Interaction Collection | Mahojin Check-in to Earn Points; KAIO Waitlist Application (February 23)

Odaily星球日报Опубліковано о 2026-02-23Востаннє оновлено о 2026-02-23

Анотація

Original Author: Asher (@Asher_ 0210) from Odaily Planet Daily (@OdailyChina) **Mahojin** An AI infrastructure platform aiming to become the "GitHub" for AI model creators and dataset developers. It enables intellectual property tracking and compensates original contributors. On April 1, 2025, Mahojin announced a $5 million funding round co-led by a16z CSX and Maelstrom. **Interaction Guide:** 1. Visit the official website, sign in, and link your X account. 2. Click "MAHO Points" and complete daily check-ins to earn points. 3. Upgrade your plan for additional point bonuses. **KAIO** A Layer 1 blockchain focused on institutional-grade RWA tokenization. In 2023, KAIO raised $11 million in funding, led by Laser Digital (Nomura’s digital asset subsidiary) and WebN Group. **Interaction Guide:** 1. Join the waitlist by connecting your wallet and submitting required information. 2. Complete related tasks on Galxe. **Edgen** An intelligent trading assistant for crypto assets, providing real-time market intelligence and actionable trading insights for retail and institutional traders. It has raised a total of $11 million across two funding rounds, with investments from Framework Ventures and North Island Ventures. **Interaction Guide:** 1. Log in via X account on the official website. 2. Complete daily check-in tasks in the "Task Center." 3. Additional tasks include beginner, weekly, and social missions.

Original | Odaily Planet Daily (@OdailyChina)

Author | Asher (@Asher_ 0210)

Mahojin: AI Infrastructure Platform

Project Introduction

Mahojin aims to build a "GitHub" for AI model creators and dataset developers. Its platform enables intellectual property tracking and pays rewards to the original contributors of models and datasets.

On April 1, 2025, Mahojin announced the completion of a $5 million funding round, co-led by a16z CSX and Maelstrom.

Interaction Tutorial

STEP 1. Go to the official website (Link: https://app.mahojin.ai/maho-point), click "Sign in" to log into your account, and bind your X account.

STEP 2. Click "MAHO Points" and then click "Check-in" to complete the daily check-in and earn points.

STEP 3. Upgrading your plan provides a points bonus.

KAIO: L1 Focused on Institutional-Grade RWA Tokenization

Project Introduction

KAIO is an L1 blockchain focused on institutional-grade Real World Asset (RWA) tokenization. The project completed an $11 million funding round in 2023, led by Laser Digital (Nomura's digital asset subsidiary) and WebN Group.

Interaction Tutorial

STEP 1. Go to the waitlist application website (Link: https://waitlist.kaio.xyz/), connect your wallet, fill in the relevant information, and complete the application.

STEP 2. Complete the relevant tasks for KAIO on Galxe (Link: https://app.galxe.com/quest/KAIO?sort=Trending).

Edgen: Intelligent Trading Assistant for Crypto Assets

Project Introduction

Edgen is an intelligent trading assistant for crypto assets, designed to provide comprehensive, real-time, and actionable market intelligence and trading suggestions for all types of crypto traders, from retail to institutional, falling under the InfoFi sector. It is not only a "full-time financial analyst" but also an intelligent assistant for retail traders to compete against whales, helping traders seize opportunities in the rapidly changing market.

According to ROOTDATA, Edgen has currently completed two funding rounds, totaling $11 million, with investment from institutions including Framework Ventures and North Island Ventures.

Interaction Tutorial

STEP 1. Go to the official website (Link: https://www.edgen.tech/zh/task/), and log in using your X account.

STEP 2. Click on "Task Center" and then complete the daily check-in task.

STEP 3. In addition to the daily check-in task, you can also complete beginner tasks, weekly tasks, social tasks, etc.

Пов'язані питання

QWhat is the main purpose of Mahojin's platform?

AMahojin aims to build a 'GitHub' for AI model creators and dataset developers, enabling intellectual property tracking and paying rewards to the original contributors of models and datasets.

QHow much funding did Mahojin announce on April 1, 2025, and who were the lead investors?

AMahojin announced a $5 million funding round on April 1, 2025, co-led by a16z CSX and Maelstrom.

QWhat is KAIO blockchain focused on, and how much funding did it raise in 2023?

AKAIO is a Layer 1 blockchain focused on institutional-grade RWA (Real World Asset) tokenization. It raised $11 million in funding in 2023, led by Laser Digital (Nomura's digital asset subsidiary) and WebN Group.

QWhat type of service does Edgen provide, and what is its total raised funding amount according to ROOTDATA?

AEdgen is an intelligent trading assistant for crypto assets, providing comprehensive, real-time market intelligence and trading advice. According to ROOTDATA, it has raised a total of $11 million across two funding rounds.

QWhat are the common interactive steps users can take for Mahojin and Edgen to earn rewards?

AFor Mahojin, users can sign in, link their X account, and complete daily check-ins to earn points. For Edgen, users can log in with their X account, go to the task center, and complete daily check-in tasks, as well as other tasks like newcomer tasks, weekly tasks, and social tasks.

Пов'язані матеріали

a16z: AI's 'Amnesia', Can Continuous Learning Cure It?

The article "a16z: AI's 'Amnesia' – Can Continual Learning Cure It?" explores the limitations of current large language models (LLMs), which, like the protagonist in the film *Memento*, are trapped in a perpetual present—unable to form new memories after training. While methods like in-context learning (ICL), retrieval-augmented generation (RAG), and external scaffolding (e.g., chat history, prompts) provide temporary solutions, they fail to enable true internalization of new knowledge. The authors argue that compression—the core of learning during training—is halted at deployment, preventing models from generalizing, discovering novel solutions (e.g., mathematical proofs), or handling adversarial scenarios. The piece introduces *continual learning* as a critical research direction to address this, categorizing approaches into three paths: 1. **Context**: Scaling external memory via longer context windows, multi-agent systems, and smarter retrieval. 2. **Modules**: Using pluggable adapters or external memory layers for specialization without full retraining. 3. **Weights**: Enabling parameter updates through sparse training, test-time training, meta-learning, distillation, and reinforcement learning from feedback. Challenges include catastrophic forgetting, safety risks, and auditability, but overcoming these could unlock models that learn iteratively from experience. The conclusion emphasizes that while context-based methods are effective, true breakthroughs require models to compress new information into weights post-deployment, moving from mere retrieval to genuine learning.

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An individual manipulated a weather sensor at Paris Charles de Gaulle Airport with a portable heat source, causing a Polymarket weather market to settle at 22°C and earning $34,000. This incident highlights a fundamental issue in prediction markets: when a market aims to reflect reality, it also incentivizes participants to influence that reality. Prediction markets operate on two layers: platform rules (what outcome counts as a win) and data sources (what actually happened). While most focus on rules, the real vulnerability lies in the data source. If reality is recorded through a specific source, influencing that source directly affects market settlement. The article categorizes markets by their vulnerability: 1. **Single-point physical data sources** (e.g., weather stations): Easily manipulated through physical interference. 2. **Insider information markets** (e.g., MrBeast video details): Insiders like team members use non-public information to trade. Kalshi fined a剪辑师 $20,000 for insider trading. 3. **Actor-manipulated markets** (e.g., Andrew Tate’s tweet counts): The subject of the market can control the outcome. Evidence suggests Tate’sociated accounts coordinated to profit. 4. **Individual-action markets** (e.g., WNBA disruptions): A single person can execute an event to profit from their pre-placed bets. Kalshi and Polymarket handle these issues differently. Kalshi enforces strict KYC, publicly penalizes insider trading, and reports to regulators. Polymarket, with its anonymous wallet-based system, has historically been more permissive, arguing that insider information improves market accuracy. However, it cooperated with authorities in the "Van Dyke case," where a user traded on classified government information. The core paradox is reflexivity: prediction markets are designed to discover truth, but their financial incentives can distort reality. The more valuable a prediction becomes, the more likely participants are to influence the event itself. The market ceases to be a mirror of reality and instead shapes it.

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