SMCI Involved in AI Smuggling, Munger's Advice for Ordinary People to Turn Their Lives Around...

比推Опубліковано о 2026-03-20Востаннє оновлено о 2026-03-20

Анотація

This article summarizes recent discussions among crypto influencers on X platform. Key topics include: 1. **SMCI AI Smuggling Case**: Reports detail an incident involving the alleged smuggling of AI technology, with linked images and an extended reading source. 2. **Charlie Munger's Advice for 2026**: The late investor's principles are highlighted, emphasizing the pitfalls of hesitation (lack of conviction) and going all-in (allowing no room for error). The advice is praised as timeless wisdom that resonates more with experience. 3. **Making 1 Million in Crypto**: A discussion on the perceived simplicity versus the actual difficulty of achieving significant profits in the cryptocurrency market. 4. **Macroeconomics, Policy & Black Swans**: Commentary on broader economic conditions, potential policy changes, and unforeseen market risks. The article concludes with links to the publisher's social media channels and a disclaimer that the content consists of personal opinions and does not constitute investment advice.

Hello dear readers~

<p style='margin-top: 0px; margin-bottom: 极速5分钟,了解加密圈KOL热议话题<br/></p> <p style='margin-top: 0px; margin-bottom: 1.5rem; padding: 0px; border: 0px; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-variant-alternates: inherit; font-variant-position: inherit; font-variant-emoji: inherit; font-stretch: inherit; line-height: 25.6极速5分钟,了解加密圈KOL热议话题

<span style='margin: 0px; padding: 0px; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; font-size: 14px; line-height: inherit; font-family: -apple极速5分钟,了解加密圈KOL热议话题<br/></p> <h3>SMCI AI Smuggling Case</h3> <p>Extended reading:https://www.bitpush.news/articles/7621608</p> <p><div><img style="max-width:100%;overflow:hidden;" src="https://d1x7dwosqaosdj.cloudfront.net/images/2026-03/bfad009a3d8f45baa96c867d152584a2.png" alt=""></div></p> <p><div><img style="极速5分钟,了解加密圈KOL热议话题<br/></p> <h3>Munger's 2026 Turnaround Formula for Ordinary People

Popular replies:

Munger says the two biggest taboos in investing are hesitation and going all-in. Hesitation means lacking one's own certainty; going all-in means not allowing for any unexpected events. The former lacks technique, the latter lacks principle, both are deficiencies in mindset and rules.

Read more, read often. When I was too young, I didn't feel anything reading Munger, but now it all rings true.

Is It Easy to Make 1 Million in the Crypto Circle...?

Macroeconomy, Policy & Black Swans

</极速5分钟,了解加密圈KOL热议话题


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Original link:https://www.bitpush.news/articles/7621712

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Пов'язані питання

QWhat is the SMCI AI smuggling case mentioned in the article?

AThe article references an 'SMCI AI smuggling case' but does not provide specific details. It includes images and a link for extended reading, suggesting it involves the alleged illegal export or trafficking of AI-related technology or components by the company SMCI.

QAccording to the article, what was Charlie Munger's advice for ordinary people to improve their situation in 2026?

AThe article states that Charlie Munger's 'equation for turning things around in 2026' advises against two things in investing: hesitation and going all-in (full allocation). Hesitation indicates a lack of conviction, while going all-in leaves no room for unexpected events.

QWhat is the general sentiment in the crypto community about making 1 million dollars, as discussed in the article?

AThe article presents a mixed sentiment with the title 'Is it simple to make 1 million in the crypto circle... or not?' It includes social media posts that likely debate the ease or difficulty of achieving this financial goal, highlighting that it is a topic of discussion but not a simple feat.

QWhat types of topics are covered under the section 'Macroeconomy, Policy, and Black Swans'?

AThis section discusses broad, large-scale economic trends, government or regulatory policies, and potential unforeseen, high-impact events ('Black Swans') that could affect the market. The article includes several images of social media posts, likely containing charts and commentary on these subjects.

QWhich platforms does the article cite as its sources for the discussions?

AThe article states that the content is compiled from the X platform (formerly known as Twitter). It also provides links to its own Twitter account (@BitpushNewsCN) and Telegram channels for further discussion and updates.

Пов'язані матеріали

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

This article introduces a novel training paradigm for generative models called Explorative Modeling (XM), which enables true end-to-end training. Traditionally, powerful generative models like autoregressive and diffusion models are not trained end-to-end. They are trained to predict a single small step but require iterative multi-step sampling for inference. This "exposure bias" leads to error accumulation and limits performance. The core challenge XM addresses is "mode blurring." In generative tasks, a single input (e.g., "generate a dog") corresponds to many valid outputs (multiple modes). Standard training objectives like reconstruction loss force the model to average these modes, producing unrealistic, blurry outputs. To avoid this, existing models break generation into many small, almost deterministic steps, sacrificing end-to-end training. XM tackles this by restructuring the training loop itself. Its key insight is to amplify "generative expressivity." For each training input, instead of generating one sample, the model generates K candidate outputs. Only the candidate closest to the real data is used for computing the loss and updating the model via backpropagation. This simple "best-of-K" mechanism is implemented as a short for-loop. By exploring multiple possibilities, the model learns to distribute its guesses across different modes rather than collapsing to their uninformative average. The paper demonstrates that "exploration" acts as a new, powerful scaling axis. Gains from XM increase with model size, data scale, and compute. Experiments show improvements in FID scores for image generation and significant efficiency gains, sometimes outperforming larger models without exploration. When pushed to the limit, XM enables fully single-step, end-to-end generative models. In robotics tasks, an "Explorative Policy" matched the performance of a 100-step Diffusion Policy with a single forward pass, drastically improving inference speed. While the best-of-K concept is not entirely new, the authors' contribution lies in formally understanding it as a direct method to boost generative expressivity without fragmenting the generation process. This work suggests that as models scale, enhancing exploration during training may become crucial for overcoming fundamental performance bottlenecks.

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Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

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Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

The AI boom is facing an unexpected bottleneck: a severe shortage of skilled construction workers and electricians. As tech giants like Meta, OpenAI, and Alphabet race to build massive data centers—such as OpenAI's $16 billion "Stargate" project—they are hitting a critical labor wall. The U.S. needs an estimated 130,000 more electricians, 240,000 construction workers, and 150,000 supervisors by 2030 for AI infrastructure alone, but tens of thousands of electrician jobs go unfilled each year. While AI companies offer high premiums, with electricians earning up to $280,000 annually, worker scarcity still causes massive losses—delays on a single project can cost $14.2 million per month. The complexity of building AI data centers, which require immense power (equivalent to powering hundreds of thousands of homes), sophisticated electrical systems, and advanced liquid cooling solutions, demands highly skilled technicians who are in short supply. To combat this, companies are investing heavily in training. Meta has committed $115 million to a free training school offering tuition, housing, and stipends, targeting 5,000 new workers. OpenAI is partnering with unions to secure skilled labor. These efforts are paying off, with a significant rise in Gen Z interest in trade schools over college. However, the power demands are staggering. AI data centers are driving a rapid surge in electricity consumption, projected to account for up to 12% of U.S. power use by 2028 and raising costs for consumers. Furthermore, the construction boom is project-based, leading to a potential future glut of trained workers once building peaks, which could depress wages industry-wide. The race for AI supremacy now depends as much on skilled hands as on advanced chips.

marsbit2 год тому

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

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