"The United States, the only country to vote against the resolution, said it promoted an "ideological agenda" and was a distraction from the "genuine problems of international peace, prosperity or good relations"."
"AMD fires back at Nvidia, claiming 256-core Zen 6 'Venice' CPU beats Vera by 3.3x in rack-level performance — company shares first estimated EPYC Venice benchmarks"
"GLM-5.3 is the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench. It also achieve open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam."
"Open Source: We will release the weights in two weeks after launch, once safety evaluation and hardening are complete."
"OpenZL delivers high compression ratios while preserving high speed, a level of performance that is out of reach for generic compressors. OpenZL takes a description of your data and builds from it a specialized compressor optimized for your specific format."
OpenZL is nice, but it's often less useful than you think - it requires that you know the structure of your data, and don't care about inspecting that data outside of your program. I've extracted one too many png files from a word document (by renaming .docx to .zip) to desire OpenZL everywhere... It might be better as a short-term "data in transit" compression than for long term storage.
Please check the OpenZL v0.2 + Silesia corpus benchmark.
"OpenZL to offer 10% faster compression speed and 70% faster decompression speed compared to Zstandard level 1 on the Silesia corpus in our benchmarks."
"OpenZL now ships its own LZ codec, exposed as ZL_GRAPH_LZ, and the serial profile in zli. It is still being actively developed to expand its feature set and improve performance on small inputs."
"The unreasonable effectiveness of our first foray into training leads us to believe that the graph model is uniquely positioned to facilitate ML-guided generation of compressors. We are tempted to view this as “the next big thing” in production-scale compression. Whereas compression research has up to now eluded those without domain expertise, we believe the future of application-specific compressors will be unlocked via investment in automated learning methods."
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