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Deta

deta

We're thrilled to announce the forthcoming Lumoz’s esMOZ airdrop, distributed to all sorts of contributors! This milestone wouldn't have been possible without the dedication and contributions of our vibrant community. Over the last two years, our community has supported Lumoz through key phases: the Pre-Alpha testnet engaged 87,567 active participants, the Alpha testnet attracted 286,179 followers, and participation in the Quidditch testnet exceeded one million. These numbers underscore the community’s strong support and trust in Lumoz. Your contributions have driven invaluable feedback, strengthening Lumoz’s technical foundation and shaping our ecosystem's growth. This airdrop is our way of expressing gratitude for your continued support and is a significant step toward our journey into the next chapter. Airdrop Rules Airdrop Inquiry Link: https://lumoz.org/airdrop Lumoz Tokens have a total supply of 10 billion, with 10% (1 billion esMOZ tokens) dedicated to this airdrop. Based on our recent valuation, this represents a value of approximately $30 million. The exchange rate for esMOZ to MOZ will be 1:1, and the exchange will follow an unlocking schedule as detailed in our official documentation (https://docs.lumoz.org/). As the largest airdrop in Lumoz's history, it will target early adopters, Lumoz PoW & node holders, ecosystem contributors, and active participants from ecosystems like ETH, Celestia, and the Move Ecosystem. Detailed Eligibility Criteria: Early Lumoz Participants:  Lumoz Point holders will receive esMOZ on a 1:1 basis for points held. Users with over 500 points will also receive bonus esMOZ airdrops.


Lumoz PoW Testnet Participants: From November 5 to December 5, Lumoz will host ZK mining on the testnet. Participants will earn esMOZ airdrops proportional to MOZ test tokens mined during this period. Updated rewards will be available on the airdrop page.


Lumoz zkVerifier Node Holders:  Holders of the Lumoz zkVerifier License, as captured in the October 26 snapshot, are eligible for airdrops.


Lumoz Ecosystem Users: This includes users of Lumoz RaaS services in projects like Merlin Chain, ZKFair, Ultiverse, and Matr1x. Active users in these ecosystems will receive direct airdrops, visible on the official airdrop page starting November 5. This portion of the airdrop will consist of two parts: The first part will be directly airdropped to active addresses in the ecosystem, which can be checked on the Lumoz official airdrop page starting November 5. The second part will be airdropped to users participating in ecosystem-related activities. After the events conclude, the esMOZ earned through participation will be updated on the Lumoz official airdrop page. Please follow official updates from each ecosystem for specific activity times. Active Users in Ecosystems like ETH, Celestia, and Move: Eligible users include: Participants interacting with major DeFi projects in ETH, including Ethereum, Polygon, and Arbitrum over the past six months.


Significant stakers in the Celestia network. Active users engaging with leading DeFi projects in Move, primarily Aptos and Sui, within the past six months. Please note that the airdrop inquiry period will last one month, during which, specific claim details will be announced. NFT Activity Coming Soon After obtaining esMOZ, users are naturally most concerned about the exchange rules. Lumoz's official documentation has previously stated that the exchange ratio between esMOZ and MOZ is 1:1, although there will be a certain unlocking period. While this setup is designed for Lumoz's long-term development, there have been voices in the community expressing a desire for an earlier unlocking. We value the feedback from every community user, which is why we have introduced an NFT section that will allow users to directly exchange a certain amount of esMOZ for MOZ. NFTs will be set at different levels, with each level offering varying esMOZ exchange limits. In the future, NFTs will also support free circulation, allowing users to exchange according to their needs for flexible asset management. We will announce the rules for obtaining NFTs shortly, so stay tuned! This airdrop marks just the beginning for Lumoz. Follow our official Twitter for updates and stay informed on our journey forward. About Lumoz Lumoz is a leading modular computing layer and ZK-RaaS platform, delivering computation for Rollups, ZK-ML, and ZKP verification through its PoW mining system. Earlier this year, Lumoz completed its Pre-A funding round, raising $14 million with backing from prominent investors like OKX Ventures, HashKey Capital, KuCoin Ventures, Polygon, and IDG Blockchain. With over 1.5 million users, 200+ projects, 28,137 validators, and 145 PoW miners engaged, Lumoz’s mainnet is set to launch by year’s end. We're thrilled to announce the forthcoming Lumoz’s esMOZ airdrop, distributed to all sorts of contributors! This milestone wouldn't have been possible without the dedication and contributions of our vibrant community. Over the last two years, our community has supported Lumoz through key phases: the Pre-Alpha testnet engaged 87,567 active participants, the Alpha testnet attracted 286,179 followers, and participation in the Quidditch testnet exceeded one million. These numbers underscore the community’s strong support and trust in Lumoz. Your contributions have driven invaluable feedback, strengthening Lumoz’s technical foundation and shaping our ecosystem's growth. This airdrop is our way of expressing gratitude for your continued support and is a significant step toward our journey into the next chapter. Airdrop Rules Airdrop Inquiry Link: https://lumoz.org/airdrop https://lumoz.org/airdrop https://lumoz.org/airdrop Lumoz Tokens have a total supply of 10 billion, with 10% (1 billion esMOZ tokens) dedicated to this airdrop. Based on our recent valuation, this represents a value of approximately $30 million. The exchange rate for esMOZ to MOZ will be 1:1, and the exchange will follow an unlocking schedule as detailed in our official documentation ( https://docs.lumoz.org/ ). https://docs.lumoz.org/ As the largest airdrop in Lumoz's history, it will target early adopters, Lumoz PoW & node holders, ecosystem contributors, and active participants from ecosystems like ETH, Celestia, and the Move Ecosystem. Detailed Eligibility Criteria: Detailed Eligibility Criteria: Early Lumoz Participants:  Lumoz Point holders will receive esMOZ on a 1:1 basis for points held. Users with over 500 points will also receive bonus esMOZ airdrops. Lumoz PoW Testnet Participants: From November 5 to December 5, Lumoz will host ZK mining on the testnet. Participants will earn esMOZ airdrops proportional to MOZ test tokens mined during this period. Updated rewards will be available on the airdrop page. Lumoz zkVerifier Node Holders:  Holders of the Lumoz zkVerifier License, as captured in the October 26 snapshot, are eligible for airdrops. Lumoz Ecosystem Users: This includes users of Lumoz RaaS services in projects like Merlin Chain, ZKFair, Ultiverse, and Matr1x. Active users in these ecosystems will receive direct airdrops, visible on the official airdrop page starting November 5. This portion of the airdrop will consist of two parts: Early Lumoz Participants:  Lumoz Point holders will receive esMOZ on a 1:1 basis for points held. Users with over 500 points will also receive bonus esMOZ airdrops. Early Lumoz Participants:  Lumoz Point holders will receive esMOZ on a 1:1 basis for points held. Users with over 500 points will also receive bonus esMOZ airdrops. Lumoz PoW Testnet Participants: From November 5 to December 5, Lumoz will host ZK mining on the testnet. Participants will earn esMOZ airdrops proportional to MOZ test tokens mined during this period. Updated rewards will be available on the airdrop page. Lumoz PoW Testnet Participants: From November 5 to December 5, Lumoz will host ZK mining on the testnet. Participants will earn esMOZ airdrops proportional to MOZ test tokens mined during this period. Updated rewards will be available on the airdrop page. Lumoz zkVerifier Node Holders:  Holders of the Lumoz zkVerifier License, as captured in the October 26 snapshot, are eligible for airdrops. Lumoz zkVerifier Node Holders:  Holders of the Lumoz zkVerifier License, as captured in the October 26 snapshot, are eligible for airdrops. Lumoz Ecosystem Users: This includes users of Lumoz RaaS services in projects like Merlin Chain, ZKFair, Ultiverse, and Matr1x. Active users in these ecosystems will receive direct airdrops, visible on the official airdrop page starting November 5. This portion of the airdrop will consist of two parts: Lumoz Ecosystem Users: This includes users of Lumoz RaaS services in projects like Merlin Chain, ZKFair, Ultiverse, and Matr1x. Active users in these ecosystems will receive direct airdrops, visible on the official airdrop page starting November 5. This portion of the airdrop will consist of two parts: The first part will be directly airdropped to active addresses in the ecosystem, which can be checked on the Lumoz official airdrop page starting November 5. The first part will be directly airdropped to active addresses in the ecosystem, which can be checked on the Lumoz official airdrop page starting November 5. The second part will be airdropped to users participating in ecosystem-related activities. After the events conclude, the esMOZ earned through participation will be updated on the Lumoz official airdrop page. Please follow official updates from each ecosystem for specific activity times. The second part will be airdropped to users participating in ecosystem-related activities. After the events conclude, the esMOZ earned through participation will be updated on the Lumoz official airdrop page. Please follow official updates from each ecosystem for specific activity times. Active Users in Ecosystems like ETH, Celestia, and Move: Eligible users include: Active Users in Ecosystems like ETH, Celestia, and Move: Eligible users include: Participants interacting with major DeFi projects in ETH, including Ethereum, Polygon, and Arbitrum over the past six months. Significant stakers in the Celestia network. Participants interacting with major DeFi projects in ETH, including Ethereum, Polygon, and Arbitrum over the past six months. Participants interacting with major DeFi projects in ETH, including Ethereum, Polygon, and Arbitrum over the past six months. Significant stakers in the Celestia network. Significant stakers in the Celestia network. Active users engaging with leading DeFi projects in Move, primarily Aptos and Sui, within the past six months. Please note that the airdrop inquiry period will last one month, during which, specific claim details will be announced. Active users engaging with leading DeFi projects in Move, primarily Aptos and Sui, within the past six months. Please note that the airdrop inquiry period will last one month, during which, specific claim details will be announced. NFT Activity Coming Soon NFT Activity Coming Soon After obtaining esMOZ, users are naturally most concerned about the exchange rules. Lumoz's official documentation has previously stated that the exchange ratio between esMOZ and MOZ is 1:1, although there will be a certain unlocking period. While this setup is designed for Lumoz's long-term development, there have been voices in the community expressing a desire for an earlier unlocking. We value the feedback from every community user, which is why we have introduced an NFT section that will allow users to directly exchange a certain amount of esMOZ for MOZ. NFTs will be set at different levels, with each level offering varying esMOZ exchange limits. In the future, NFTs will also support free circulation, allowing users to exchange according to their needs for flexible asset management. We will announce the rules for obtaining NFTs shortly, so stay tuned! This airdrop marks just the beginning for Lumoz. Follow our official Twitter for updates and stay informed on our journey forward. About Lumoz About Lumoz Lumoz is a leading modular computing layer and ZK-RaaS platform, delivering computation for Rollups, ZK-ML, and ZKP verification through its PoW mining system. Earlier this year, Lumoz completed its Pre-A funding round, raising $14 million with backing from prominent investors like OKX Ventures, HashKey Capital, KuCoin Ventures, Polygon, and IDG Blockchain. With over 1.5 million users, 200+ projects, 28,137 validators, and 145 PoW miners engaged, Lumoz’s mainnet is set to launch by year’s end.

Lumoz Unveils esMOZ Airdrop Details

Lumoz revela detalles del lanzamiento aéreo de esMOZ

Lumoz Devwale Detay Airdrop esMOZ

A Lumoz bemutatja az esMOZ Airdrop részleteit

Lumoz svela i dettagli dell'airdrop esMOZ

LumozがesMOZエアドロップの詳細を発表

Lumoz បង្ហាញព័ត៌មានលម្អិត esMOZ Airdrop

Lumoz onthult esMOZ Airdrop-details

Lumoz ujawnia szczegóły zrzutu esMOZ

Lumoz Desvela esMOZ Airdrop Detalles

Lumoz zbulon detajet e esMOZ Airdrop

Lumoz тафсилоти esMOZ Airdrop-ро ошкор мекунад

Lumoz esMOZ Airdrop tafsilotlarini ochib beradi

I chose to share some aspects of my design process, as there is an abundance of fascinating content out there, ranging from general information to specific details. Sometimes, I find it valuable to compare my proсess with others and gain insights from their experiences. This text is a continuation of the previous one, related to the project I am currently working as a solo designer. I recommend that you start by spending 5 minutes reading the first article at the following link to gain more context. https://hackernoon.com/the-implementation-of-design-guidelines-for-small-and-medium-projects?embedable=true In my previous post, I discussed my experience with using design systems. The design development stage is almost finished now that this phase has been completed successfully, and I have recently begun building a website on Framer. Towards the end of the article I plan to write a couple of lines about working with Framer, but I'll go in order. Notable is a company in the engineering field that focuses on chemical product areas. The company has three distinct product lines: metering pumps, chemical pumps, and chemical reagents. The company has built a strong presence in its industry through over a decade of successful operation in the B2B market. The project's current aims include boosting brand awareness and growing the base of loyal customers. The primary issue stems from the company having three distinct operating divisions, each catering to unique target audiences and requirements. In other words, a user searching for specific information may become confused by irrelevant information, making their experience more complicated. Furthermore, the detailed technical information included in product descriptions can make it challenging to organize the visual hierarchy of content and may result in overwhelming the reader with too much information. This is how the issue was initially explained in the project. However, if you dig deeper, you will need to untangle a complex web of various questions. Gain an understanding of the user including their motives for visiting the site, the situations they are in, the questions they ask, and especially what expectations they have from the business. Conversely, the business aims to operate and grow successfully. In essence, you must strike a balance between assisting the user in navigating their current path more efficiently and aiding the business in generating revenue. Highlighting the main benefits of the service and ironing out any potential issues is essential for this task. Every business has its bottlenecks, but it's not always convenient for your client to discuss them. Based on Notable's accumulated experience, the most common ways users find a company's website have been identified: During the design phase of an object, a request is made for a specific technological solution with the involvement of key product components. The user is typically a design engineer with expertise in technology and familiarity with our products. The main benefit for the user is the ease of accessing information and navigation. It is not necessary to provide the full specification, but it is crucial to include specific key elements to ensure the designer fully grasps the requirements. The request has come up because of an unexpected incident at the facility and needs to be resolved quickly. The main focus is on navigation, providing relevant information, and establishing a clear visual hierarchy to simplify the process and guide the user. In this situation, we are not working with a designer, but instead with a manager from the procurement department. And it is probable that his technical expertise concerning the requested product will not be as extensive as it was in the prior situation. The situation occurs when a user is looking for a trustworthy business partner. In this instance, the important values are expertise, reliability, stability and brand awareness. Throughout Notable's operational period, the business has also gone through transformation. The previous top sellers have been replaced by other products. There are new solutions that have surfaced. The company naturally aims to differentiate itself in the market by creating a fresh and distinctive brand identity. In analyzing the industry and the branding of companies within similar fields, it became evident that the color balance is heavily skewed towards shades of blue. After a while, we decided to go with this color palette, which mirrors the gradient in the company logo. I believe that this decision allows us to evoke the appropriate emotions. Additionally, the logo's gradient and depth of color highlight the company's accumulated expertise over the years. Returning to the idea of making navigation easier, I thought about using this gradient on the homepage to smoothly move from hero section to the next until reaching the bottom of the page. Our target audiences vary greatly, so the solution needs to be easily understandable for all. The concept is for the user to scroll through the main page to find the relevant section, select a specific color scheme, such as Purple Heart for chemical reagents, and then continue navigating the site horizontally in this Monochromatic color scheme. The user is greeted by the start screen in Columbia Blue, followed by the dosing equipment block in Medium Purple, chemical reagents in Purple Heart, chemical pumps in Persian, and finally, Indigo tones in the footer. Therefore, the main page was designed with an Analogous color scheme to help users easily concentrate on the content they need. I also added horizontal color palettes to each block using color theory and a better sequence for the user. For instance, when browsing the Purple Heart – chemical reagents pages, the user will stay within the color scheme of the corresponding Monochromatic palette. Website structure. A decision was made to shift from a tree-like structure to a combined, linear one in order to facilitate and accelerate the user's journey. The target pages for the company's top-earning products can now be accessed with just one click. The Bento system made this possible. Cards and an improved visual hierarchy were used to highlight the top products in each Bento block. Therefore, the revised website now consists of just 25 pages, as opposed to the original 61. The achievement was partly a result of the easier-to-use content delivery system and the specific product pages. Therefore, the information architecture on Notable's corporate website now appears as follows: homepage;
Two intermediate pages;
form page;
3 key product pages;
2 service pages;
16 product pages. Three breakpoints were implemented for the main page, intermediate pages, form pages, key product pages, and service pages to ensure a responsive design: Desktop 1200 px, Tablet 1199-810 px и Phone 809 px. On Desktop, the content area is set to 1200 px; I used a simple 12-column grid with a 24 px gutter. The design for Tablet and Phone is completely adaptable. In the end, the design system I developed for this project included: 1389 components;
288 styles;
166 variants. This case is one of those where you don't have enough high quality graphic content. That's just how it turned out. It was necessary to gather everything available, to upscale in some areas, and to attempt to enhance the quality in others. It was challenging to create a large amount of content in Midjourney and other AIs because the neural network is unable to produce technical materials of this quality, this leads to numerous errors and inaccuracies, which quickly and greatly diminish the user's confidence in Notable's expertise. I edited the graphics in Photoshop, incorporated them into a unified color palette, and created the mockups. During my journey, Midjourney has been incredibly useful to me when dealing with abstract graphics representing the movement of liquid. I was fortunate in this instance as I utilized it on the home page start screens, intermediate screens, and the page featuring a key product. The style is consistent, but my main page features a light theme, the product page has a dark theme, and for the intermediate page, as a variation, I animated this abstract flow in Pika. Next I proceeded to construct the website on Framer. Initially, I perceived this stage as a specific challenge due to my lack of experience with Framer. I have experience using different website builders in the past and now have the opportunity to test this one. There is one particular drawback: the expense. Nevertheless, in all other aspects, there are only benefits at this point in time. Usually, one may face several limitations regarding the layout while collaborating with site builders. You have to pursue compromises and depart from the initial design. In the case of Framer, the process involves transferring the layout from Figma with the assistance of plugins, and the use of components is essential. Consequently, this procedure requires a certain amount of time. The animation is generated with minimal effort utilizing only a few mouse clicks. I have a sincere passion for animating layouts in Figma, and I frequently observe a point at which loading speeds noticeably decrease. However, in Framer, any layout is brought to life in a captivating and engaging manner, without requiring a significant time investment. By simply watching a couple of videos from their academy on YouTube, one can quickly gain the skills to enhance dynamic elements. Currently, I will suspend further discussion, and anticipate sharing my impressions following the release in my upcoming post. I chose to share some aspects of my design process, as there is an abundance of fascinating content out there, ranging from general information to specific details. Sometimes, I find it valuable to compare my proсess with others and gain insights from their experiences. This text is a continuation of the previous one, related to the project I am currently working as a solo designer. I recommend that you start by spending 5 minutes reading the first article at the following link to gain more context. https://hackernoon.com/the-implementation-of-design-guidelines-for-small-and-medium-projects?embedable=true https://hackernoon.com/the-implementation-of-design-guidelines-for-small-and-medium-projects?embedable=true In my previous post, I discussed my experience with using design systems. The design development stage is almost finished now that this phase has been completed successfully, and I have recently begun building a website on Framer. Towards the end of the article I plan to write a couple of lines about working with Framer, but I'll go in order. Notable is a company in the engineering field that focuses on chemical product areas. The company has three distinct product lines: metering pumps, chemical pumps, and chemical reagents. The company has built a strong presence in its industry through over a decade of successful operation in the B2B market. The project's current aims include boosting brand awareness and growing the base of loyal customers. The primary issue stems from the company having three distinct operating divisions, each catering to unique target audiences and requirements. In other words, a user searching for specific information may become confused by irrelevant information, making their experience more complicated. Furthermore, the detailed technical information included in product descriptions can make it challenging to organize the visual hierarchy of content and may result in overwhelming the reader with too much information. This is how the issue was initially explained in the project. However, if you dig deeper, you will need to untangle a complex web of various questions. Gain an understanding of the user including their motives for visiting the site, the situations they are in, the questions they ask, and especially what expectations they have from the business. Conversely, the business aims to operate and grow successfully. In essence, you must strike a balance between assisting the user in navigating their current path more efficiently and aiding the business in generating revenue. Highlighting the main benefits of the service and ironing out any potential issues is essential for this task. Every business has its bottlenecks, but it's not always convenient for your client to discuss them. Based on Notable's accumulated experience, the most common ways users find a company's website have been identified: During the design phase of an object, a request is made for a specific technological solution with the involvement of key product components. The user is typically a design engineer with expertise in technology and familiarity with our products. The main benefit for the user is the ease of accessing information and navigation. It is not necessary to provide the full specification, but it is crucial to include specific key elements to ensure the designer fully grasps the requirements. During the design phase of an object, a request is made for a specific technological solution with the involvement of key product components. The request has come up because of an unexpected incident at the facility and needs to be resolved quickly. The main focus is on navigation, providing relevant information, and establishing a clear visual hierarchy to simplify the process and guide the user. In this situation, we are not working with a designer, but instead with a manager from the procurement department. And it is probable that his technical expertise concerning the requested product will not be as extensive as it was in the prior situation. The request has come up because of an unexpected incident at the facility and needs to be resolved quickly. The situation occurs when a user is looking for a trustworthy business partner. In this instance, the important values are expertise, reliability, stability and brand awareness. The situation occurs when a user is looking for a trustworthy business partner. Throughout Notable's operational period, the business has also gone through transformation. The previous top sellers have been replaced by other products. There are new solutions that have surfaced. The company naturally aims to differentiate itself in the market by creating a fresh and distinctive brand identity. In analyzing the industry and the branding of companies within similar fields, it became evident that the color balance is heavily skewed towards shades of blue. After a while, we decided to go with this color palette, which mirrors the gradient in the company logo. I believe that this decision allows us to evoke the appropriate emotions. Additionally, the logo's gradient and depth of color highlight the company's accumulated expertise over the years. Returning to the idea of making navigation easier, I thought about using this gradient on the homepage to smoothly move from hero section to the next until reaching the bottom of the page. Our target audiences vary greatly, so the solution needs to be easily understandable for all. The concept is for the user to scroll through the main page to find the relevant section, select a specific color scheme, such as Purple Heart for chemical reagents, and then continue navigating the site horizontally in this Monochromatic color scheme. The user is greeted by the start screen in Columbia Blue, followed by the dosing equipment block in Medium Purple, chemical reagents in Purple Heart, chemical pumps in Persian, and finally, Indigo tones in the footer. Therefore, the main page was designed with an Analogous color scheme to help users easily concentrate on the content they need. I also added horizontal color palettes to each block using color theory and a better sequence for the user. For instance, when browsing the Purple Heart – chemical reagents pages, the user will stay within the color scheme of the corresponding Monochromatic palette. Website structure. A decision was made to shift from a tree-like structure to a combined, linear one in order to facilitate and accelerate the user's journey. The target pages for the company's top-earning products can now be accessed with just one click. The Bento system made this possible. Cards and an improved visual hierarchy were used to highlight the top products in each Bento block. Therefore, the revised website now consists of just 25 pages, as opposed to the original 61. The achievement was partly a result of the easier-to-use content delivery system and the specific product pages. Therefore, the information architecture on Notable's corporate website now appears as follows: homepage; Two intermediate pages; form page; 3 key product pages; 2 service pages; 16 product pages. homepage; Two intermediate pages; form page; 3 key product pages; 2 service pages; 16 product pages. Three breakpoints were implemented for the main page, intermediate pages, form pages, key product pages, and service pages to ensure a responsive design: Desktop 1200 px, Tablet 1199-810 px и Phone 809 px. On Desktop, the content area is set to 1200 px; I used a simple 12-column grid with a 24 px gutter. The design for Tablet and Phone is completely adaptable. In the end, the design system I developed for this project included: 1389 components; 288 styles; 166 variants. 1389 components; 288 styles; 166 variants. This case is one of those where you don't have enough high quality graphic content. That's just how it turned out. It was necessary to gather everything available, to upscale in some areas, and to attempt to enhance the quality in others. It was challenging to create a large amount of content in Midjourney and other AIs because the neural network is unable to produce technical materials of this quality, this leads to numerous errors and inaccuracies, which quickly and greatly diminish the user's confidence in Notable's expertise. I edited the graphics in Photoshop, incorporated them into a unified color palette, and created the mockups. During my journey, Midjourney has been incredibly useful to me when dealing with abstract graphics representing the movement of liquid. I was fortunate in this instance as I utilized it on the home page start screens, intermediate screens, and the page featuring a key product. The style is consistent, but my main page features a light theme, the product page has a dark theme, and for the intermediate page, as a variation, I animated this abstract flow in Pika. Next I proceeded to construct the website on Framer. Initially, I perceived this stage as a specific challenge due to my lack of experience with Framer. I have experience using different website builders in the past and now have the opportunity to test this one. There is one particular drawback: the expense. Nevertheless, in all other aspects, there are only benefits at this point in time. Usually, one may face several limitations regarding the layout while collaborating with site builders. You have to pursue compromises and depart from the initial design. In the case of Framer, the process involves transferring the layout from Figma with the assistance of plugins, and the use of components is essential. Consequently, this procedure requires a certain amount of time. The animation is generated with minimal effort utilizing only a few mouse clicks. I have a sincere passion for animating layouts in Figma, and I frequently observe a point at which loading speeds noticeably decrease. However, in Framer, any layout is brought to life in a captivating and engaging manner, without requiring a significant time investment. By simply watching a couple of videos from their academy on YouTube, one can quickly gain the skills to enhance dynamic elements. Currently, I will suspend further discussion, and anticipate sharing my impressions following the release in my upcoming post.

Specific Details of the Design Process

Authors:
(1) Qian Yang, Zhejiang University, Equal contribution. This work was conducted during Qian Yang’s internship at Alibaba Group;
(2) Jin Xu, Alibaba Group, Equal contribution;
(3) Wenrui Liu, Zhejiang University;
(4) Yunfei Chu, Alibaba Group;
(5) Xiaohuan Zhou, Alibaba Group;
(6) Yichong Leng, Alibaba Group;
(7) Yuanjun Lv, Alibaba Group;
(8) Zhou Zhao, Alibaba Group and Corresponding to Zhou Zhao (zhaozhou@zju.edu.cn);
(9) Yichong Leng, Zhejiang University
(10) Chang Zhou, Alibaba Group and Corresponding to Chang Zhou (ericzhou.zc@alibaba-inc.com);
(11) Jingren Zhou, Alibaba Group. Table of Links Abstract and 1. Introduction 2 Related Work 3 AIR-Bench and 3.1 Overview 3.2 Foundation Benchmark 3.3 Chat Benchmark 3.4 Evaluation Strategy 4 Experiments 4.1 Models 4.2 Main Results 4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias 5 Conclusion and References A Detailed Results of Foundation Benchmark A Detailed Results of Foundation Benchmark In Table 5, we delineate the performance assessment for each model across the various tasks on the foundation benchmark. With the exception of Speaker Gender Recognition and Synthesized Voice Detection, which are binary-choice tasks, all other tasks necessitate a selection from four options. As such, a random selection in the Speaker Gender Recognition and Synthesized Voice Detection datasets would theoretically achieve an accuracy of 50%, while the expected accuracy for random choices across the remaining datasets stands at 25%. Consequently, any performance metrics that approximate these random baselines are indicative of an absence of discernible proficiency in the respective tasks. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Qian Yang, Zhejiang University, Equal contribution. This work was conducted during Qian Yang’s internship at Alibaba Group; (2) Jin Xu, Alibaba Group, Equal contribution; (3) Wenrui Liu, Zhejiang University; (4) Yunfei Chu, Alibaba Group; (5) Xiaohuan Zhou, Alibaba Group; (6) Yichong Leng, Alibaba Group; (7) Yuanjun Lv, Alibaba Group; (8) Zhou Zhao, Alibaba Group and Corresponding to Zhou Zhao (zhaozhou@zju.edu.cn); (9) Yichong Leng, Zhejiang University (10) Chang Zhou, Alibaba Group and Corresponding to Chang Zhou (ericzhou.zc@alibaba-inc.com); (11) Jingren Zhou, Alibaba Group. Authors: Authors: (1) Qian Yang, Zhejiang University, Equal contribution. This work was conducted during Qian Yang’s internship at Alibaba Group; (2) Jin Xu, Alibaba Group, Equal contribution; (3) Wenrui Liu, Zhejiang University; (4) Yunfei Chu, Alibaba Group; (5) Xiaohuan Zhou, Alibaba Group; (6) Yichong Leng, Alibaba Group; (7) Yuanjun Lv, Alibaba Group; (8) Zhou Zhao, Alibaba Group and Corresponding to Zhou Zhao (zhaozhou@zju.edu.cn); (9) Yichong Leng, Zhejiang University (10) Chang Zhou, Alibaba Group and Corresponding to Chang Zhou (ericzhou.zc@alibaba-inc.com); (11) Jingren Zhou, Alibaba Group. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 AIR-Bench and 3.1 Overview 3 AIR-Bench and 3.1 Overview 3.2 Foundation Benchmark 3.2 Foundation Benchmark 3.3 Chat Benchmark 3.3 Chat Benchmark 3.4 Evaluation Strategy 3.4 Evaluation Strategy 4 Experiments 4.1 Models 4.1 Models 4.2 Main Results 4.2 Main Results 4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias 4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias 5 Conclusion and References 5 Conclusion and References A Detailed Results of Foundation Benchmark A Detailed Results of Foundation Benchmark A Detailed Results of Foundation Benchmark In Table 5, we delineate the performance assessment for each model across the various tasks on the foundation benchmark. With the exception of Speaker Gender Recognition and Synthesized Voice Detection, which are binary-choice tasks, all other tasks necessitate a selection from four options. As such, a random selection in the Speaker Gender Recognition and Synthesized Voice Detection datasets would theoretically achieve an accuracy of 50%, while the expected accuracy for random choices across the remaining datasets stands at 25%. Consequently, any performance metrics that approximate these random baselines are indicative of an absence of discernible proficiency in the respective tasks. 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

Detailed Results of the Foundation Benchmark

SAN FRANCISCO, USA / California, October 10th, 2024/Chainwire/--Constellation Network's “HyDef ‘24” conference will take place on Thursday, October 24, 2024, featuring a free daylong virtual event combined with a live in-person event for a nominal fee at 1 Hotel in San Francisco. Constellation Network is a unique Web3 framework with new open-source tooling that empowers companies and individuals to build blockchain networks for Big Data, creating trust and transparency around data collection, validation, and transacting. Nicknamed “America’s Blockchain,” Constellation Network actively works with entities such as the U.S. Military, The Digital Chamber, the Texas Blockchain Association, Space ISAC, and the National DigiFoundry. Constellation has been validated and approved by the U.S. Department of Defense through the Air Force Research Laboratory (AFRL) as a, “Scalable, Secure and Defense-Approved Blockchain technology.” “So much work has been going on behind the scenes which is why HyDef is a great forum to bring it to the forefront around one action packed event. Since the launch of Metagraphs, it's been hard to keep up with all the inbound interest from federal, enterprise and academia. We are excited to not only give updates on what has matured but also unveil new expressions that truly separate Constellation apart in the Web3 industry.” - Benjamin Diggles, Constellation Co-founder and CSO. Constellation's HyDef ‘24 conference will deliver a jam-packed day of ground-breaking insights. Conference sessions cover secure information-sharing in a zero-trust world, the future of finance and blockchain, the evolving regulatory environment, government and blockchain, and much more. Hackathon-winning projects will be showcased at the event where developers have leveraged Constellation’s big data transaction and validation capabilities to build apps that gather and validate data at scale. These apps feed the data into AI or causal models to give businesses and individuals insights based on more input than we’ve heretofore been able to process. Another HyDef highlight will be the long-awaited reveal of the details of the working relationship between Constellation and Panasonic. The work the two companies are doing together has the potential to bring Constellation’s technology and blockchain-secured edge computing to the world en masse in a meaningful way. “The Constellation Community is the backbone to our ecosystem. HyDef is a time for us all to come together, celebrate the progress in our ecosystem, give visibility to our partners, and gear up for the future of Constellation. By bringing together thought leaders, builders, and new product development we paint a picture for new opportunities in distributed networks and the communities that build them."- Ben Jorgensen, Constellation Co-founder and CEO. The in-person event will be held at the 1 Hotel in San Francisco and will require a nominal $150 fee. Attendees include representatives from Panasonic, the Greer Institute at Intel, Forward Edge AI, venture capitalists, The Digital Chamber, the Constellation leadership team, along with Stardust Collective community leaders. The virtual event is free to attend with access via livestream to all keynote speeches, panel discussions, and hackathon showcases in real time.Virtual attendees can engage directly with speakers and panelists through Q&A and live chat features, and may connect with other attendees in virtual event spaces. "Our metagraph application layer just released to the public, and this is the perfect time for us to showcase our Hackathon winners, our partners, and our progress. I'm excited for the chance to bring the industry together in this unique format and share ideas and ways we can work together and build together."- Altif Brown, Constellation co-founder, Head of Community, and conference organizer. To find out more about the event and register, users can visit stardust's event site at https://stardust-collective.org/HyDef-Conference About Constellation Network Constellation Network, founded in 2017, is a Blockchain ecosystem powered by the Hypergraph Transfer Protocol (HGTP), designed to secure, validate, and process data for Web3 applications. HGTP enables seamless and secure Blockchain communication, akin to how HTTP functions for the web. Constellation’s tools support building Blockchain networks for big data, fostering trust and transparency. Validated by the U.S. Department of Defense via the Air Force Research Laboratory (AFRL), Constellation is recognized as a scalable and secure Blockchain solution. Website: www.constellationnetwork.io Twitter: https://twitter.com/Conste11ation Telegram: https://t.me/constellationcommunity Contact Constellation co-founder, Head of Community, and conference organizer Altif Brown Constellation Network, Inc. altif@constellationnetwork.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. SAN FRANCISCO, USA / California, October 10th, 2024/Chainwire/--Constellation Network's “HyDef ‘24” conference will take place on Thursday, October 24, 2024, featuring a free daylong virtual event combined with a live in-person event for a nominal fee at 1 Hotel in San Francisco. Constellation Network is a unique Web3 framework with new open-source tooling that empowers companies and individuals to build blockchain networks for Big Data, creating trust and transparency around data collection, validation, and transacting. Nicknamed “America’s Blockchain,” Constellation Network actively works with entities such as the U.S. Military, The Digital Chamber, the Texas Blockchain Association, Space ISAC, and the National DigiFoundry. Constellation has been validated and approved by the U.S. Department of Defense through the Air Force Research Laboratory (AFRL) as a, “Scalable, Secure and Defense-Approved Blockchain technology.” “So much work has been going on behind the scenes which is why HyDef is a great forum to bring it to the forefront around one action packed event. Since the launch of Metagraphs, it's been hard to keep up with all the inbound interest from federal, enterprise and academia. We are excited to not only give updates on what has matured but also unveil new expressions that truly separate Constellation apart in the Web3 industry.” - Benjamin Diggles, Constellation Co-founder and CSO. “So much work has been going on behind the scenes which is why HyDef is a great forum to bring it to the forefront around one action packed event. Since the launch of Metagraphs, it's been hard to keep up with all the inbound interest from federal, enterprise and academia. We are excited to not only give updates on what has matured but also unveil new expressions that truly separate Constellation apart in the Web3 industry.” - Benjamin Diggles, Constellation Co-founder and CSO. Constellation's HyDef ‘24 conference will deliver a jam-packed day of ground-breaking insights. Conference sessions cover secure information-sharing in a zero-trust world, the future of finance and blockchain, the evolving regulatory environment, government and blockchain, and much more. Hackathon-winning projects will be showcased at the event where developers have leveraged Constellation’s big data transaction and validation capabilities to build apps that gather and validate data at scale. These apps feed the data into AI or causal models to give businesses and individuals insights based on more input than we’ve heretofore been able to process. Another HyDef highlight will be the long-awaited reveal of the details of the working relationship between Constellation and Panasonic. The work the two companies are doing together has the potential to bring Constellation’s technology and blockchain-secured edge computing to the world en masse in a meaningful way. “The Constellation Community is the backbone to our ecosystem. HyDef is a time for us all to come together, celebrate the progress in our ecosystem, give visibility to our partners, and gear up for the future of Constellation. By bringing together thought leaders, builders, and new product development we paint a picture for new opportunities in distributed networks and the communities that build them."- Ben Jorgensen, Constellation Co-founder and CEO. “The Constellation Community is the backbone to our ecosystem. HyDef is a time for us all to come together, celebrate the progress in our ecosystem, give visibility to our partners, and gear up for the future of Constellation. By bringing together thought leaders, builders, and new product development we paint a picture for new opportunities in distributed networks and the communities that build them."- Ben Jorgensen, Constellation Co-founder and CEO. The in-person event will be held at the 1 Hotel in San Francisco and will require a nominal $150 fee. Attendees include representatives from Panasonic, the Greer Institute at Intel, Forward Edge AI, venture capitalists, The Digital Chamber, the Constellation leadership team, along with Stardust Collective community leaders. The virtual event is free to attend with access via livestream to all keynote speeches, panel discussions, and hackathon showcases in real time.Virtual attendees can engage directly with speakers and panelists through Q&A and live chat features, and may connect with other attendees in virtual event spaces. "Our metagraph application layer just released to the public, and this is the perfect time for us to showcase our Hackathon winners, our partners, and our progress. I'm excited for the chance to bring the industry together in this unique format and share ideas and ways we can work together and build together."- Altif Brown, Constellation co-founder, Head of Community, and conference organizer. "Our metagraph application layer just released to the public, and this is the perfect time for us to showcase our Hackathon winners, our partners, and our progress. I'm excited for the chance to bring the industry together in this unique format and share ideas and ways we can work together and build together."- Altif Brown, Constellation co-founder, Head of Community, and conference organizer. To find out more about the event and register, users can visit stardust's event site at https://stardust-collective.org/HyDef-Conference https://stardust-collective.org/HyDef-Conference https://stardust-collective.org/HyDef-Conference About Constellation Network Constellation Network , founded in 2017, is a Blockchain ecosystem powered by the Hypergraph Transfer Protocol (HGTP), designed to secure, validate, and process data for Web3 applications. Constellation Network Constellation Network HGTP enables seamless and secure Blockchain communication, akin to how HTTP functions for the web. Constellation’s tools support building Blockchain networks for big data, fostering trust and transparency. Validated by the U.S. Department of Defense via the Air Force Research Laboratory (AFRL), Constellation is recognized as a scalable and secure Blockchain solution. Website: www.constellationnetwork.io www.constellationnetwork.io www.constellationnetwork.io Twitter: https://twitter.com/Conste11ation https://twitter.com/Conste11ation https://twitter.com/Conste11ation Telegram: https://t.me/constellationcommunity https://t.me/constellationcommunity https://t.me/constellationcommunity Contact Constellation co-founder, Head of Community, and conference organizer Altif Brown Constellation Network, Inc. altif@constellationnetwork.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

Constellation Network, The DoD-vetted Blockchain For Big Data, Unveils Panasonic Partnership Details

Constellation Network, правераны Міністэрствам абароны блокчэйн для вялікіх даных, раскрывае падрабязнасці партнёрства Panasonic

Constellation Network, la cadena de blocs aprovada pel DoD per a grans dades, revela els detalls de l'associació de Panasonic

Constellation Network, la cadena de bloques aprobada por el Departamento de Defensa para Big Data, revela detalles de su asociación con Panasonic

Constellation Network, Blockchain yang Diuji DoD untuk Big Data, Mengungkap Detail Kemitraan Panasonic

国防総省認定のビッグデータ向けブロックチェーン、コンステレーションネットワークがパナソニックとの提携の詳細を発表

Constellation Network, DoD тарабынан текшерилген чоң маалыматтар үчүн блокчейн, Panasonic өнөктөштүк чоо-жайын ачат

Tambajotra Constellation, Blockchain nohamarinin'ny DoD ho an'ny angon-drakitra lehibe, dia manambara ny antsipiriany momba ny fiaraha-miasa amin'ny Panasonic

Constellation Network, de door het Amerikaanse ministerie van Defensie goedgekeurde blockchain voor big data, onthult details over partnerschap met Panasonic

Constellation Network, den DoD-bevakade blockkedjan för Big Data, avslöjar detaljer om Panasonics partnerskap

Шабакаи Constellation, Blockchain аз ҷониби DoD санҷидашуда барои маълумоти калон, тафсилоти шарикии Panasonic -ро ошкор мекунад

Styldyzlar tory, Uly maglumatlar üçin DoD-barlagly blokchein, Panasonic Partnership Jikme-jikliklerini açýar

Inethiwekhi yeConstellation, iBlockchain ehlolwe yiDoD yeDatha enkulu, ityhila iiNkcukacha zoBudlelwane bePanasonic

Authors:
(1) Liang Wang, Microsoft Corporation, and Correspondence to (wangliang@microsoft.com);
(2) Nan Yang, Microsoft Corporation, and correspondence to (nanya@microsoft.com);
(3) Xiaolong Huang, Microsoft Corporation;
(4) Linjun Yang, Microsoft Corporation;
(5) Rangan Majumder, Microsoft Corporation;
(6) Furu Wei, Microsoft Corporation and Correspondence to (fuwei@microsoft.com). Table of Links Abstract and 1 Introduction 2 Related Work 3 Method 3.1 Synthetic Data Generation 3.2 Training 4 Experiments 4.1 Statistics of the Synthetic Data 4.2 Model Fine-tuning and Evaluation 4.3 Main Results 4.4 Multilingual Retrieval 5 Analysis 5.1 Is Contrastive Pre-training Necessary? 5.2 Extending to Long Text Embeddings and 5.3 Analysis of Training Hyperparameters 6 Conclusion and References A Implementation Details B Test Set Contamination Analysis C Prompts for Synthetic Data Generation D Instructions for Training and Evaluation A Implementation Details The model and dataset release information is available at https://github.com/microsoft/ unilm/tree/master/e5. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Liang Wang, Microsoft Corporation, and Correspondence to (wangliang@microsoft.com); (2) Nan Yang, Microsoft Corporation, and correspondence to (nanya@microsoft.com); (3) Xiaolong Huang, Microsoft Corporation; (4) Linjun Yang, Microsoft Corporation; (5) Rangan Majumder, Microsoft Corporation; (6) Furu Wei, Microsoft Corporation and Correspondence to (fuwei@microsoft.com). Authors: Authors: (1) Liang Wang, Microsoft Corporation, and Correspondence to (wangliang@microsoft.com); (2) Nan Yang, Microsoft Corporation, and correspondence to (nanya@microsoft.com); (3) Xiaolong Huang, Microsoft Corporation; (4) Linjun Yang, Microsoft Corporation; (5) Rangan Majumder, Microsoft Corporation; (6) Furu Wei, Microsoft Corporation and Correspondence to (fuwei@microsoft.com). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Method 3.1 Synthetic Data Generation 3.1 Synthetic Data Generation 3.2 Training 3.2 Training 4 Experiments 4.1 Statistics of the Synthetic Data 4.1 Statistics of the Synthetic Data 4.2 Model Fine-tuning and Evaluation 4.2 Model Fine-tuning and Evaluation 4.3 Main Results 4.3 Main Results 4.4 Multilingual Retrieval 4.4 Multilingual Retrieval 5 Analysis 5.1 Is Contrastive Pre-training Necessary? 5.1 Is Contrastive Pre-training Necessary? 5.2 Extending to Long Text Embeddings and 5.3 Analysis of Training Hyperparameters 5.2 Extending to Long Text Embeddings and 5.3 Analysis of Training Hyperparameters 6 Conclusion and References 6 Conclusion and References A Implementation Details A Implementation Details B Test Set Contamination Analysis B Test Set Contamination Analysis C Prompts for Synthetic Data Generation C Prompts for Synthetic Data Generation D Instructions for Training and Evaluation D Instructions for Training and Evaluation A Implementation Details The model and dataset release information is available at https://github.com/microsoft/ unilm/tree/master/e5. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

Improving Text Embeddings with
Large Language Models: Implementation Details

Authors:
(1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google;
(2) Dawei Yang, Google Research;
(3) Tyler Zhu, Google Research;
(4) Fitsum Reda, Google Research;
(5) William Chan, Google Research;
(6) Chitwan Saharia, Google Research;
(7) Mohammad Norouzi, Google Research;
(8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research. Table of Links Abstract and 1. Introduction 2. Related Work 3. Method 3.1. Cascaded Diffusion Models for Try-On 3.2. Parallel-UNet 4. Experiments 5. Summary and Future Work and References Appendix A. Implementation Details B. Additional Results A. Implementation Details A.1. Parallel-UNet A.2. Training and Inference TryOnDiffusion was implemented in JAX [4]. All three diffusion models are trained on 32 TPU-v4 chips for 500K iterations (around 3 days for each diffusion model). After trained, we run the inference of the whole pipeline on 4 TPU-v4 chips with batch size 4, which takes around 18 seconds for one batch. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google; (2) Dawei Yang, Google Research; (3) Tyler Zhu, Google Research; (4) Fitsum Reda, Google Research; (5) William Chan, Google Research; (6) Chitwan Saharia, Google Research; (7) Mohammad Norouzi, Google Research; (8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research. Authors: Authors: (1) Luyang Zhu, University of Washington and Google Research, and work done while the author was an intern at Google; (2) Dawei Yang, Google Research; (3) Tyler Zhu, Google Research; (4) Fitsum Reda, Google Research; (5) William Chan, Google Research; (6) Chitwan Saharia, Google Research; (7) Mohammad Norouzi, Google Research; (8) Ira Kemelmacher-Shlizerman, University of Washington and Google Research. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. Method 3. Method 3.1. Cascaded Diffusion Models for Try-On 3.1. Cascaded Diffusion Models for Try-On 3.2. Parallel-UNet 3.2. Parallel-UNet 4. Experiments 4. Experiments 5. Summary and Future Work and References 5. Summary and Future Work and References Appendix Appendix A. Implementation Details A. Implementation Details B. Additional Results B. Additional Results A. Implementation Details A.1. Parallel-UNet A.2. Training and Inference TryOnDiffusion was implemented in JAX [4]. All three diffusion models are trained on 32 TPU-v4 chips for 500K iterations (around 3 days for each diffusion model). After trained, we run the inference of the whole pipeline on 4 TPU-v4 chips with batch size 4, which takes around 18 seconds for one batch. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

The Nuts and Bolts of Parallel-UNet: Implementation Details

Table of Links Abstract and 1 Introduction 1.1 The twincode platform 1.2 Related Work 2 Research Questions 3 Variables 3.1 Independent Variables 3.2 Dependent Variables 3.3 Confounding Variables 4 Participants 5 Execution Plan and 5.1 Recruitment 5.2 Training and 5.3 Experiment Execution 5.4 Data Analysis Acknowledgments and References 5.2 Training One week or so before the experiment execution, we plan to provide a short training session about the twincode platform, so students become familiar with it and any concerns about how to use the platform were dispelled, thus reducing any potential anxiety for using a new platform. 5.3 Experiment Execution For experiment execution, all registered students must log into the twincode platform, which will automatically allocate them into the control and experimental groups. This allocation is random and gender–balanced, i.e., the proportion of men and women in each group is as close as possible. Once all students are allocated to groups, they are randomly allocated into control–experimental pairs, as shown in Figure 2. After subject allocation, the pairs are presented a programming exercise that they must solve collaboratively (labeled as Task #1 in Figure 3) in the twincode platform. They are given 20 minutes to do it, and in case they finished earlier, another exercise of increasing difficulty is presented. At the end of the 20–minute period, they are asked to individually fill in a questionnaire about the perceived productivity compared to solo programming and about the perceived partner’s technical competency compared to their own. They are given 10 minutes to fill in the questionnaire. After filling the first questionnaire, the students are presented another programming exercise to be solved individually in 10 minutes (Task #2 in Figure 3). As in the previous task, another exercise of increasing difficulty is presented if they finish earlier. The main purpose of this individual task is to make students forget about their first partners, i.e. their way of writing dialog messages or source code, so they do not recognize them in the second in–pair task. After the individual task, pairs are presented again a new collaborative programming exercise that they must solve in similar conditions to the exercise in Task #1. In this second in–pair exercise, the avatar gender (which in this initial study will always indicate binary gender) is swapped with respect to the first exercise for the subjects in the experimental group. Note that pairs are kept the same in order to reduce the variability due to the subjects themselves, which could possibly have had a confounding effect in case of a new pair allocation for Task #3. Once Task #3 is finished, students are asked to fill in the same questionnaire than they filled after Task #1, and another questionnaire comparing the perceived genders and skills of the first and second partners. They are given 15 minutes for responding both questionnaires. Finally, they are informed about the actual goal of the study and that the pairs remained the same during the experiment. At this moment, they are allowed to withdraw their data if they wish to do so. Authors:
(1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es);
(2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es);
(3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es);
(4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu);
(5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu);
(6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). This paper is available on arxiv under CC BY 4.0 DEED license. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 The twincode platform 1.1 The twincode platform 1.2 Related Work 1.2 Related Work 2 Research Questions 2 Research Questions 3 Variables 3 Variables 3.1 Independent Variables 3.1 Independent Variables 3.2 Dependent Variables 3.2 Dependent Variables 3.3 Confounding Variables 3.3 Confounding Variables 4 Participants 4 Participants 5 Execution Plan and 5.1 Recruitment 5 Execution Plan and 5.1 Recruitment 5.2 Training and 5.3 Experiment Execution 5.2 Training and 5.3 Experiment Execution 5.4 Data Analysis 5.4 Data Analysis Acknowledgments and References Acknowledgments and References 5.2 Training One week or so before the experiment execution, we plan to provide a short training session about the twincode platform, so students become familiar with it and any concerns about how to use the platform were dispelled, thus reducing any potential anxiety for using a new platform. 5.3 Experiment Execution For experiment execution, all registered students must log into the twincode platform, which will automatically allocate them into the control and experimental groups. This allocation is random and gender–balanced, i.e., the proportion of men and women in each group is as close as possible. Once all students are allocated to groups, they are randomly allocated into control–experimental pairs, as shown in Figure 2. After subject allocation, the pairs are presented a programming exercise that they must solve collaboratively (labeled as Task #1 in Figure 3) in the twincode platform. They are given 20 minutes to do it, and in case they finished earlier, another exercise of increasing difficulty is presented. At the end of the 20–minute period, they are asked to individually fill in a questionnaire about the perceived productivity compared to solo programming and about the perceived partner’s technical competency compared to their own. They are given 10 minutes to fill in the questionnaire. After filling the first questionnaire, the students are presented another programming exercise to be solved individually in 10 minutes (Task #2 in Figure 3). As in the previous task, another exercise of increasing difficulty is presented if they finish earlier. The main purpose of this individual task is to make students forget about their first partners, i.e. their way of writing dialog messages or source code, so they do not recognize them in the second in–pair task. After the individual task, pairs are presented again a new collaborative programming exercise that they must solve in similar conditions to the exercise in Task #1. In this second in–pair exercise, the avatar gender (which in this initial study will always indicate binary gender) is swapped with respect to the first exercise for the subjects in the experimental group. Note that pairs are kept the same in order to reduce the variability due to the subjects themselves, which could possibly have had a confounding effect in case of a new pair allocation for Task #3. Once Task #3 is finished, students are asked to fill in the same questionnaire than they filled after Task #1, and another questionnaire comparing the perceived genders and skills of the first and second partners. They are given 15 minutes for responding both questionnaires. Finally, they are informed about the actual goal of the study and that the pairs remained the same during the experiment. At this moment, they are allowed to withdraw their data if they wish to do so. Authors: (1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es); (2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es); (3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es); (4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu); (5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu); (6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). Authors: Authors: (1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es); (2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es); (3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es); (4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu); (5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu); (6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). 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

Detailed Execution Plan for Remote Pair Programming Study

Authors:
(1) Suriya Gunasekar, Microsoft Research;
(2) Yi Zhang, Microsoft Research;
(3) Jyoti Aneja, Microsoft Research;
(4) Caio C´esar Teodoro Mendes, Microsoft Research;
(5) Allie Del Giorno, Microsoft Research;
(6) Sivakanth Gopi, Microsoft Research;
(7) Mojan Javaheripi, Microsoft Research;
(8) Piero Kauffmann, Microsoft Research;
(9) Gustavo de Rosa, Microsoft Research;
(10) Olli Saarikivi, Microsoft Research;
(11) Adil Salim, Microsoft Research;
(12) Shital Shah, Microsoft Research;
(13) Harkirat Singh Behl, Microsoft Research;
(14) Xin Wang, Microsoft Research;
(15) S´ebastien Bubeck, Microsoft Research;
(16) Ronen Eldan, Microsoft Research;
(17) Adam Tauman Kalai, Microsoft Research;
(18) Yin Tat Lee, Microsoft Research;
(19) Yuanzhi Li, Microsoft Research. Table of Links Abstract and 1. Introduction
2 Training details and the importance of high-quality data
2.1 Filtering of existing code datasets using a transformer-based classifier
2.2 Creation of synthetic textbook-quality datasets
2.3 Model architecture and training
3 Spikes of model capability after finetuning on CodeExercises, 3.1 Finetuning improves the model’s understanding, and 3.2 Finetuning improves the model’s ability to use external libraries
4 Evaluation on unconventional problems with LLM grading
5 Data pruning for unbiased performance evaluation
5.1 N-gram overlap and 5.2 Embedding and syntax-based similarity analysis
6 Conclusion and References
A Additional examples for Section 3
B Limitation of phi-1
C Examples for Section 5 2 Training details and the importance of high-quality data As alluded to in the title of the paper, the central ingredient our model relies on textbook-quality training data. Unlike previous work that used standard sources of text data for code generation, such as The Stack [KLA+ 22] (which contains sourcecode from repositories with permissive licenses) and other web-based datasets (e.g., StackOverflow and CodeContest [LCC+ 22]), we argue that these sources are not optimal for teaching the model how to reason and plan algorithmically. On the other hand, our model architecture and training methods are fairly conventional (Section 2.3), so we devote this section primarily to explaining how we curated our data. The standard code datasets [KLA+ 22, LCC+ 22] form a large and diverse corpus covering broad range of topics and use cases. However, based on manual inspection of random samples we observe that many of these snippets are not very instructive for learning the basics of coding, and suffer from several drawbacks: • Many samples are not self-contained, meaning that they depend on other modules or files that are external to the snippet, making them hard to understand without additional context. • Typical examples do not involve any meaningful computation, but rather consist of trivial or boilerplate code, such as defining constants, setting parameters, or configuring GUI elements. • Samples that do contain algorithmic logic are often buried inside complex or poorly documented functions, making them difficult to follow or learn from. • The examples are skewed towards certain topics or use cases, resulting in an unbalanced distribution of coding concepts and skills across the dataset. One can only imagine how frustrating and inefficient it would be for a human learner to try to acquire coding skills from these datasets, as they would have to deal with a lot of noise, ambiguity, and incompleteness in the data. We hypothesize that these issues also affect the performance of language models, as they reduce the quality and quantity of the signal that maps natural language to code. We conjecture that language models would benefit from a training set that has the same qualities as a good “textbook”: it should be clear, self-contained, instructive, and balanced. In this work, we address this challenge directly and show that by intentionally selecting and generating high-quality data, we can achieve state-of-the-art results on code-generation tasks with a much smaller model and less compute than existing approaches. Our training relies on three main datasets: • A filtered code-language dataset, which is a subset of The Stack and StackOverflow, obtained by using a language model-based classifier (consisting of about 6B tokens). • A synthetic textbook dataset consisting of <1B tokens of GPT-3.5 generated Python textbooks. • A small synthetic exercises dataset consisting of ∼180M tokens of Python exercises and solutions. We describe those datasets in more detail in the next subsections. Taken together, the above datasets contain less than 7B tokens. We refer to the combination of filtered code-language and synthetic textbook datasets as “CodeTextbook” and use it in the pretraining phase to obtain our base model phi-1-base —this model already achieves a competitive HumanEval performance of 29%. Then we use the 180M token synthetic exercises dataset, referred to as “CodeExercises”, to finetune our phi-1-base model to obtain phi-1. Despite the small size of the “CodeExercises” dataset, finetuning with this dataset is crucial not only for large improvements in generating simple Python function as shown in Figure 2.1, but more broadly to unlock many interesting emergent capabilities in our phi-1 model that are not observed in phi-1-base (see Section 3). This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Suriya Gunasekar, Microsoft Research; (2) Yi Zhang, Microsoft Research; (3) Jyoti Aneja, Microsoft Research; (4) Caio C´esar Teodoro Mendes, Microsoft Research; (5) Allie Del Giorno, Microsoft Research; (6) Sivakanth Gopi, Microsoft Research; (7) Mojan Javaheripi, Microsoft Research; (8) Piero Kauffmann, Microsoft Research; (9) Gustavo de Rosa, Microsoft Research; (10) Olli Saarikivi, Microsoft Research; (11) Adil Salim, Microsoft Research; (12) Shital Shah, Microsoft Research; (13) Harkirat Singh Behl, Microsoft Research; (14) Xin Wang, Microsoft Research; (15) S´ebastien Bubeck, Microsoft Research; (16) Ronen Eldan, Microsoft Research; (17) Adam Tauman Kalai, Microsoft Research; (18) Yin Tat Lee, Microsoft Research; (19) Yuanzhi Li, Microsoft Research. Authors: Authors: (1) Suriya Gunasekar, Microsoft Research; (2) Yi Zhang, Microsoft Research; (3) Jyoti Aneja, Microsoft Research; (4) Caio C´esar Teodoro Mendes, Microsoft Research; (5) Allie Del Giorno, Microsoft Research; (6) Sivakanth Gopi, Microsoft Research; (7) Mojan Javaheripi, Microsoft Research; (8) Piero Kauffmann, Microsoft Research; (9) Gustavo de Rosa, Microsoft Research; (10) Olli Saarikivi, Microsoft Research; (11) Adil Salim, Microsoft Research; (12) Shital Shah, Microsoft Research; (13) Harkirat Singh Behl, Microsoft Research; (14) Xin Wang, Microsoft Research; (15) S´ebastien Bubeck, Microsoft Research; (16) Ronen Eldan, Microsoft Research; (17) Adam Tauman Kalai, Microsoft Research; (18) Yin Tat Lee, Microsoft Research; (19) Yuanzhi Li, Microsoft Research. Table of Links Abstract and 1. Introduction 2 Training details and the importance of high-quality data 2.1 Filtering of existing code datasets using a transformer-based classifier 2.2 Creation of synthetic textbook-quality datasets 2.3 Model architecture and training 3 Spikes of model capability after finetuning on CodeExercises, 3.1 Finetuning improves the model’s understanding, and 3.2 Finetuning improves the model’s ability to use external libraries 4 Evaluation on unconventional problems with LLM grading 5 Data pruning for unbiased performance evaluation 5.1 N-gram overlap and 5.2 Embedding and syntax-based similarity analysis 6 Conclusion and References A Additional examples for Section 3 B Limitation of phi-1 C Examples for Section 5 Abstract and 1. Introduction Abstract and 1. Introduction 2 Training details and the importance of high-quality data 2 Training details and the importance of high-quality data 2.1 Filtering of existing code datasets using a transformer-based classifier 2.1 Filtering of existing code datasets using a transformer-based classifier 2.2 Creation of synthetic textbook-quality datasets 2.2 Creation of synthetic textbook-quality datasets 2.3 Model architecture and training 2.3 Model architecture and training 3 Spikes of model capability after finetuning on CodeExercises, 3.1 Finetuning improves the model’s understanding, and 3.2 Finetuning improves the model’s ability to use external libraries 3 Spikes of model capability after finetuning on CodeExercises, 3.1 Finetuning improves the model’s understanding, and 3.2 Finetuning improves the model’s ability to use external libraries 4 Evaluation on unconventional problems with LLM grading 4 Evaluation on unconventional problems with LLM grading 5 Data pruning for unbiased performance evaluation 5 Data pruning for unbiased performance evaluation 5.1 N-gram overlap and 5.2 Embedding and syntax-based similarity analysis 5.1 N-gram overlap and 5.2 Embedding and syntax-based similarity analysis 6 Conclusion and References 6 Conclusion and References A Additional examples for Section 3 A Additional examples for Section 3 B Limitation of phi-1 B Limitation of phi-1 C Examples for Section 5 C Examples for Section 5 2 Training details and the importance of high-quality data As alluded to in the title of the paper, the central ingredient our model relies on textbook-quality training data. Unlike previous work that used standard sources of text data for code generation, such as The Stack [KLA+ 22] (which contains sourcecode from repositories with permissive licenses) and other web-based datasets (e.g., StackOverflow and CodeContest [LCC+ 22]), we argue that these sources are not optimal for teaching the model how to reason and plan algorithmically. On the other hand, our model architecture and training methods are fairly conventional (Section 2.3), so we devote this section primarily to explaining how we curated our data. The standard code datasets [KLA+ 22, LCC+ 22] form a large and diverse corpus covering broad range of topics and use cases. However, based on manual inspection of random samples we observe that many of these snippets are not very instructive for learning the basics of coding, and suffer from several drawbacks: • Many samples are not self-contained, meaning that they depend on other modules or files that are external to the snippet, making them hard to understand without additional context. • Typical examples do not involve any meaningful computation, but rather consist of trivial or boilerplate code, such as defining constants, setting parameters, or configuring GUI elements. • Samples that do contain algorithmic logic are often buried inside complex or poorly documented functions, making them difficult to follow or learn from. • The examples are skewed towards certain topics or use cases, resulting in an unbalanced distribution of coding concepts and skills across the dataset. One can only imagine how frustrating and inefficient it would be for a human learner to try to acquire coding skills from these datasets, as they would have to deal with a lot of noise, ambiguity, and incompleteness in the data. We hypothesize that these issues also affect the performance of language models, as they reduce the quality and quantity of the signal that maps natural language to code. We conjecture that language models would benefit from a training set that has the same qualities as a good “textbook”: it should be clear, self-contained, instructive, and balanced. In this work, we address this challenge directly and show that by intentionally selecting and generating high-quality data, we can achieve state-of-the-art results on code-generation tasks with a much smaller model and less compute than existing approaches. Our training relies on three main datasets: • A filtered code-language dataset, which is a subset of The Stack and StackOverflow, obtained by using a language model-based classifier (consisting of about 6B tokens). filtered code-language • A synthetic textbook dataset consisting of <1B tokens of GPT-3.5 generated Python textbooks. synthetic textbook • A small synthetic exercises dataset consisting of ∼180M tokens of Python exercises and solutions. synthetic exercises We describe those datasets in more detail in the next subsections. Taken together, the above datasets contain less than 7B tokens. We refer to the combination of filtered code-language and synthetic textbook datasets as “CodeTextbook” and use it in the pretraining phase to obtain our base model phi-1-base —this model already achieves a competitive HumanEval performance of 29%. Then we use the 180M token synthetic exercises dataset, referred to as “CodeExercises”, to finetune our phi-1-base model to obtain phi-1. Despite the small size of the “CodeExercises” dataset, finetuning with this dataset is crucial not only for large improvements in generating simple Python function as shown in Figure 2.1, but more broadly to unlock many interesting emergent capabilities in our phi-1 model that are not observed in phi-1-base (see Section 3). 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

Textbooks are All You Need: Training Details and the Importance of High-quality Data

How to Send Detailed Slack Notifications From GitHub Actions

Authors:
(1) Zhan Ling, UC San Diego and equal contribution;
(2) Yunhao Fang, UC San Diego and equal contribution;
(3) Xuanlin Li, UC San Diego;
(4) Zhiao Huang, UC San Diego;
(5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research
(6) Roland Memisevic, Qualcomm AI Research;
(7) Hao Su, UC San Diego. Table of Links Abstract and Introduction Related work Motivation and Problem Formulation Deductively Verifiable Chain-of-Thought Reasoning Experiments Limitations Conclusion, Acknowledgements and References A Deductive Verification with Vicuna Models B More Discussion on Improvements of Deductive Verification Accuracy Versus Improvements on Final Answer Correctness C More Details on Answer Extraction D Prompts E More Deductive Verification Examples C More Details on Answer Extraction In this section, we describe our process to extract the final answer from language models’ responses. The process begins by selecting the last three non-empty lines. Then, these lines are processed through the following pipeline: Firstly, we use a list of regular expressions to identify "No-Answer" patterns within the text, such as "we cannot answer (this|the) question". This process helps us ascertain whether the model can provide a conclusive answer. If any such patterns appear in the text, we mark "No answer!" as the final answer. However, if we don’t detect these patterns, we proceed to the next steps for extracting the final answer.


Secondly, if any "Answer-Split" patterns are found in the text, we divide the text into several blocks using the identified pattern. The last block of text is then utilized for extracting the answer.


Lastly, we use regular expressions, as outlined in Tab. 11, to scan the remaining text for possible final answers. If multiple matches are found for the pattern, we select the first match as the final answer. If no pattern matches are found in the remaining text, we default the final response to "No answer!". “No-Answer” Patterns: "we cannot provide an answer to this question with (this|the) given information", "we cannot answer (this|the) question", "we cannot determine", "we can’t determine", "we do not have enough information to answer (this|the) question", "we do not have enough information to provide a definitive answer to (this|the) question", "the answer(.*?)is unknown", "answer is not listed among the answer choices". “Answer-Split” Patterns: "answer is", "final answer:", "answer to the question is", "answer to this question is", "concatenated letters are", "concatenate the letters -", "The answer of ". This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Zhan Ling, UC San Diego and equal contribution; (2) Yunhao Fang, UC San Diego and equal contribution; (3) Xuanlin Li, UC San Diego; (4) Zhiao Huang, UC San Diego; (5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research (6) Roland Memisevic, Qualcomm AI Research; (7) Hao Su, UC San Diego. Authors: Authors: (1) Zhan Ling, UC San Diego and equal contribution; (2) Yunhao Fang, UC San Diego and equal contribution; (3) Xuanlin Li, UC San Diego; (4) Zhiao Huang, UC San Diego; (5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research (6) Roland Memisevic, Qualcomm AI Research; (7) Hao Su, UC San Diego. Table of Links Abstract and Introduction Abstract and Introduction Related work Related work Motivation and Problem Formulation Motivation and Problem Formulation Deductively Verifiable Chain-of-Thought Reasoning Deductively Verifiable Chain-of-Thought Reasoning Experiments Experiments Limitations Limitations Conclusion, Acknowledgements and References Conclusion, Acknowledgements and References A Deductive Verification with Vicuna Models A Deductive Verification with Vicuna Models B More Discussion on Improvements of Deductive Verification Accuracy Versus Improvements on Final Answer Correctness B More Discussion on Improvements of Deductive Verification Accuracy Versus Improvements on Final Answer Correctness C More Details on Answer Extraction C More Details on Answer Extraction D Prompts D Prompts E More Deductive Verification Examples E More Deductive Verification Examples C More Details on Answer Extraction In this section, we describe our process to extract the final answer from language models’ responses. The process begins by selecting the last three non-empty lines. Then, these lines are processed through the following pipeline: Firstly, we use a list of regular expressions to identify "No-Answer" patterns within the text, such as "we cannot answer (this|the) question". This process helps us ascertain whether the model can provide a conclusive answer. If any such patterns appear in the text, we mark "No answer!" as the final answer. However, if we don’t detect these patterns, we proceed to the next steps for extracting the final answer. Secondly, if any "Answer-Split" patterns are found in the text, we divide the text into several blocks using the identified pattern. The last block of text is then utilized for extracting the answer. Lastly, we use regular expressions, as outlined in Tab. 11, to scan the remaining text for possible final answers. If multiple matches are found for the pattern, we select the first match as the final answer. If no pattern matches are found in the remaining text, we default the final response to "No answer!". Firstly, we use a list of regular expressions to identify "No-Answer" patterns within the text, such as "we cannot answer (this|the) question". This process helps us ascertain whether the model can provide a conclusive answer. If any such patterns appear in the text, we mark "No answer!" as the final answer. However, if we don’t detect these patterns, we proceed to the next steps for extracting the final answer. Firstly, we use a list of regular expressions to identify "No-Answer" patterns within the text, such as "we cannot answer (this|the) question". This process helps us ascertain whether the model can provide a conclusive answer. If any such patterns appear in the text, we mark "No answer!" as the final answer. However, if we don’t detect these patterns, we proceed to the next steps for extracting the final answer. Secondly, if any "Answer-Split" patterns are found in the text, we divide the text into several blocks using the identified pattern. The last block of text is then utilized for extracting the answer. Secondly, if any "Answer-Split" patterns are found in the text, we divide the text into several blocks using the identified pattern. The last block of text is then utilized for extracting the answer. Lastly, we use regular expressions, as outlined in Tab. 11, to scan the remaining text for possible final answers. If multiple matches are found for the pattern, we select the first match as the final answer. If no pattern matches are found in the remaining text, we default the final response to "No answer!". Lastly, we use regular expressions, as outlined in Tab. 11, to scan the remaining text for possible final answers. If multiple matches are found for the pattern, we select the first match as the final answer. If no pattern matches are found in the remaining text, we default the final response to "No answer!". “No-Answer” Patterns : "we cannot provide an answer to this question with (this|the) given information", "we cannot answer (this|the) question", "we cannot determine", "we can’t determine", "we do not have enough information to answer (this|the) question", "we do not have enough information to provide a definitive answer to (this|the) question", "the answer(.*?)is unknown", "answer is not listed among the answer choices". “No-Answer” Patterns “Answer-Split” Patterns: "answer is", "final answer:", "answer to the question is", "answer to this question is", "concatenated letters are", "concatenate the letters -", "The answer of ". “Answer-Split” Patterns: 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

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