Spicy Commentary | Michael Saylor's 'Player Talk'; 60-Year-Old Aunt Liquidated After 'Scamming a Young Man'

Foresight NewsPublished on 2026-06-13Last updated on 2026-06-13

Abstract

**"Spicy Commentary": Three Tales of Crypto's Wild Week** This week's "Spicy Commentary" column highlights three dramatic stories from the cryptocurrency world. First, **MicroStrategy's Michael Saylor** addressed the controversy over his company potentially selling Bitcoin. At the BTC Prague event, he clarified, "I never said the company can't sell Bitcoin. I told *you* never to sell *your* Bitcoin." This "do as I say, not as I do" stance was criticized by netizens as peak linguistic gymnastics, noting a history of him previously stating the company would "never" sell. Second, a **bizarre fraud case** emerged from Beijing. A 60-year-old woman, obsessed with getting rich from crypto but unwilling to risk her own savings, posed online as the 20-something "god-daughter" of a high-ranking official. She catfished a young man, convincing him to give her over 200,000 yuan for fabricated emergencies. She then invested all the stolen money into cryptocurrency with 10x leverage, only to lose everything in a market crash. The woman was sentenced to four years in prison for fraud. Finally, a **sobering trader's tale** surfaced on Reddit. A user posted "Tale of a crypto trader," confessing their net worth had plummeted from a peak of $45 million to roughly $17,200, primarily due to holding meme coins too long. The post, described as a crypto "book of confessions," sparked reactions ranging from sympathy to critique about greed, poor risk management, and the perils of treating meme coi...

Written by: Nicky, Foresight News

Welcome back to Spicy Commentary, where we review the week's notorious scenes in the crypto world involving 'stubborn talk, romance scams, and shaky hands'.

This week features three stories: 'A Big Shot's Stubborn Talk, An Old Lady's Romance Scam, and a Trader's Tearful Tale of Turning 40 Million in Profit to Dust'.

Michael Saylor: I Said 'You' Shouldn't Sell, Of Course the Company Can Sell

MicroStrategy founder Michael Saylor recently addressed the controversy over 'the company selling Bitcoin' at BTC Prague.

He said: 'I never said the company can't sell Bitcoin. What I said to you was: never sell your own Bitcoin. Anyone who has listened to our earnings calls, read the disclosure documents, or has half a brain knows that. Of course, the company will sell Bitcoin when necessary.'

Netizens' Spicy Comments:

'So the fine print behind 'never sell' is — refers to retail investors.'

I can, but you can't 😅.

Some netizens dug up old videos proving he had repeatedly said the company would never sell Bitcoin.

Dog🐶 and bee🐝?

Now it seems to have become a double-edged sword.

It's become brainwashing propaganda 😭.

So 'I' will hold until after MicroStrategy sells, right 🥺.

So will everyone's combined holdings exceed 845,000 coins?

Hold until the end of time!

This clarification is the pinnacle of linguistic art: I said 'you' shouldn't sell, I didn't say 'I' wouldn't sell.

Sixty-Year-Old Aunt Impersonates the 'Goddaughter' of a 20-Year-Old Senior Official, Scams Young Man Out of Over 200,000 RMB in Online Romance, Loses It All on 10x Leverage Crypto Trading

On June 5th, Beijing's Haidian District Procuratorate announced a bizarre case.

A sixty-year-old Ms. Meng, after retirement, became obsessed with cryptocurrencies. She dreamed of getting rich quick but was unwilling to risk her own pension money. So she hatched a devious plan: gamble with someone else's money.

She met a mother on a short-video platform who was worried about finding a partner for her honest son. Ms. Meng lied, saying she had a 20-something 'goddaughter' named Xiao Hong who worked for a central ministry, gentle and respectable. In reality, 'Xiao Hong' was an alter ego she created using another WeChat account. She changed her profile picture to a young woman and imitated a girl's tone of voice, starting an online romance with the young man.

The young man repeatedly asked to meet, but was always put off with excuses like 'work is busy, discipline is strict.' He had never seen 'Xiao Hong,' but had met Ms. Meng in person, always considering her a kind-hearted 'godmother.'

As their 'relationship' warmed up, Ms. Meng, using reasons like 'family illness, studying abroad,' swindled over 200,000 RMB from the young man. He gave her all his savings and even borrowed money.

The young man finally sensed something was off: her chat vocabulary was old-fashioned, and the 'photos from abroad' had a backdrop of a domestic KTV. He reported the case.

When apprehended, Ms. Meng tried to argue it was a 'loan,' but with solid evidence, she eventually confessed. All the over 200,000 RMB she swindled went into cryptocurrency, with 10x leverage, all in one go. The result: the market crashed, she got liquidated, and everything turned to zero🫪.

Recently, Haidian Court sentenced Ms. Meng to four years in prison for fraud, fined her, and ordered her to compensate the victim for his losses.

Sixty is the prime of life for hustling!

The old grandma👵 is still ahead of the curve!

After scamming old men, now scamming young men🤯.

The young sister turned out to be a 60-year-old aunt🤪.

So, should people be bolder🤣.

'Forced wealth creation,' huh🫢.

The age seems just right too🫠.

There really could be a fall guy🤔!

Do you want to learn from the aunt?

Take bold risks while you're 'young'!

Gotta work harder💪!

From 45 Million to 17 Thousand, Ask Me Anything

On the Reddit CryptoCurrency subreddit, someone posted:

'Tale of a crypto trader'

With a caption stating: Peak net worth $45 million, now roughly $17,200 left. Ask me anything. This trader had grown his funds to a peak of $45 million through Memecoin holdings.

The comment section was instantly flooded with 'eager' netizens:

'Have you ever thought about if you had bought index funds back then...'

'Bro, you're still alive, right?'

The post sparked massive resonance and gawking, a veritable crypto version of Confessions.

Netizen's Spicy Comment: 'I allocated 20% of my portfolio to POOPCAT' , that's just crazy. (POPCAT is currently trading at $0.041, having peaked at $2 in November 2024.)

Netizen said, he just wanted $100 million? Poor guy. (Looks like he just needed to double, but fell into the abyss.)

Imagine taking profit at $4 million and then enjoying life.

Netizen's Spicy Comment: But those screenshots of unrealized profits are eternal😎.
Comment reply: Could mint it as an NFT.

A netizen commented, Holding Memecoins like they were gold, if he had cashed out then, with the low liquidity of Memecoins, maybe he'd only get 50% of the value. (But that's still $20 million!)

I don't see a trader, more like a gambling addict🤑.

A netizen shared his own story: I was heartbroken at the time for holding a token too long, went from $6000 profit down to $2000 before realizing disaster was coming. If he can still post after losing $45 million (not counting time and other immeasurable resource inputs), then I'm fine too.

When people say you should diversify, they don't mean buying a million dollars worth of Memecoins. Why not buy some gold, silver, stocks, etc.? It had to be Memecoins🤣. It's a real shame.

Interestingly, I also made a lot from WIF and BONK, both dog-related coins, but ever since the TRUMP coin launched, all coins have crashed.

From Saylor's 'word games,' to the aunt's '10x leverage scam,' to the Reddit bro's '$45 million to $17 thousand,' this week had someone playing rhetoric, someone playing psychology, and someone playing heart palpitations.

The world is one big amateur stage, but Spicy Commentary is here to help you clip these brilliant scenes together!

Wishing everyone a happy weekend, see you next week!

(This week's content is based on public information and online discussions, interpreted lightly, for reference only.)

Past Reviews: Spicy Commentary| Three Stories Tell You: In the Crypto World, The Harder You Try, The More 'Miserable' You Become
Spicy Commentary| The 'King of Retail' Got His Account Hacked by His Brother for 'Scamming'? AI 'Archaeological Discovery' Unearths Bitcoin...
Spicy Commentary| Coinbase Layoffs Hit a Vital Artery? Can You Take a Time-Traveling Car Back to Before You Bought...

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

**Raydium Exploit Reveals DeFi's Hidden Risk: Forgotten "Zombie" Contracts** A recent attack on Raydium's deprecated V3 AMM pools resulted in a loss of approximately $1.34 million. The hacker exploited pools that were no longer supported by Raydium's current UI or SDK but remained fully functional and accessible on-chain. This incident highlights a critical, often overlooked category of risk in DeFi: inactive or legacy smart contracts that projects fail to properly decommission. Since March 2025, there have been at least 8 publicly reported attacks targeting such abandoned contracts, with total losses around $10.8 million. Including older pools and deprecated features, the count rises to 10 incidents with roughly $22.5 million in losses. These "zombie contracts" represent a lifecycle management failure rather than a code vulnerability, yet they are typically misclassified under general "code bug" categories in security reports, masking the true scale of the problem. The root cause is that projects often merely document a contract as "deprecated" without taking essential technical steps to secure it: withdrawing remaining assets, disabling external call functions, and implementing ongoing monitoring. These forgotten, under-monitored components become prime targets for attackers. To address this, the industry needs to recognize "zombie contracts" as a distinct risk category and establish standardized decommissioning protocols. Essential steps should include: 1) a formal retirement announcement, 2) removal of all front-end integrations, 3) withdrawal of locked assets, 4) disabling key contract functions, 5) ongoing security monitoring, 6) clear user communication, and 7) a post-mortem analysis. The value of a DeFi project lies not only in its current TVL but also in the security of its historical codebase, which has now become a new attack surface.

Foresight News1m ago

The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

Foresight News1m ago

Solayer Launches Margin Trade to Consolidate Perpetual Multi-Asset Trading on Mainnet

Solayer, a high-performance SVM-compatible Layer 1 blockchain, has launched its Margin Trade platform on mainnet. This unified cross-asset perpetual trading platform merges traditional and digital finance, developed with input from institutional and crypto trading experts. It offers trading for a diverse range of assets, including top cryptocurrencies, commodities like gold and oil, and equities such as the synthetic MT500 index. Key features include cross-margin trading with a unified collateral pool for improved capital efficiency, real-time execution, and full on-chain transparency with non-custodial asset ownership. The launch includes the first perpetual market for Pearl Research ($PRL) with up to 3x leverage. Future plans involve expanding asset listings and adding isolated margin functionality. Built on the high-throughput Solayer network capable of 330,000 TPS, the platform aims to provide the speed required for serious trading. Following successful testnet phases, Margin Trade's mainnet debut marks a significant step in Solayer's goal to unify traditional and crypto markets on-chain with institutional-grade decentralized finance infrastructure.

TheNewsCrypto35m ago

Solayer Launches Margin Trade to Consolidate Perpetual Multi-Asset Trading on Mainnet

TheNewsCrypto35m ago

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

Robots have started to 'consume data,' driving the formation of a new industrial supply chain focused on producing training data for embodied AI. Unlike large language models, which are trained on vast internet text corpora, embodied AI models face a 'data desert' in the physical world. This has created a massive demand for first-person perspective video data (Ego Data), captured by workers wearing cameras in places like Indian garment factories. Companies like Neocambrian AI are establishing 'data factories' where workers perform standardized tasks (e.g., sorting clothes, kitchen organization) to generate thousands of hours of video. Research, such as NVIDIA's EgoScale, demonstrates that scaling this human demonstration data predictably improves robot performance, particularly for dexterous manipulation. This has validated a training path combining large-scale human data for pre-training with smaller amounts of robot-specific data for fine-tuning. The value of different data types varies significantly, forming a 'data pyramid.' The base consists of low-cost, large-scale internet and Ego Data. Higher layers include more expensive motion-capture data (e.g., from data gloves), simulation/synthetic data, and the most costly and scarce layer: real robot teleoperation data. This demand has spawned a layered ecosystem of data suppliers: low-cost data factories, motion capture and alignment specialists, robot-native teleoperation service providers, simulation data companies, and platforms aiming for data standardization. Robot companies themselves are adopting a 'layered procurement' strategy: outsourcing generic Ego Data while building in-house capabilities for robot-specific adaptation data and the critical deployment/failure data generated in real-world applications. The industry is shifting focus from hardware and basic mobility to the data pipelines required for general-purpose capability. While parallels exist to data labeling companies like Scale AI in the LLM boom, the physical complexity of robot data—involving action success ambiguity and sim-to-real gaps—requires more integrated solutions for data collection, annotation, and a continuous feedback loop. The race is on to build the data engines that will teach robots to operate reliably in the unstructured real world.

marsbit2h ago

Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

marsbit2h ago

Tremble Humans, AI Continues Its Accelerated Sprint

Trembling, Humans: AI Continues Its Accelerated Sprint Yes, AI is still rapidly accelerating. While deep learning seemed to stall quickly in its early years, large models after years of development show no sign of hitting their ceiling. At the Zhiyuan Conference 2026, the focus is on enabling AI to move from the digital world into the physical world. Scaling Law remains effective, continuing to drive advancements in both large language models and multimodal models. The industry is now entering a phase of pursuing World Models, though unresolved technical paths and data issues mean this exploration may take 3-5 more years. Concurrently, breakthroughs in Agents are accelerating AI's real-world application in fields like healthcare and meetings. Making Agents truly useful requires key hardware-software co-design, evident from the strong presence of chip vendors at the conference. We stand at a new historical threshold where AI is becoming a foundational force reshaping the world. The first day of the conference highlighted AI's evolution from "knowing how to chat" to "knowing how to work." Scaling Law persists, World Models are the next key battleground, and Agents are transitioning from usable to好用 (user-friendly). Scaling Law is not ending but diversifying. New models like Anthropic's Fable 5 demonstrate scaling through parameter size, synthetic data, and reinforcement learning. Advancements in AI Coding and Agent deployment are enabling a trend of AI self-evolution, potentially allowing AI to take over digital world iterations. World Models represent the next frontier for large models extending into the physical realm, but no current model is truly impressive at solving real-world problems. Technical consensus is lacking, with debates on data sources (video, simulation, real-world). Different approaches are emerging: language-centric, pixel-centric, 3D-structure-centric, and visual-representation-centric models. Zhiyuan Institute is exploring a fifth path: unified latent space modeling fusing language and visual representations, and introduced its own under-development World Model, Physis-v0.1. On the product side, Agents are key to bringing AI into daily life. Since 2025, the "Year of the Agent," products have become more proactive and capable of complex tasks. Zhiyuan showcased four vertical Agents for cardiac diagnosis, autonomous research, meeting summarization, and protein risk discovery. However, technical challenges remain, particularly in context engineering like memory and orchestration. "Harness" – the engineering framework around an Agent – is crucial for maximizing its capabilities by clarifying intent, designing workflows, and incorporating validation and feedback. In summary, AI's breakneck pace continues on multiple fronts: foundational model scaling, the ambitious pursuit of World Models for physical understanding, and the ongoing refinement of practical Agents. The journey from capable to truly reliable and useful AI systems is well underway.

marsbit3h ago

Tremble Humans, AI Continues Its Accelerated Sprint

marsbit3h ago

The Backside of Musk's Trillion-Dollar Fortune: 85% Can't Be Sold

Elon Musk becomes the world's first trillionaire, driven by SpaceX's IPO valuing the company at $1.77 trillion. However, his vast wealth is largely illiquid: he holds over 85% voting control, likely through super-voting shares that are subject to lock-ups and selling restrictions. While his net worth surpasses $1 trillion across SpaceX, Tesla, and private holdings, only a tiny fraction (potentially under 2% annually) could be converted to cash without jeopardizing control and market confidence. SpaceX's IPO also creates paper millionaires for roughly 4,400 employees, but their holdings face lock-up periods, exercise costs, and taxes, delaying and reducing actual cash proceeds. Only 4.2% of total shares are initially available for public trading, making the stock price highly sensitive to limited net buying or selling pressure. A major test will come when lock-ups expire for the remaining 96% of shares. The article contrasts SpaceX's wealth distribution with potential AI IPOs. Anthropic and OpenAI could generate employee wealth pools 20 times larger than SpaceX's in paper value, due to their higher valuations relative to revenue and potentially more distributed ownership. However, sustaining those high price-to-sales multiples post-IPO is uncertain. A key financial puzzle for SpaceX investors is its xAI unit. While it has locked in an estimated $26 billion in annual compute revenue from clients like Anthropic and Google, the unit reported a $6.4 billion loss in 2025. More critically, estimated annual capital expenditures of ~$30.8 billion exceed that revenue. The long-term viability of SpaceX's AI narrative hinges on whether this compute income can eventually cover the unit's massive ongoing investments and losses.

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The Backside of Musk's Trillion-Dollar Fortune: 85% Can't Be Sold

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EVM Compatibility: Developers can effortlessly migrate decentralised applications from EVM chains to the Solana environment using Sonic’s HyperGrid interpreter, increasing the accessibility and integration of various dApps. Ecosystem Support for Developers: By exposing native composable gaming primitives, Sonic facilitates a sandbox-like environment where developers can experiment and implement business logic, greatly enhancing the overall development experience. Monetisation Infrastructure: Sonic natively supports growth and monetisation efforts, providing frameworks for traffic generation, payments, and settlements, thereby ensuring that gaming projects are not only viable but also sustainable financially. Timeline of Sonic The evolution of Sonic has been marked by several key milestones. 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1.7k Total ViewsPublished 2024.04.04Updated 2024.12.03

What is $S$

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54 Total ViewsPublished 2024.12.17Updated 2024.12.17

What is AGENT S

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This dual approach empowers it to build a rich repository of domain-specific knowledge, enhancing its performance in task execution. Planning Over Long Task Horizons: Agent S employs experience-augmented hierarchical planning, a strategic approach that facilitates efficient breakdown and execution of intricate tasks. This feature significantly enhances its ability to manage multiple subtasks efficiently and effectively. Handling Dynamic, Non-Uniform Interfaces: The project introduces the Agent-Computer Interface (ACI), an innovative solution that enhances the interaction between agents and users. Utilizing Multimodal Large Language Models (MLLMs), Agent S can navigate and manipulate diverse graphical user interfaces seamlessly. Through these pioneering features, Agent S provides a robust framework that addresses the complexities involved in automating human interaction with machines, setting the stage for myriad applications in AI and beyond. Who is the Creator of Agent S? While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

723 Total ViewsPublished 2025.01.14Updated 2025.01.14

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