HeFi

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COPA Claims Craig Wright’s LaTeX Documents on Bitcoin Concepts are Fabricated

Twitter is currently the hottest communication and marketing channel for the NFT community, used for discussions and promotions of new and emerging NFT projects. While other popular social media sites like YouTube and Instagram are also prevalent in the NFT community, the microblogging platform has made several advances toward integrating some crypto features. In September 2021, Twitter a new service for their tipping function, allowing users to tip seamlessly in Bitcoin by using a payments application, Strike, and has since integrated payments in ETH. As of January 2022, Twitter the NFT Profile Pictures feature that allows linking an NFT as a profile picture while authenticating the ownership. announced launched It is getting harder to keep up in times of information overload, new , and emerging NFT collections. Therefore, this post will focus on NFT influencers on Twitter to follow, who share the most meaningful insights into the blockchain, NFT, and crypto ecosystem. crypto terms As the world of NFTs is so new and many have been recently enthused by crypto and DeFi, this list involves several people who have shifted their career focus to NFTs and crypto. We have considered and checked these NFT personas by their follower count, the quality of information they share, and the types of insights to various audiences. 1. Farokh Sarmad @Farokh - 268k followers Farokh, aka Farokh Sarmad, is a serial entrepreneur and a social media influencer who started his career in social media in 2011 when he founded his blog "Mr. Goodlife." It led to the creation of Goodlife & Media communications agency, where Farokh is the founder and CEO. Today, he helps other entrepreneurs grow their social media presence, build engaged communities, and join the NFT revolution. Being at the forefront of the crypto industry growth, Farokh has established himself in the NFT space through the NFT room chat on Clubhouse and has discussions about his investments and NFTs through his founded channel . Rug Radio NFT investments, community discussions, community engagement. Follow for: , , Also on: Discord LinkedIn Opensea 2. Gary Vaynerchuk @garyvee @veefriends - 3m / 218k followers Gary Vaynerchuk is a serial entrepreneur, chairman of media company VaynerX, and the founder and CEO of advertising agency VaynerMedia. As a keen crypto and NFT enthusiast, already a part of the online marketing and communications industry, Gary established a project called VeeFriends - a community for people in the NFT world. The project serves as a knowledge base for the whole NFT community aimed at all levels. Users can not only keep up to date with his latest collections, but they can also attend educational sessions about the blockchain world to improve their knowledge. Gary offers both guidance as well as creates NFTs himself. emerging NFT collections, educational sessions for beginners, own NFTs. Follow for: YouTube, , Also on: LinkedIn Opensea 3. Tom Bilyeu @TomBilyeu - 162k followers Tom Bilyeu is the co-founder of nutritional food company Quest Nutrition and is the CEO and host of a weekly show, Impact Theory. He is also an NFT enthusiast who got mesmerized by the world of crypto. He is active on Twitter, promoting his opinions and views on new collections and projects, talking about NFTs, Metaverse, business, and entrepreneurship; he has an excellent account to follow on Twitter. learn about NFTs & Metaverse, advice, ideas, investing in crypto. Follow for: , Also on: YouTube LinkedIn 4. Rac.eth @RAC - 58k followers RAC (Remix Artist Collective) aka André Allen Anjos started his career as a musician, mixing tapes out of radio broadcasts. He then found himself enthused by the world of crypto, establishing himself as an expert in the field, combining crypto, music, and NFTs, an unanticipated combination at the time. He already has his own crypto-token and made his first audiovisual NFT in 2021, which sold for $26,000. He was even named one of the Future 25 shaping the music industry by RollingStone magazine, thanks to his achievements in the NFT music space! NFT music, innovation in the crypto and NFT world, new creations. Follow for: , Also on: Instagram Opensea 5. Carly Reilly @carlypreilly - 16.5k followers Carly Reilly's background includes a career in politics, media, and finance. However, as an NFT and crypto enthusiast, she has recently moved over to the crypto world and now works as a Chief of Staff at a creative blockchain lab, BLOCK. What is more, she's launched and is currently hosting a podcast called "Overpriced JPEGS" in collaboration with network, one of the , where she discusses all-things-NFTs and the Metaverse with guest speakers. Banklesshq best crypto YouTube channels Each week she breaks down the latest news and insights into the NFT industry and explains what makes particular NFTs worth so much. You can keep up to date with all the latest episodes and NFT insights on her Twitter profile. behind-the-scenes insights into the NFT world, female NFT influencers. Follow for: , Also on: YouTube LinkedIn 6. Carlo D'Angelo @DeFiDefenseLaw - 2.6k followers Carlo D'Angelo has a background of working as a private criminal defense attorney and public defender, and he is also a published author. He has made a move to become a DeFi, Crypto, NFT & Criminal Defense lawyer, focusing on the new area. As many regulations are still vague as the world of crypto and NFTs grows and develops, individuals like him will become even more in demand. Moreover, he is also a regular contributor to the Rug Radio channel, where he primarily focuses on topics around new regulations and how they will shape and change the industry. Follow his account for the latest update regarding the latest news and advice. He doesn't have quite the following as other accounts yet; however, we expect it to grow as the industry develops and the need for crypto lawyers increases. DeFi, crypto, NFTs criminal defense law, latest news, regulations, updates. Follow for: Also on: LinkedIn 7. Kevin Rose @kevinrose - 1.6M followers Kevin Rose is one of the most followed NFT influencers and entrepreneurs in the NFT space on Twitter. He is a collector and a crypto startups investor who also has his podcasts @proof_xyz and @modern_fi and is a co-founder of a PFP NFT project @moonbirds_xyz. His day job is also closely related to this, as he works as a partner in True Ventures, a venture capital firm that focuses on early-stage technology startups. He regularly shares his opinions on the future of NFTs and crypto, investing in Web3, and the overall impact of blockchain technology. future of NFTs, the impact of blockchain technology, investing in Web3. Follow for: , Also on: LinkedIn Opensea 8. Matty @DCLBlogge r - 214k followers Matty is an NFT influencer who is obsessed with the emerging Web3 industry. He has been featured on CBC radio, Coindesk, and Reuters for his ideas in space. Of course, he also has his own YouTube channel, "follow my NFT," where he expresses his opinions and talks about NFT growth, community, and blockchain technology. He is both a collector and has created some NFT art himself. His background includes advising on community growth, marketing, sales, and business development in the blockchain tech industry. He is very active on the platform and has got interesting views on emerging NFTs and general tips in the industry, for example, video content about the best metaverse jobs. His Twitter account is worth a follow, as is his YouTube account. NFT investing and coverage, NFT industry growth. Follow for: , , Also on: YouTube LinkedIn Opensea 9. @NFTEthics - 80k followers In the emerging NFT industry, it is crucial to do your research before investing in any new NFT collections, and this Twitter channel will help you avoid suspected scams. As the blockchain industry is built on anonymity and pseudonymous profiles, it can be more difficult to identify bad actors, so this one is a good one to follow to avoid falling victim to scams. NFT ethics, fraudulent accounts, and NFT scams. Follow for: 10. @Ohhshiny - 144k followers This community account remains pseudonymous and is hosting the popular OohShiny show, a program on Twitter Spaces that interviews people in the NFT industry. OohShiny account tweets about the most recent happenings and news in the Metaverse, highlighting any new and unique NFT projects. They have discussions with new creators who wish to get advice on the progress of their projects. emerging projects in the NFT space, advice for creators launching new projects. Follow for: Also on: Opensea 11. @Digitalartchick - 155k followers Another pseudonymous account is @Artchick NFT influencer on Twitter with over 155k followers, very active on the platform, sharing views and opinions about the space. She is an NFT creator with a background in UX design and painting and clothing and pattern design in the physical world. One of few women in the crypto and NFT world, she promotes other women to join and calls for attention to other female creators and collectors. women in crypto, female NFT influencers, NFT creations. Follow for: Also on: Website 12. Snoop Dogg @snoopdogg - 19M followers Even Snoop Dogg has been attracted by the world of NFTs and regularly shares his opinions, investments, and creations in the space. At the beginning of March 2022, he released a Mixtape & EDM NFT collection, "Death Row EDM Vol. 1" on Opensea, and just a few days ago, in April 2022, new "Death Row EDM Vol. 2" was released. He has even hinted at plans to relaunch his Death Row Records as an NFT record label. He has admitted to having saved $17 million worth of digital tokens under an alias and has launched an NFT collection, The Doggies, in partnership with The Sandbox in the Metaverse. NFT creations, music NFTs, emerging collections. Follow for: , Also on: Opensea Opensea 13. Kode Abdo @Bosslogic - 496k followers Bosslogic, aka Kode Abdo, started as a graphic designer with digital arts, photoshop, and digital photography expertise. He is a self-made digital artist and one of the top NFT influencers in the space. He has a large following on his Twitter account and is also very active on his other social media platforms, Instagram and YouTube. new NFT projects, NFT art, NFT creations. Follow for: , Also on: Instagram YouTube 14. @RyandCrypto - 327k followers Another pseudonymous profile with a large following is an NFT influencer active on Twitter, @RyandCrypto, who doesn't create any NFT art himself but shares his investments, opinions, and thoughts about new and existing projects on Twitter and YouTube channels. He shares where he sees the potential and provides the latest news, updates, and trends in the NFT industry. news, updates, and opinions about new NFT projects. Follow for: Also on: YouTube 15. Richerd @Richerd.eth - 158k followers Richerd has a background in software development and building consumer products for web-based platforms. He is also the founder of Manifold, a company that focuses on equipping NFT creators with the new and innovative tools they need to develop NFT collections quickly. On his Twitter account, Richerd brings his knowledge and influence surrounding the security of NFTs and the power and impact of blockchain technology, crypto, and NFTs on the physical world. influence of NFTs, security of NFTs, discussions about new NFT projects. Follow for: Also on: Opensea Summing up Following NFT influencers on Twitter is great to learn more about the crypto world, engage in community discussions, keep updated with the latest news, and discover innovative new NFT or other DeFi projects. However, it is crucial to ensure they are also credible: shilling, rug-pulls, and pump-and-dump schemes are nothing new in the DeFi world. As the Web3 space grows, the community of fake identities spreading false information will also increase.

15 Crypto and NFT Influencers to Follow on Twitter in 2022

Authors:
(1) Tony Lee, Stanford with Equal contribution;
(2) Michihiro Yasunaga, Stanford with Equal contribution;
(3) Chenlin Meng, Stanford with Equal contribution;
(4) Yifan Mai, Stanford;
(5) Joon Sung Park, Stanford;
(6) Agrim Gupta, Stanford;
(7) Yunzhi Zhang, Stanford;
(8) Deepak Narayanan, Microsoft;
(9) Hannah Benita Teufel, Aleph Alpha;
(10) Marco Bellagente, Aleph Alpha;
(11) Minguk Kang, POSTECH;
(12) Taesung Park, Adobe;
(13) Jure Leskovec, Stanford;
(14) Jun-Yan Zhu, CMU;
(15) Li Fei-Fei, Stanford;
(16) Jiajun Wu, Stanford;
(17) Stefano Ermon, Stanford;
(18) Percy Liang, Stanford. Table of Links Abstract and 1 Introduction 2 Core framework 3 Aspects 4 Scenarios 5 Metrics 6 Models 7 Experiments and results 8 Related work 9 Conclusion 10 Limitations Author contributions, Acknowledgments and References A Datasheet B Scenario details C Metric details D Model details E Human evaluation procedure 9 Conclusion We introduced Holistic Evaluation of Text-to-Image Models (HEIM), a new benchmark to assess 12 important aspects in text-to-image generation, including alignment, quality, aesthetics, originality, reasoning, knowledge, bias, toxicity, fairness, robustness, multilinguality, and efficiency. Our evaluation of 26 recent text-to-image models reveals that different models excel in different aspects, opening up research avenues to study whether and how to develop models that excel across multiple aspects. To enhance transparency and reproducibility, we release our evaluation pipeline, along with the generated images and human evaluation results. We encourage the community to consider the different aspects when developing text-to-image models. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Tony Lee, Stanford with Equal contribution; (2) Michihiro Yasunaga, Stanford with Equal contribution; (3) Chenlin Meng, Stanford with Equal contribution; (4) Yifan Mai, Stanford; (5) Joon Sung Park, Stanford; (6) Agrim Gupta, Stanford; (7) Yunzhi Zhang, Stanford; (8) Deepak Narayanan, Microsoft; (9) Hannah Benita Teufel, Aleph Alpha; (10) Marco Bellagente, Aleph Alpha; (11) Minguk Kang, POSTECH; (12) Taesung Park, Adobe; (13) Jure Leskovec, Stanford; (14) Jun-Yan Zhu, CMU; (15) Li Fei-Fei, Stanford; (16) Jiajun Wu, Stanford; (17) Stefano Ermon, Stanford; (18) Percy Liang, Stanford. Authors: Authors: (1) Tony Lee, Stanford with Equal contribution; (2) Michihiro Yasunaga, Stanford with Equal contribution; (3) Chenlin Meng, Stanford with Equal contribution; (4) Yifan Mai, Stanford; (5) Joon Sung Park, Stanford; (6) Agrim Gupta, Stanford; (7) Yunzhi Zhang, Stanford; (8) Deepak Narayanan, Microsoft; (9) Hannah Benita Teufel, Aleph Alpha; (10) Marco Bellagente, Aleph Alpha; (11) Minguk Kang, POSTECH; (12) Taesung Park, Adobe; (13) Jure Leskovec, Stanford; (14) Jun-Yan Zhu, CMU; (15) Li Fei-Fei, Stanford; (16) Jiajun Wu, Stanford; (17) Stefano Ermon, Stanford; (18) Percy Liang, Stanford. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Core framework 2 Core framework 3 Aspects 3 Aspects 4 Scenarios 4 Scenarios 5 Metrics 5 Metrics 6 Models 6 Models 7 Experiments and results 7 Experiments and results 8 Related work 8 Related work 9 Conclusion 9 Conclusion 10 Limitations 10 Limitations Author contributions, Acknowledgments and References Author contributions, Acknowledgments and References A Datasheet A Datasheet B Scenario details B Scenario details C Metric details C Metric details D Model details D Model details E Human evaluation procedure E Human evaluation procedure 9 Conclusion We introduced Holistic Evaluation of Text-to-Image Models (HEIM), a new benchmark to assess 12 important aspects in text-to-image generation, including alignment, quality, aesthetics, originality, reasoning, knowledge, bias, toxicity, fairness, robustness, multilinguality, and efficiency. Our evaluation of 26 recent text-to-image models reveals that different models excel in different aspects, opening up research avenues to study whether and how to develop models that excel across multiple aspects. To enhance transparency and reproducibility, we release our evaluation pipeline, along with the generated images and human evaluation results. We encourage the community to consider the different aspects when developing text-to-image models. 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

Paving the Way for Better AI Models: Insights from HEIM’s 12-Aspect Benchmark

Authors:
(1) Tony Lee, Stanford with Equal contribution;
(2) Michihiro Yasunaga, Stanford with Equal contribution;
(3) Chenlin Meng, Stanford with Equal contribution;
(4) Yifan Mai, Stanford;
(5) Joon Sung Park, Stanford;
(6) Agrim Gupta, Stanford;
(7) Yunzhi Zhang, Stanford;
(8) Deepak Narayanan, Microsoft;
(9) Hannah Benita Teufel, Aleph Alpha;
(10) Marco Bellagente, Aleph Alpha;
(11) Minguk Kang, POSTECH;
(12) Taesung Park, Adobe;
(13) Jure Leskovec, Stanford;
(14) Jun-Yan Zhu, CMU;
(15) Li Fei-Fei, Stanford;
(16) Jiajun Wu, Stanford;
(17) Stefano Ermon, Stanford;
(18) Percy Liang, Stanford. Table of Links Abstract and 1 Introduction 2 Core framework 3 Aspects 4 Scenarios 5 Metrics 6 Models 7 Experiments and results 8 Related work 9 Conclusion 10 Limitations Author contributions, Acknowledgments and References A Datasheet B Scenario details C Metric details D Model details E Human evaluation procedure 5 Metrics To evaluate the 12 aspects (§3), we also curate a diverse and realistic set of metrics. Table 3 presents an overview of all the metrics and their descriptions. Compared to previous metrics, our metrics are more realistic and broader. First, in addition to automated metrics, we use human metrics (top rows in Table 3) to perform realistic evaluation that reflects human judgment [25, 26, 27]. Specifically, we employ human metrics for the overall text-image alignment and photorealism, which are used for many evaluation aspects, including alignment, quality, knowledge, reasoning, fairness, robustness, and multilinguality. We also employ human metrics for overall aesthetics and originality, for which capturing the nuances of human judgment is important. To conduct human evaluation, we employ crowdsourcing following the methodology described in Otani et al.,[35]. Concrete English definitions are provided for each human evaluation question and rating choice, and a minimum of 5 participants evaluate each image. We use at least 100 image samples for each aspect. For more details about the crowdsourcing procedure, please refer to Appendix E. The second contribution is introducing new metrics for aspects that have received limited attention in existing evaluation efforts, namely fairness, robustness, multilinguality, and efficiency, as discussed in §3. The new metrics aim to close the evaluation gaps. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Tony Lee, Stanford with Equal contribution; (2) Michihiro Yasunaga, Stanford with Equal contribution; (3) Chenlin Meng, Stanford with Equal contribution; (4) Yifan Mai, Stanford; (5) Joon Sung Park, Stanford; (6) Agrim Gupta, Stanford; (7) Yunzhi Zhang, Stanford; (8) Deepak Narayanan, Microsoft; (9) Hannah Benita Teufel, Aleph Alpha; (10) Marco Bellagente, Aleph Alpha; (11) Minguk Kang, POSTECH; (12) Taesung Park, Adobe; (13) Jure Leskovec, Stanford; (14) Jun-Yan Zhu, CMU; (15) Li Fei-Fei, Stanford; (16) Jiajun Wu, Stanford; (17) Stefano Ermon, Stanford; (18) Percy Liang, Stanford. Authors: Authors: (1) Tony Lee, Stanford with Equal contribution; (2) Michihiro Yasunaga, Stanford with Equal contribution; (3) Chenlin Meng, Stanford with Equal contribution; (4) Yifan Mai, Stanford; (5) Joon Sung Park, Stanford; (6) Agrim Gupta, Stanford; (7) Yunzhi Zhang, Stanford; (8) Deepak Narayanan, Microsoft; (9) Hannah Benita Teufel, Aleph Alpha; (10) Marco Bellagente, Aleph Alpha; (11) Minguk Kang, POSTECH; (12) Taesung Park, Adobe; (13) Jure Leskovec, Stanford; (14) Jun-Yan Zhu, CMU; (15) Li Fei-Fei, Stanford; (16) Jiajun Wu, Stanford; (17) Stefano Ermon, Stanford; (18) Percy Liang, Stanford. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Core framework 2 Core framework 3 Aspects 3 Aspects 4 Scenarios 4 Scenarios 5 Metrics 5 Metrics 6 Models 6 Models 7 Experiments and results 7 Experiments and results 8 Related work 8 Related work 9 Conclusion 9 Conclusion 10 Limitations 10 Limitations Author contributions, Acknowledgments and References Author contributions, Acknowledgments and References A Datasheet A Datasheet B Scenario details B Scenario details C Metric details C Metric details D Model details D Model details E Human evaluation procedure E Human evaluation procedure 5 Metrics To evaluate the 12 aspects (§3), we also curate a diverse and realistic set of metrics. Table 3 presents an overview of all the metrics and their descriptions. Compared to previous metrics, our metrics are more realistic and broader. First, in addition to automated metrics, we use human metrics (top rows in Table 3) to perform realistic evaluation that reflects human judgment [25, 26, 27]. Specifically, we employ human metrics for the overall text-image alignment and photorealism, which are used for many evaluation aspects, including alignment, quality, knowledge, reasoning, fairness, robustness, and multilinguality. We also employ human metrics for overall aesthetics and originality, for which capturing the nuances of human judgment is important. To conduct human evaluation, we employ crowdsourcing following the methodology described in Otani et al.,[35]. Concrete English definitions are provided for each human evaluation question and rating choice, and a minimum of 5 participants evaluate each image. We use at least 100 image samples for each aspect. For more details about the crowdsourcing procedure, please refer to Appendix E. The second contribution is introducing new metrics for aspects that have received limited attention in existing evaluation efforts, namely fairness, robustness, multilinguality, and efficiency, as discussed in §3. The new metrics aim to close the evaluation gaps. 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

Evaluating AI Models with HEIM Metrics for Fairness, Robustness, and More

Authors:
(1) Tony Lee, Stanford with Equal contribution;
(2) Michihiro Yasunaga, Stanford with Equal contribution;
(3) Chenlin Meng, Stanford with Equal contribution;
(4) Yifan Mai, Stanford;
(5) Joon Sung Park, Stanford;
(6) Agrim Gupta, Stanford;
(7) Yunzhi Zhang, Stanford;
(8) Deepak Narayanan, Microsoft;
(9) Hannah Benita Teufel, Aleph Alpha;
(10) Marco Bellagente, Aleph Alpha;
(11) Minguk Kang, POSTECH;
(12) Taesung Park, Adobe;
(13) Jure Leskovec, Stanford;
(14) Jun-Yan Zhu, CMU;
(15) Li Fei-Fei, Stanford;
(16) Jiajun Wu, Stanford;
(17) Stefano Ermon, Stanford;
(18) Percy Liang, Stanford. Table of Links Abstract and 1 Introduction 2 Core framework 3 Aspects 4 Scenarios 5 Metrics 6 Models 7 Experiments and results 8 Related work 9 Conclusion 10 Limitations Author contributions, Acknowledgments and References A Datasheet B Scenario details C Metric details D Model details E Human evaluation procedure 2 Core framework We focus on evaluating text-to-image models, which take textual prompts as input and generate images. Inspired by HELM [1], we decompose the model evaluation into four key components: aspect, scenario, adaptation, and metric (Figure 4). An aspect refers to a specific evaluative dimension. Examples include image quality, originality, and bias. Evaluating multiple aspects allows us to capture diverse characteristics of generated images. We evaluate 12 aspects, listed in Table 1, through a combination of scenarios and metrics. Each aspect is defined by a scenario-metric pair. A scenario represents a specific use case and is represented by a set of instances, each consisting of a textual input and optionally a reference output image. We consider various scenarios reflecting different domains and tasks, such as descriptions of common objects (MS-COCO) and logo design (Logos). The complete list of scenarios is provided in Table 2. Adaptation is the specific procedure used to run a model, such as translating the instance input into a prompt and feeding it into the model. Adaptation strategies include zero-shot prompting, few-shot prompting, prompt engineering, and finetuning. We focus on zero-shot prompting. We also explore prompt engineering techniques, such as Promptist [28], which use language models to refine the inputs before feeding into the model. A metric quantifies the quality of image generations according to some standard. A metric can be human (e.g., humans rate the overall text-image alignment on a 1-5 scale) or automated (e.g., CLIPScore). We use both human and automated metrics to capture both subjective and objective assessments. The metrics are listed in Table 3. In the subsequent sections of the paper, we delve into the details of aspects (§3), scenarios (§4), metrics (§5), and models (§6), followed by the discussion of experimental results and findings in §7. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Tony Lee, Stanford with Equal contribution; (2) Michihiro Yasunaga, Stanford with Equal contribution; (3) Chenlin Meng, Stanford with Equal contribution; (4) Yifan Mai, Stanford; (5) Joon Sung Park, Stanford; (6) Agrim Gupta, Stanford; (7) Yunzhi Zhang, Stanford; (8) Deepak Narayanan, Microsoft; (9) Hannah Benita Teufel, Aleph Alpha; (10) Marco Bellagente, Aleph Alpha; (11) Minguk Kang, POSTECH; (12) Taesung Park, Adobe; (13) Jure Leskovec, Stanford; (14) Jun-Yan Zhu, CMU; (15) Li Fei-Fei, Stanford; (16) Jiajun Wu, Stanford; (17) Stefano Ermon, Stanford; (18) Percy Liang, Stanford. Authors: Authors: (1) Tony Lee, Stanford with Equal contribution; (2) Michihiro Yasunaga, Stanford with Equal contribution; (3) Chenlin Meng, Stanford with Equal contribution; (4) Yifan Mai, Stanford; (5) Joon Sung Park, Stanford; (6) Agrim Gupta, Stanford; (7) Yunzhi Zhang, Stanford; (8) Deepak Narayanan, Microsoft; (9) Hannah Benita Teufel, Aleph Alpha; (10) Marco Bellagente, Aleph Alpha; (11) Minguk Kang, POSTECH; (12) Taesung Park, Adobe; (13) Jure Leskovec, Stanford; (14) Jun-Yan Zhu, CMU; (15) Li Fei-Fei, Stanford; (16) Jiajun Wu, Stanford; (17) Stefano Ermon, Stanford; (18) Percy Liang, Stanford. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Core framework 2 Core framework 3 Aspects 3 Aspects 4 Scenarios 4 Scenarios 5 Metrics 5 Metrics 6 Models 6 Models 7 Experiments and results 7 Experiments and results 8 Related work 8 Related work 9 Conclusion 9 Conclusion 10 Limitations 10 Limitations Author contributions, Acknowledgments and References Author contributions, Acknowledgments and References A Datasheet A Datasheet B Scenario details B Scenario details C Metric details C Metric details D Model details D Model details E Human evaluation procedure E Human evaluation procedure 2 Core framework We focus on evaluating text-to-image models, which take textual prompts as input and generate images. Inspired by HELM [1], we decompose the model evaluation into four key components: aspect, scenario, adaptation, and metric (Figure 4). An aspect refers to a specific evaluative dimension. Examples include image quality, originality, and bias. Evaluating multiple aspects allows us to capture diverse characteristics of generated images. We evaluate 12 aspects, listed in Table 1, through a combination of scenarios and metrics. Each aspect is defined by a scenario-metric pair. aspect A scenario represents a specific use case and is represented by a set of instances, each consisting of a textual input and optionally a reference output image. We consider various scenarios reflecting different domains and tasks, such as descriptions of common objects (MS-COCO) and logo design (Logos). The complete list of scenarios is provided in Table 2. scenario Adaptation is the specific procedure used to run a model, such as translating the instance input into a prompt and feeding it into the model. Adaptation strategies include zero-shot prompting, few-shot prompting, prompt engineering, and finetuning. We focus on zero-shot prompting. We also explore prompt engineering techniques, such as Promptist [28], which use language models to refine the inputs before feeding into the model. Adaptation A metric quantifies the quality of image generations according to some standard. A metric can be human (e.g., humans rate the overall text-image alignment on a 1-5 scale) or automated (e.g., CLIPScore). We use both human and automated metrics to capture both subjective and objective assessments. The metrics are listed in Table 3. metric In the subsequent sections of the paper, we delve into the details of aspects (§3), scenarios (§4), metrics (§5), and models (§6), followed by the discussion of experimental results and findings in §7. 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

HEIM’s Core Framework: A Comprehensive Approach to Text-to-Image Model Assessment

The Cyclicality of the Crypto Markets Shows Its Hand Once Again

As a participant in this year's Hacktoberfest, I created an AI chatbot called Technical Writer. This AI chatbot, built with Coze, is designed for beginners and technical writing experts. It is also integrated into the Technical Writing Mentorship Program (TWMP) learning site as a contribution to the TWMP community, founded by Nwokocha Wisdom. With his team, they have successfully trained over thirty technical writers, including me, in various technical fields. Technical Writer helps technical writers by: Providing the right answers and career paths in technical writing.
Suggesting projects for technical writers to build their portfolios.
Simplifying technical writing terminologies with real-life examples. Why Technical Writers Matter in Technical Writing As a technical advocate who frequently shares the importance of technical writing on social media, I often receive direct messages from people asking questions like: How can I start in technical writing?
What courses should I take to become a technical writer? And many more questions. Sometimes, I find these questions overwhelming, even though I enjoy answering them. I built this chatbot on available courses on TWMP’s site and an API documentation course by Tom Johnson to help answer and guide technical writers in their journey. Demo https://youtu.be/aVrPfzu0W-s?embedable=true Coze Agent Store: TechnicalWriter Discord Chatbot: TechnicalWriter How I Built Technical Writer These are the steps I took in building this chatbot: Adding the Character and skills I described my chatbot character and skill in the Persona & Prompt field and then optimized the character and skill. Knowledge Resources As mentioned earlier, the chatbot response will be based on the courses available on TWMP and API documentation course. To do this, I added these courses using the Knowledge feature. I batch-added the TWMP and API doc Course base URLs which took about 20 mins to process. I added these resources using “Add to agent.” The reasons why I used only these resources are: The courses were written by experts in technical writing with proven records.


These courses have helped me and many others become top technical writers and excellent contributors for reputable organizations like Google and JSON Schema. Chat Experience Welcoming Note I added a friendly welcoming note to the chatbot which gives an overview of what the chatbot is all about using the “opening text” feature. Auto-suggestion To enhance further engagement and clarity for the users, I added the auto-suggestion feature with a custom prompt to keep the chatbot from within the scope of the chatbot. Voice Chat I also added a voice chat feature to the chatbot to ensure that people with visual impairments are not excluded. This promotes accessibility for users worldwide. Furthermore, I enable communication in all available languages such as Arabic, Japanese, French, etc. Discord Integration Since TWMP’s community is on Discord, I integrated this chatbot into a Discord bot so that community members can interact with it directly. I created a New application and named it after the chatbot. Then I copied the Bot token and added it to the chatbot. This link was sent to the group admin and was added to the server. Submitting the Chatbot to the Agent Store To ensure this chatbot is not used solely by the local community, I submitted it to the Coze agent store to help technical writers worldwide attain their skills and expertise in technical writing. It was submitted under the 'Writing’ category. So if you head over to the Writing category, and search for “TechnicalWriter”, you will see it there Don’t forget to drop a comment, like, and share with others Adding the Chatbot to the Learning Site I copied the chatbot Web SDK.



I added it to the repo. Result: The chatbot icon is displayed on the site. As it responds to the user, it also adds a reference, as shown above. Final Note This process took me one week to complete because I built this chatbot for an open-source community, so it had to go through the proper procedures. I was asked to change the icon to match the community logo. Some of the challenges I faced were: Trying to modify the height of the chatbot.
I couldn't add an avatar or image on behalf of the user. Despite these challenges, I believe more features will be added. Thanks to the documentation and my experience creating a Telegram bot, I also learned how to create a Discord chatbot for the first time. If you find this chatbot helpful, don’t hesitate to add it to your community. As a participant in this year's Hacktoberfest, I created an AI chatbot called Technical Writer . This AI chatbot, built with Coze , is designed for beginners and technical writing experts. It is also integrated into the Technical Writing Mentorship Program (TWMP) learning site as a contribution to the TWMP community, founded by Nwokocha Wisdom . With his team, they have successfully trained over thirty technical writers, including me, in various technical fields. Technical Writer Coze Technical Writing Mentorship Program (TWMP) Technical Writing Mentorship Program (TWMP) Nwokocha Wisdom Technical Writer helps technical writers by: Technical Writer Providing the right answers and career paths in technical writing. Suggesting projects for technical writers to build their portfolios. Simplifying technical writing terminologies with real-life examples. Providing the right answers and career paths in technical writing. Suggesting projects for technical writers to build their portfolios. Simplifying technical writing terminologies with real-life examples. Why Technical Writers Matter in Technical Writing As a technical advocate who frequently shares the importance of technical writing on social media, I often receive direct messages from people asking questions like: How can I start in technical writing? What courses should I take to become a technical writer? How can I start in technical writing? What courses should I take to become a technical writer? And many more questions. Sometimes, I find these questions overwhelming, even though I enjoy answering them. I built this chatbot on available courses on TWMP’s site and an API documentation course by Tom Johnson to help answer and guide technical writers in their journey. API documentation course Tom Johnson Demo https://youtu.be/aVrPfzu0W-s?embedable=true https://youtu.be/aVrPfzu0W-s?embedable=true Coze Agent Store: TechnicalWriter TechnicalWriter Discord Chatbot: TechnicalWriter TechnicalWriter How I Built Technical Writer These are the steps I took in building this chatbot: Adding the Character and skills I described my chatbot character and skill in the Persona & Prompt field and then optimized the character and skill. Persona & Prompt Knowledge Resources As mentioned earlier, the chatbot response will be based on the courses available on TWMP and API documentation course. To do this, I added these courses using the Knowledge feature. Knowledge I batch-added the TWMP and API doc Course base URLs which took about 20 mins to process. I batch-added the TWMP and API doc Course base URLs which took about 20 mins to process. I added these resources using “Add to agent.” I added these resources using “Add to agent.” I added these resources using “ Add to agent.” Add to agent.” The reasons why I used only these resources are: The courses were written by experts in technical writing with proven records. These courses have helped me and many others become top technical writers and excellent contributors for reputable organizations like Google and JSON Schema. The courses were written by experts in technical writing with proven records. The courses were written by experts in technical writing with proven records. These courses have helped me and many others become top technical writers and excellent contributors for reputable organizations like Google and JSON Schema. These courses have helped me and many others become top technical writers and excellent contributors for reputable organizations like Google and JSON Schema. Chat Experience Welcoming Note I added a friendly welcoming note to the chatbot which gives an overview of what the chatbot is all about using the “ opening text” feature. opening text” Auto-suggestion To enhance further engagement and clarity for the users, I added the auto-suggestion feature with a custom prompt to keep the chatbot from within the scope of the chatbot. Voice Chat I also added a voice chat feature to the chatbot to ensure that people with visual impairments are not excluded. This promotes accessibility for users worldwide. Furthermore, I enable communication in all available languages such as Arabic, Japanese, French, etc. Discord Integration Since TWMP’s community is on Discord, I integrated this chatbot into a Discord bot so that community members can interact with it directly. I created a New application and named it after the chatbot. I created a New application and named it after the chatbot. Then I copied the Bot token and added it to the chatbot. Then I copied the Bot token and added it to the chatbot. Then I copied the Bot token and added it to the chatbot. This link was sent to the group admin and was added to the server. Submitting the Chatbot to the Agent Store To ensure this chatbot is not used solely by the local community, I submitted it to the Coze agent store to help technical writers worldwide attain their skills and expertise in technical writing. It was submitted under the ' Writing’ category. Writing’ So if you head over to the Writing category, and search for “TechnicalWriter”, you will see it there “ TechnicalWriter ”, TechnicalWriter Don’t forget to drop a comment, like, and share with others Don’t forget to drop a comment, like, and share with others Adding the Chatbot to the Learning Site I copied the chatbot Web SDK. I added it to the repo. I copied the chatbot Web SDK. I copied the chatbot Web SDK. I added it to the repo. I added it to the repo. Result: The chatbot icon is displayed on the site. As it responds to the user, it also adds a reference, as shown above. Final Note This process took me one week to complete because I built this chatbot for an open-source community, so it had to go through the proper procedures. I was asked to change the icon to match the community logo. Some of the challenges I faced were: Trying to modify the height of the chatbot. I couldn't add an avatar or image on behalf of the user. Trying to modify the height of the chatbot. I couldn't add an avatar or image on behalf of the user. Despite these challenges, I believe more features will be added. Thanks to the documentation and my experience creating a Telegram bot, I also learned how to create a Discord chatbot for the first time. If you find this chatbot helpful, don’t hesitate to add it to your community. If you find this chatbot helpful, don’t hesitate to add it to your community.

How I Built Technical Writer: An AI Chatbot For an Open Source Project using Coze

iOS vs. Android Monetization: Why Not Understanding the Differences Could Harm Your Business

SAN FRANCISCO, USA, November 19th, 2024/Chainwire/--The Keanu(KNU)token, a new Solana-based meme coin inspired by cultural icon Keanu Reeves, has officially hit the market via a fair launch on Pump.fun. The release comes amid a surge in meme coin market activity, with the Keanu team positioning the project as a community-driven initiative leveraging viral potential. The broader meme coin market has seen massive growth in recent months, with notable projects contributing to an industry market cap of $125 billion. The team aims to capitalize on this momentum with a token launch designed to appeal to a broad demographic, from crypto enthusiasts to pop culture fans. This launch perfectly aligns with what investor and commentator Murad Mahmudov has described as an unprecedented "meme coin supercycle," creating fertile ground for viral, community-focused crypto projects. Keanu (KNU): A Cultural and Community-Driven Token Keanu leverages the widespread popularity and meme culture heritage of actor Keanu Reeves, a figure celebrated across generational and cultural divides. The token seeks to distinguish itself through its ethos, emphasizing positivity and inclusivity—triggering speculation that this could be the making of a new crypto cult. Keanu’s branding incorporates community-oriented principles such as “Be Excellent to Each Other,” signaling a departure from the often intense and competitive nature of the crypto landscape. The team behind Keanu plans to cultivate an engaged community by focusing on a balance between entertainment and functionality. This strategy positions the token as an accessible entry point for new users exploring the world of cryptocurrencies while fostering a strong connection with existing crypto participants. Bridging Pop Culture and Cryptocurrency Meme coins have historically gained traction for their simplicity and relatability, making them an accessible entry point for newbies. While Dogecoin has been the most prominent example of a meme coin achieving mainstream recognition, the Keanu project seeks to expand on this foundation, offering a culturally resonant alternative designed to engage a diverse audience. Unlike many projects that rely solely on novelty or fleeting trends, Keanu’s developers emphasize its potential for sustained community growth. By targeting both experienced cryptocurrency participants and mainstream audiences, the token aspires to build a broader cultural and economic bridge between these spaces. Exploring Keanu's Adoption Potential Keanu launches with a total supply of 1 billion KNU tokens, structured to encourage widespread participation. The project’s debut on Pump.fun represents the first phase of its roadmap, with plans for additional listings to follow. By tapping into Keanu Reeves’ universal popularity and the current momentum of the meme coin market, the token seeks to carve out its place in the evolving crypto landscape. For updates on the project’s progress, Keanu encourages the community to follow its official channels on X (formerly Twitter) and Telegram. About Keanu Keanu (KNU) is a meme token built on the Solana blockchain. It prioritizes community engagement and inclusivity, aiming to foster a positive cultural movement within the cryptocurrency space. The token launched via a fair listing on Pump.fun, with plans for expanded availability on decentralized exchanges and additional trading platforms. Contact Keanu pr@keanu.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here SAN FRANCISCO, USA, November 19th, 2024/Chainwire/--The Keanu(KNU)token, a new Solana-based meme coin inspired by cultural icon Keanu Reeves, has officially hit the market via a fair launch on Pump.fun. The release comes amid a surge in meme coin market activity, with the Keanu team positioning the project as a community-driven initiative leveraging viral potential. The broader meme coin market has seen massive growth in recent months, with notable projects contributing to an industry market cap of $125 billion . The team aims to capitalize on this momentum with a token launch designed to appeal to a broad demographic, from crypto enthusiasts to pop culture fans. market cap of $125 billion market cap of $125 billion This launch perfectly aligns with what investor and commentator Murad Mahmudov has described as an unprecedented " meme coin supercycle ," creating fertile ground for viral, community-focused crypto projects. meme coin supercycle meme coin supercycle Keanu (KNU): A Cultural and Community-Driven Token Keanu leverages the widespread popularity and meme culture heritage of actor Keanu Reeves, a figure celebrated across generational and cultural divides. The token seeks to distinguish itself through its ethos, emphasizing positivity and inclusivity—triggering speculation that this could be the making of a new crypto cult. widespread popularity widespread popularity Keanu’s branding incorporates community-oriented principles such as “Be Excellent to Each Other,” signaling a departure from the often intense and competitive nature of the crypto landscape. The team behind Keanu plans to cultivate an engaged community by focusing on a balance between entertainment and functionality. This strategy positions the token as an accessible entry point for new users exploring the world of cryptocurrencies while fostering a strong connection with existing crypto participants. Bridging Pop Culture and Cryptocurrency Meme coins have historically gained traction for their simplicity and relatability, making them an accessible entry point for newbies. While Dogecoin has been the most prominent example of a meme coin achieving mainstream recognition, the Keanu project seeks to expand on this foundation, offering a culturally resonant alternative designed to engage a diverse audience. Unlike many projects that rely solely on novelty or fleeting trends, Keanu’s developers emphasize its potential for sustained community growth. By targeting both experienced cryptocurrency participants and mainstream audiences, the token aspires to build a broader cultural and economic bridge between these spaces. Exploring Keanu's Adoption Potential Keanu launches with a total supply of 1 billion KNU tokens, structured to encourage widespread participation. The project’s debut on Pump.fun represents the first phase of its roadmap, with plans for additional listings to follow. By tapping into Keanu Reeves’ universal popularity and the current momentum of the meme coin market, the token seeks to carve out its place in the evolving crypto landscape. For updates on the project’s progress, Keanu encourages the community to follow its official channels on X (formerly Twitter) and Telegram . X X Telegram Telegram About Keanu Keanu (KNU) is a meme token built on the Solana blockchain. It prioritizes community engagement and inclusivity, aiming to foster a positive cultural movement within the cryptocurrency space. The token launched via a fair listing on Pump.fun, with plans for expanded availability on decentralized exchanges and additional trading platforms. Keanu Keanu Contact Keanu pr@keanu.xyz 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

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