Imagine if today all you had was Fable 5 from Anthropic & GPT 5.5 from OpenAI – No Local Inference – No OpenCode : Hermes – No GPUs / DGX Sparks / Mac Studios Just a hostage situation to a few corps willing to rugpull you any sec Now you understand why we need Opensource AI
LLMS
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DiffusionGemma: lightning-fast, 4x faster than Gemma 4
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Awesome to see this innovation in text diffusion. DiffusionGemma is lightning fast, 4x faster than other Gemma 4 models! Congrats to @bodonoghue85 and the team who worked so hard on this – excited to see what people build with it! https://t.co/AwDNx8bm7J
— Demis Hassabis (@demishassabis) 11 juin 2026Great to see this innovation in text diffusion. DiffusionGemma is lightning-fast, 4x faster than other Gemma 4 models! Congratulations to @bodonoghue85 and the team who worked so hard on this project – can't wait to see what people will build with it!
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Code mode makes discussion irrelevant, model writes JavaScript
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With code mode the whole discussion isn’t really relevant anymore. It’s now all the same, model simply writes JavaScript.
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RAG System: The Complete Zero-to-Hero Guide 2026
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RAG System: The Complete Zero-to-Hero Guide – 2026! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding
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Google Research Adds Agentic RAG to AI Toolkit
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Google Research Adds Agentic RAG! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
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First technical blog on building LLM infrastructure
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first in a series of technical blogs of how we build llm infra
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Few-shot Autoregressive Density Estimation paper referenced by GPT3
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We wrote this: Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions. https://
arxiv.org/abs/1710.10304 Soon after, the GPT3 paper had this to say: “Metalearning in language models has been utilized in [RWC+19], though with much more limited results and no -
Shoutouts to multi-agent LLM systems, DSPy, and GRASP papers
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shoutouts:
• why multi-agent LLM systems fail? (arXiv:2503.13657) — @mertcemri @melissapan + @istoica05 @matei_zaharia @profjoeyg @adityagp & team • DSPy (arXiv:2310.03714) — @lateinteraction + @hazyresearch lab & co-authors
• GRASP (arXiv:2605.29668) — Jonas Moll, -
AI agents improve their own control harnesses
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"Auto-Harness: Harnesses That Improve Themselves" What if an AI agent improved the harness that controls how it acts? Thus, instead of humans adjusting prompts, tools, retry rules, and verification for each model, this article explores
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Only boomers fix typos; LLMs understand despite mistakes
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only boomers fix typos in prompts. llms perfectly understand you even if you mistype.