To Those Still Holding On in Crypto: What's More Frightening Than a Bear Market is Collective Silence

marsbitPublished on 2026-06-05Last updated on 2026-06-05

Abstract

To those still persevering in Crypto: what's more terrifying than a bear market is collective silence. Author: haotian. Key lessons for anyone wishing to remain in Crypto: 1) Looking back at past cycles, the crypto industry almost "dies" once each cycle before rebounding. Plunges of 90%, 80%, 70% followed by V-shaped recoveries are the norm. While extreme volatility is foundational, so is the industry's remarkable resilience—it simply won't die. 2) Centralized exchanges (CEX) are not the saviors of crypto; in many ways, they don't even belong to the industry. Running a platform to collect fees is their perpetual goal, regardless of whether the assets traded are major cryptocurrencies, memecoins, stock futures, oil, or precious metals. 3) While this cycle's on-chain narrative innovation is rife with VC-funded "scams," it will ultimately be grand, sweeping narratives that pull the industry out of stagnation. Examples include DeFi in 2020, NFTs in 2022, inscriptions in 2023, and AI Agents in 2024. The scale and persistence of these narratives determine a bull market's strength and the difficulty of post-bubble recovery. An absence of "innovative narratives" would spell real trouble for crypto. 4) The noisy, often polarized, and conflicting voices on Crypto Twitter (CT)—ranging from FOMO to sharp criticism—are largely a manifestation of poor market conditions. It's fine to observe for entertainment, but remember: if the crypto industry were to collapse catastrophically, no o...

Author: haotian

If you want to keep making a living in Crypto, you must understand these principles:

1) Looking back at past cycles, the crypto industry has nearly died and reborn almost every cycle. It's normal to see crashes of 90%, 80%, or 70% followed by a V-shaped recovery and takeoff. High volatility is indeed a fundamental characteristic of the industry, but don't forget, so is its incredible resilience. It just won't die.

2) CEX exchanges have never been the saviors of the crypto industry; to some extent, they don't even belong to it. For trading platforms, collecting 'fees' is eternal. Whether what's traded on their platform are major cryptocurrencies, meme coins, U.S. stock futures, crude oil, or precious metals doesn't really matter.

3) While this cycle's on-chain narrative innovation indeed contains many VC project 'scams,' what will ultimately pull the crypto industry out of its slump will still be grand narratives. Just like DeFi in 2020, NFTs in 2022, Inscriptions in 2023, and AI Agents in 2024, the sustainability and scale of these narratives determine the depth of a bull market and the difficulty of rebuilding after the bubble bursts. But a crypto industry without 'innovative narratives' is truly unsustainable.

4) Crypto Twitter is flooded with voices—some driven by FOMO, some sharp and critical, some antagonistic—all of which are ultimately manifestations of a poor secondary market. Look at them, use them for entertainment, but that's it. If the crypto industry ever faces a catastrophic collapse, no one will be spared. So, as they say, all you great 'stock traders,' please do respect the path you came from.

5) Some vested interests or early OG beneficiaries have withdrawn into seclusion, some have retreated to the background to cause mischief, and some are still persevering and preaching. This isn't the scariest part. What's more frightening is when the majority chooses 'silence.' The cost of this silence will undoubtedly be the continuous intensification of 'bad money driving out good,' and the damage this inflicts on the industry's 'consensus' is the most fatal blow.

In the Last 2 Minutes Before SK Hynix Market Open, TradeXYZ Achieved Price Accuracy Within 0.13%

Traditionally, asset price discovery halts when markets close. However, decentralized exchanges like Hyperliquid, enabling 24/7 trading of Real-World Asset (RWA) perpetuals, are changing this. A case study involving SK Hynix stock on Hyperliquid's xyz:SKHX market demonstrates this shift during a weekend when the Korean Exchange (KRX) was closed. While the KRX closed at 2,070,000 KRW on June 5th, active trading continued on-chain. By 08:56 KST on Monday, June 8th, just before the KRX open, the chain price had fallen to 1200.0 USDC, implying a -10.21% drop. Three minutes later, the KRX opened at 1,856,000 KRW, an actual drop of -10.34%. The on-chain price had predicted the opening decline with remarkable accuracy, missing by only 0.13 percentage points. In the final two minutes before the open (08:58-08:59 KST), the on-chain market saw a significant volume spike and a +2.31% price rebound. This wasn't a prediction error but likely the market front-running the expected post-open bounce. Indeed, after opening at its low, the KRX price rebounded approximately +2.64% by 09:03 KST. This event illustrates how 24/7 on-chain markets can act as a leading price discovery venue, not only anticipating opening prices but also trading the immediate post-open dynamics before traditional markets even begin.

marsbit8m ago

In the Last 2 Minutes Before SK Hynix Market Open, TradeXYZ Achieved Price Accuracy Within 0.13%

marsbit8m ago

Farewell to Traditional Bulls and Bears: The Market Has Entered an Era of Rotating Bubbles

Farewell to traditional bull and bear markets; we have entered an era of rolling bubbles. This article uses a meteorological analogy to explain the modern market's shift from slow-moving, long-term trends to a chain of rapid, successive speculative frenzies. The old market resembled "stratiform" weather—slow, broad cycles lasting years. Today's market is like a "mesoscale convective system," where isolated storms (bubbles in sectors like AI, GLP-1 drugs, or crypto) form in sequence. Each is triggered by the outflow of capital and sentiment from the previous one, creating a self-perpetuating chain of booms and busts. This structural change is driven by eight permanent shifts: the democratization of speculation (zero-commission trading, retail options activity), perpetual buying from defined-contribution retirement plans, the dominance of passive investing (creating price-insensitive flows), the rise of multi-strategy funds and high-frequency trading (weakening price discovery), suppressed volatility that erupts violently, an index composition now dominated by long-duration, narrative-driven tech stocks, the elimination of information delays, and a permissive fiscal/monetary backdrop. These conditions ensure that rolling bubbles are the new normal. To navigate this environment, investors should either become deep-sector experts who understand the underlying technologies and business models or become adept observers of trends and capital flows. While chaotic from within each "storm," a higher-altitude view reveals a predictable pattern of serial booms. The key is to avoid being emotionally swept up in any single narrative and to recognize the market's new, permanent structure.

marsbit9m ago

Farewell to Traditional Bulls and Bears: The Market Has Entered an Era of Rotating Bubbles

marsbit9m ago

The Right Way to Use Skills: 5 Reflections After Anthropic Publicly Shared Its Internal Methodology

A deep dive into Anthropic's internal methodology for building effective AI "Skills" reveals five key insights for maximizing their value. First, Skills should focus on capturing "Gotchas" and tacit organizational knowledge—like common pitfalls and undocumented rules—rather than restating general information the AI already knows. Second, think of Skills as a form of "Context Engineering"; they are best structured as folders, not monolithic documents. A core `SKILL.md` file should act as a navigational index, progressively pulling in detailed references, examples, and assets only as needed to avoid overwhelming the model's context window. Third, whenever possible, automate repetitive tasks with scripts. This preserves the model's reasoning capacity for judgment and analysis, while scripts reliably handle the execution, saving tokens and improving accuracy. Instructions within a Skill provide the "why" and the expert judgment, while scripts provide the concrete "how." Fourth, a Skill's description is critical and often misunderstood. It should not be a list of features but a routing rule that clearly signals *when* the Skill should be triggered based on user intent and common phrasing. Finally, as Skills scale from personal tools to team-wide assets, management is crucial. Anthropic advocates for a lightweight, organic approach: let new Skills spread organically within small groups first. Those that prove genuinely useful through adoption naturally graduate to a formal marketplace, ensuring the curated library contains only high-value, battle-tested tools.

marsbit26m ago

The Right Way to Use Skills: 5 Reflections After Anthropic Publicly Shared Its Internal Methodology

marsbit26m ago

Vying for the AI Payment Track: Traditional Card Networks Face Off Against Coinbase

As AI agents increasingly conduct commercial transactions, a battle for control over the underlying payment infrastructure is unfolding. The competition centers on two divergent and incompatible technical approaches for autonomous AI payments. One camp, led by traditional card networks Visa and Mastercard, relies on tokenized card credentials within the established banking rails. Visa's "Intelligent Commerce" and Mastercard's "Agent Pay" services extend their existing tokenization technology to authorized AI agents for consumer retail transactions, leveraging decades of fraud protection and dispute resolution systems. Their partners include major AI firms like Anthropic, OpenAI, and Microsoft. The opposing camp, spearheaded by Coinbase, advocates for an open internet protocol using stablecoins. Coinbase's x402 protocol utilizes the HTTP 402 status code to enable direct, machine-to-machine micropayments with USDC on-chain. This model eliminates card fees and is designed for high-frequency, low-value transactions between AI agents, such as paying for API calls or data streams, where traditional card costs are prohibitive. Currently, application scenarios are clearly divided. Mainstream consumer-facing AI shopping services (e.g., ChatGPT's "one-click checkout," Amazon's AI-assisted shopping) predominantly use card channels due to their mature consumer protections and merchant networks. Conversely, the stablecoin channel dominates machine-to-machine payments, as seen in Amazon Bedrock's core payment service using Base blockchain. Significantly, traditional card networks are not solely defending their turf; they are also investing in the stablecoin arena. Visa has rapidly expanded its stablecoin settlement volume and partnered with Coinbase on interoperability, while Mastercard moved to acquire stablecoin platform BVNK. This dual-strategy indicates their intent to become the fee-collecting gateway for all payment flows, regardless of the underlying rail. The short-term outlook is for coexistence: cards for personal retail, stablecoins for machine transactions. The long-term outcome hinges on whether AI-driven commerce will resemble traditional retail or evolve into a vast network of machine micropayments. Visa and Mastercard's hedging strategy suggests they are prepared for either future, while companies betting on a single channel face greater risk.

Foresight News27m ago

Vying for the AI Payment Track: Traditional Card Networks Face Off Against Coinbase

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For the First Time, Pure Human Video Pretrained VLA for Dexterous Manipulation: Deployable with Minimal Fine-Tuning Data

For the first time, a purely human-video-pretrained Vision-Language-Action (VLA) model for dexterous manipulation requires only a small amount of data for fine-tuning to achieve successful real-world deployment. Achieving human-level dexterous manipulation remains a core challenge in robotics. While multi-fingered hands offer hardware potential, Visual-Language-Action (VLA) models lag behind due to the high cost of collecting diverse, high-quality robot data. A novel framework, VITRA, developed by Microsoft Research Asia and Tsinghua University, addresses this by automatically transforming massive, unlabeled real-world human activity videos into a structured V-L-A training dataset. Key innovations include precise 3D hand motion annotation from monocular video, atomic action segmentation based on hand-speed minima, and automated instruction generation using VLMs combined with 3D trajectory visualization. This process created a massive dataset of 1 million clips. Pretrained exclusively on this human video data, the VLA model (combining a VLM backbone with a Diffusion Transformer action expert) demonstrates strong zero-shot hand motion prediction in unseen environments. Crucially, it requires minimal fine-tuning (~1.2k demonstrations) on real robot data to achieve high-success-rate dexterous manipulation tasks like grasping, placing, pouring, and sweeping on hardware like the Realman robot with the XHAND1 dexterous hand. The model shows exceptional generalization to novel objects and environments. The research also observes promising scaling behavior, where performance improves with more pretraining data, paving the way for more generalized embodied intelligence.

marsbit38m ago

For the First Time, Pure Human Video Pretrained VLA for Dexterous Manipulation: Deployable with Minimal Fine-Tuning Data

marsbit38m ago

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To Those Still Holding On in Crypto: What's More Frightening Than a Bear Market is Collective Silence

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