"We are so back," posted OpenAI CEO Sam Altman. In other words, ChatGPT can now browse the internet again. A week after the ChatGPT creator launched a Browse with Bing plugin, the feature was ...

ChatGPT internet browsing is back after being disabled for months

We’re in a very strange moment for the internet. We all know it’s broken. That’s not news. But there’s something in the air—a vibe shift, a sense that things are about to change.

How to fix the internet

CWG-Internet is limited to Member States, with open consultation to all stakeholders. Previously, this group was established as the Dedicated Group as an integral part of WG WSIS, open only to all Member States, in accordance with Resolution 75 (WTSA ...

Council Working Group on International Internet-related Public Policy Issues (CWG-Internet)

These solution providers are focused on an assortment of opportunities in the IoT space, whether it’s protecting industrial systems from cyberattacks, integrating AI-powered surveillance systems ...

Internet of things

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Who is using the internet the most in the UK? The UK’s most active internet users were individuals between 30 and 39 years old. This age group made up almost 30 percent of all internet users in ...

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A line drawing of the Internet Archive headquarters building façade. An illustration of a magnifying glass. An illustration of a magnifying glass.

Spring, 1957 / McIninch Greenhouses.

Internet accessibility may be improving in South Africa, but its quality is still below the global average. The recent Census from Stats SA showed that internet connectivity in South Africa has ...

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Over two million people in the Gaza Strip face complete isolation from the world, with internet and power access shrinking as Israel continues to retaliate against Hamas atrocities. Gaza is ...

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Gigabit Extra + Choice TV Internet 1200 Mbps $95.00 Gigabit + Choice TV + Xfinity Voice Internet 1000 Mbps $100.00 Gigabit Extra + Choice TV + Xfinity Voice Internet 1200 Mbps $105.00

Best Internet Providers in Jacksonville, FL

INGIC

ingic

If you’re in a search of a renowned and reliable Android app development company in the USA, here is your comprehensive list of top companies. I agree that choosing the right app development partner for your Android app project is like looking for a needle in a haystack. To make this task a bit easy, here I give a comprehensive list of twenty top Android app development companies in the USA. Let’s start straightaway: (Delmar, Delaware) Solution Analysts Employee Strength- 100+ Established in- 2011 Since inception in the year 2011, this company has remained committed to app quality and performance. Irrespective of scale and size, Solution Analysts has achieved the client’s happiness in every app project. With an in-house team of , the company is striving for meeting the deadlines without compromising in quality. experienced Android app developers Integration of cutting-edge tools and advancements from emerging technologies like IoT, AR, and VR in enterprise-grade app solutions give this company an edge over its peers. With client-oriented approach and cost-effectiveness, the company has supported many startups and small businesses globally to grow with 360-degree IT solutions in web and app domains. (Culver, California) 2. Swenson He Employee Strength- 50+ Established in- 2014 Swenson He is known for developing high impact mobile app solutions. The company has experienced developers who can build robust and sophisticated mobile apps for Android platform. Swenson He takes care of every app development aspect from conception to completion while developing interactive apps. The company focuses on design and project execution to fulfill the ever-changing requirements of corporate clients. Swenson He has achieved fame for executing complex app projects. , LLC (NYC, New York) 3. The Sneakers Agency Employee Strength- 50+ Established in- 2014 The Sneakers Agency focuses on creating innovative app solutions. They emphasize the design aspect for making the user-friendly app solutions. The company also offers customized solutions in web and systems apart from Android and iOS platforms. Sneakers has forayed into IoT recently. The company believes in providing an interactive digital experience and developing tailored software solutions for enterprises across different industry sectors. ( San Diego, California) 4. Ingic Employee Strength- 50+ Established in- 2012 Ingic comes in top five positions because it offers an excellent mobile app strategy and development services to corporate clients. The company has a team of experienced developers for crafting robust and reliable Android apps. Ingic has focused on high conversion rate through custom app development. Apart from mobile app development, the company also provides software development, web development, and digital marketing services. ( NYC, New York) 5. Blue Label Labs Employee Strength- 100+ Established in- 2009 Blue Label Labs is one of the leading mobile app development companies of USA. The company has partnered with entrepreneurs and digital agencies to develop feature-rich apps for Android and iOS. Apart from mobile app development, the company also works in building apps for the tablet, watch and TV. Blue Label Labs also offers digital marketing services for customized app solutions. The company assists enterprises to grow with high-quality apps for different devices. (San Francisco, California) 6. ArcTouch Employee Strength- 100+ Established in- 2007 ArcTouch helps businesses of all size with feature-rich apps for mobile and connected devices. The company houses experienced product strategists, designers, and engineers to simplify the business processes of the clients. The company also provides cross-platform mobile app development services and blockchain app development services. ArcTouch also develops IoT and AR-based solutions for forward-looking businesses. Conversational bots and apps are specialties of the company. (Atlanta, Georgia) 7. Big Nerd Ranch Employee Strength- 100+ Established in- 2001 Big Nerd Ranch is known for developing web and mobile app solutions with excellent UX and UI design. The company has experienced Android app developers who can complete the project in the given time. The company has in-house teams of designers, web developers and mobile app developers for iOS and Android. Big Nerd Ranch also runs team training programs for the app developers. Employees with the real-world app development experience and thorough understanding run these programs. (NYC, New York) 8. Blue Whale Apps Employee Strength- 50+ Established in- 2006 It is one of the leading company that houses experienced professionals in the domains of technology, advertising, and design domains. Blue Whale Apps is a company dedicated to only mobile app development. It takes care of every aspect of app development from conceptualization to completion. Apart from Android app development, the company also offers quality iPhone and iPad app development services also. (Los Angeles, California) 9. Citrusbyte Employee Strength- 50+ Established in- Citrusbyte focuses on product design, planning, and delivery of customized app solution. The company has gained fame for providing solutions that not only address the current needs but also customizable to meet future needs. The company’s core focus remains on UI/UX design while developing app solutions. The company has also forayed into VR domain for creating app solutions with immersive experience on the VR infrastructure. (San Jose, California) 10. Clavax Employee Strength- 200+ Established in- 2011 Clavax is a reliable software, web, and mobile app development company. The company is known for offering innovative and scalable IT solutions to global clients across the world. The company has experienced developers for developing customized app solutions for leading platforms. The company helps entrepreneurs and startups to transform their app ideas into robust and reliable mobile apps. (NYC, New York) 11. Dom&Tom Employee Strength- 100+ Established in- 2009 The company helps the next-gen startups to grow. The company supports the client’s strategic initiatives with open collaboration and wonderful user experience. The company offers both web and mobile app development services across the USA. Dom&Tom also offers cross-platform app development services and emerging technologies-related solutions. Dom&Tom has a team of experienced developers, designers, and business strategists. Though it was started to develop iPhone apps, the company offers excellent Android app development services. (Chicago, Illinois) 12. Ethervision Employee Strength- 50+ Established in- 2008 As an enterprise mobile application development company, Ethervision offers iPhone, iPad, and Android app development services to corporate clients. The company has experienced mobile app developers who help enterprises come up with innovative app solutions with enterprise-friendly features. The company has some experienced strategists that guide entrepreneurs for building enterprise-grade iOS or Android applications. (NYC, New York) 13. Fueled Employee Strength- 100+ Established in- 2007 Fueled has various teams consists of expert app developers, designers, and software strategies. The company is known for developing cutting-edge mobile apps for startups and big enterprises alike. The company builds iPhone apps and blockchain-based apps along with Android applications. Fueled also offers branding, website development, and strategic consultancy services to the businesses of all sizes. (Brooklyn, New York) 14. Fuzz Productions Employee Strength-50+ Established in- When it comes to high-quality web and mobile app solutions, Fuzz Productions can develop customized and seamlessly-performing apps. The company has multiple teams comprise strategists, designers, and engineers to develop feature-rich apps for Android and iOS platforms. The company can provide teams for working with the client’s product team. Fuzz Productions can develop customized software for various companies across different industry sectors. (Palo Alto, California) 15. Intellectsoft Employee Strength- 100+ Established in- 2007 It is a custom software development company that provides high-quality solutions to meet the challenges and ever-changing business requirements. The company is known for integrating the advancements of emerging technologies like blockchain, AR (Augmented Reality), and AI (Artificial Intelligence). The company also offers UX and UI services and product engineering services. Intellectsoft focuses on the advanced practices for developing high-quality app solutions. (Los Angeles, California) 16. ISBX Employee Strength-100+ Established in- 2009 ISBX is a leading software developer that provides both web and mobile app solutions to the enterprises. As a full range marketing and software development services provider, ISBX has served many companies across the world. Apart from the mobile app and web development, the company also offers designing and technology services. The company also forays into the blockchain and cryptocurrency-related development services with excellent consulting services. (San Francisco, California) 17. Pivotal Employee Strength-500+ Established in- 2013 Pivotal combines modern approach and cutting-edge tools along with domain expertise and experience. The company builds customized software with innovative and lean approaches for building the next-gen app solutions. Pivotal help startups with a greater agility through the advanced apps. Pivotal builds excellent apps by combining platform, tools, and methodology to deliver pleasant user experiences. Quality and reduced time-to-market are other characteristics of the company. (Reston, Virginia) 18. Savvy Apps Employee Strength- 50+ Established in-2009 Savvy Apps offers strategy, branding, mobile app development, digital marketing, web development, and UI/UX design. The company has a team of experienced app developers who have worked on hundreds of app projects. The company also offers cloud computing services and digital marketing services. The company offers strategic services like competitive analysis, user research, etc. Branding and designing are other services. (Charlottesville, Virginia) 19. WillowTree Employee Strength-50+ Established in-2007 WillowTree has engineered customized software solutions. The company ensures seamless app solutions for excellent digital experiences. WillowTree has experienced teams of developers who have built apps, responsive websites, and bots through following a predetermined mobile strategy and product design. Apart from Android apps, WillowTree also offers iOS app development and prototyping services for the businesses. (Sunnyvale, California) 20. Zapps Employee Strength-100+ Established in-2007 The company offers customized solutions to simplify complex business processes. The company has teams of experienced developers who can deliver apps across a wide range of mobile technologies. Zapps create hybrid and native apps for both iOS and Android platforms. What’s more, the company also offers consultancy and marketing services while keeping focus on the mobile app domain. We have also included these two companies in our list: (Pleasanton, California) 21. CitrusBits Employee Strength-50+ Established in-2005 CitrusBits is a noteworthy mobile app development firm that has earned fame for developing and designing tailored mobile apps. The company offers 360-degree solutions through agile approach across all mobile platforms. With effective digital strategy and excellent support, the company builds apps that can mitigate risk and help companies expand the business. The company has also forayed into the AR and VR app development domains. Emerging technologies like blockchain and IoT are also integrated by the company to create game-changing apps for the corporate sector. (NYC, New York) 22. Appetizer Mobile Employee Strength-50+ Established in-2009 Appetizer Mobile LLC builds enterprise-grade app solutions for iOS (iPhone, iPad, Watch, etc.), Android (phones, tablets, etc.), HTML5, and Unity platforms. Apart from mobile application development, the company offers app consulting and marketing services. Innovative UI and UX design and strategic branding are other domains in which the Appetizer Mobile provide high-quality services. Wrapping Up you time, money, and efforts. It is, therefore, necessary for you to find the right development partner. Android app development costs Do let me know what do you think about this list, and send me your suggestions regarding the addition of other reliable and reputed company. With the belief that this exhausting list will remain useful for you, I am signing off.

Top 20 Android Application Development Companies in USA

The internet user base in India is set to surpass 900 million by 2025, driven by the growing use of Indic languages for digital content. The ‘Internet in India Report 2024,’ jointly prepared by the Internet and Mobile Association of India (IAMAI) and KANTAR, reveals that the number of Active Internet Users in India reached 886 million in 2024, marking a robust 8% year-on-year growth. Rural India, with 488 million users, leads this growth and now accounts for 55% of the total internet population. The report highlights the increasing role of Indic languages in shaping internet consumption patterns. Nearly all internet users (98%) accessed content in Indic languages, with Tamil, Telugu, and Malayalam emerging as the most popular due to their extensive availability. Over half (57%) of urban internet users prefer consuming content in regional languages, underscoring the growing demand for local language content across platforms. Biswapriya Bhattacharya, Director B2B and Technology, Kantar Insights – South Asia commented, “AI has emerged as a significant game changer over the past year. Nine out of ten internet users have interacted with apps featuring embedded AI capabilities. The benefits of AI are no longer confined to a small segment of internet users but extend across the entire user spectrum. The widespread acceptance and enthusiasm surrounding AI should encourage digital companies to introduce more next-generation AI features in India. At the same time, it is crucial for these companies to ensure their solutions are both accurate and ethical.” The benefits of AI are no longer confined to a small segment of internet users but extend across the entire user spectrum. — Biswapriya Bhattacharya, Director B2B and Technology, Kantar Insights – South Asia The report highlights that the digital gender gap in India is steadily narrowing, with 47 % of all internet users in the country being women, so far the highest. Female internet users now form a significant portion of shared device users in rural India: 58%. This marks substantial progress in making digital access more inclusive and equitable over the years. While internet penetration continues to grow, the pace has moderated, especially in urban areas, compared to the exponential growth seen in previous years. Rural India, however, continues to witness double the growth rate of urban regions, reflecting the untapped potential in these areas. Urban India leads in the adoption of non-traditional devices such as smart TVs and smart speakers, which have grown by 54% between 2023 and 2024. Concurrently, mobile devices remain the primary means of accessing the internet across both urban and rural demographics. Rural India dominates online engagement for top activities, including OTT video and music streaming, online communication, and social media usage, outpacing urban users in these categories. Conversely, activities like net commerce, digital payments, and online education are still more prevalent among urban populations. Navanwita Sachdev, Editor, The Tech Panda The internet user base in India is set to surpass 900 million by 2025, driven by the growing use of Indic languages for digital content. The ‘Internet in India Report 2024,’ jointly prepared by the Internet and Mobile Association of India (IAMAI) and KANTAR , reveals that the number of Active Internet Users in India reached 886 million in 2024, marking a robust 8% year-on-year growth. Rural India, with 488 million users, leads this growth and now accounts for 55% of the total internet population. Internet and Mobile Association of India (IAMAI) KANTAR The report highlights the increasing role of Indic languages in shaping internet consumption patterns. Nearly all internet users (98%) accessed content in Indic languages, with Tamil, Telugu, and Malayalam emerging as the most popular due to their extensive availability. Over half (57%) of urban internet users prefer consuming content in regional languages, underscoring the growing demand for local language content across platforms. Biswapriya Bhattacharya, Director B2B and Technology, Kantar Insights – South Asia commented, “AI has emerged as a significant game changer over the past year. Nine out of ten internet users have interacted with apps featuring embedded AI capabilities. The benefits of AI are no longer confined to a small segment of internet users but extend across the entire user spectrum. The widespread acceptance and enthusiasm surrounding AI should encourage digital companies to introduce more next-generation AI features in India. At the same time, it is crucial for these companies to ensure their solutions are both accurate and ethical.” The benefits of AI are no longer confined to a small segment of internet users but extend across the entire user spectrum. — Biswapriya Bhattacharya, Director B2B and Technology, Kantar Insights – South Asia The benefits of AI are no longer confined to a small segment of internet users but extend across the entire user spectrum. — Biswapriya Bhattacharya, Director B2B and Technology, Kantar Insights – South Asia The benefits of AI are no longer confined to a small segment of internet users but extend across the entire user spectrum. The benefits of AI are no longer confined to a small segment of internet users but extend across the entire user spectrum. The benefits of AI are no longer confined to a small segment of internet users but extend across the entire user spectrum. The report highlights that the digital gender gap in India is steadily narrowing, with 47 % of all internet users in the country being women, so far the highest. Female internet users now form a significant portion of shared device users in rural India: 58%. This marks substantial progress in making digital access more inclusive and equitable over the years. While internet penetration continues to grow, the pace has moderated, especially in urban areas, compared to the exponential growth seen in previous years. Rural India, however, continues to witness double the growth rate of urban regions, reflecting the untapped potential in these areas. Urban India leads in the adoption of non-traditional devices such as smart TVs and smart speakers, which have grown by 54% between 2023 and 2024. Concurrently, mobile devices remain the primary means of accessing the internet across both urban and rural demographics. Rural India dominates online engagement for top activities, including OTT video and music streaming, online communication, and social media usage, outpacing urban users in these categories. Conversely, activities like net commerce, digital payments, and online education are still more prevalent among urban populations. Navanwita Sachdev, Editor, The Tech Panda Navanwita Sachdev, Editor, The Tech Panda Navanwita Sachdev, Editor, The Tech Panda

India’s Internet Users Surge Past 900M, Driven by Indic Language Adoption

Indië se internetgebruikers styg verby 900 miljoen, aangedryf deur Indiese taalaanneming

Hindistanda İnternet istifadəçilərinin sayı 900 milyonu ötüb, hind dilinin mənimsənilməsi ilə idarə olunur

ভারতের ইন্টারনেট ব্যবহারকারীরা 900M অতিক্রম করেছে, ভারতীয় ভাষা গ্রহণের দ্বারা চালিত

Los usuarios de Internet en la India superan los 900 millones, impulsados por la adopción del idioma indio

Intian Internet-käyttäjien määrä ylitti 900 miljoonaa Intian kielen omaksumisen vetämänä

Os usuarios de Internet da India superan os 900 millóns, impulsados pola adopción da lingua india

インドのインターネットユーザー数が9億人を超える、インド系言語の普及が牽引

Ba usagers ya Internet ya Inde Surge Past 900M, Etambwisami na Adoption ya Langue Indic

Indijas interneta lietotāju skaits pārsniedz 900 miljonus, un to veicina indiešu valodas pārņemšana

India’s Internet Users Surge Past 900M, E Kgannwa ke go Amogelwa ga Polelo ya Indic

Кількість користувачів Інтернету в Індії перевищила 900 мільйонів завдяки прийняттю індійської мови

Abasebenzisi be-Intanethi baseIndiya badlula kwi-900M edlulileyo, eqhutywa luLwimi lwe-Indic

There are more AI models than what you see in the news and on social media. There are hundreds, including open-source models, private ones, and the tech giant’s own: Gemini, Claude, OpenAI, Grok, Deepseek. What is a model really? Is it just a block box of data? Almost! You can think of it as a zip file of the internet with a bit of C++ code that communicates with the zip file. I credit this analogy to Andrej Karpathy, although I’m not sure if it was his idea, but he is the real industry expert. An AI model is a neural network utilizing a set of massive data to recognize specific patterns. Now is the time to take advantage of them and choose wisely whether for business, personal assistance, or creativity enhancement. The objective of this guide is not about “model training”; it is geared for individuals new to the field of AI who want a better understanding and to leverage the technology. You can build with AI, not over it, so after reading this guide, the knowledge gained shall be understanding general concepts, usage, and measuring accuracy. In this AI guide, you will learn the following, so you can skip to any section, but if you’re a beginner, read this entire article: Category of Models
Corresponding Tasks of Models
Naming Convention of Models
Accuracy Performance of Models
Benchmark References As a beginner or just hearing about the popular tools, make note that there isn’t one type, multi-use case model that does everything you ask it to do. From the interface, it may appear that you are just typing to a chatbot, but there is a lot more being executed in the background. Business analysts, product managers, and engineers adopting AI can identify the objective they have and select from a category of AI models. Here are 4 categories of models among many: Natural Language Processing (general)
Generative (Image, Video, Audio, Text, Code)
Discriminative (Computer Vision, Text Analysis)
Reinforcement Learning While most models specialize in one category, others are multi-modal with different levels of accuracy. Every model has been trained on specific data and, therefore, can do specific tasks related to the data it was trained on. Here’s a list of common tasks each category of models can do: Natural Language Processing Enables computers to interpret, understand, and generate natural human language using tokenization and statistical models. Examples are chatbots, and the most common one is ChatGPT, which stands for “generative pre-trained transformer”. Most models are actually pre-trained transformers. Generative (Image, Video, Audio, Text, Code) They are Generative Adversarial Networks (GANs), which use two sub-models known as a generator and a discriminator. Realistic imagery, audio, text, and code can be produced based on tons of data it was trained on. Stable Diffusion is the most popular method of generating images and videos. Discriminative (Computer Vision, Text Analysis) These use algorithms designed to learn different classes of datasets for decision-making. They include sentiment analysis, optical recognition, and sentiment analysis. Reinforcement Learning Using trial-and-error methods and human enforcement to produce goal-oriented outcomes, such as robotics, game playing, and autonomous driving. Naming Convention of Models Now that you understand the types of models and their tasks, the next step is to identify the model quality and performance. This begins with the name of the models. Let’s break down a model naming. There’s the official convention for naming AI models, but the most popular ones will just have a name followed by the version number, such as chatGPT#, Claude #, Grok #, Gemini #. However, the smaller open-source and task-specific models will have longer names. This can be seen on huggingface.co, which will contain the organization name, model name, parameter size, and lastly the context size. Let’s elaborate with examples: mistralai/Mistral-Small-3.1-24B-Instruct-2053 Mistralai is the organization
Mistral-Small is the model name
3.1 is the version number
24B-Instruct is the parameter count in billions of training data
2053 is the context size or token count google/gemma-3-27b Google is the organization
Gemma is the model name
3 is the version number
27B is the parameter size in billions Additional details, which you will see and need to know, are the quantization format in bits. The higher the quantization format, the more computer RAM and storage are required to operate the model. A quantization format is represented in floating point, such as 4, 6, 8, and 16. Other formats can include GPTQ, NF4, and GGML, which indicate usage for specific hardware configurations. Accuracy Performance of Models If you’ve seen news headlines about a new model release, do not immediately trust the results that are claimed. AI performance competition is so competitive right now that companies cook up the performance numbers for marketing hype. How many people will test them on their own instead of trusting the marketing hype? Not many at all, so don’t fall for the “hallucinated figures”. References https://techcrunch.com/2025/04/07/meta-exec-denies-the-company-artificially-boosted-llama-4s-benchmark-scores/ and https://lmarena.ai/?leaderboard The real way to determine model quality is to check benchmark scores and leaderboards. There have been several tests which you could say are semi-standardized or maybe fully standardized, but in reality, we are testing “black boxes” with tons of variables. The best measure is to check answers and responses from AI with facts and other scientific sources. Leaderboard websites will show sortable rankings with votes, confidence interval scores, usually in a percentage value. The common benchmarks are tests that prompt the AI model with questions and get answers that are measured. They can include: AI2 Reasoning Challenge, HellaSwag, MMLU, TruthfulQA, WinoGrande, GSM8K, HumanEval. Here are brief descriptions of the benchmarking methods: AI2 Reasoning Challenge (ARC) – 7787 multiple-choice science questions from grade school HellaSwag – commonsense reasoning exercises through sentence completion MMLU – Massive multitask language understanding for problem solving TruthfulQA – assess truthfulness by encouraging falsehoods and avoiding responses like “I’m not sure”. WinoGrande – Winograd schema challenge with two near-identical sentences based on a trigger word GSM8K – 8,000 grade school level math questions HumanEval – measures the ability to generate correct Python code across 164 challenges Leaderboard websites for reference: https://www.vellum.ai/blog/llm-benchmarks-overview-limits-and-model-comparison https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/ https://scale.com/leaderboardhttps://artificialanalysis.ai/leaderboards/models https://epoch.ai/data/notable-ai-models https://openlm.ai/chatbot-arena/ https://lmarena.ai/?leaderboard Originally published here There are more AI models than what you see in the news and on social media. There are hundreds, including open-source models, private ones, and the tech giant’s own: Gemini, Claude, OpenAI, Grok, Deepseek. What is a model really? Is it just a block box of data? Almost! You can think of it as a zip file of the internet with a bit of C++ code that communicates with the zip file. I credit this analogy to Andrej Karpathy, although I’m not sure if it was his idea, but he is the real industry expert. An AI model is a neural network utilizing a set of massive data to recognize specific patterns. Now is the time to take advantage of them and choose wisely whether for business, personal assistance, or creativity enhancement. The objective of this guide is not about “model training”; it is geared for individuals new to the field of AI who want a better understanding and to leverage the technology. You can build with AI, not over it, so after reading this guide, the knowledge gained shall be understanding general concepts, usage, and measuring accuracy. In this AI guide, you will learn the following, so you can skip to any section, but if you’re a beginner, read this entire article: Category of Models Corresponding Tasks of Models Naming Convention of Models Accuracy Performance of Models Benchmark References Category of Models Corresponding Tasks of Models Naming Convention of Models Accuracy Performance of Models Benchmark References As a beginner or just hearing about the popular tools, make note that there isn’t one type, multi-use case model that does everything you ask it to do. From the interface, it may appear that you are just typing to a chatbot, but there is a lot more being executed in the background. Business analysts, product managers, and engineers adopting AI can identify the objective they have and select from a category of AI models. Here are 4 categories of models among many: Natural Language Processing (general) Generative (Image, Video, Audio, Text, Code) Discriminative (Computer Vision, Text Analysis) Reinforcement Learning Natural Language Processing (general) Generative (Image, Video, Audio, Text, Code) Discriminative (Computer Vision, Text Analysis) Reinforcement Learning While most models specialize in one category, others are multi-modal with different levels of accuracy. Every model has been trained on specific data and, therefore, can do specific tasks related to the data it was trained on. Here’s a list of common tasks each category of models can do: Natural Language Processing Natural Language Processing Enables computers to interpret, understand, and generate natural human language using tokenization and statistical models. Examples are chatbots, and the most common one is ChatGPT, which stands for “generative pre-trained transformer”. Most models are actually pre-trained transformers. Generative (Image, Video, Audio, Text, Code) Generative (Image, Video, Audio, Text, Code) They are Generative Adversarial Networks (GANs), which use two sub-models known as a generator and a discriminator. Realistic imagery, audio, text, and code can be produced based on tons of data it was trained on. Stable Diffusion is the most popular method of generating images and videos. Discriminative (Computer Vision, Text Analysis) Discriminative (Computer Vision, Text Analysis) These use algorithms designed to learn different classes of datasets for decision-making. They include sentiment analysis, optical recognition, and sentiment analysis. Reinforcement Learning Reinforcement Learning Using trial-and-error methods and human enforcement to produce goal-oriented outcomes, such as robotics, game playing, and autonomous driving. Naming Convention of Models Naming Convention of Models Now that you understand the types of models and their tasks, the next step is to identify the model quality and performance. This begins with the name of the models. Let’s break down a model naming. There’s the official convention for naming AI models, but the most popular ones will just have a name followed by the version number, such as chatGPT#, Claude #, Grok #, Gemini #. However, the smaller open-source and task-specific models will have longer names. This can be seen on huggingface.co , which will contain the organization name, model name, parameter size, and lastly the context size. huggingface.co Let’s elaborate with examples: mistralai/Mistral-Small-3.1-24B-Instruct-2053 mistralai/Mistral-Small-3.1-24B-Instruct-2053 Mistralai is the organization Mistral-Small is the model name 3.1 is the version number 24B-Instruct is the parameter count in billions of training data 2053 is the context size or token count Mistralai is the organization Mistral-Small is the model name 3.1 is the version number 24B-Instruct is the parameter count in billions of training data 2053 is the context size or token count google/gemma-3-27b google/gemma-3-27b Google is the organization Gemma is the model name 3 is the version number 27B is the parameter size in billions Google is the organization Gemma is the model name 3 is the version number 27B is the parameter size in billions Additional details, which you will see and need to know, are the quantization format in bits. The higher the quantization format, the more computer RAM and storage are required to operate the model. A quantization format is represented in floating point, such as 4, 6, 8, and 16. Other formats can include GPTQ, NF4, and GGML, which indicate usage for specific hardware configurations. Accuracy Performance of Models Accuracy Performance of Models If you’ve seen news headlines about a new model release, do not immediately trust the results that are claimed. AI performance competition is so competitive right now that companies cook up the performance numbers for marketing hype. How many people will test them on their own instead of trusting the marketing hype? Not many at all, so don’t fall for the “hallucinated figures”. References https://techcrunch.com/2025/04/07/meta-exec-denies-the-company-artificially-boosted-llama-4s-benchmark-scores/ and https://lmarena.ai/?leaderboard https://techcrunch.com/2025/04/07/meta-exec-denies-the-company-artificially-boosted-llama-4s-benchmark-scores/ https://lmarena.ai/?leaderboard The real way to determine model quality is to check benchmark scores and leaderboards. There have been several tests which you could say are semi-standardized or maybe fully standardized, but in reality, we are testing “black boxes” with tons of variables. The best measure is to check answers and responses from AI with facts and other scientific sources. Leaderboard websites will show sortable rankings with votes, confidence interval scores, usually in a percentage value. The common benchmarks are tests that prompt the AI model with questions and get answers that are measured. They can include: AI2 Reasoning Challenge, HellaSwag, MMLU, TruthfulQA, WinoGrande, GSM8K, HumanEval. Here are brief descriptions of the benchmarking methods: AI2 Reasoning Challenge (ARC) – 7787 multiple-choice science questions from grade school AI2 Reasoning Challenge (ARC) HellaSwag – commonsense reasoning exercises through sentence completion HellaSwag MMLU – Massive multitask language understanding for problem solving MMLU TruthfulQA – assess truthfulness by encouraging falsehoods and avoiding responses like “I’m not sure”. TruthfulQA WinoGrande – Winograd schema challenge with two near-identical sentences based on a trigger word WinoGrande GSM8K – 8,000 grade school level math questions GSM8K HumanEval – measures the ability to generate correct Python code across 164 challenges HumanEval Leaderboard websites for reference: Leaderboard websites for reference: https://www.vellum.ai/blog/llm-benchmarks-overview-limits-and-model-comparison https://www.vellum.ai/blog/llm-benchmarks-overview-limits-and-model-comparison https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/ https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/ https://scale.com/leaderboard https://artificialanalysis.ai/leaderboards/models https://scale.com/leaderboard https://artificialanalysis.ai/leaderboards/models https://epoch.ai/data/notable-ai-models https://epoch.ai/data/notable-ai-models https://openlm.ai/chatbot-arena/ https://openlm.ai/chatbot-arena/ https://lmarena.ai/?leaderboard https://lmarena.ai/?leaderboard Originally published here here

Which AI Model Should You Use? (Check Benchmarks)

How AI transforms personalized ads, dynamic pricing, and user retention across leading digital platforms Imagine your smartphone not just displaying ads but knowing exactly what and when to offer you, making you genuinely interested in the purchase. This is the reality ushered in by a new wave of artificial intelligence, already contributing to substantial additional revenues for companies. Monetization is undergoing a revolution thanks to AI. According to a McKinsey survey, while overall AI adoption remains steady at around 55%, only 23% of respondents reported that at least 5% of their organizations' EBIT last year was attributable to AI usage, indicating significant room for growth. This underscores a global challenge: how to harness AI's potential to balance revenue growth while maintaining a quality user experience.​ Timofey Popov, an experienced product management specialist with proficiency in implementing technology-driven solutions across fintech and banking sectors, has dedicated the last eight years to perfecting digital monetization strategies. His career spans from creating financial products within major banking institutions to spearheading AI-powered product initiatives at organizations including SberMarket and McDonald's, where he has consistently revolutionized business revenue approaches. Additionally, he serves as a respected industry authority, participating as a jury member for the distinguished CX Awards and Globee Awards, where he assesses innovative developments in customer experience and digital transformation. This article will examine how his knowledge of adaptive algorithms, dynamic pricing systems, and personalization techniques has revolutionized these platforms, establishing new benchmarks for AI-enhanced monetization. From Big Macs to Big Data: How AI Transformed McDonald's Digital Sales When McDonald's decided to upgrade its mobile app, it turned to Popov, who had a unique vision for AI-driven monetization. What happened next would revolutionize how one of the world's largest fast-food chains approaches digital sales. "AI analyzed dozens of factors: time of day, order history, popular meal combinations, even the weather. Based on this data, each user was offered a personalized set of dishes and deals. For example, if someone often ordered coffee in the morning, the system could offer them a combo deal with a croissant," Popov explains. The results were staggering: the average check grew by 16%, repeat orders increased by 34%, and overall sales through the app skyrocketed by 480%. What started as a simple ordering tool became a cornerstone of McDonald's digital strategy, proving that AI-driven personalization could work globally. The Marketplace Revolution: Scaling success in the largest classified platform in the world Building on his success at McDonald's, Popov went on to lead a team of 14 at the largest classifieds platform in the world. There, he focused on three key areas: promotional products for sellers, advertising inventory, and improving algorithms using Generative AI and transformer models. "The classifieds market is always a delicate balance between the interests of buyers and sellers. Using AI, we were able to personalize advertising offers and dynamically adjust bids. This increased revenues by 10% while simultaneously improving the user experience," Popov explains. The implementation of predictive models that analyze user behavior and predict optimal ad placements improved conversion rates by 17%, setting new standards for marketplace monetization. Popov also shared his insights on AI-driven advertising strategies at industry-leading conferences, including Epic Growth, where he presented on "ML in the Tasks of Expanding Advertising Inventory." Smart Pricing: Reinventing Retail at SberMarket At SberMarket, one of the largest Russian marketplaces, Popov's team tackled a universal challenge in grocery retail — how to optimize sales with perishable inventory and volatile demand. Their solution? An AI-powered dynamic pricing system that would revolutionize how supermarkets approach inventory management. "Imagine a supermarket where prices automatically adjust to demand every hour. In the morning, milk might cost more because it's in high demand for breakfast, while in the evening, the price drops to ensure it sells before its expiration date. This is exactly the system we created using AI," Popov explains. The implementation of these "smart" prices brought the company a 19% increase in revenue. The system also learned to predict customer behavior. If AI noticed that a customer regularly ordered certain products, it would alert suppliers in advance to increase stock levels. This helped boost the number of loyal customers by 22% through proactive inventory management and personalized recommendations. The Future of AI-driven Monetization Popov actively shares his expertise on professional platforms. His publications can be found in leading business outlets, including RBC and Cossa. He has previously spoken at the Epic Growth and Matemarketing conferences, presenting on implementing ML solutions in business processes. The transformation of monetization through AI is not just a trend but a new reality for businesses. Over eight years of working with product innovations, Popov has progressed from implementing basic algorithms to creating complex monetization systems based on artificial intelligence. His experience demonstrates that success in the digital era is determined not only by technological capabilities but also by the ability to balance business goals with user needs. "We are on the brink of a new era where autonomous monetization systems will become the foundation of the digital economy. In the future, we will see fully autonomous systems that will adapt to market changes independently, taking into account microeconomic fluctuations and user behavior," concludes Popov, highlighting how the smartphone in your pocket might soon become even smarter about understanding what you really want to buy. How AI transforms personalized ads, dynamic pricing, and user retention across leading digital platforms How AI transforms personalized ads, dynamic pricing, and user retention across leading digital platforms Imagine your smartphone not just displaying ads but knowing exactly what and when to offer you, making you genuinely interested in the purchase. This is the reality ushered in by a new wave of artificial intelligence, already contributing to substantial additional revenues for companies. Monetization is undergoing a revolution thanks to AI. According to a McKinsey survey , while overall AI adoption remains steady at around 55%, only 23% of respondents reported that at least 5% of their organizations' EBIT last year was attributable to AI usage, indicating significant room for growth. This underscores a global challenge: how to harness AI's potential to balance revenue growth while maintaining a quality user experience.​ survey survey Timofey Popov, an experienced product management specialist with proficiency in implementing technology-driven solutions across fintech and banking sectors, has dedicated the last eight years to perfecting digital monetization strategies. His career spans from creating financial products within major banking institutions to spearheading AI-powered product initiatives at organizations including SberMarket and McDonald's, where he has consistently revolutionized business revenue approaches. Additionally, he serves as a respected industry authority, participating as a jury member for the distinguished CX Awards and Globee Awards, where he assesses innovative developments in customer experience and digital transformation. This article will examine how his knowledge of adaptive algorithms, dynamic pricing systems, and personalization techniques has revolutionized these platforms, establishing new benchmarks for AI-enhanced monetization. From Big Macs to Big Data: How AI Transformed McDonald's Digital Sales From Big Macs to Big Data: How AI Transformed McDonald's Digital Sales When McDonald's decided to upgrade its mobile app, it turned to Popov, who had a unique vision for AI-driven monetization. What happened next would revolutionize how one of the world's largest fast-food chains approaches digital sales. "AI analyzed dozens of factors: time of day, order history, popular meal combinations, even the weather. Based on this data, each user was offered a personalized set of dishes and deals. For example, if someone often ordered coffee in the morning, the system could offer them a combo deal with a croissant," Popov explains. "AI analyzed dozens of factors: time of day, order history, popular meal combinations, even the weather. Based on this data, each user was offered a personalized set of dishes and deals. For example, if someone often ordered coffee in the morning, the system could offer them a combo deal with a croissant," The results were staggering: the average check grew by 16%, repeat orders increased by 34%, and overall sales through the app skyrocketed by 480%. What started as a simple ordering tool became a cornerstone of McDonald's digital strategy, proving that AI-driven personalization could work globally. The Marketplace Revolution: Scaling success in the largest classified platform in the world The Marketplace Revolution: Scaling success in the largest classified platform in the world Building on his success at McDonald's, Popov went on to lead a team of 14 at the largest classifieds platform in the world. There, he focused on three key areas: promotional products for sellers, advertising inventory, and improving algorithms using Generative AI and transformer models. "The classifieds market is always a delicate balance between the interests of buyers and sellers. Using AI, we were able to personalize advertising offers and dynamically adjust bids. This increased revenues by 10% while simultaneously improving the user experience," Popov explains. The implementation of predictive models that analyze user behavior and predict optimal ad placements improved conversion rates by 17%, setting new standards for marketplace monetization. Popov also shared his insights on AI-driven advertising strategies at industry-leading conferences, including Epic Growth, where he presented on "ML in the Tasks of Expanding Advertising Inventory." Smart Pricing: Reinventing Retail at SberMarket Smart Pricing: Reinventing Retail at SberMarket At SberMarket, one of the largest Russian marketplaces, Popov's team tackled a universal challenge in grocery retail — how to optimize sales with perishable inventory and volatile demand. Their solution? An AI-powered dynamic pricing system that would revolutionize how supermarkets approach inventory management. "Imagine a supermarket where prices automatically adjust to demand every hour. In the morning, milk might cost more because it's in high demand for breakfast, while in the evening, the price drops to ensure it sells before its expiration date. This is exactly the system we created using AI," Popov explains. The implementation of these "smart" prices brought the company a 19% increase in revenue. "Imagine a supermarket where prices automatically adjust to demand every hour. In the morning, milk might cost more because it's in high demand for breakfast, while in the evening, the price drops to ensure it sells before its expiration date. This is exactly the system we created using AI," The system also learned to predict customer behavior. If AI noticed that a customer regularly ordered certain products, it would alert suppliers in advance to increase stock levels. This helped boost the number of loyal customers by 22% through proactive inventory management and personalized recommendations. The Future of AI-driven Monetization The Future of AI-driven Monetization Popov actively shares his expertise on professional platforms. His publications can be found in leading business outlets, including RBC and Cossa. He has previously spoken at the Epic Growth and Matemarketing conferences, presenting on implementing ML solutions in business processes. The transformation of monetization through AI is not just a trend but a new reality for businesses. Over eight years of working with product innovations, Popov has progressed from implementing basic algorithms to creating complex monetization systems based on artificial intelligence. His experience demonstrates that success in the digital era is determined not only by technological capabilities but also by the ability to balance business goals with user needs. "We are on the brink of a new era where autonomous monetization systems will become the foundation of the digital economy. In the future, we will see fully autonomous systems that will adapt to market changes independently, taking into account microeconomic fluctuations and user behavior," concludes Popov, highlighting how the smartphone in your pocket might soon become even smarter about understanding what you really want to buy. "We are on the brink of a new era where autonomous monetization systems will become the foundation of the digital economy. In the future, we will see fully autonomous systems that will adapt to market changes independently, taking into account microeconomic fluctuations and user behavior,"

How AI Monetizes Your Preferences – Insights from Product Manager Timofey Popov

Hvordan AI monetiserer dine præferencer – indsigt fra produktchef Timofey Popov

Cómo la IA monetiza sus preferencias – Insights de Product Manager Timofey Popov

Kako AI monetarizira vaše preferencije – uvid od menadžera proizvoda Timofeya Popova

Bagaimana AI Menghasilkan Uang dari Preferensi Anda – Pandangan dari Manajer Produk Timofey Popov

Come l’intelligenza artificiale monetizza le tue preferenze – Insight dal Product Manager Timofey Popov

AIがあなたの好みをどのように稼げるか - 製品マネージャーTimofey Popovからの洞察

Алдыңғы мақалаЧТО НАДО ДЕЛАТЬ ДЛЯ РАЗВИТИЯ ПРОМЫШЛЕННОСТИ?

Kaip AI uždirba pinigus iš jūsų pageidavimų – įžvalgos iš produkto vadovo Timofey Popov

Како вештачката интелигенција ги монетизира вашите преференции – увид од менаџерот за производи Timofey Popov

Bagaimana AI menghasilkan wang daripada keutamaan anda – wawasan daripada Pengurus Produk Timofey Popov

Ako AI peniaze z vašich preferencií – názory od manažéra produktu Timofeyho Popova

วิธีการสร้างรายได้จากอัจฉริยะอัจฉริยะของคุณ – ความเห็นจากผู้อํานวยการผลิตภัณฑ์ Timofey Popov

In the financial world, timing is everything. And in January 2025, Carl “Moon” Runefelt made a call that is now almost impossible to ignore. A familiar name in the crypto space, Runefelt tweeted: “Debts are rising alongside greed and euphoria in the stock market. The signs of a financial crisis are growing. I believe we will see a big financial meltdown… Bitcoin is the Noah’s Ark in the economic flood that is coming. Get in the boat.” At the time, markets were hot. Bitcoin had just pushed past the $100,000 mark, and stocks were continuing to climb. Optimism was everywhere, both in traditional finance and across crypto communities. Carl’s warning didn’t make headlines. It was written off as another bearish opinion during a time when nobody wanted to hear it. But fast forward to April, and that same tweet is back in circulation for a reason. Markets have taken a hit. Bitcoin has fallen to around $76,000. Ethereum is down too, and interest in DeFi and NFTs has cooled off. On the stock market side, the S&P 500 has dropped roughly 14 percent in just three trading days. That makes it one of the fastest and steepest short-term corrections since 1987. Suddenly, people are going back to Carl’s January post and wondering how he saw it coming. According to Carl, it came down to one thing: the federal funds effective rate. In a follow-up YouTube video, he explained that his prediction wasn’t just a guess. He was looking at a pattern that has repeated itself over the last few decades. Each time the US begins cutting interest rates after a period of raising them, a recession tends to follow. The chart is simple. Rate cuts, then a gray recession bar. Again and again. Carl noticed that interest rates were peaking and starting to turn. To him, that signaled not just the possibility of a downturn, but the likely timing of one. Combined with high debt levels, soaring credit card usage, and the overheated mood in the markets, it all pointed to an incoming reset. That’s why in January, he said a major pullback was coming within months. And now, that timeline is starting to play out. On top of that, Carl doubled down with a follow-up tweet including a chart of the federal funds rate to back his claim. He wrote: “One of the main indicators I look at when I predict this coming stock market crash and recession is the federal funds rate. Over the past decades, when they start lowering the rate, a stock market crash and recession usually follow.” This chart, showing the relationship between falling rates and recessions, is central to Carl’s view that the current environment was unsustainable. But more importantly, Carl has a clear outlook on what happens next. He believes the Federal Reserve will eventually bring interest rates down to zero and begin printing massive amounts of money again. When that happens, he says Bitcoin will be the best way to protect wealth. He continues to describe Bitcoin as a safe haven asset, calling it “the Noah’s Ark” for those trying to escape the consequences of a broken financial system. Runefelt first rose to prominence through his YouTube channel The Moon Show. His story is well known in the crypto space. He went from working as a cashier in Sweden to building a global following, mostly by breaking down complex market movements in a way that’s easy to digest. He doesn’t just look at charts. He connects macro shifts with digital assets and speaks to the bigger picture. Even with prices falling, Carl hasn’t lost confidence in Bitcoin. In his latest commentary, he says he still believes Bitcoin could hit $300,000 by the end of the year. His more bullish outlook is $1 million per BTC, especially if the Fed turns the money printer back on. If quantitative easing returns in full force, Carl believes Bitcoin could soar to all-time highs once again. He also pointed out that short-term drops are part of the cycle. When markets panic, everything falls. But long-term investors, he says, should see this as an opportunity to buy. He even joked about selling his Bugatti to buy Ethereum, calling it “the trade of the century” if ETH rebounds to five figures. Carl’s big takeaway is that crypto and traditional markets are more linked than ever. When stocks fall, crypto usually follows. And that connection is exactly what he was watching when he made his prediction in January. The federal funds rate, market behavior, and liquidity trends all paint the same picture. Now, that prediction is no longer just a hot take. It’s a case study in spotting the signs before they show up in the headlines. Looking back, Carl’s message wasn’t exaggerated. It was right on time. And today, a lot more people are starting to pay attention. In the financial world, timing is everything. And in January 2025, Carl “Moon” Runefelt made a call that is now almost impossible to ignore. A familiar name in the crypto space, Runefelt tweeted : tweeted tweeted “Debts are rising alongside greed and euphoria in the stock market. The signs of a financial crisis are growing. I believe we will see a big financial meltdown… Bitcoin is the Noah’s Ark in the economic flood that is coming. Get in the boat.” “Debts are rising alongside greed and euphoria in the stock market. The signs of a financial crisis are growing. I believe we will see a big financial meltdown… Bitcoin is the Noah’s Ark in the economic flood that is coming. Get in the boat.” At the time, markets were hot. Bitcoin had just pushed past the $100,000 mark, and stocks were continuing to climb. Optimism was everywhere, both in traditional finance and across crypto communities. Carl’s warning didn’t make headlines. It was written off as another bearish opinion during a time when nobody wanted to hear it. But fast forward to April, and that same tweet is back in circulation for a reason. Markets have taken a hit. Bitcoin has fallen to around $76,000. Ethereum is down too, and interest in DeFi and NFTs has cooled off. On the stock market side, the S&P 500 has dropped roughly 14 percent in just three trading days. That makes it one of the fastest and steepest short-term corrections since 1987. Suddenly, people are going back to Carl’s January post and wondering how he saw it coming. According to Carl, it came down to one thing: the federal funds effective rate. In a follow-up YouTube video , he explained that his prediction wasn’t just a guess. He was looking at a pattern that has repeated itself over the last few decades. Each time the US begins cutting interest rates after a period of raising them, a recession tends to follow. The chart is simple. Rate cuts, then a gray recession bar. Again and again. YouTube video YouTube video Carl noticed that interest rates were peaking and starting to turn. To him, that signaled not just the possibility of a downturn, but the likely timing of one. Combined with high debt levels, soaring credit card usage, and the overheated mood in the markets, it all pointed to an incoming reset. That’s why in January, he said a major pullback was coming within months. And now, that timeline is starting to play out. On top of that, Carl doubled down with a follow-up tweet including a chart of the federal funds rate to back his claim. He wrote: follow-up tweet follow-up tweet “One of the main indicators I look at when I predict this coming stock market crash and recession is the federal funds rate. Over the past decades, when they start lowering the rate, a stock market crash and recession usually follow.” “One of the main indicators I look at when I predict this coming stock market crash and recession is the federal funds rate. Over the past decades, when they start lowering the rate, a stock market crash and recession usually follow.” This chart, showing the relationship between falling rates and recessions, is central to Carl’s view that the current environment was unsustainable. But more importantly, Carl has a clear outlook on what happens next. He believes the Federal Reserve will eventually bring interest rates down to zero and begin printing massive amounts of money again. When that happens, he says Bitcoin will be the best way to protect wealth. He continues to describe Bitcoin as a safe haven asset, calling it “the Noah’s Ark” for those trying to escape the consequences of a broken financial system. Runefelt first rose to prominence through his YouTube channel The Moon Show . His story is well known in the crypto space. He went from working as a cashier in Sweden to building a global following, mostly by breaking down complex market movements in a way that’s easy to digest. He doesn’t just look at charts. He connects macro shifts with digital assets and speaks to the bigger picture. The Moon Show Even with prices falling, Carl hasn’t lost confidence in Bitcoin. In his latest commentary, he says he still believes Bitcoin could hit $300,000 by the end of the year. His more bullish outlook is $1 million per BTC, especially if the Fed turns the money printer back on. If quantitative easing returns in full force, Carl believes Bitcoin could soar to all-time highs once again. He also pointed out that short-term drops are part of the cycle. When markets panic, everything falls. But long-term investors, he says, should see this as an opportunity to buy. He even joked about selling his Bugatti to buy Ethereum, calling it “the trade of the century” if ETH rebounds to five figures. Carl’s big takeaway is that crypto and traditional markets are more linked than ever. When stocks fall, crypto usually follows. And that connection is exactly what he was watching when he made his prediction in January. The federal funds rate, market behavior, and liquidity trends all paint the same picture. Now, that prediction is no longer just a hot take. It’s a case study in spotting the signs before they show up in the headlines. Looking back, Carl’s message wasn’t exaggerated. It was right on time. And today, a lot more people are starting to pay attention.

Carl “Moon” Runefelt Predicted the Market Crash — And It’s Happening Now

Carl "Moon" Runefelt የሽያጭ ገበያ መቁረጥ - እና ይህ አሁን ይሆናል

كارل "ماون" رينفيلد يوصي بالخسارة في السوق - وربما يحدث الآن

Карл "Луна" Рънфелт предсказа пазара - и това се случва сега

Carl "Moon" Runefelt predijo el colapso del mercado - Y está sucediendo ahora

Carl "मून" Runefelt ने बाजार दुर्घटना का पूर्वानुमान किया - और यह अब हो रहा है

Carl "Moon" Runefelt ha predetto il crash del mercato - E sta accadendo ora

Carl “Moon” Runefelt が市場崩壊を予測しました - そしてそれは今起こっています

Carl "Moon" Runefelt ການຄາດຄະເນການຕະຫຼາດ crash - ແລະມັນແມ່ນເກີດຂຶ້ນໃນປັດຈຸບັນ

Carl "Moon" Runefelt alitabiri kuanguka kwa soko - na inatokea sasa

คาร์ล "ดวงจันทร์" Runefelt การคาดการณ์ความล้มเหลวของตลาด - และมันเกิดขึ้นตอนนี้

کارل "مون" رینفیلٹ نے مارکیٹ کا بحران پیش کیا - اور یہ اب ہو رہا ہے

卡尔“月亮”Runefelt预测市场崩溃 - 现在发生了

Authors:
(1) Yanpeng Ye, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia, GreenDynamics Pty. Ltd, Kensington, NSW, Australia, and these authors contributed equally to this work;
(2) Jie Ren, GreenDynamics Pty. Ltd, Kensington, NSW, Australia, Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong, China, and these authors contributed equally to this work;
(3) Shaozhou Wang, GreenDynamics Pty. Ltd, Kensington, NSW, Australia (shaozhou@greendynamics.com.au);
(4) Yuwei Wan, GreenDynamics Pty. Ltd, Kensington, NSW, Australia and Department of Linguistics and Translation, City University of Hong Kong, Hong Kong, China;
(5) Imran Razzak, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia;
(6) Tong Xie, GreenDynamics Pty. Ltd, Kensington, NSW, Australia and School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW, Australia (tong@greendynamics.com.au);
(7) Wenjie Zhang, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia (wenjie.zhang@unsw.edu.au). Editor’s note: This article is part of a broader study. You’re reading Part 7 of 9. Read the rest below. Table of Links Abstract and Introduction
Methods
Data preparation and schema design
LLMs training, evaluation and inference
Entity resolution
Knowledge graph construction
Result
Discussion
Conclusion and References Result In Table 1, the performance comparison of LLMs includes Darwin, LLaMA, and LLaMA2 in NER, RE and ER task within the materials science domain is presented. Darwin achieves markedly higher F1 scores in both NER and RE tasks than the LLaMA 7b and LLaMA2 7b models. This indicates that Darwin can perform more satisfactory results in text tasks in the field of materials science. However, there has been no significant difference in their effectiveness for ER tasks between these models. We speculate that this is due to insufficient contextual memory capabilities of the LLMs. In response to the challenge, entity resolution was applied to the inference outputs, which substantially improved the performance in Standardization (ER) tasks. This refinement process has also led to enhancements in the NER and Relation RE tasks as well. The improvements can be attributed to the ER task removing some of the incorrect inferences that resulted from hallucinations and providing a secondary adjustment of the extracted relations. To gain a more comprehensive understanding of the improvements brought by each step in the standardization process, we conducted ablation experiments, the results are shown in Table 2 which clearly shows that each method contributes to the improvement of ER. The result indicates the most effective method is to optimize through the expert dictionary, which indicates that in addition to the accuracy of the expert dictionary, it can also fully cover all other labels except for the three labels representing the material. The improvement of ER-NF/A is also significant, as this process can eliminate most erroneous materials, and even has a stronger improvement on NER than ER-OL. Besides, ER-N/F pay attention to adjust the relation error between "Name" and "Formula", from a contribution perspective, it is also an indispensable part of ER. We also evaluated the LLM classifier by selecting 200 manually labeled data, and the statistical evaluation results in Table 3 shows the strong performance of LLM used in classification task. The triples are transferred from normalized data to construct the FMKG which contains 162,605 nodes and 731,772 edges, the schematic diagram shows in Figure 3. In the graph, there are 11 types of nodes and 13 types of relations. The FMKG store large amount of information in functional materials includes battery, catalyst and solar cell. For example, as the Figure 4 shows, by analyzing the frequency of material appearances in the battery domain, we observe that Co2O3 is the most frequently occurring material, followed by MoS2, graphite, TiO2, LiCoO2 etc which are commonly used in the battery domain. Likewise, it is evident that the most prevalent application within the battery field is lithium-ion batteries, which appear with a frequency significantly higher than other applications in the battery domain. These insights align with our understanding of these domains and materials, confirming that our knowledge graph effectively stores factual knowledge and offers a knowledge platform for material researchers. For further validation of FMKG, we extract the 500 triples are randomly extracted except the "DOI" and "Domain" nodes, which are informative information, and checked by the experts in relevant material science. After splitting these triples apart, annotator can obtain 1000 entities and 500 relations. The report from annotator shows in Table 4. “Name” is the highest priority core label and only serves as the head in the knowledge graph, so it will not be used as a relation. Notably, the labels "Application", "Structure/Phase", "Synthesis" and "Characterization" exhibit 100% accuracy in both entity and relation. This is due to the rigorous standardization of entities under these categories, using our expert dictionary. In contrast, the "Descriptor" and "Property", characterized by their vast diversity and broad spectrum, are applied a less stringent standardization process which lead to a certain degree of imprecision in entity and relation. But the results are still satisfactory. "Name" and "Acronym" has also achieved a commendable 100% entity accuracy. However, when it comes to analyzing relations, the ChemDataExtractor encounters certain limitations. A detailed analysis of "Name" and "Acronym" misclassifications reveals that most of these wrong entities originate from "Formula", which is due to the reasonable error of LLM"s binary classification of "Formula" and "Name" and ChemDataExtractor. The impact of this kind of error is not significant, as fundamentally, the "Name", "Formula" and "Acronym" from same source all represent the same material. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Yanpeng Ye, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia, GreenDynamics Pty. Ltd, Kensington, NSW, Australia, and these authors contributed equally to this work; (2) Jie Ren, GreenDynamics Pty. Ltd, Kensington, NSW, Australia, Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong, China, and these authors contributed equally to this work; (3) Shaozhou Wang, GreenDynamics Pty. Ltd, Kensington, NSW, Australia (shaozhou@greendynamics.com.au); (4) Yuwei Wan, GreenDynamics Pty. Ltd, Kensington, NSW, Australia and Department of Linguistics and Translation, City University of Hong Kong, Hong Kong, China; (5) Imran Razzak, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia; (6) Tong Xie, GreenDynamics Pty. Ltd, Kensington, NSW, Australia and School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW, Australia (tong@greendynamics.com.au); (7) Wenjie Zhang, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia (wenjie.zhang@unsw.edu.au). Authors: Authors: (1) Yanpeng Ye, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia, GreenDynamics Pty. Ltd, Kensington, NSW, Australia, and these authors contributed equally to this work; (2) Jie Ren, GreenDynamics Pty. Ltd, Kensington, NSW, Australia, Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong, China, and these authors contributed equally to this work; (3) Shaozhou Wang, GreenDynamics Pty. Ltd, Kensington, NSW, Australia (shaozhou@greendynamics.com.au); (4) Yuwei Wan, GreenDynamics Pty. Ltd, Kensington, NSW, Australia and Department of Linguistics and Translation, City University of Hong Kong, Hong Kong, China; (5) Imran Razzak, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia; (6) Tong Xie, GreenDynamics Pty. Ltd, Kensington, NSW, Australia and School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW, Australia (tong@greendynamics.com.au); (7) Wenjie Zhang, School of Computer Science and Engineering, University of New South Wales, Kensington, NSW, Australia (wenjie.zhang@unsw.edu.au). Editor’s note: This article is part of a broader study. You’re reading Part 7 of 9. Read the rest below. Editor’s note: This article is part of a broader study. You’re reading Part 7 of 9. Read the rest below. Editor’s note: This article is part of a broader study. You’re reading Part 7 of 9. Read the rest below. Table of Links Abstract and Introduction Methods Data preparation and schema design LLMs training, evaluation and inference Entity resolution Knowledge graph construction Result Discussion Conclusion and References Abstract and Introduction Abstract and Introduction Methods Methods Data preparation and schema design Data preparation and schema design LLMs training, evaluation and inference LLMs training, evaluation and inference Entity resolution Entity resolution Knowledge graph construction Knowledge graph construction Result Result Discussion Discussion Conclusion and References Conclusion and References Result In Table 1, the performance comparison of LLMs includes Darwin, LLaMA, and LLaMA2 in NER, RE and ER task within the materials science domain is presented. Darwin achieves markedly higher F1 scores in both NER and RE tasks than the LLaMA 7b and LLaMA2 7b models. This indicates that Darwin can perform more satisfactory results in text tasks in the field of materials science. However, there has been no significant difference in their effectiveness for ER tasks between these models. We speculate that this is due to insufficient contextual memory capabilities of the LLMs. In response to the challenge, entity resolution was applied to the inference outputs, which substantially improved the performance in Standardization (ER) tasks. This refinement process has also led to enhancements in the NER and Relation RE tasks as well. The improvements can be attributed to the ER task removing some of the incorrect inferences that resulted from hallucinations and providing a secondary adjustment of the extracted relations. To gain a more comprehensive understanding of the improvements brought by each step in the standardization process, we conducted ablation experiments, the results are shown in Table 2 which clearly shows that each method contributes to the improvement of ER. The result indicates the most effective method is to optimize through the expert dictionary, which indicates that in addition to the accuracy of the expert dictionary, it can also fully cover all other labels except for the three labels representing the material. The improvement of ER-NF/A is also significant, as this process can eliminate most erroneous materials, and even has a stronger improvement on NER than ER-OL. Besides, ER-N/F pay attention to adjust the relation error between "Name" and "Formula", from a contribution perspective, it is also an indispensable part of ER. We also evaluated the LLM classifier by selecting 200 manually labeled data, and the statistical evaluation results in Table 3 shows the strong performance of LLM used in classification task. The triples are transferred from normalized data to construct the FMKG which contains 162,605 nodes and 731,772 edges, the schematic diagram shows in Figure 3. In the graph, there are 11 types of nodes and 13 types of relations. The FMKG store large amount of information in functional materials includes battery, catalyst and solar cell. For example, as the Figure 4 shows, by analyzing the frequency of material appearances in the battery domain, we observe that Co2O3 is the most frequently occurring material, followed by MoS2, graphite, TiO2, LiCoO2 etc which are commonly used in the battery domain. Likewise, it is evident that the most prevalent application within the battery field is lithium-ion batteries, which appear with a frequency significantly higher than other applications in the battery domain. These insights align with our understanding of these domains and materials, confirming that our knowledge graph effectively stores factual knowledge and offers a knowledge platform for material researchers. For further validation of FMKG, we extract the 500 triples are randomly extracted except the "DOI" and "Domain" nodes, which are informative information, and checked by the experts in relevant material science. After splitting these triples apart, annotator can obtain 1000 entities and 500 relations. The report from annotator shows in Table 4. “Name” is the highest priority core label and only serves as the head in the knowledge graph, so it will not be used as a relation. Notably, the labels "Application", "Structure/Phase", "Synthesis" and "Characterization" exhibit 100% accuracy in both entity and relation. This is due to the rigorous standardization of entities under these categories, using our expert dictionary. In contrast, the "Descriptor" and "Property", characterized by their vast diversity and broad spectrum, are applied a less stringent standardization process which lead to a certain degree of imprecision in entity and relation. But the results are still satisfactory. "Name" and "Acronym" has also achieved a commendable 100% entity accuracy. However, when it comes to analyzing relations, the ChemDataExtractor encounters certain limitations. A detailed analysis of "Name" and "Acronym" misclassifications reveals that most of these wrong entities originate from "Formula", which is due to the reasonable error of LLM"s binary classification of "Formula" and "Name" and ChemDataExtractor. The impact of this kind of error is not significant, as fundamentally, the "Name", "Formula" and "Acronym" from same source all represent the same material. 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

AI Model Reads Thousands of Studies, Nails Battery Science Better Than Expected

Kingstown, Saint Vincent and the Grenadines, April 17th, 2025/Chainwire/--SimpleFX has announced a significant expansion of its trading offer, increasing its portfolio to include over 1,000 financial assets spanning global markets. This development marks a strategic extension of the platform’s multi-asset trading capabilities, aimed at providing broader market access to its users. over 1,000 financial assets over 1,000 financial assets Expanded Equities and Geographic Coverage The update introduces hundreds of new equities from regions including Australia, Malaysia, Hong Kong, the Republic of South Africa, and Singapore. SimpleFX has also extended its coverage in key markets such as Brazil, Germany, France, Japan, Sweden, and Turkey. This broader market reach enables traders to access equities across various time zones, offering increased flexibility in portfolio diversification. Users can also trade these equities in conjunction with related instruments, including commodities, metals, cryptocurrencies, and forex pairs. metals Enhanced Cryptocurrency Access The platform has expanded its cryptocurrency offerings by adding 19 altcoins, including Ripple (XRP) and Toncoin (TON), allowing traders to engage with a wider selection of digital assets. These additions complement an already diverse range of crypto-fiat pairs available on SimpleFX. Comprehensive U.S. Market Integration As part of the expansion, SimpleFX has included all equities listed on the NASDAQ-100 (US100) and S&P 500 (US500) indices. These indices are widely regarded as benchmarks of the U.S. high-technology sector and the broader American economy. With over 500 U.S. equities now available, traders can utilize the SimpleFX WebTrader platform’s analytical tools to explore opportunities across major sectors such as technology, defense, pharmaceuticals, energy, and consumer goods. Key Global Additions Noteworthy new instruments include: BHP Group (BHP.AU): A leading mining conglomerate, accounting for a substantial portion of the Australian share market.
Tencent Holdings (HKG: 0700): A major technology and entertainment firm based in Hong Kong, known for products such as WeChat and its gaming investments.
Intel Corporation (INTC.US): A long-standing player in the semiconductor space, currently focusing on AI integration.
RTX Corporation (RTX.US): A significant entity in aerospace and defense, producing military-grade technologies.
Ripple (XRPUSD): A digital asset linked to a blockchain-based payment protocol used for cross-border financial transactions. BHP Group (BHP.AU): A leading mining conglomerate, accounting for a substantial portion of the Australian share market. BHP BHP Tencent Holdings (HKG: 0700): A major technology and entertainment firm based in Hong Kong, known for products such as WeChat and its gaming investments. HKG: 0700 HKG: 0700 Intel Corporation (INTC.US): A long-standing player in the semiconductor space, currently focusing on AI integration. INTC.US INTC.US RTX Corporation (RTX.US): A significant entity in aerospace and defense, producing military-grade technologies. RTX.US RTX.US Ripple (XRPUSD): A digital asset linked to a blockchain-based payment protocol used for cross-border financial transactions. XRPUSD XRPUSD Platform Enhancements and Regulatory Options In addition to the instrument expansion, SimpleFX has broadened its registration options, allowing users to register under either the Saint Vincent and the Grenadines (SVG) or Mauritius (MU) jurisdictions. Furthermore, the platform now supports deposits and withdrawals via Mastercard and Visa for USD and EUR accounts, complementing its existing 20 cryptocurrency-based payment methods. These additions aim to improve transaction speed and security while offering more flexibility in account funding. About SimpleFX SimpleFX is a global trading platform offering access to a wide range of financial markets, including forex, equities, cryptocurrencies, indices, and commodities. Established in 2014, the platform is known for its user-friendly WebTrader interface, comprehensive market access, and commitment to innovation in multi-asset trading. SimpleFX operates under multiple jurisdictions and provides users with various registration and payment options, catering to both retail and professional traders worldwide. SimpleFX SimpleFX Contacts SimpleFX Partnership Team marketing@simplefx.com SimpleFX Support Team support@simplefx.com 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

SimpleFX Expands Global Trading Offer With Over 1000 Instruments

SimpleFX brei wêreldwye handelsaanbod uit met meer as 1000 instrumente

SimpleFX ከ 1000 በላይ መሳሪያዎች ጋር ዓለም አቀፍ የንግድ አቅርቦት ይሰጣል

SimpleFX пашырае сусветную гандлёвую прапанову з больш чым 1000 інструментамі

SimpleFX proširuje globalnu ponudu trgovanja sa više od 1000 instrumenata

SimpleFX amplía su oferta de negociación global con más de 1000 instrumentos

A SimpleFX több mint 1000 eszközzel bővíti globális kereskedési kínálatát

SimpleFX Memperluas Penawaran Perdagangan Global dengan Lebih dari 1000 Instrumen

SimpleFX、1000以上のツールでグローバルな取引オファーを拡大

SimpleFX ផ្តល់ជូននូវសេវាកម្មពាណិជ្ជកម្មទូទាំងពិភពលោកជាមួយនឹងច្រើនជាង 1000 ឧបករណ៍

SimpleFX د نړۍ د سوداګرۍ وړاندیز سره د 1000 څخه زيات انټرنټونه پراختیا کوي

SimpleFX zgjeron ofertën globale të tregtisë me më shumë se 1000 instrumente

SimpleFX 1000 سے زائد آلات کے ساتھ عالمی ٹریڈنگ کی پیشکش کو بڑھاتا ہے

Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 MindEye2 and 2.1 Shared-Subject Functional Alignment 2 MindEye2 and 2.1 Shared-Subject Functional Alignment 2.2 Backbone, Diffusion Prior, & Submodules 2.2 Backbone, Diffusion Prior, & Submodules 2.3 Image Captioning and 2.4 Fine-tuning Stable Diffusion XL for unCLIP 2.3 Image Captioning and 2.4 Fine-tuning Stable Diffusion XL for unCLIP 2.5 Model Inference 2.5 Model Inference 3 Results and 3.1 fMRI-to-Image Reconstruction 3 Results and 3.1 fMRI-to-Image Reconstruction 3.2 Image Captioning 3.2 Image Captioning 3.3 Image/Brain Retrieval and 3.4 Brain Correlation 3.3 Image/Brain Retrieval and 3.4 Brain Correlation 3.5 Ablations 3.5 Ablations 4 Related Work 4 Related Work 5 Conclusion 5 Conclusion 6 Acknowledgements and References 6 Acknowledgements and References A Appendix A Appendix A.1 Author Contributions A.1 Author Contributions A.2 Additional Dataset Information A.2 Additional Dataset Information A.3 MindEye2 (not pretrained) vs. MindEye1 A.3 MindEye2 (not pretrained) vs. MindEye1 A.4 Reconstruction Evaluations Across Varying Amounts of Training Data A.4 Reconstruction Evaluations Across Varying Amounts of Training Data A.5 Single-Subject Evaluations A.5 Single-Subject Evaluations A.6 UnCLIP Evaluation A.6 UnCLIP Evaluation A.7 OpenCLIP BigG to CLIP L Conversion A.7 OpenCLIP BigG to CLIP L Conversion A.8 COCO Retrieval A.8 COCO Retrieval A.9 Reconstruction Evaluations: Additional Information A.9 Reconstruction Evaluations: Additional Information A.10 Pretraining with Less Subjects A.10 Pretraining with Less Subjects A.11 UMAP Dimensionality Reduction A.11 UMAP Dimensionality Reduction A.12 ROI-Optimized Stimuli A.12 ROI-Optimized Stimuli A.13 Human Preference Experiments A.13 Human Preference Experiments A.13 Human Preference Experiments We conducted two-alternative forced-choice experiments on 58 human raters online. We probed three comparisons intermixed into the same behavioral experiment, with each comparison consisting of 1200 trials sampled evenly from the 1000 NSD test samples across the 4 subjects who completed all 40 scanning sessions (subjects 1, 2, 5, 7). The total 3600 experimental trials were shuffled and 87 trials were presented to each subject. Our subjects were recruited through the Prolific platform, with our experimental tasks hosted on Meadows. All other experiment details follow the protocol used in Kneeland et al. (2023c). A.13.1 MINDEYE2 VS. MINDEYE2 (RANDOM) The first comparison was a two-way identification task in which participants were asked to select which of two images was more similar to a ground truth image. The two images provided for comparison were both reconstructions from MindEye2 (40-hour), one being a randomly selected reconstruction from the test set and the other being the correct, corresponding reconstruction. Raters correctly identified the corresponding reconstruction 97.82% of the time (p < 0.001). This establishes a new SOTA for human-rated image identification accuracy, as the only other papers to perform such an experiment were the original method proposed by (Takagi and Nishimoto, 2022), whose method achieved 84.29%, and the MindEye1 + BOI (brain-optimized inference) method proposed by (Kneeland et al., 2023c), whose enhancement to the MindEye1 method achieved 95.62%. The method in (Takagi and Nishimoto, 2022) is different from the "+Decoded Text" method we compare against in Table 1, which was released in a later technical report (Takagi and Nishimoto, 2023), and which does not report human subjective evaluations. A.13.2 MINDEYE2 (REFINED) VS. MINDEYE2 (UNREFINED) The second comparison was the same task as the first but this time comparing refined MindEye2 (40-hour) reconstructions against unrefined MindEye2 reconstructions (both correctly corresponding to the appropriate fMRI activity). This comparison was designed to empirically confirm the subjective improvements in naturalistic quality provided by MindEye2’s refinement step. This is particularly important to confirm because the quantitative evaluation metrics displayed in Table 4 sometimes preferred the unrefined reconstructions. Refined reconstructions were rated as more similar to the ground truth images 71.94% of the time (p < 0.001), demonstrating that the final refinement step improves reconstruction quality and accuracy when assessed by humans. A.13.3 MINDEYE2 (1-HOUR) VS. BRAIN DIFFUSER (1-HOUR) The final comparison was likewise the same task but this time comparing reconstructions from MindEye2 against reconstructions from the Brain Diffuser method (Ozcelik and VanRullen, 2023), where both methods were trained using only the first hour of scanning data from the 4 NSD subjects. This experiment demonstrated that the MindEye2 reconstructions were preferred 53.01% of the time (p = 0.044), demonstrating a statistically significant improvement in scaling performance compared to the previous state-of-the-art model for reconstructions using only 1 hour of training data. This confirms the results in the main text that MindEye2 achieves SOTA in the low-sample 1-hour setting. We visualized cases where Brain Diffuser (1-hour) was preferred over MindEye2 (1-hour) in Appendix Figure 12. We observed that Brain Diffuser reconstructions were often preferred in situations where both MindEye2 and Brain Diffuser reconstructions were low quality, but MindEye2 reconstructions were "confidently" wrong (in the sense that MindEye2 reconstructions enforce a naturalistic prior from the refinement step) whereas Brain Diffuser reconstructions were producing distorted outputs that contained subtle elements corresponding to the target image. This may indicate human raters prefer distorted outputs with recognizable features, and disfavored the model that enforces a naturalistic prior, and may lose these features. 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 Authors:
(1) Paul S. Scotti, Stability AI and Medical AI Research Center (MedARC);
(2) Mihir Tripathy, Medical AI Research Center (MedARC) and a Core contribution;
(3) Cesar Kadir Torrico Villanueva, Medical AI Research Center (MedARC) and a Core contribution;
(4) Reese Kneeland, University of Minnesota and a Core contribution;
(5) Tong Chen, The University of Sydney and Medical AI Research Center (MedARC);
(6) Ashutosh Narang, Medical AI Research Center (MedARC);
(7) Charan Santhirasegaran, Medical AI Research Center (MedARC);
(8) Jonathan Xu, University of Waterloo and Medical AI Research Center (MedARC);
(9) Thomas Naselaris, University of Minnesota;
(10) Kenneth A. Norman, Princeton Neuroscience Institute;
(11) Tanishq Mathew Abraham, Stability AI and Medical AI Research Center (MedARC). Authors: Authors: (1) Paul S. Scotti, Stability AI and Medical AI Research Center (MedARC); (2) Mihir Tripathy, Medical AI Research Center (MedARC) and a Core contribution; (3) Cesar Kadir Torrico Villanueva, Medical AI Research Center (MedARC) and a Core contribution; (4) Reese Kneeland, University of Minnesota and a Core contribution; (5) Tong Chen, The University of Sydney and Medical AI Research Center (MedARC); (6) Ashutosh Narang, Medical AI Research Center (MedARC); (7) Charan Santhirasegaran, Medical AI Research Center (MedARC); (8) Jonathan Xu, University of Waterloo and Medical AI Research Center (MedARC); (9) Thomas Naselaris, University of Minnesota; (10) Kenneth A. Norman, Princeton Neuroscience Institute; (11) Tanishq Mathew Abraham, Stability AI and Medical AI Research Center (MedARC).

Human Evaluation Validates MindEye2's Superior Image Reconstruction Quality

Welcome to HackerNoon’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. Welcome to HackerNoon’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. HackerNoon The link for the template is HERE. 1. What is your company in 2–5 words? Denis Alaev: EODHD APIs is a top-rated financial data provider. Denis Alaev: Denis Alaev: Denis Alaev top-rated top-rated 2. Why is now the time for your company to exist? The demand for reliable and affordable financial data is higher than ever. As more businesses and individuals rely on data-driven decision-making, there's a growing need for accessible APIs that deliver accurate financial data without the hefty costs of traditional providers. 3. What do you love about your team, and why are you the ones to solve this problem? Our team combines deep technical expertise with product development experience from international tech companies. We understand both the technical challenges of financial data and the user experience side, which makes us uniquely positioned to build a product that's both robust and easy to use. 4. If you weren’t building your startup, what would you be doing? I’d likely still be building products — either in big tech or a fast-paced startup. With my background in computer science and product management, I'd continue solving technical problems that matter. 5. At the moment, how do you measure success? What are your metrics? At the moment, we measure success primarily by the number of active users — we currently have tens of thousands, which is our key product metric. Additionally, we closely monitor customer satisfaction through our Trustpilot rating and user feedback. Positive reviews and high satisfaction scores are strong indicators that we’re delivering real value and meeting user expectations. 6. In a few sentences, what do you offer to whom? We provide high-quality, affordable APIs for historical and fundamental financial market data. Our customers range from individual investors and developers to fintech companies and startups looking to integrate clean and accurate financial data into their products. 7. What’s most exciting about your traction to date? What excites us most is how quickly we grew after pivoting from a B2C investment analysis platform to a B2B API-first company. We are bootstrapped, have reached a six-figure MRR and built a loyal client base by solving a real market pain point. 8. Where do you think your growth will be next year? We expect growth to come from expanding our data sources, improving API performance, and reaching new markets — especially in regions underserved by traditional financial data providers. In addition, we see strong growth potential through the integration of AI into our services, particularly in data checking and validation. This will not only improve data quality but also enable us to offer new, intelligent services that better meet our users' needs. 9. Tell us about your first paying customer and revenue expectations over the next year. Our first paying customer signed up during the launch weekend after we built a simple landing page. It was an early sign that we were solving a real problem. Based on our current trajectory, we anticipate sustained revenue growth throughout the next year. 10. What’s your biggest challenge? Our biggest challenge is managing the transition from a small, fast-moving team to a growing organization with evolving processes. As we scale, things change rapidly — which requires us to stay agile, maintain control, and constantly anticipate what’s coming next. We need to combine strong operational discipline with visionary thinking to stay ahead of market shifts and external events. For example, when the market is booming, subscriber numbers grow. But when the market slows down or crashes, interest in trading often declines due to psychological factors — people lose confidence or become discouraged. Navigating these fluctuations and keeping users engaged through both market highs and lows is a constant challenge we’re focused on. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders.
Would you like to take a stab at answering some of these questions? The link for the template is HERE. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. HackerNoon Founder & CEO David Smooke ten questions for startup founders Would you like to take a stab at answering some of these questions? The link for the template is HERE. The link for the template is HERE.

How EODHD APIs Quietly Built a Six-Figure Business by Fixing Financial Data

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