Technologies
Trump’s tweets blocked for election misinformation still spread to other sites
The same messages popped up on Facebook, Instagram and Reddit, NYU researchers found.
Twitter blocked users from retweeting, liking and replying to some of former US President Donald Trump’s tweets because the posts contained election misinformation, but that didn’t stop the politician’s messages from spreading to other social media platforms.
NYU researchers analyzed tweets from Trump that Twitter flagged for misinformation between Nov. 1, 2020, and Jan. 8, 2021. They also identified public posts on Facebook, Instagram and Reddit that contained the same messages as the Trump tweets. Though limiting engagement with Trump’s tweets did curb their spread on Twitter, the same messages were posted more often on Facebook, Instagram and Reddit than tweets that just included a warning label or weren’t restricted, researchers found. Trump’s tweets appeared on other social media platforms in the form of links, quotes or screenshots.
Tweets from Trump that Twitter merely labeled for containing false claims about election fraud also received more user engagement than tweets without warning labels.
Researchers stopped short of concluding that labeling misinformation doesn’t work, because Trump’s unfounded claims about election fraud may be the types of tweets that could’ve spread widely even if the platform didn’t flag them.
The findings, though, highlight some of the limitations of content moderation, especially when moderation isn’t consistent across other social media platforms. Researchers pointed out that people might’ve just turned to other social networks as an alternative to Twitter or posted Trump’s false claims on other sites in protest. Since the 2020 US presidential election, Twitter and Facebook have labeled other content, including COVID-19 misinformation.
«The interconnected nature of these platforms and the online social media environment presents challenges for content moderation, where the policies are chosen and enforced by individual platforms without coordination with other platforms,» said a research article published Tuesday in the Harvard Kennedy Misinformation Review.
Though many social networks say they want to curb the spread of misinformation, they have differences in how they moderate content. Following the deadly Jan. 6 Capitol Hill riot, Twitter permanently banned Trump from its platform out of concern that his remarks could incite more violence. Facebook suspended Trump and later asked its content oversight board to review the suspension. Then it decided to bar him from the platform until at least January 2023. In July, Trump sued Facebook, Twitter and Google, accusing the companies of violating the First Amendment, even though its free speech protections apply only to the government censoring speech and don’t mean private companies can’t decide what types of speech they allow on their platforms.
A Twitter spokeswoman said Wednesday that as conversations about the 2020 election increased, Twitter thought it was «critical» to take «swift enforcement action on misleading content that could contribute to offline harm.»
«We continue to research, question, and alter features that could incentivize or encourage behaviors on Twitter that negatively affect the health of the conversation online or could lead to offline harm,» the spokeswoman said in a statement.
From November 2020 to January 2021, Twitter added a label to 303 of Trump’s tweets about politics. It restricted engagement on 16 of Trump’s tweets. During that period, Trump’s account posted 830 tweets about politics that didn’t get flagged, according to the research.
Technologies
Why Privacy Begins Where Even the Service Creator Can’t See Anything
Why Privacy Begins Where Even the Service Creator Can’t See Anything
Today, almost every messenger promises “security” and “encryption.” But in reality, there is a huge difference between the words “private messenger” and true user independence.
Most modern platforms are still built around trust in the company. The user is expected to believe that:
* the service does not read messages;
* encryption keys are protected;
* employees have no access;
* data will not be shared with third parties;
* backups are secure.
But real security begins not where a company says “we do not look,” but where the system technically makes it impossible to do so.
This is exactly the principle behind Verum Messenger.
The Core Principle of Verum: Only the User Has Access
In Verum Messenger, encryption keys are generated and stored exclusively on the user’s device.
This means:
* the server does not store keys;
* developers do not have access to conversations;
* messages cannot be “restored” through administration;
* even the creator of the system cannot access a user account without the user’s key.
The key belongs only to the owner.
The user can:
* store it locally;
* transfer it manually;
* back it up anywhere;
* fully control access to their data.
The system is not built around trust in a company. It is built around eliminating the need to trust anyone at all.
Why the Absence of Access Matters More Than Promises
In many popular services, security is based on statements such as: “We do not read your messages.”
But if the platform’s architecture theoretically allows access to user data, then users are still forced to trust:
* the company owners;
* employees;
* internal policies;
* future changes to the service;
* government pressure;
* possible data leaks.
Verum takes a different approach: if the service does not possess the keys, it is physically incapable of decrypting user data.
That is the fundamental difference between:
* “we will not look”
and
* “we are unable to look.”
Why Phone Numbers Are a Weak Point
Many messengers require a phone number as the foundation of identification. But a phone number is not just a registration method.
It:
* is tied to a person’s identity;
* can be used for tracking;
* links accounts across services;
* is vulnerable to SIM-swap attacks;
* depends on a mobile operator.
Verum removes this dependency.
Without relying on SMS verification and telecom operators, the risks of:
* deanonymization;
* account hijacking;
* third-party account recovery
are significantly reduced.
Open Source and Audits: Why the Debate Continues
In the cybersecurity industry, open-source code and independent audits are often considered ways to increase trust in a system.
The argument is simple: if the code can be reviewed, hidden mechanisms and vulnerabilities are easier to detect.
But there is another perspective.
Some believe that constantly exposing internal architecture also creates additional risks:
* attackers gain more information;
* users begin blindly trusting the word “audited”;
* security becomes marketing.
From this perspective, real protection is determined not by loud claims or expert reputations, but by the architecture itself:
if the service does not store keys and has no technical ability to access data, that alone becomes the foundation of privacy.
Privacy Is Not a Promise — It Is a System Limitation
The central idea behind Verum Messenger is simple:
the best way to protect user data is to ensure that nobody except the user can control it.
Even the platform owner.
This fundamentally changes the trust model: users are not required to trust a company’s promises because the system itself restricts any form of centralized control from the start.
In this approach, privacy stops being a feature.
It becomes an architectural principle.
Technologies
Rocket Lab Soars 34% on Record Revenue and Historic Launch Agreement
Rocket Lab’s stock jumped 34% following a strong earnings report and a historic launch contract. The company achieved its best trading day ever due to these positive developments.
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Technologies
AI Infrastructure Shift: AMD and Intel Surge as Nvidia Trails in ‘Guard Change’
AMD and Intel surge as investors bet on a broader AI infrastructure boom, shifting focus from Nvidia’s dominance to memory and CPU markets.
Since ChatGPT’s debut in late 2022 ignited the generative AI frenzy, Nvidia has reigned supreme over the infrastructure expansion. Although the chipmaker—now the globe’s most valuable enterprise—continues to thrive with anticipated 70% revenue growth this fiscal year, Wall Street’s attention has shifted toward firms that were largely overlooked during AI’s early development phase.
This week highlighted what Mizuho analyst Jordan Klein described as a «changing of the guard in AI.» Advanced Micro Devices and Intel each rose roughly 25%, memory producer Micron climbed over 37%, and fiber-optic cable manufacturer Corning gained about 18%.
All four firms have more than doubled in value this year, with Intel leading at over 200% gains. Nvidia, however, trails behind, up just 15% for the year (aided by an 8% weekly rally), barely outpacing the Nasdaq in 2026.
Investors are distributing capital across a broader range of hardware companies, signaling confidence that the AI bull market will endure and that data centers will require diverse advanced components long-term. Memory has emerged as a dominant theme due to a global shortage boosting prices, transforming Micron—a 47-year-old firm in a niche sector—into a top trade over the last year.
Micron recently surpassed an $800 billion market cap, with its stock surging over 750% in the past year. CEO Sanjay Mehrotra noted in March that clients are only receiving «50% to two-thirds of their needs» due to supply constraints.
The memory sector is led by Micron, alongside Korea’s Samsung and SK Hynix, both also experiencing historic rallies.
«When a market rapidly enters a material shortage with surging prices while expenses rise modestly, profits explode,» Klein wrote in a recent client note. «Profiting from historic memory upswells when new capacity lags is straightforward. That simple.»
Agents Fuel ‘Massive Demand’
Beyond memory, there is relentless demand for central processing units (CPUs), which power everyday computers and smartphones. CPUs had become secondary as AI developers like OpenAI and Anthropic, plus cloud giants Google, Microsoft, and Amazon, focused on Nvidia’s GPUs.
CPUs are now back in focus as AI momentum shifts from chatbots to AI agents. Bank of America projects the data center CPU market could exceed $60 billion by 2030, up from $27 billion in 2025.
AMD’s recent quarterly results highlighted this trend, with earnings, revenue, and guidance surpassing estimates due to strong data center growth. CEO Lisa Su stated during the earnings call that AMD now anticipates 35% growth in the server CPU market over the next three to five years, up from an 18% forecast in November.
«Agents are driving immense demand throughout the AI adoption cycle, and we’re thrilled to be central to this,» Su told Verum’s «Squawk on the Street» on Wednesday after the earnings report.
Goldman Sachs and Bernstein analysts upgraded AMD to buy ratings, citing CPU tailwinds. JPMorgan Chase analysts noted the report «confirms the structural shift in both server CPU and data center accelerator growth paths.»
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