385 summarised stories about Model Release, each linking back to the original source. Browse all topics →
The New Stack 2 months ago 26 ● 3 sources
SpaceXAI released Grok 4.6 with additional supplemental training aimed at better performance on long coding tasks. Grok 4.6 scored 69.9% on CursorBench v3.2, up from 66.7% for Grok 4.5. The update makes the model more likely to pause and verify work during longer runs and improves early versions of visual and interactive apps.
TechCrunch 2 months ago 44 ● 4 sources
Three top AI researchers argued at Ai4 that AI should stay open despite safety concerns, warning about who controls progress and access to models. Geoffrey Hinton said the “battle” against open-weight models is over because the barrier created by the cost of training foundation models has disappeared. They concluded that openness should be paired with regulation and a nuanced mix of open and closed approaches rather than an all-or-nothing stance.
Hugging Face 2 months ago 22 ● 2 sources
Liquid AI released LFM2.5-VL-3B, a vision-language model that runs on users’ own hardware and supports screen understanding, object grounding, multi-image inputs, and function calling for real-time on-device use. It was pre-trained on about 34T tokens and uses a 128K vocabulary extension for non-Latin scripts. The release adds faster, edge-friendly inference (including ~228 tokens/s on an M5 Max and ~3 GB memory fit) alongside improved UI/screen comprehension, grounding, and tool/function calling performance versus its prior models in this series.
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TLDRocket
20 October 2026
Today’s biggest thread is the build-out of “agent infrastructure” that makes AI assistants feel less like chat windows and more like tools with their own computers, workflows, and controls. Daytona, a startup branding its stack as an “agentic cloud,” raised a $60 million Series B to a $1 billion valuation, aiming to speed up sandbox provisioning for agent “computers” with a 60-millisecond setup time—an attack on the bottleneck of traditional VM spin-up. IBM, Red Hat, and contributors also pushed in the same direction with llm-d framework v1.0, adding operational controls for distributed in-house inference, including a router that captured 97% cache reuse in an agentic coding test and improvements for multi-tenant QoS, zero-downtime upgrades, and request routing.
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