Month: June 2025

What You See Isn’t Always What You Get: The Hidden Strategy Behind Product Design

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Have you ever wondered why some products just feel right, perfectly integrated, even when they’re made of many different parts? Or why a seemingly simple design can be a powerful competitive weapon? A fascinating study by Sheen S. Levine and Johannes M. Pennings, titled “What You See is not What You Get: Product Architecture and … Read more

Bitcoin Could Spike to $120K, Here Are 4 Factors Boosting the Case for a BTC Bull Run

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Multiple analysts have repeatedly pointed to $120,000 as bitcoin’s BTC price target this year. Recent developments have strengthened that bullish case, driven by four key factors: the spot price, central bank policy, energy market trend, and technical setup. Let’s take a look at those in detail. BTC’s love affair with $100K Recently, a crypto trader … Read more

Closing the Feedback Loop: Building AI That Learns from Its Users

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In the rapidly evolving landscape of artificial intelligence, the journey from a promising AI model to a successful AI product is rarely linear. It’s an iterative process, constantly refined by real-world interaction. While model metrics like accuracy, precision, and F1-score are crucial during development, they often tell only half the story. The true litmus test … Read more

Dogecoin Surges 7% as Bulls Break Key Resistance

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Dogecoin surged 6.56% over the past 24 hours, bouncing from a two-month low of $0.1508 to a session high of $0.1632, as traders stepped in aggressively following a technical breakout. The move came amid heightened geopolitical tensions between the U.S. and Iran, which triggered broad volatility across crypto markets but failed to derail DOGE’s momentum. … Read more

GPT-2 Architecture and Training Details: Parameters & Cross-Entropy Loss

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Table of Links Abstract and 1 Introduction 2 Related Work 3 Model and 3.1 Associative memories 3.2 Transformer blocks 4 A New Energy Function 4.1 The layered structure 5 Cross-Entropy Loss 6 Empirical Results and 6.1 Empirical evaluation of the radius 6.2 Training GPT-2 6.3 Training Vanilla Transformers 7 Conclusion and Acknowledgments Appendix A. Deferred … Read more

Theoretical Derivations: Cross-Entropy Loss and Energy Functions in LLMs

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Table of Links Abstract and 1 Introduction 2 Related Work 3 Model and 3.1 Associative memories 3.2 Transformer blocks 4 A New Energy Function 4.1 The layered structure 5 Cross-Entropy Loss 6 Empirical Results and 6.1 Empirical evaluation of the radius 6.2 Training GPT-2 6.3 Training Vanilla Transformers 7 Conclusion and Acknowledgments Appendix A. Deferred … Read more