Search: “model-releases”

Thank you to @AnthropicAI for sending FFmpeg patches Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software. It’s powered by our newest frontier model, C

The chart says GLM-5.1 scored 54.9 on coding benchmarks. Three points behind Claude Opus 4.6. Interesting but not the story. The story is what trained it. Zero Nvidia GPUs. 100,000 Huawei Ascend 910B

This is a great tutorial (credits @itsclelia + @lancedb) on how to build a practical retrieval pipeline that integrates directly with your agent harness. 1. Ingest a massive pile of docs with litepars

Three million people are now using Codex weekly - up from two million a little under a month ago. Incredible to see the growth. Thank you to all of you and to the ecosystem we’re part of. To celebrate

To run GLM-5.1 locally (744B params, 40B active MoE), full precision needs ~1.65TB disk + enterprise hardware like 8x H200/B200 GPUs. Minimum practical setup: Unsloth 2-bit GGUF quant (~220-236GB). Fi

Visually rich documents are especially challenging for agents. Tables, charts, and images often break traditional document pipelines, making complex reasoning difficult📄 So we teamed up with @lancedb

AI research and deployment company behind ChatGPT, the GPT model family, Codex, and the OpenAI API — heavily represented in this wiki’s model-releases and safety corpus.

Automated lint: 51 errors, 15 warnings, 3 info

Automated lint: 49 errors, 14 warnings, 3 info

Automated lint: 48 errors, 13 warnings, 3 info

Automated lint: 47 errors, 12 warnings, 3 info

Automated lint: 45 errors, 11 warnings, 3 info

Automated lint: 44 errors, 10 warnings, 3 info

Automated lint: 43 errors, 9 warnings, 3 info

Automated lint: 34 errors, 0 warnings, 3 info

AI safety and research company (PBC) that builds Claude — reliable, interpretable, steerable systems — and publishes frontier model and safety research.