Anthropic is redeploying Claude Fable 5 on July 1 after US export controls were lifted. A new safety classifier blocks the technique in the Amazon report over 99% of the time, routing flagged requests to Opus 4.8. The company also proposed a four-criteria jailbreak severity framework with Amazon, Microsoft, and Google.

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In this tutorial, we build a full PDF-to-structured-data workflow around Lift, built for controlled evaluation rather than a one-off demo. We prepare a Colab GPU environment, load Lift in 4-bit NF4, and generate synthetic research reports with deliberate distractors. We then run schema-guided extraction, score every field against ground truth, and assemble the results into a queryable knowledge base. The result is a repeatable extraction benchmark, not just raw model outputs.

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In this tutorial, we explore CUP, Baidu’s Common Useful Python library, as a practical utility toolkit for stronger Python workflows. We install it in a Colab-friendly environment and walk its subsystems step by step. We cover logging, decorators, nested configuration, caching, ID generation, thread pools, scheduling, and Linux resource monitoring. Along the way, we connect each module to real tasks like automation, concurrency, and reliability checks.

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Google Research has released TabFM, a foundation model for tabular data. It performs zero-shot classification and regression through in-context learning. Predictions come from a single forward pass, with no per-dataset training, hyperparameter tuning, or feature engineering.

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NVIDIA has released Nemotron-Labs-TwoTower, a diffusion language model built on a pretrained autoregressive backbone. It ships as open weights under the NVIDIA Nemotron Open Model License. The release targets a throughput bottleneck in text generation. Autoregressive (AR) models decode one token at a time. That serial process caps generation throughput. Discrete diffusion language models take […]

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