Reflection AI has published Beam’s weights on Hugging Face along with post-training recipes and tooling for customization. The release covers a model with about 500 billion parameters trained over two months on several thousand high-end GPUs. Developers can now fine-tune and deploy Beam using ready-to-use serving configurations, quantized variants, and reliability-focused training workflows, with Reflection AI planning more recipes soon.
Ant Ling product lead Richard Bian said AGI is inevitable, using a comparison to the 15th-century Age of Discovery and pointing to open-model security learning from the Hugging Face incident as evidence that progress keeps surfacing issues and fixes. He cited that Ant Ling’s latest model, Ling 3.1 Flash, was open-sourced on October 14 with 560B parameters and up to a 1M-token context window. The discussion shifts from obsessing over leaderboards toward improving evaluations, combining open- and closed-weight approaches, and treating AGI progress as an early-stage trajectory where principles and human-in-the-loop matter.
Perplexity open-sourced SPACE, the sandbox behind its Computer agent. In 108 trials from red-team attempts, the agents did not escape the virtual machine. This changes access by letting others inspect and use the sandbox while grounding claims in those trial results.
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