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CategoryAllAI research 2AI safety & security 1AI tools & infra 1
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Saving Jet Fuel

Tutorial optimizes flight paths to cut jet fuel costs using open-source Scikit-decide planning framework and OpenAP aircraft performance models.

A technical walkthrough demonstrates wind-aware flight path optimization using Scikit-decide, an open-source framework for reinforcement learning and automated planning, paired with OpenAP fuel-consumption models built by Dr. Junzi Sun at TU Delft and NOAA wind data. A Boeing 787-9 flying EWR to FCO can require roughly $68K in fuel, and adjusted routing could save thousands. The post uses Python 3.12, DuckDB with spatial extensions, and QGIS for map rendering.

Hacker News · AI · 1d agoAI tools & infraHN 45↑ · 19 comments

[AINews] AEF-1 standard emerges for Third Party Evaluators, as Xai, OpenAI, and Anthropic all cosign

xAI, OpenAI, and Anthropic cosign the AEF-1 third-party evaluation standard while Dario Amodei proposes embedded evaluators for safety verification.

The AI Evaluator Forum published AEF-1, a baseline standard for independent third-party AI evaluations covering access, conflicts of interest, funding relationships, recusal, and transparency, cosigned by xAI, OpenAI, and Anthropic. Dario Amodei wrote a rare personal blogpost proposing embedded evaluators such as METR with desks, badges, company laptops, and internal-risk-team-level access to verify safety commitments, plus democratic and global coordination frameworks. The roundup also covers the pacing debate: Bilal Chughtai left Google DeepMind arguing progress may outrun alignment, while critics including Aidan Gomez and Cohere push back against slowdowns and lab gatekeeping. Additional items include Cline Desktop's launch with open-weight model support.

Latent Space · 2d agoAI safety & security

VC-Attention: Value Smoothing and Softmax Casting for Low-bit Attention

VC-Attention is a training-free low-bit attention method for diffusion transformers, achieving 1.46-1.59x kernel speedups on datacenter GPUs with higher fidelity.

VC-Attention is a training-free low-bit attention framework for diffusion transformers that pairs V-Smooth value smoothing via lightweight online clustering with ExpCast-FP8, which maps log-domain scores directly to E4M3 FP8 probability codes and eliminates the FP32 softmax exponential. It is implemented for B200, B300, H200, RTX PRO 6000, and RTX 5090 GPUs. Across Wan2.2, LongCat-Video, HunyuanVideo-1.5, and MiniMax-H3, it improves fidelity over low-bit baselines and speeds attention 1.46-1.59x over BF16 FlashAttention-4 on datacenter Blackwell and Hopper GPUs and 2.3-3.6x on workstation cards, with 1.13-1.70x faster end-to-end clip generation.

Hugging Face daily papersupdated · 7h agofirst · 3d agoAI research 2 sources