24H Hot Posts Overview: TA Made 100K U by 'Shorting Eggs' on Prediction Markets!

比推Pubblicato 2026-02-27Pubblicato ultima volta 2026-02-27

Introduzione

This 24-hour roundup covers key discussions from crypto influencers. One trader reportedly earned $100K by consistently shorting egg futures on Polymarket, with commentators highlighting the importance of discipline and market-specific expertise over complex models. In regulatory news, ZachXBT exposed alleged insider trading at Axiom exchange, sparking warnings about the risks of centralized platforms and comparisons of crypto to "online Myanmar." A technical thread detailed arbitrage strategies between Kalshi and Polymarket, including common pitfalls. Another topic explored the experimental use of AI trading bots, with mixed results. (Note: All content represents personal opinions from X platform and not investment advice.)

Dear readers, hello~

What have crypto KOLs been talking about in the past 24 hours?

Note: The following content is compiled from the X platform, represents personal opinions, does not represent the stance of this platform, and does not constitute investment advice.

Made 100K U by 'Shorting Eggs Every Month' on Polymarket!

Hot Replies:

People who focus on one category are more sensitive to pricing deviations in this market than anyone else; this is the real edge, not some complex model;

The volatility of eggs is indeed outrageous, but the most impressive part is that they consistently short it every month. If it were me, I probably would have been shaken out by the weekly fluctuations long ago—discipline is the core skill.

This is hedging in a strongly correlated industry, right? Every trade has its master;

Egg futures are just bizarre. The domestic egg futures market has been manipulated by a few people because the market cap is too small.

Zach's Hammer Falls: Insider Trading at Axiom Exchange

Hot Replies:

Suggest everyone not be too quick to kick them while they're down. For centralized custodial services, users' private keys and funds are in the hands of the project team. Push them too hard, and they might just pull the plug and run.

Institutions manipulate Bitcoin, exchanges engage in insider trading, project teams pump and dump... Nothing is surprising in the crypto world, which is famously called the 'Online Northern Myanmar'.

Common Pitfalls and Experience in Kalshi vs. Polymarket Arbitrage (Technical干货)

Experience post link:https://x.com/mirrorzk/status/2023303202196570420

Letting AI Bots/Agents Trade Crypto for You: How Did It Go?

Twitter:https://twitter.com/BitpushNewsCN

BitPush TG Discussion Group:https://t.me/BitPushCommunity

BitPush TG Subscription: https://t.me/bitpush

Original link:https://www.bitpush.news/articles/7614958

Domande pertinenti

QWhat strategy did the trader use to earn 100,000 USDT on Polymarket?

AThe trader consistently shorted eggs on the prediction market, capitalizing on pricing inefficiencies and high volatility in the egg market.

QAccording to the article, what is considered the real 'edge' in prediction markets?

AThe real edge is having deep knowledge and sensitivity to pricing deviations in a specific category, rather than relying on complex models.

QWhat major issue was exposed involving Axiom Exchange?

AAxiom Exchange was involved in an insider trading scandal, as highlighted by Zach's investigation.

QWhat platform comparison is discussed for arbitrage opportunities in the article?

AThe article discusses arbitrage between Kalshi and Polymarket, including common pitfalls and technical insights.

QWhat caution is advised regarding centralized exchanges in the context of the Axiom scandal?

AUsers are cautioned against provoking centralized exchanges, as they control private keys and funds, and might disconnect services or exit scam if pressured.

Letture associate

‘Death markets!’ U.S lawmaker slams Polymarket bets on lost U.S soldiers in Iran

U.S. lawmaker Seth Moulton strongly criticized the prediction market platform Polymarket for allowing users to bet on the fate of a missing American soldier in Iran-downed aircraft, calling it a "disgusting death market." He also accused former President Donald Trump, an investor in Polymarket, of potentially profiting from access to non-public intelligence. Polymarket quickly removed the market, citing integrity violations and launched an internal review. This incident is part of a broader pattern; the platform previously faced scrutiny when insiders reportedly earned over $1.2 million from contracts related to U.S. strikes in Iran and the death of its former leader. Senator Chris Murphy announced plans to introduce legislation to make such markets illegal, highlighting growing ethical and regulatory concerns around prediction platforms.

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‘Death markets!’ U.S lawmaker slams Polymarket bets on lost U.S soldiers in Iran

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Zhejiang University Research Team Proposes New Approach: Teaching AI How the Human Brain Understands the World

A research team from Zhejiang University published a paper in *Nature Communications* challenging the prevailing notion that larger AI models inherently think more like humans. They found that while model performance on recognizing concrete concepts improved as parameters increased (from 74.94% to 85.87%), performance on abstract concept tasks slightly declined (from 54.37% to 52.82%) in models like SimCLR, CLIP, and DINOv2. The key difference lies in how concepts are organized. Humans naturally form hierarchical categories (e.g., grouping a swan and an owl into "birds"), enabling them to apply past knowledge to new situations. Models, however, rely heavily on statistical patterns in data and struggle to form stable, abstract categories. The team proposed a novel solution: using human brain signals (recorded when viewing images) to supervise and guide the model's internal organization of concepts. This method, termed transferring "human conceptual structures," helped the model learn a brain-like categorical system. In experiments, the model showed improved few-shot learning and generalization, with a 20.5% average improvement on a task requiring abstract categorization like distinguishing living vs. non-living things, even outperforming much larger models. This research shifts the focus from simply scaling model size ("bigger is better") to designing smarter internal structures ("structured is smarter"). It highlights a new pathway for developing AI that possesses more human-like abstract reasoning and adaptive learning capabilities.

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Zhejiang University Research Team Proposes New Approach: Teaching AI How the Human Brain Understands the World

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Altcoins show strength, Solana draws capital – Is it 2023 all over again?

Altcoins are showing signs of strength as Bitcoin dominance (BTC.D) struggles to break the 60% resistance level, historically a reversal point that triggers altcoin rallies. However, unlike the 2021 bull run led by Ethereum’s breakout against Bitcoin, ETH/BTC is down nearly 10% this year due to ETF outflows. Instead, Solana (SOL) is emerging as a potential leader. Although SOL/BTC is down 16% year-to-date, Solana has seen relatively stronger ETF inflows compared to Bitcoin and Ethereum, along with generating twice the revenue of Ethereum in the last 24 hours. While a full altseason isn't guaranteed, a SOL/BTC breakout combined with weakening BTC.D could signal the early stages of a Solana-led altcoin rally.

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Altcoins show strength, Solana draws capital – Is it 2023 all over again?

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Dialogue with Bloomberg ETF Analyst: Why Bitcoin ETF Holders Did Not Sell During the 50% Plunge

In a recent interview on Coin Stories, Bloomberg Intelligence Senior ETF Analyst James Seyffart discussed the resilience of Bitcoin ETF holders, who largely held their positions despite a 50% price drop, contrary to expectations of panic selling. Seyffart noted that while there was a $9 billion outflow from Bitcoin ETFs starting October 10, it was minor compared to the $250-300 billion inflows prior, and outflows have since reversed by $20-25 billion. He attributed this "diamond hands" behavior to educated investors who understand Bitcoin’s volatility and typically allocate only a small portion (e.g., 1-5%) of their portfolios, leading to rebalancing rather than selling during dips. The conversation also covered the entry of major institutions like Morgan Stanley, which is launching its own Bitcoin ETF, leveraging its vast client assets. Seyffart highlighted the growing efficiency of ETFs, with physical redemptions now allowed, potentially enabling direct Bitcoin transfers to holders in the future. However, he expressed concern over the concentration of Bitcoin custody with Coinbase. Additionally, Seyffart discussed the inverse flow trends between Bitcoin and Gold ETFs recently, with Bitcoin acting more like a risk-on growth asset. He remains optimistic about Bitcoin ETFs eventually surpassing Gold ETFs in size due to Bitcoin’s diverse use cases. Finally, he emphasized the importance of diversification in the current volatile market, where traditional hedges have largely failed, and cash.

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Dialogue with Bloomberg ETF Analyst: Why Bitcoin ETF Holders Did Not Sell During the 50% Plunge

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Who Cannot Be Distilled into a Skill?

"This article explores the concerning trend of AI systems distilling human workers into replaceable 'skills,' using the viral 'Colleague.skill' phenomenon as a key example. It argues that the most diligent employees—those who meticulously document their work, write detailed analyses, and transparently share decision-making logic—are paradoxically the most vulnerable to being replaced. Their high-quality 'context' (communication records, documents, and decision trails) becomes the perfect fuel for AI agents, extracted from corporate platforms like Feishu and DingTalk. The piece warns of a deeper ethical crisis: the reduction of human relationships to functional APIs, as seen in derivatives like 'Ex.skill' or 'Boss.skill,' which reduce complex individuals to mere utilities. This reflects a shift from Martin Buber's 'I-Thou' relationship (seeing others as whole beings) to an 'I-It' dynamic (seeing them as tools). While AI can capture explicit knowledge (written documents, replies), it fails to capture tacit knowledge—the intuition, experience, and unspoken insights that define human expertise. However, a greater danger emerges when AI-generated content, based on distilled human data, is used to train future models, leading to 'model collapse' and homogenized, mediocre outputs—a process likened to 'electronic patina' degrading information over time. The article concludes by noting a small but symbolic resistance, such as the 'anti-distill' tool that generates meaningless text to protect valuable knowledge. Ultimately, it suggests that while AI can capture a static snapshot of a person, humans remain 'fluid algorithms' capable of continuous growth and adaptation, leaving their AI shadows behind."

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