OpenAI Blog
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3 months ago
Anthropic updated its Codex app to include computer use capabilities, web browsing, image generation, memory features, and plugin support on macOS and Windows. The app now enables developers to automate tasks across their entire system rather than working within a single application. This integration of multiple tools into one interface is intended to streamline development processes and reduce context-switching between separate applications.
Hugging Face Blog
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3 months ago
The Sentence Transformers library now supports training and finetuning multimodal embedding models that handle text, images, audio, and video across tasks like visual document retrieval and semantic search. A finetuned Qwen3-VL-Embedding-2B model achieved an NDCG@10 score of 0.947 on visual document retrieval, compared to the base model's 0.888 and outperforming all tested competitors including models four times larger. Users can now train domain-specific multimodal models using the same SentenceTransformerTrainer component used for text-only models, with automatic handling of image preprocessing and support for multiple loss functions including CachedMultipleNegativesRankingLoss.
Hugging Face Blog
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3 months ago
The MLX team created a Skill and test harness to help contributors port language models from the transformers library to mlx-lm using AI agents, while maintaining code quality standards. The Skill automates scaffolding tasks like downloading model variants and running per-layer comparisons, but produces PRs that disclose they were agent-assisted and include generation examples and numerical comparisons for reviewer confidence. This approach aims to make new transformers models available in mlx-lm shortly after their release, while ensuring maintainers can review agent-generated code without being overwhelmed by volume.
Hugging Face Blog
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3 months ago
Researchers extended a reinforcement-learning framework to train e-commerce conversational agents across eight realistic shopping tasks (product discovery, cart building, returns, order tracking, and others) using procedurally generated problems and algorithmically verifiable rewards. The system uses a 12-axis difficulty curriculum that scales task complexity across dimensions like constraint count, distractor products, and mid-conversation stock changes, with training conducted over 300 steps on a Qwen 3 8B model. This approach eliminates the need for human annotation or LLM judges by making all outcomes—product correctness, variant selection, hallucination detection—directly verifiable through code.
