You also want to give these Gemma 4 assistant GGUF builds a try on @huggingface →
https://
huggingface.co/collections/At
omicChat/gemma-4-assistant-gguf
…
AI
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Try Gemma 4 Assistant GGUF Builds on Hugging Face
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Faster Inference for Gemma 4 on LLaMA.cpp with Multi-Token Prediction
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🚨 STOP WHAT YOU ARE DOING AND LOOK AT THESE BENCHMARKS@atomic_chat_hq just unlocked 1.5x faster inference for Gemma 4 on LLaMA.cpp using Multi-Token Prediction.
— Charly Wargnier (@DataChaz) 8 mai 2026
138 tokens per second on a local 26B model is pure sorcery 👀
Get the code and GGUFs below ↓ https://t.co/o4aF64B5eeSTOP WHAT YOU ARE DOING AND LOOK AT THESE BENCHMARKS @atomic_chat_hq just unlocked 1.5x faster inference for Gemma 4 on LLaMA.cpp using Multi-Token Prediction. 138 tokens per second on a local 26B model is pure sorcery Get the code and GGUFs below ↓
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GPT 5.5 Impresses in Deep Learning Research
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After another week of intensive work with GPT 5.5, I’m once again confirming my initial conclusions: it’s an impressive model. For deep learning auto-research tasks, the improvement over versions 5.2 and 5.4 is highly noticeable! It’s not just about execution and implementation—
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AI compute crunch and its impact on chatbots
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What is the #AI compute crunch—and how will it affect #Chatbots?
by @denibechard @sciam Learn more: https://
bit.ly/4ukDNhE #ArtificialIntelligence #MachineLearning #ML #MI -
Major AI news today
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Top stories in AI today: – OpenAI’s reasoning upgrade for voice agents
– Google folds Fitbit into its AI health play
– Test multiple AI models with same prompt
– Anthropic plans for AI that builds itself
– 4 new AI tools, community workflows, and more -
SecretaryMEITY: Orchestration Layer for AI in Indian Healthcare
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In a panel discussion on Building AI for Indian Healthcare at the AB PM-JAY Auto-Adjudication Hackathon Showcase 2026, @SecretaryMEITY spoke about the need for an orchestration layer that leverages multiple model architectures — LLMs, SLMs and VLMs — to address critical
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Pourquoi l’approche modulaire fonctionne en pratique
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Why the building block approach actually scales in the real world: → You can compose agents instead of rebuilding them
→ You control behavior by design, not by prompt hacking
→ You can change one block without breaking the whole system This is how you go from experiments to -
AI as a System of Small Functions, Not a Giant Brain
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The biggest “aha” from this conversation with Arno at Elastic: AI should not be one giant brain. → It should be a system of small, well-defined functions
→ Each function has a clear role
→ Each agent only gets the power it needs, nothing more This is how you avoid chaos as