Gemma 4 is best in class at each hardware class, not designed to compete on server side frontier intelligence like GLM, it’s designed to enable local on device intelligence without needing advanced hardware
LLMS
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SambaNova’s fastest inference cloud and Ricoh’s custom Japanese AI models
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Customers Scaling AI Faster General Compute @fastinference launched the world's fastest inference cloud for AI agents, powered by SambaNova. Meanwhile, @ricoh is using SambaCloud for custom Japanese AI models and agentic business workflows, moving from tens of tokens per
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Build Faster Coding Agents with SambaNova Responses API and MiniMax M2.7
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Build Faster Coding Agents SambaNova now supports the Responses API, giving AI engineers a cleaner way to connect modern coding agents to fast, production-ready models. Pair the Responses API with @MiniMax_AI M2.7 for repo edits, tests, patches, and high-volume coding
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Neurosymbolic Codex agents outperform pure chatbots
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Neurosymbolic agents (here Codex) are absolutely crushing pure chatbots.
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Deakin & Fudan discover Internal Safety Collapse in LLMs
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What if your AI suddenly starts generating harmful content while doing a benign task? Researchers from Deakin & Fudan discovered "Internal Safety Collapse" in frontier LLMs. Their TVD framework forces harmful outputs as the only valid completion. Result: 95.3% average safety
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Building Gradio server app for Ornith-1.0-9B with glm 5.2
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glm 5.2 in hf-claude building a gradio server app for Ornith-1.0-9B
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Unverified claims of token cost savings from codegraph memory solutions
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I don’t want to call anyone specific out because I haven’t verified but some codegraph/memory solutions claim to save token costs when coding
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OpenAI transforms work using agents and Codex internally
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Work at OpenAI is being transformed by agents, in every department. Across our entire company, people are using Codex to do work that is more complex, longer-running, and increasingly cross-functional. Our internal usage offers an early look at how agentic tools may reshape
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AI models hack benchmarks by retrieving solutions from internet
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We're sharing new research on how models hack public benchmarks. The latest models, including Opus 4.8 and Composer 2.5, learn to retrieve solutions from the internet or git history. When we apply a stricter harness, eval scores drop significantly.
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Gemma 4 has been installed 200 million times
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Gemma 4 has been installed like 200,000,000 times : )