🤖 AI Research - Digital Twins | Drishtant Ghosh (Drix10)

🤖 AI Research - Digital Twins

A new study from @pewresearch in which they created rich digital twins and then compared responses to actual survey-takers. Large average error. But interestingly, when I just asked frontier models for the question response distribution, the result is far more accurate than the original survey.

Key Points:

Digital Twins vs AI Models: The study compared the accuracy of digital twins and AI models in predicting survey responses, with AI models showing significantly better results.
Trade-offs and Limitations: The study highlights the limitations of digital twins in accurately predicting human behavior, and the potential benefits of using AI models for this purpose.
Actionable Takeaway: This study demonstrates the potential of AI models in improving the accuracy of survey responses, and highlights the need for further research in this area.

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🚀 Astra AI Growth

At @Astra__AI we are now adding +150.000 new users each DAY! Our growth curve continues to look steep, and we've done it bootstrapped from the start, with zero VC investment so far. To put that in perspective, in the first 2.5 years, Instagram grew by +110k users per day.

Key Points:

Astra AI Growth: Astra AI has experienced rapid growth, with +150,000 new users added each day, and a growth curve that continues to look steep.
Bootstrapped Growth: Astra AI has achieved this growth without any VC investment, demonstrating the potential for bootstrapped growth in the AI space.
Actionable Takeaway: This growth demonstrates the potential for AI companies to achieve rapid growth without relying on VC investment.

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🚫 AI-Scale Problems

“You cannot fight AI-scale problems with human-scale solutions.” — @CredibleCrypto That's the problem @umanitek is tackling with what he calls an “immune system” for the internet, built on OriginTrail CrediBULL has experienced it firsthand: 500+ fake accounts taken down in a

Key Points:

AI-Scale Problems: The problem of AI-scale problems is highlighted, where human-scale solutions are insufficient to address the complexity of AI systems.
OriginTrail Immune System: OriginTrail is tackling this problem with an “immune system” for the internet, which has already shown success in taking down 500+ fake accounts.
Actionable Takeaway: This highlights the need for more sophisticated solutions to address AI-scale problems, and the potential of OriginTrail's immune system to address this issue.

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🏨 AI in Hospitality

The President and COO of Marriott Vacations Worldwide is reimagining hospitality with AI. On The New Normal, @rubenharris and @jeffherzfeld sat down with Mike Flaskey to map it across 700,000 owner families and 120 resorts. Here is where AI fits, and where it never will.

Key Points:

AI in Hospitality: The President and COO of Marriott Vacations Worldwide is using AI to reimagine hospitality, with a focus on mapping AI solutions across 700,000 owner families and 120 resorts.
AI Applications: The article highlights where AI fits in the hospitality industry, and where it does not.
Actionable Takeaway: This demonstrates the potential of AI in the hospitality industry, and highlights the need for careful consideration of where AI solutions are most effective.

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📚 ResearchRabbit

A good reading strategy is not about speed (or AI summaries ). It's about feeling confident knowing what to read first. Use ResearchRabbit’s citation map to spot foundational, influential, and emerging papers before diving into your reading list. Prioritize first. Read

Key Points:

ResearchRabbit: ResearchRabbit is a tool that helps researchers prioritize their reading list by providing a citation map of foundational, influential, and emerging papers.
Citation Map: The citation map helps researchers identify the most important papers to read first, and prioritize their reading list accordingly.
Actionable Takeaway: This demonstrates the potential of ResearchRabbit in improving research efficiency, and highlights the need for careful consideration of where to focus reading efforts.

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🚀 Tera Wallet

Tera Wallet is live on @tryteraai . Sends and swaps pay 0.50%. Incoming transfers are not skimmed. Card top-up buys API credits, not a token. Hold official $TCODE to claim monthly credits. Keys stay in the browser. https:// teraai.chat/wallet @talocode

Key Points:

Tera Wallet: Tera Wallet is a new wallet that allows users to send and swap cryptocurrencies with low fees, and provides a secure way to hold and manage their assets.
Features: The wallet provides a range of features, including low fees, secure storage, and easy access to API credits.
Actionable Takeaway: This demonstrates the potential of Tera Wallet in providing a secure and convenient way to manage cryptocurrencies.

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📚 Engaged Classrooms

What does an engaged classroom actually look like? Jillian Pratt at Comal ISD names two things. Students enjoying the work, and students choosing how to do it. Watch the full story: https:// hubs.la/Q04xX27L0

Key Points:

Engaged Classrooms: The article highlights the importance of creating engaged classrooms, where students are motivated and interested in the learning process.
Student Engagement: The article discusses two key factors that contribute to student engagement: students enjoying the work, and students choosing how to do it.
Actionable Takeaway: This demonstrates the potential of creating engaged classrooms, and highlights the need for careful consideration of how to motivate and engage students.

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🤖 Looped Diffusion Transformer

Looped Diffusion Transformer (Looped-DiT) is a new twist on text-to-image models: instead of piling on more parameters, it loops the same Transformer blocks inside every denoising step—gaining depth without the bloat. Here’s what stands out: — A 260M-parameter Looped-DiT beats

Key Points:

Looped Diffusion Transformer: The Looped Diffusion Transformer is a new text-to-image model that uses a looped structure to gain depth without increasing the number of parameters.
Advantages: The model has several advantages, including the ability to beat state-of-the-art models on certain tasks.
Actionable Takeaway: This demonstrates the potential of the Looped Diffusion Transformer in improving text-to-image models, and highlights the need for careful consideration of how to design and train these models.

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