cross-functional team alignment allowed AvidXchange to increase productivity by 300%, resulting in a key product launching 6 months earlier than scheduled. By 2021, they made a successful IPO at a $2 billion valuation. Revenue expectations are always an exciting topic when looking ahead to next year. Clear goals and solid planning ensure a successful and sustainable year. 10. What’s your biggest threat? Our biggest threat is the temptation for leaders to revert to old habits, especially when faced with pressure or uncertainty. It’s easy for teams to fall back into reacting to urgent issues instead of sticking to a disciplined execution process. This lack of focus can derail even the best plans. Additionally, as more tools and software solutions enter the market, companies risk getting distracted by “shiny objects” that promise quick fixes but don’t deliver real results. Staying laser-focused on helping our clients achieve measurable outcomes – and ensuring they build the habits to sustain those results – keeps us ahead of these challenges. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. Would you like to take a stab at answering some of these questions? The link for the template is HERE. “The best founders turn obstacles into opportunities and setbacks into setups for success.” “The best founders turn obstacles into opportunities and setbacks into setups for success.” 1. HackerNoon : What is your company in 2–5 words? HackerNoon Patrick Thean: Strategy alignment and execution. Patrick Thean : Patrick Thean 2. Why is now the time for your company to exist? With a wave of high-profile CEOs stepping down – Peloton, Chevron, and others – it’s clear that today’s corporate leaders are under extraordinary pressure. Economic uncertainty, rising inflation, and the growing emphasis on ESG priorities have created an environment where the usual playbook no longer works. In this climate, companies must operate with sharper focus, tighter alignment, and better execution to navigate these challenges and thrive. Our approach helps organizations do that, equipping teams with the clarity and tools they need to succeed under today’s demanding conditions. 3. What do you love about your team, and why are you the ones to solve this problem? I love my team’s relentless drive to make things simpler and more effective for leaders and their teams. They bring deep experience, a strong sense of accountability, and a genuine passion for helping others succeed. Our ability to translate complex problems into practical, actionable solutions that deliver results sets us apart. We’ve lived through the same challenges our clients face – misalignment, unclear priorities, and execution gaps – and we’ve built a proven system that works because we’ve tested it in real-world scenarios. It’s not just about the tools we provide; it’s about the mindset and discipline we help instill to create lasting change. 4. If you weren’t building your startup, what would you be doing? If I weren’t building my company, I’d dedicate more of my time and energy to supporting causes I deeply care about, like Samaritan’s Feet. This incredible organization aimed to put 10 million pairs of shoes on the feet of 10 million children in 10 years, and their mission aligns with my passion for creating opportunities and hope for underserved communities. Whether through fundraising, hands-on involvement in shoe distribution events, or helping spread their message, I’d focus on expanding the impact of initiatives like this to transform lives and give kids a chance to dream bigger. 5. At the moment, how do you measure success? What are your metrics? Success is measured by our tangible impact on the leaders and teams we support. Our Rhythm Systems software helps organizations track success by focusing on clear priorities, creating alignment, and executing with discipline. The platform enables leaders to measure what matters most – achieving strategic goals, improving team accountability, or hitting key performance metrics. We look at client goal achievement rates, the consistency of execution rhythms, and the alignment of leadership teams. Beyond the data, success is also about the stories we hear – how our system has transformed how leaders manage their teams, navigate challenges, and achieve results faster and more effectively. When our clients win, that’s the ultimate measure of our success. Rhythm Systems 6. In a few sentences, what do you offer to whom? We help CEOs and leadership teams of growth-oriented mid-market companies achieve their goals faster and with less friction. We provide the tools, methodology, and coaching to clarify strategic priorities, align teams, and drive accountability with measurable KPIs. Our approach ensures leaders focus on what matters most, execute effectively, and make better decisions using real-time data. 7. What’s most exciting about your traction to date? What excites me most is seeing our system's real-world impact on our clients. Companies like MobilityWorks, which has achieved record revenue and profits, and AvidXchange, which has scaled from $40M to $100M in just two years, prove that our approach works. It’s not just about the numbers – it’s hearing from CEOs and teams who say our tools and methodology have transformed how they operate, helping them focus, align, and win. Watching our clients break through their ceilings and accomplish things they once thought impossible keeps us motivated daily. 8. Where do you think your growth will be next year? Next year, our growth continues to help CEOs and their leadership teams navigate the complexity of scaling their businesses. With the increasing challenges of hybrid work, economic uncertainty, and rapidly changing markets, more leaders seek practical tools to stay aligned and execute effectively. Rhythm Systems is positioned to meet that need with our platform, simplifying strategic planning and execution by investing in enhancements like AI-driven insights for better decision-making. Additionally, we’ll focus on expanding our reach through referrals and partnerships and sharing more success stories from clients who’ve used our system to achieve record results. It’s exciting as more companies realize the value of working with clarity, focus, and accountability. 9. Tell us about your first paying customer and revenue expectations over the next year. From start-up to IPO: AvidXchange became our first paying customer 16 years ago. They were growing dramatically and faced internal challenges as headcount quickly grew from 50 to 1,600 employees. Keeping teams aligned to meet key deliverables as the organization transformed was critical – but hard. Product launches, acquisitions, R&D, and customer success required seamless collaboration among multiple departments and teams. The risk of missing handoffs and falling short of customer commitments was greater than ever. How Rhythm Helped: Closing the communication gap around goals. Michael Praeger, CEO and Co-Founder of AvidXchange, committed to his team’s success through vertical and horizontal alignment. Rhythm launched a company-wide training initiative, focusing on how to use the software and methodology to promote transparency around goals. Rhythm coaches pinpointed areas of misalignment, which helped teams make crucial planning and execution adjustments to bridge the communication gap and pivot quickly when necessary. Key Learnings: Cross-functional team alignment allowed AvidXchange to increase productivity by 300%, resulting in a key product launching 6 months earlier than scheduled. By 2021, they made a successful IPO at a $2 billion valuation. Revenue expectations are always an exciting topic when looking ahead to next year. Clear goals and solid planning ensure a successful and sustainable year. 10. What’s your biggest threat? Our biggest threat is the temptation for leaders to revert to old habits, especially when faced with pressure or uncertainty. It’s easy for teams to fall back into reacting to urgent issues instead of sticking to a disciplined execution process. This lack of focus can derail even the best plans. Additionally, as more tools and software solutions enter the market, companies risk getting distracted by “shiny objects” that promise quick fixes but don’t deliver real results. Staying laser-focused on helping our clients achieve measurable outcomes – and ensuring they build the habits to sustain those results – keeps us ahead of these challenges. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke ’s ten questions for startup founders . HackerNoon Founder & CEO David Smooke ten questions for startup founders Would you like to take a stab at answering some of these questions? The link for the template is HERE. Would you like to take a stab at answering some of these questions? The link for the template is HERE. The link for the template is HERE.

"Lack of Focus Can Derail Even the Best Plans" says Rhythm Systems CEO Patrick Thean

Authors:
(1) Hoon Kim, Beeble AI, and contributed equally to this work;
(2) Minje Jang, Beeble AI, and contributed equally to this work;
(3) Wonjun Yoon, Beeble AI, and contributed equally to this work;
(4) Jisoo Lee, Beeble AI, and contributed equally to this work;
(5) Donghyun Na, Beeble AI, and contributed equally to this work;
(6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 6 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction
2. Related Work
3. SwitchLight and 3.1. Preliminaries
3.2. Problem Formulation
3.3. Architecture
3.4. Objectives
4. Multi-Masked Autoencoder Pre-training
5. Data
6. Experiments
7. Conclusion Appendix A. Implementation Details
B. User Study Interface
C. Video Demonstration
D. Additional Qualitative Results & References 3.4. Objectives We supervise both intrinsic image attributes and relit images using their corresponding ground truths, obtained from the lightstage. We employ a combination of reconstruction, perceptual [24], adversarial [22], and specular [34] losses. Final Loss. The SwitchLight is trained in an end-to-end manner using the weighted sum of the above losses: We empirically determined the weighting coefficients. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Authors: Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 6 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 6 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 6 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 6 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.3. Architecture 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 5. Data 6. Experiments 7. Conclusion Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.2. Problem Formulation 3.3. Architecture 3.3. Architecture 3.4. Objectives 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 4. Multi-Masked Autoencoder Pre-training 5. Data 5. Data 6. Experiments 6. Experiments 7. Conclusion 7. Conclusion Appendix A. Implementation Details B. User Study Interface C. Video Demonstration D. Additional Qualitative Results & References A. Implementation Details A. Implementation Details B. User Study Interface B. User Study Interface C. Video Demonstration C. Video Demonstration D. Additional Qualitative Results & References D. Additional Qualitative Results & References 3.4. Objectives We supervise both intrinsic image attributes and relit images using their corresponding ground truths, obtained from the lightstage. We employ a combination of reconstruction, perceptual [24], adversarial [22], and specular [34] losses. Final Loss. The SwitchLight is trained in an end-to-end manner using the weighted sum of the above losses: Final Loss. We empirically determined the weighting coefficients. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Beeble Researchers Develop AI That Can Make Any Photo Look Perfectly Lit—Even in the Darkest Room

Authors:
(1) Hanoona Rasheed, Mohamed bin Zayed University of AI and equally contributing first authors;
(2) Muhammad Maaz, Mohamed bin Zayed University of AI and equally contributing first authors;
(3) Sahal Shaji, Mohamed bin Zayed University of AI;
(4) Abdelrahman Shaker, Mohamed bin Zayed University of AI;
(5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University;
(6) Hisham Cholakkal, Mohamed bin Zayed University of AI;
(7) Rao M. Anwer, Mohamed bin Zayed University of AI and Aalto University;
(8) Eric Xing, Mohamed bin Zayed University of AI and Carnegie Mellon University;
(9) Ming-Hsuan Yang, University of California - Merced and Google Research;
(10) Fahad S. Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 9 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Table of Links Abstract and 1 Introduction
2. Related Work
3. Method
4. Data Annotation Pipeline
5. Experiments
6. Conclusion and References Supplementary Material (Part 1) A. Additional Implementation Details
B. Additional Downstream Tasks
C. Additional Qualitative Results Supplementary Material (Part 2) D. Dataset Visualization
E. Limitations and Future Work
F. Ethics and Societal Impact C. Additional Qualitative Results In this section, we provide more qualitative examples to better understand the capacity of GLaMM. C.1. Grounded Conversation Generation (GCG) Fig. 7 shows qualitative results of GLaMM finetuned on GranDf dataset. The model could produce dense captions and provide dense pixel-level groundings of the caption. C.2. Referring Segmentation Fig. 8 shows the effectiveness of GLaMM in understanding the natural language query and segmenting the corresponding objects. Note that GLaMM can also segment multiple objects via multi-round conversations. C.3. Region-level Captioning Fig. 9 shows the qualitative results of GLaMM for regionlevel understanding. Our model can generate detailed descriptions about the user-specified regions in an image. C.4. Image-level Captioning Fig. 10 shows GLaMM’s qualitative results on captioning tasks. Our model can generate dense captions for images. C.5. Conditional Image Generation Fig. 12 shows GLaMM’s seamless integration for generative tasks. We use the Stable Diffusion inpainting model stable-diffusion-xl-1.0-inpainting [41] for this task. We first generate a segmentation mask using our GlaMM model based on the user query. This segmentation mask along with the user prompt is given as the input to the Stable Diffusion inpainting model, which generates the final output. Figure 14. Dataset samples from GranDf. The figure shows the GPT4 [34] prompts used and the created dataset samples from Grandf dataset. This repurposed human-annotated dataset provides rich semantics to GLaMM for GCG task. C.6. Conversations Fig. 13 illustrates the unique functionality of GLaMM to engage in multi-purpose task conversations. GLaMM is a generic conversational model that can accept prompts in the form of text and/or region and can answer in the form of text and/or segmentation masks. Note that our model is not explicitly trained to handle such scenarios, and this behavior emerges mainly due to our pretraining on GranD dataset, where an image is presented to LMM in different contexts. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Hanoona Rasheed, Mohamed bin Zayed University of AI and equally contributing first authors; (2) Muhammad Maaz, Mohamed bin Zayed University of AI and equally contributing first authors; (3) Sahal Shaji, Mohamed bin Zayed University of AI; (4) Abdelrahman Shaker, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Hisham Cholakkal, Mohamed bin Zayed University of AI; (7) Rao M. Anwer, Mohamed bin Zayed University of AI and Aalto University; (8) Eric Xing, Mohamed bin Zayed University of AI and Carnegie Mellon University; (9) Ming-Hsuan Yang, University of California - Merced and Google Research; (10) Fahad S. Khan, Mohamed bin Zayed University of AI and Linköping University. Authors: Authors: (1) Hanoona Rasheed, Mohamed bin Zayed University of AI and equally contributing first authors; (2) Muhammad Maaz, Mohamed bin Zayed University of AI and equally contributing first authors; (3) Sahal Shaji, Mohamed bin Zayed University of AI; (4) Abdelrahman Shaker, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Hisham Cholakkal, Mohamed bin Zayed University of AI; (7) Rao M. Anwer, Mohamed bin Zayed University of AI and Aalto University; (8) Eric Xing, Mohamed bin Zayed University of AI and Carnegie Mellon University; (9) Ming-Hsuan Yang, University of California - Merced and Google Research; (10) Fahad S. Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 9 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Editor's Note: This is Part 9 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Editor's Note: This is Part 9 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Editor's Note: This is Part 9 of 10 of a study detailing the development of an AI model that is designed to describe images to users. Read the rest below. Table of Links Abstract and 1 Introduction 2. Related Work 3. Method 4. Data Annotation Pipeline 5. Experiments 6. Conclusion and References Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Work 2. Related Work 3. Method 3. Method 4. Data Annotation Pipeline 4. Data Annotation Pipeline 5. Experiments 5. Experiments 6. Conclusion and References 6. Conclusion and References Supplementary Material (Part 1) Supplementary Material (Part 1) A. Additional Implementation Details B. Additional Downstream Tasks C. Additional Qualitative Results A. Additional Implementation Details A. Additional Implementation Details B. Additional Downstream Tasks B. Additional Downstream Tasks C. Additional Qualitative Results C. Additional Qualitative Results Supplementary Material (Part 2) Supplementary Material (Part 2) D. Dataset Visualization E. Limitations and Future Work F. Ethics and Societal Impact D. Dataset Visualization D. Dataset Visualization E. Limitations and Future Work E. Limitations and Future Work F. Ethics and Societal Impact F. Ethics and Societal Impact C. Additional Qualitative Results In this section, we provide more qualitative examples to better understand the capacity of GLaMM. C.1. Grounded Conversation Generation (GCG) Fig. 7 shows qualitative results of GLaMM finetuned on GranD f dataset. The model could produce dense captions and provide dense pixel-level groundings of the caption. f C.2. Referring Segmentation Fig. 8 shows the effectiveness of GLaMM in understanding the natural language query and segmenting the corresponding objects. Note that GLaMM can also segment multiple objects via multi-round conversations. C.3. Region-level Captioning Fig. 9 shows the qualitative results of GLaMM for regionlevel understanding. Our model can generate detailed descriptions about the user-specified regions in an image. C.4. Image-level Captioning Fig. 10 shows GLaMM’s qualitative results on captioning tasks. Our model can generate dense captions for images. C.5. Conditional Image Generation Fig. 12 shows GLaMM’s seamless integration for generative tasks. We use the Stable Diffusion inpainting model stable-diffusion-xl-1.0-inpainting [41] for this task. We first generate a segmentation mask using our GlaMM model based on the user query. This segmentation mask along with the user prompt is given as the input to the Stable Diffusion inpainting model, which generates the final output. Figure 14. Dataset samples from GranDf. The figure shows the GPT4 [34] prompts used and the created dataset samples from Grandf dataset. This repurposed human-annotated dataset provides rich semantics to GLaMM for GCG task. Dataset samples from GranD f . f C.6. Conversations Fig. 13 illustrates the unique functionality of GLaMM to engage in multi-purpose task conversations. GLaMM is a generic conversational model that can accept prompts in the form of text and/or region and can answer in the form of text and/or segmentation masks. Note that our model is not explicitly trained to handle such scenarios, and this behavior emerges mainly due to our pretraining on GranD dataset, where an image is presented to LMM in different contexts. 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

This New AI Can See, Talk, and Even Edit Images in a Single Conversation

The Sneaky Way Web Browsers Are Identifying You (Even When You Turn Off Cookies)

Die skelm manier waarop webblaaiers jou identifiseer (selfs wanneer jy koekies afskakel)

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