Hands-On Guide to Participating in CZ-Supported predict.fun

Odaily星球日报Опубліковано о 2025-12-19Востаннє оновлено о 2025-12-19

Анотація

Odaily Planet Daily introduces predict.fun, a prediction market platform founded by dingaling, a former Binance employee, and backed by CZ and YZi Labs. Launched on December 17, the platform airdropped initial points to eligible users, including BNB Chain meme traders and Aster participants. Unlike traditional prediction markets like Polymarket, predict.fun allows users to earn additional yield on funds used for predictions. The step-by-step guide includes: registering on the website, checking airdrop eligibility by wallet address, depositing at least $10 to unlock a referral link, sharing the invite on Twitter, verifying the tweet, and trading to unlock points (e.g., $500 trading volume for 200 points). Only event betting counts toward trading volume. The platform emphasizes user engagement and integrated yield generation.

Original | Odaily Planet Daily (@OdailyChina)

Author | Asher (@Asher_ 0210)

On December 17, dingaling, the founder of the prediction market platform predict.fun, announced on X that the platform is officially open and has airdropped initial points to Meme traders on BNB Chain, Aster participants, and active users of various prediction markets.

Unlike traditional prediction markets such as Polymarket and Kalshi, predict.fun has introduced a key innovation: funds used for predictions are no longer idle but can simultaneously generate additional returns during the prediction period.

It is worth mentioning that on December 4, Binance founder CZ posted on X, stating that the founder of predict.fun had worked at Binance several years ago and that the project was incubated by YZi Labs and received "investment" (for more details, read: CZ Reconciles and Joins Forces, dingaling Returns to the Prediction Trend with predict.fun).

Next, Odaily Planet Daily will guide you step-by-step through the interactive experience of participating in predict.fun.

Step-by-Step Participation Tutorial

STEP 1. After logging into the project’s official website (link: https://predict.fun/), first-time users should click "Sign Up" to register and log in.

STEP 2. Click on "Airdrop" at the top to check airdrop eligibility: on the page, click "Check Wallet Eligibility" and enter your wallet address to view (as shown in the prompt below, indicating eligibility).

STEP 3. If eligible, click on "Deposit" at the top to deposit at least $10 into the platform and unlock your personal referral link.

STEP 4. Click "Tweet" in step 3 to share the referral tweet. The system will automatically generate a personal referral tweet, or you can edit the content yourself.

STEP 5. Fill in the link to the published referral tweet and click "Verify Tweet" to complete verification.

STEP 6. Unlock personal points by trading on the platform. As shown in the image, the account needs to complete $500 in trading volume to unlock 200 points. Note that only event-based bets count as valid trades.

Трендові криптовалюти

CitreaCTR

wrapped stUSDTWSTUSDT

Velodrome FinanceVELODROME

BrevisBREV

ZRX（0X）ZRX

Пов'язані питання

QWhat is the key innovation of predict.fun compared to traditional prediction markets like Polymarket and Kalshi?

Apredict.fun allows users' funds to generate additional yields during the prediction period, unlike traditional platforms where funds remain idle.

QWho is the official launch of predict.fun announced by on X platform?

AThe official launch was announced by predict.fun's founder, dingaling, on the X platform.

QWhat is the minimum deposit required to unlock a personal referral link on predict.fun?

AA minimum deposit of at least $10 is required to unlock the personal referral link.

QWhich user groups received the initial airdrop of points from predict.fun?

AThe initial points airdrop was distributed to Meme traders on BNB Chain, Aster participants, and active users of various prediction markets.

QWhat is the relationship between CZ and predict.fun's founder as mentioned in the article?

ACZ mentioned that predict.fun's founder previously worked at Binance several years ago, and the project is incubated and invested in by YZi Labs.

Пов'язані матеріали

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

Ethereum Q1 2026 Report: Fees Down, Users & Transactions Hit New Highs Token Terminal's Q1 2026 report on Ethereum presents a pivotal development: the network achieved record highs in monthly active users (13.2M, +85.9% YoY), total transactions (200.4M, +81.5% YoY), and throughput (25.78 TPS), while transaction fees on the mainnet plummeted by 47.9% quarter-over-quarter. This shift is attributed to the network's strategic move into a "low fees for scale" phase, exemplified by the Fusaka upgrade which increased data capacity and lowered block space costs, releasing pent-up demand (a manifestation of Jevons's Paradox). The report highlights a core narrative shift for Ethereum: from a DeFi-centric blockchain to a global financial settlement layer. It maintains a dominant position in tokenized assets, holding majority market shares among top chains in stablecoins (61.8%), tokenized funds (73.0%), and tokenized commodities (84.0%). Growth in tokenized funds (+73.1% YoY) and commodities (+325.9% YoY) was particularly strong, driven by institutions like BlackRock and JPMorgan entering the space. Contrasting these usage gains, several USD-denominated value metrics declined in Q1: fully diluted market cap fell 30.3% QoQ, total value locked (TVL) dropped 11.0%, and ecosystem transaction volume decreased 24.0%. The report interprets this as Ethereum prioritizing long-term network expansion and cementing its role as the default settlement layer for finance over short-term fee capture. The commentary from Etherealize argues that, much like the early internet, Ethereum's open, permissionless model is poised to win over closed alternatives as institutional tokenization accelerates.

marsbit17 хв тому

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

marsbit17 хв тому

Intel CEO Liwu Chen's First Podcast Interview: Our Goal is '10x in 5-10 Years', Betting on Advanced Packaging, Glass Substrate, and Synthetic Diamond

Intel CEO Chen Liwu outlines a bold vision for the company's transformation in a podcast interview, targeting a "10x return in 5-10 years." He emphasizes a strategic shift beyond traditional process node scaling, focusing on advanced packaging (like EMIB), new materials (glass substrates, GaN, SiC, InP, synthetic diamond), and system-level integration. Key to this is Intel's foundry business, where he prioritizes building trust through superior yield, defect density, and cycle times. Chen sees strong CPU demand resurgence driven by agent AI and inference workloads. He details the Terafab collaboration with Elon Musk to address semiconductor infrastructure gaps and stresses the strategic importance of U.S.-based advanced manufacturing. Acknowledging Intel is still in the "crawl" phase of his crawl-walk-run framework, Chen believes the company's full potential—extending from its PC base into edge computing and physical AI—will become apparent by 2030-2032.

marsbit23 хв тому

Intel CEO Liwu Chen's First Podcast Interview: Our Goal is '10x in 5-10 Years', Betting on Advanced Packaging, Glass Substrate, and Synthetic Diamond

marsbit23 хв тому

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Pete Florence, a former senior research scientist at Google DeepMind and a key contributor to the Vision-Language-Action (VLA) model architecture, is deliberately distancing his startup, Generalist AI, from the trendy "world model" label. He argues that the industry should prioritize concrete goals over buzzwords. His goal is to create robots that can perform a vast range of unseen tasks with high speed and success rates, without needing task-specific training data. Recently, his company raised $400 million (¥2.7 billion) at a $2 billion valuation. Notable investors include NVIDIA's NVentures, Bezos Expeditions, NFDG, as well as Xiaomi co-founder Lin Bin, Zoom founder Eric Yuan, and renowned AI scientist Fei-Fei Li. Florence's approach stems from his academic background at MIT under Professor Russ Tedrake, focusing on understanding the physical world. After joining DeepMind, he developed models like Transporter Network and co-created the VLA framework. He left in 2025 to found Generalist AI. The company has launched two models: GEN-0, which demonstrated that scaling laws apply to physical motion, and GEN-1. GEN-1 was trained on over 500,000 hours of physical interaction data collected via a specialized wearable device. It achieves a 99% success rate on precise mechanical tasks like folding boxes and maintains performance three times faster than its predecessor. Florence believes GEN-1 is reaching a commercial utility threshold similar to the GPT-3 inflection point. The substantial funding round, following GEN-1's release, signifies strong investor confidence in Generalist AI's practical, goal-driven path to creating versatile, useful robots, regardless of the "world model" terminology.

marsbit24 хв тому

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

marsbit24 хв тому

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

In three days, Google lost two AI legends. On June 18, Noam Shazeer, co-author of the seminal "Attention is All You Need" paper and Gemini co-lead, left for OpenAI. Just 48 hours later, John Jumper, 2024 Nobel laureate and AlphaFold lead, departed DeepMind for Anthropic. This follows Andrej Karpathy joining Anthropic in May. These moves highlight a structural trend: top AI talent is concentrating at mission-driven, pre-IPO firms like OpenAI and Anthropic, while Google becomes a primary source. The exodus stems from a core mission mismatch. Google's ad-centric model often subordinates AI research to product and revenue goals, creating friction for pioneers like Shazeer, who returned in 2024 only to leave again. In contrast, OpenAI and Anthropic offer singular focus on pushing AI boundaries, whether towards AGI or safety-aligned models, which deeply appeals to top researchers like Jumper. Financial incentives amplify the pull. With both OpenAI and Anthropic nearing IPO, employees stand to gain immensely from equity, an upside Google's mature stock cannot match. Furthermore, the 2023 merger of Google Brain and DeepMind, intended to consolidate strength, has instead created cultural tension and slowed the path from research to product, as evidenced by Gemini's pace. This talent redistribution is reshaping the AI landscape. While Google retains vast data and compute resources, its true crisis is the quiet, continuous loss of the people who define the field's future. The real moat in AI is not infrastructure, but the concentration of brilliant minds—a battle Google is currently losing.

marsbit2 год тому

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

marsbit2 год тому

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

Beyond the familiar performance charts like MMLU-Pro and MMMU, which major AI models strive to ace, stands a key "examiner": Chinese-Canadian researcher Wenhu Chen. An assistant professor at the University of Waterloo and founder of TIGERLab, Chen addresses the crucial need for more rigorous AI evaluation. As models like GPT-4 began scoring near-perfect results on older benchmarks like MMLU, it became difficult to distinguish their true capabilities. In response, Chen introduced MMLU-Pro in 2024, featuring harder, more reasoning-focused questions with more answer choices, successfully reintroducing meaningful performance gaps. His work extends to multi-modal evaluation with MMMU and its enhanced version, MMMU-Pro. These benchmarks test a model's ability to understand and reason with complex information from images, charts, and text across diverse academic subjects, exposing the significant challenges even top models face in genuine comprehension. Chen's background in complex QA, table reasoning, and his experience at Google DeepMind on projects like Gemini inform his approach. He understands that effective benchmarks must anticipate how models might "cheat" by memorizing data or avoiding visual analysis. His lab also actively researches video understanding and generation models (e.g., UniVideo, Vamba), ensuring his evaluation work is grounded in practical model-building challenges. Now at Meta's Super Intelligence Lab, Chen continues his focus on multi-modal data and evaluation, representing the deep yet often unseen contributions of Chinese talent in shaping the fundamental tools of the AI industry.

marsbit2 год тому

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

marsbit2 год тому

Торгівля

Спот

Ф'ючерси

Обговорення

Ласкаво просимо до спільноти HTX. Тут ви можете бути в курсі останніх подій розвитку платформи та отримати доступ до професійної ринкової інформації. Нижче представлені думки користувачів щодо ціни FUN (FUN).

Hands-On Guide to Participating in CZ-Supported predict.fun

Анотація

Step-by-Step Participation Tutorial

Трендові криптовалюти

Пов'язані питання

Пов'язані матеріали

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

Intel CEO Liwu Chen's First Podcast Interview: Our Goal is '10x in 5-10 Years', Betting on Advanced Packaging, Glass Substrate, and Synthetic Diamond

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

Торгівля

Популярні статті

Як купити FUN

Обговорення