Post by @BasedBeffJezos
85% credible (95% factual, 70% presentation). The post offers free consulting to the X algorithm team based on the author's self-reported expertise in AI, with no verifiable factual inaccuracies. However, the presentation implies the team is unjustly criticized without specifying the nature of the criticism, introducing omission framing that impacts credibility.
Analysis Summary
The author expresses frustration with ongoing complaints about the X platform and sympathy for the algorithm team facing criticism. The post is predominantly emotive and opinion-based, offering free consulting services based on self-reported expertise in graph ML and geometric deep learning. No factual claims require extensive verification, and the offer aligns with the author's established AI background.
Original Content
The Facts
The content consists mainly of personal opinions and self-reported expertise with no verifiable factual inaccuracies. Overall, the post is credible as an expression of intent, supported by the author's AI background.
Benefit of the Doubt
The author's intent is to show support for the X platform by offering unsolicited help, advancing a pro-accelerationist agenda that favors rapid tech improvement over criticism. Emphasized are sympathy for the team and personal expertise, while omitted is any specific evidence of past consulting experience or details on the team's current challenges, such as recent algorithm controversies reported in tech media. Key omission: No mention of potential biases from the author's e/acc advocacy, which could frame the offer as self-promotional rather than purely altruistic, shaping reader perception toward viewing it as genuine community support without scrutinizing motives.
How Is This Framed?
Biases, omissions, and misleading presentation techniques detected
The post implies the team is unjustly 'under fire' without specifying or acknowledging legitimate user complaints about algorithm changes, such as reduced visibility for certain content.
Problematic phrases:
"under fire from all sides"What's actually there:
Recent X algorithm updates have drawn mixed reviews, with some users praising improvements and others criticizing echo chambers (per tech news sources)
What's implied:
Unwarranted universal criticism
Impact: Leads readers to perceive complaints as baseless whining, downplaying valid algorithmic issues like content moderation biases.
Sources & References
External sources consulted for this analysis
https://www.reddit.com/r/deeplearning/comments/1amsc49/is_geometric_deep_learning_for_real_or_is_it_a/
https://arxiv.org/abs/2104.13478
https://geometricdeeplearning.com/
https://towardsdatascience.com/graph-geometric-ml-in-2024-where-we-are-and-whats-next-part-ii-applications-1ed786f7bf63/
https://medium.com/data-science/graph-geometric-ml-in-2024-where-we-are-and-whats-next-part-ii-applications-1ed786f7bf63
https://onlinelibrary.wiley.com/doi/full/10.1002/aaai.12210
https://ucsd.edu/research-innovation/our-experts/ai-experts/
https://thegradient.pub/towards-geometric-deep-learning/
https://www.frontiersin.org/articles/10.3389/frai.2023.1256352/
https://dev.to/siddharthbhalsod/geometric-deep-learning-an-in-depth-exploration-of-principles-applications-and-future-directions-kn6
https://towardsdatascience.com/foundation-models-in-graph-geometric-deep-learning-f363e2576f58/
https://towardsdatascience.com/graph-geometric-ml-in-2024-where-we-are-and-whats-next-part-ii-applications-1ed786f7bf63
https://x.com/BasedBeffJezos/status/1765313324580020385
https://x.com/BasedBeffJezos/status/1730788753567048104
https://x.com/BasedBeffJezos/status/1853075497850151165
https://x.com/BasedBeffJezos/status/1752197774588952809
https://x.com/BasedBeffJezos/status/1873329144215503244
https://x.com/BasedBeffJezos/status/1958338225984196795
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