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Towards One-for-All Robustness Across a Continuum of Threat Levels

Adversarially robust models often overfit to a specific attack budget, necessitating multiple specialized models for diverse and dynamic adversarial environments, a strategy that becomes fundamentally intractable as the threat space grows. This raises an open challenge: can we achieve strong robustness across a continuum of threat levels within a single model? We propose the Threat Conditional…

We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.

Read the original at arxiv.org →

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On-Device AI in Kotlin

In this Kotlin tutorial, you'll learn how to run a large language model (LLM) directly on a user's device: no server, no API key needed.

  • Kotlin tutorial teaches running LLM on user device
  • Advantages: offline, privacy, low latency, no cloud costs
  • NobodyWho library wraps llama.cpp for Kotlin, Python, React Native

I got sick of regenerating AI ads over one typo, so I made this

While making social media creatives for clients. AI tools design them fine now, but the output is one flat image. So when there's a typo or a wrong price (there's always something), you can't fix it.

  • Tool generates layout file using Python
  • Node script creates PSD file with editable layers
  • PSD allows Photoshop or Photopea editing

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