Криптобиржа Coinbase запустит собственную платформу для прогнозирования

investing.ruPublished on 2025-11-19Last updated on 2025-11-19

Happycoin.club - Криптовалютная биржа Coinbase работает над созданием сайта для платформы прогнозирования. Эту информацию опубликовала исследовательница в области технологий и блогер Джейн Манчун Вонг, известная тем, что находит функции, находящиеся в разработке, на крупных технологических сайтах.

Она представила скриншоты, которые подписала словами о том, что Coinbase «работает над рынком предсказаний».

На одном скриншоте указано, что продукт будет предлагаться подразделением биржи Coinbase Financial Markets, занимающимся деривативами, «через Kalshi», что предполагает, что сервис будет опираться на нормативно-правовую базу Kalshi. На других изображениях показан интерфейс рынка прогнозов, который, судя по всему, позволяет пользователям участвовать в торгах с использованием USDC или долларов США. Категории включают экономику, спорт, науку, политику и технологии.

Компания ранее заявляла о своей заинтересованности в рынках прогнозов в рамках плана по превращению в «универсальную биржу». 13 ноября Coinbase и Kalshi объявили о партнёрстве, которое позволит площадке выступать в качестве кастодиана для контрактов Kalshi на основе USDC.

Недавно Crypto.com запустила продукт для рынка прогнозов в партнёрстве с Trump Media, а биржа Gemini объявила о планах создать аналогичную платформу в рамках готовящегося к запуску приложения.

Такой интерес криптокомпаний неудивителен. Рынки прогнозов стали одним из самых популярных криптовалютных предложений в этом году, а объёмы торгов на таких платформах, как Kalshi и Polymarket, резко возросли.

Читайте оригинальную статью на сайте Happycoin.club

Related Reads

Sam Altman's ChatGPT Parenting Method Stirs Controversy

Sam Altman, CEO of OpenAI, recently faced significant backlash after sharing a concept for using ChatGPT to manage family life. He suggested feeding the AI family schedules and children's interests to generate a personalized morning podcast for car rides, covering topics from soccer games to birthdays alongside news for parents. The post, intended to showcase a "cool" AI application, was widely criticized online. Critics, including "Gravity Falls" creator Alex Hirsch, argued the idea undermined genuine parent-child interaction, with Hirsch's rebuttal—"You could just... talk to your kids?"—gaining far more engagement than Altman's original post. This incident highlights a perceived contradiction in Altman's persona, who has previously expressed the pain of missing time with his child while now proposing AI-mediated conversations. This is not Altman's first promotion of AI-assisted parenting; he has previously described using ChatGPT as a 24/7 parenting encyclopedia for newborn care questions. However, the latest suggestion—having AI script family conversations—crossed a line for many, shifting his image from an anxious new dad to primarily an OpenAI promoter. The controversy coincides with OpenAI's strategic push into the "family AI" space, as evidenced by hiring a family product manager and shifting user demographics toward older parents. While AI can efficiently manage schedules and information, the strong public reaction underscores the sensitivity of applying AI to core human relationships. Critics emphasize that AI should assist with logistical tasks to free up time for family, not replace the empathy, spontaneous connection, and shared experiences that define genuine caregiving. The debate points to crucial boundaries for AI in the home, where efficiency is less important than authentic human connection.

marsbit4m ago

Sam Altman's ChatGPT Parenting Method Stirs Controversy

marsbit4m ago

Just Now, OpenAI's Largest Pre-trained Model Doug Exposed

On August 9, X user ChrisGPT reported that OpenAI is advancing a new large-scale pre-training model codenamed **Doug**, which is said to be its largest such project to date and distinct from GPT-6. ChrisGPT later suggested GPT-6 is likely Astra, a model OpenAI recently paused due to safety concerns, with Doug potentially launching by November. This aligns with a July 9 research memo from SemiAnalysis, which stated OpenAI has overcome pre-training issues and is actively developing a much larger model codenamed Doug. If accurate, this signals a potential shift: after nearly two years of relying primarily on post-training, reinforcement learning (RL), and inference-time compute for capability gains—evidenced by models like o1, o3, and the GPT-5 system—OpenAI may be restarting large-scale foundational model scaling. The backdrop includes competitive pressure from Google's Gemini 3 release in November 2025, which reportedly prompted internal focus at OpenAI. In December 2025, The Information reported OpenAI was developing a pre-training model codenamed **Garlic**, which showed promising results and incorporated key bug fixes. This project reportedly paved the way for an "even bigger and better model"—likely Doug. In summary, Doug may represent OpenAI's return to significant base model scaling, building on resolved pre-training challenges and aiming to push capabilities beyond the limits of the current GPT-4o-era foundation enhanced by advanced post-training techniques.

marsbit24m ago

Just Now, OpenAI's Largest Pre-trained Model Doug Exposed

marsbit24m ago

OpenAI Researcher: We Don't Read Papers Anymore

An OpenAI researcher's remark that top AI labs "no longer read papers" sparked widespread discussion, highlighting a deepening crisis of trust in academic publishing. This sentiment followed exposure of questionable practices in an ICLR paper, where exceptional results were linked to undisclosed "tricks." A large-scale "experimental review" by SAI of 168 Oral papers from ICML 2026 revealed severe reproducibility issues. Of the 105 papers fully replicated, only 8 successfully verified over 80% of their claims, with a median verification rate of just 28-30%. Common problems included non-runnable code, missing files, incomplete documentation, and results mismatching those reported. Some papers even relied on now-offline models, making verification impossible. Specific cases involved an 8x inflation in claimed trained parameters and missing evaluation models from released code. Verifying a single ICML Oral paper had a median cost of around $8,900, with 17 exceeding $100,000. This creates a perverse incentive: flawed research carries high rewards (citations, jobs) with minimal risk of exposure, as verification is prohibitively expensive or impossible without code. While industry researchers at well-resourced labs may rely less on papers due to internal experiments and resources, academic and early-career researchers remain heavily dependent on publications for PhD applications, faculty positions, and entry into top labs. This creates a paradoxical system where papers are increasingly distrusted as reliable knowledge sources yet retain their gatekeeping value in career advancement. The situation underscores a critical need for systemic reforms to ensure scientific integrity and reproducibility in AI research.

marsbit34m ago

OpenAI Researcher: We Don't Read Papers Anymore

marsbit34m ago

Trading

Spot
活动图片