gekko

doing the math for you

Category: LLM

  • Kimi K2 Thinking: China’s New Contender in the LLM Reasoning Race

    Kimi K2 Thinking: China’s New Contender in the LLM Reasoning Race

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    The global AI landscape has entered a phase of rapid escalation. Major players now outdo one another with an almost weekly cadence of new model releases—each “the best ever,” each more powerful, more capable, more efficient. And we users, fascinated and perhaps a little complicit, eagerly follow along, testing every new capability as the frontier…

  • Bridging Context Engineering in AI with Requirements Engineering

    Bridging Context Engineering in AI with Requirements Engineering

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    How Emerging AI Research Could Reinvent Context Scenarios in Software Design Hey there, fellow software enthusiasts! If you’re like me, you’ve probably spent countless hours crafting context scenarios to nail down requirements in software development projects. These narrative-driven descriptions of user interactions in specific situations provide a rock-solid foundation for understanding what a system really…

  • Transformers Are Injective: Why Your LLM Could Remember Everything (But Doesn’t)

    Transformers Are Injective: Why Your LLM Could Remember Everything (But Doesn’t)

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    The authors of “Language Models are Injective and Hence Invertible”, https://arxiv.org/abs/2510.15511, address a foundational question about transformer-based language models: do they lose information in the mapping from an input text sequence to their internal hidden activations? In more formal terms: is the model’s mapping injective (distinct inputs → distinct representations), and therefore potentially invertible (one…

  • LLM-Guided Image Editing: Embracing Mistakes for Smarter Photo Edits

    LLM-Guided Image Editing: Embracing Mistakes for Smarter Photo Edits

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    Imagine being able to tweak a photo just by telling your computer what you want. That’s the promise of text-based image editing, and Apple’s latest research takes it a step further. Apple’s team, in collaboration with UC Santa Barbara, has developed a new AI approach that lets users edit images using plain language descriptions. More…

  • The Neural Junk-Food Hypothesis

    The Neural Junk-Food Hypothesis

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    Based on the pre‐print LLMs Can Get “Brain Rot”! (arXiv:2510.13928) by Shuo Xing et al. (2025) The premise — and why this deserves attention The authors introduce an evocative metaphor: just as humans may suffer “brain rot” when indulging excessively in shallow, attention-grabbing online content, large language models (LLMs) might likewise degrade their reasoning, context-handling…