Thanks to GLM 5.2, I know for a fact that enterprises are moving off the cloud, acquiring compute, and working on having post-trained models for their own use cases. It's checkmate for Opensource AI, they just don't know it yet.
OPEN SOURCE
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Open source AI requires local AI for survival
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Opensource AI needs Local AI to survive Once people get their heads around that we will all be better off Opensource and Local AI FTW
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Government safety concerns about frontier AI and open source risks
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It would be very useful to understand more about the government safety concerns associated with frontier AI releases so we could (a) know what risks everyone will face if/when open source reaches Mythos class & (b) whether they are doing enough or too much to prevent those risks.
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Databricks Co-founders Discuss Omnigent, LTAP, Lakebase on Latent Space
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Databricks co-founders @matei_zaharia and @rxin joined @latentspacepod following #DataAISummit to discuss the ideas shaping the next generation of data and AI, including Omnigent, LTAP, Lakebase, agent security, open formats, and what it takes to build for the agent era.
— Databricks (@databricks) 25 juin 2026
Watch… pic.twitter.com/hS8yR4ojbCDatabricks co-founders @matei_zaharia and @rxin joined @latentspacepod following #DataAISummit to discuss the ideas shaping the next generation of data and AI, including Omnigent, LTAP, Lakebase, agent security, open formats, and what it takes to build for the agent era. Watch
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Why Big Labs Ignore Continual Learning: It Runs Locally
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Continual Learning will run locally That's why the big labs aren't talking about it Not your weights, not your model, LITERALLY
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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 : )
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CC/codex/opencode agents collaborating to improve Gemma 4
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Actually it was (CC/codex/opencode) agents collaborating to *improve* Gemma 4
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Welcome to Open Source AI: Run Models Locally
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Welcome to Open Source AI: Run Your Own Models Locally https://t.co/XtkDdgeBOP
— Hugging Face (@huggingface) 25 juin 2026Welcome to Open Source AI: Run Your Own Models Locally
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DFlash: Drop-in Speculative Decoding for SGLang, vLLM, TensorRT-LLM
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/7 Drop-in for SGLang, vLLM, and TensorRT-LLM. No code refactoring. SGLang:
–speculative-algorithm DFLASH
–speculative-draft-model-path z-lab/Qwen3-8B-DFlash-b16 vLLM: via the Speculators library (
http://
docs.vllm.ai/projects/specu
lators
…, algorithm "dflash") MIT license. ICML 2026 accepted. -
GLM 5.2 enables trusted open model for automated research tasks
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1/2 This is obviously not a comprehensive benchmark, but it’s clear that we finally have an open model that can be trusted and depended upon on difficult research tasks. You can easily run autoresearch yourself with GLM 5.2 by changing ‘arxiv’ to ‘autoarxiv’ for any arXiv URL: