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Week in review: Microsoft fixes two actively exploited 0-days, PAN-OS auth bypass hole plugged
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Hugging Face's new ML Intern lets anyone run machine learning experiments through a simple chat
Hugging Face launched ML Intern, a chat-based agent that autonomously selects models, trains, and ships demos within user-approved compute budgets.
Hugging Face's ML Intern lets users describe a project in chat, then finds models, datasets, and tools on the Hub, GitHub, and the web before estimating compute costs and enforcing an approved budget. It autonomously creates datasets, trains models, monitors jobs, uploads results, writes reports, and builds demos, each with its own tracking dashboard. A demo training run took about six hours and cost under $0.50. The launch comes as Hugging Face is being acquired by Nvidia, whose CEO Jensen Huang has pledged to keep the platform open and hardware-neutral.
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[AINews] OpenAI shuts off Cursor
OpenAI cut off API access to coding tool Cursor after its SpaceX acquisition, citing contract violations by Elon Musk's companies.
OpenAI disabled Cursor's access following the closing of Cursor's acquisition by SpaceX, citing its experience with Elon Musk's companies violating contracts; Cursor responded that OpenAI accounts for only 5% of its traffic. The weekly digest also covers major open-weight releases: Z.ai's GLM-5.3 (744B total/40B active, 1M context) and Tencent's Hy4-preview (770B/49B, ~#5 on Code Arena WebDev), plus Alibaba's Qwen3.8-Flash (125B/6B). vLLM published benchmarks showing no universal winner among speculative decoding methods across model families.
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TruffleHog AWS Analyze reduces remediation time on leaked AWS credentials
Truffle Security launched TruffleHog AWS Analyze, which maps leaked AWS keys' permissions and role assumptions; research found 88% of 64,024 leaked keys still active.
Truffle Security announced TruffleHog AWS Analyze, a TruffleHog Enterprise feature that enriches leaked AWS credentials with identity, effective permissions, and role-assumption context to help teams assess blast radius and prioritize remediation, extending earlier coverage of SaaS and Google Cloud to AWS. Truffle Security research on 64,024 unique leaked AWS keys found 88% still active, a median exposure of five years, only 14% rotated, 84% with full administrator access, and 1 in 6 being root keys, including 929 keys AWS had flagged via its compromised-key quarantine policy that still authenticated. A scan of 7.6 petabytes of public AI training data on Hugging Face found 3,343 live AWS keys, over 900 of which could list S3 buckets holding at least 51.7 TB of private data.
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m-a-p/YuE2-3B — new model trending #30 on Hugging Face
M-A-P released YuE2-3B, an open music generation model that outperforms Suno v5 on WildSongBench and runs locally on a 24GB GPU.
The M-A-P (multimodal-art-projection) team released YuE2-3B, an open-weights music generation model that turns lyrics and a style prompt into full songs with vocals and accompaniment. It uses an AR-NAR Mixture-of-Transformers backbone with symbolic planning and flow matching through a VAE, and supports editable scores (melody and chords, including ABC notation) plus agentic editing workflows. On 192 WildSongBench prompts it reports a SongBench average of 6.9632 (best-of-8) versus 6.8721 for Suno v5, claimed as state of the art among evaluated open and proprietary models. It runs 48 kHz stereo inference locally on a single 24GB NVIDIA GPU without quantization, with companion releases including YuE2-Vae, MERT-v2 encoders, the WildSongBench dataset, and SheetSage2.
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ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF — new model trending #3 on Hugging Face
ISTA-DASLab releases GSQ-RCO non-uniform GGUF quantizations of Qwen3.8-27B down to 2.5 bpw, with task-lossless IQ3_S matching BF16 benchmark scores.
ISTA-DASLab released GGUF quantizations of Qwen3.8-27B produced with GSQ (Gumbel-Softmax Quantization) and RCO (Riemannian Constrained Optimization), non-uniform methods that allocate per-tensor precision via gradient-based search under a total size budget. Four checkpoints range from 2.50 bpw (8.4 GB) to 3.50 bpw (11.8 GB), plus a BF16 vision projector (mmproj) enabling multimodal use. The recommended IQ3_S build is task-lossless, matching the BF16 base exactly on AIME25 (100.00) and LiveCodeBench v6 (85.71) at roughly one fifth of the BF16 size. Optional -mtp variants add a Multi-Token Prediction head for speculative decoding in llama.cpp.
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