Hugging Face is a platform for hosting open models, datasets and code. Recent coverage includes US Justice Department inquiries into Nvidia's roughly $13 billion deal to acquire the company, and the release of two open Swift packages for Apple platforms, AnySpeechRecognizer and AnyDecisionModel. Hugging Face also launched Project ProtoStar with universities to publish publicly funded science in AI-ready formats, while weights such as WhissleAI's Arc speech model and NVIDIA's Nemotron 3.5 Transcribe appeared on the platform.
Rebuilding AUTOMATIC1111 with GradioHugging Face rebuilt most of AUTOMATIC1111 as Workflow1111, a single Gradio workflow with 11 media pipelines and 73 nodes covering text-to-image, hi-res fix, image-to-image, ControlNet-style annotators and more; users run pipelines with their Hugging Face account or access token and use their own quota.
Superintelligence is coming—should we let it?TechCrunch's Equity podcast debated whether superintelligence is inevitable and controllable, noting safety incidents like OpenAI's Hugging Face breach highlight risks of more capable systems; guest Connor Leahy joined the discussion.
Desert Ant Labs launches on-device fast lightweight modelsDesert Ant Labs launched 18 tiny on-device models for audio, vision, and text (Swift/Kotlin/JS SDK), free up to 100k monthly active devices; they run in milliseconds on older phones and keep data on-device for privacy.
Hugging Face launches ML Intern to run ML experiments via chatHugging Face launched ML Intern, a chatbot-integrated assistant that lets non-experts run machine learning experiments via conversation, lowering the barrier to ML experimentation.
Boundary-aware self-distillation for safety refusalHugging Face published the paper “Safety for Whom? Boundary-Aware Self-Distillation for Controlled LLM Safety Refusal”, arguing that topic-level guards (e.g. LlamaGuard-3) and benchmarks like XSTest and OR-Bench cause safe prompts to be refused. The paper formalizes a topic universe (political prompts) containing a target-harmful subset and studies training and evaluation methods to refuse the harmful subset while answering the benign complement.
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