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Found 8 items · updated 01:10
Nov 2Mon
  1. SemiAnalysis0

    GDPx: A benchmark to measure the capabilities of cutting-edge models in the most valuable domains.

    GDPx benchmark covers valuable areas such as chip design and cybersecurity, aiming to measure the capabilities of general intelligence agents, with results showing that top models scored only 12%. Opus 5.5 and GPT-6 Astra were penalized due to their security classifier scores, this benchmark was developed by SemiAnalysis in collaboration with 14 professional data partners and ensures task quality through a strict QA process.

Nov 7Sat
Nov 3Tue
  1. Latent Space

    Claude Code can read and write neural network weights: 'Weight space programming' bypasses Autoresearch

    Claude Code can directly read and edit the weights of other models without human intervention or PyTorch/Jax code, achieving weight reading, single-time bug fixes, performance improvements, or writing network weights from scratch. This technology bypasses the traditional process of Autoresearch by running experiments in a cluster to train models, demonstrating the potential of LLMs in weight space programming, currently mainly applicable to simple games and RL policies.

Oct 22Thu
  1. Google DeepMind

    Google DeepMind releases Forest Typology (ForTy) 2020 global forest type map

    Google DeepMind and Google Research jointly release the Forest Typology (ForTy) 2020 Global Map, which classifies every 10x10 meter land on Earth into six types of forests and land use, and tracks changes from 2017 to 2025. The map is based on definitions by the United Nations Food and Agriculture Organization, accurately distinguishing native forests, plantations, monoculture forest plantations, economic crop forests, and other land types, supporting the EU's deforestation regulations with an accuracy of 90%.

    Same story · 2 outlets

    How the accounts differ:Google DeepMind states the map classifies land into six types; The Decoder mentions only four types (original forest, plantations, and tree crop farms).

Nov 3Tue
Oct 30Fri
  1. Latent Space65

    AIE CODE 2026 Highlights: Three Themes Focus on AI Programming Agent Development

    AIE CODE 2026 will explore three themes: redundant-free expansion, system-one model, and raising ambition, focusing on AI programming agent development, attracting 1000 top smart agent engineers globally. The conference will showcase the practical application of the System One model and focus on software rewrite cases from browser to Adobe Photoshop Suite.

Oct 29Thu
  1. Hugging Face Blog

    Project ProtoStar project release: Promoting the AI compatibility of publicly funded scientific research

    Project ProtoStar is announced, aiming to unlock the potential of publicly funded scientific research by making datasets, models, and code more accessible and reusable. The project brings together universities, research institutions, industry, and the open science community, supporting the release of scientific achievements in AI-compatible, machine-actionable formats, and demonstrating its value through a series of public challenges. The first challenge, hosted by Carnegie Mellon University, will launch on November 2.

Oct 28Wed
  1. Ars Technica · AI65

    Pinal released: AI tools can design proteins through natural language

    Pinal can generate proteins through natural language prompts, with a success rate meeting specific functional requirements; Hugging Face also launches a similar tool at the DNA level. Pinal uses T2struct to convert prompts into structural blueprints, which are then combined with SAProt-T for sequence generation based on probabilistic evaluation, trained based on three major databases and AlphaFold predictions.