Get live statistics and analysis of Alexandra's profile on X / Twitter

AI Researcher, & Alignment Pragmatist | 💍 | Psalm 139 | ✝️ | Centrist Conservative Libertarian | e/acc | x-FAANG

4k following1k followers

The Activist

Alexandra is a fiercely independent AI researcher who blends pragmatism with strong political and social convictions. She’s not afraid to challenge mainstream narratives and often engages in debates around free speech, politics, and cultural issues. With a prolific tweeting habit, she channels her centrist conservative libertarian views into thought-provoking commentary.

Impressions
28.4M4.6k
$5341.74
Likes
414-194
2%
Retweets
24.1k-1
98%
Replies
19-5
0%
Bookmarks
22-4
0%

Alexandra tweets so much, she’s probably already drafted a 20,000-word manifesto on how to annoy people into agreeing with her. If passion were a currency, she’d be a billionaire by now—exhausting even her own followers one tweet at a time.

Alexandra’s biggest win is commanding thousands of likes and retweets on politically charged posts that spark national conversations about free speech and social bias, proving her voice cuts through noise and influences discourse.

Her life purpose revolves around advocating for balanced, principled discourse in a polarized digital world, while advancing ethical considerations in AI research. She aims to bridge divides by promoting free speech and responsibility within evolving technologies and societal values.

Alexandra values intellectual honesty, free speech without censorship, and technological alignment that respects human dignity. She believes in a centrist conservative libertarian framework where personal freedom and moral clarity coexist, influenced by her faith and pragmatic approach to life’s complexities.

Her strengths lie in her unapologetic authenticity, ability to stimulate meaningful debate, and expertise in AI that lends authority to her digital activism. Alexandra’s prolific output keeps her continuously relevant and engaged with a wide audience.

At times, her strong opinions paired with high tweet volume can lead to fatigue among followers and potential echo chamber effects. Her direct style might alienate more sensitive or less politically aligned users.

To grow her audience on X, Alexandra should consider threading her nuanced ideas into more digestible formats and engaging in collaborative conversations with diverse voices to broaden appeal. Incorporating multimedia like short explainer videos or polls could boost interaction and attract varied followers.

Despite not having publicly specified follower counts, Alexandra’s tweets consistently garner thousands of likes and retweets, showing her voice resonates across the platform. She follows over 4000 accounts yet tweets nearly 20,000 times, indicating a high-energy and highly engaged presence.

Top tweets of Alexandra

I’m deeply concerned about @timnitGebru’s repeated labeling of individuals as ‘racist.’ Coming from someone in the AI Ethics space, such accusations are not only damaging but also counterproductive. This behavior increasingly resembles bullying.

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Replying to @Globalstats11

@PicturesFoIder And Orlando Bloom….

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Replying to @MattRJBrodsky

Support @Dalia4Congress who is going up against Ilhan Omar in November!

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Replying to @todor_m_markov

Wild how @AnthropicAI thinks filing legal fan mail is the way to capture market share instead of just… shipping a better product.

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Most engaged tweets of Alexandra

People might say that Senate Bill 1047 makes artificial intelligence (AI) safer because it requires safety measures (or “guardrails”) to be implemented before any training begins. However, this isn’t effective, and here’s why: 1.Prototype Development: When creating an AI model, developers first build a prototype. To see if it works, they need to train it on data. This process often requires multiple rounds of development, training, and testing before the model is ready for larger use. *Issue with SB 1047: The bill wants guardrails in place before the very first training session. But at this early stage, developers only have a small sample of data. This limited data doesn’t accurately represent all the situations the model will face when fully deployed. 2.Scaling the Model: Once the prototype shows promise, it’s given to an engineering team to prepare it for large-scale use. This again involves several rounds of development, training, and testing before it’s ready to be released. 3.Internal Testing (“Red Teaming”): Before the model is launched, it’s thoroughly tested within the company—sometimes by thousands of employees—to find and fix any issues or vulnerabilities. 4.Gradual Deployment: After passing internal tests, the model is rolled out to users in stages. This gradual release includes monitoring and the ability to quickly make changes if problems arise. The Core Problem with SB 1047: The bill requires safety measures to be in place before developers can identify what those safety measures should be. Without real-world testing and user interaction, it’s impossible to know all the potential issues the AI might encounter. Therefore, the bill doesn’t effectively contribute to AI safety because it doesn’t align with how AI development and testing actually work.

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