Me trying to replace Fable with an ensemble of smaller models pic.twitter.com/0dATTnoMbn
— Peter Gostev (@petergostev) 25 juin 2026
Me trying to replace Fable with an ensemble of smaller models
MACHINE LEARNING
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Trying to replace Fable with an ensemble of smaller models
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New tiny LFM2.5-230M model for ultra-low latency robotics and e-commerce
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Here's our new, tiniest model: LFM2.5-230M! 🥳
— Maxime Labonne (@maximelabonne) 25 juin 2026
We went even smaller to power ultra-low latency use cases like e-commerce and robotics.
Here's a demo of LFM2.5-230M running on a Unitree G1, decomposing user prompts into a sequence of tool calls.
Available today on @huggingface! https://t.co/JiuSWnwZCs pic.twitter.com/5PuSamwZLRHere's our new, tiniest model: LFM2.5-230M! We went even smaller to power ultra-low latency use cases like e-commerce and robotics. Here's a demo of LFM2.5-230M running on a Unitree G1, decomposing user prompts into a sequence of tool calls. Available today on @huggingface
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Anthropic’s distillation continues unabated despite update
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This is an interesting update that Anthropic published in their official letter. But in short: they apparently haven't managed to stop the distillation. It's continuing almost seamlessly, just like before.
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GuidedVLA Helps Robots Focus by Splitting Action Decoder
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Can robots stop getting distracted by visual clutter and actually focus on what matters for the task? Researchers from Fudan, SJTU, and HKU present GuidedVLA. Instead of treating the action decoder as a single black box, they split it into specialized “attention heads”—each
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Hermes Agent learns by failing and recording lessons
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Hermes /learn explained. agents usually learn the hard way. they struggle through a task live, fail a few times, find the path that works, and only then write down what they figured out. the lesson costs you a painful session before it becomes reusable. Hermes Agent by Nous
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Multi-agent collaboration boosts Gemma 4 inference speed 5x
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Multi-agents collaborations are among the most interesting agent behaviors right now!
— Thomas Wolf (@Thom_Wolf) 25 juin 2026
We did an experiment the other day with 100+ agents (an open-collaborations for a week) collaborating to improve the inference speed of Gemma 4 in vLLM. Got a 5x final improvement in speed but… pic.twitter.com/4PFS8L2mqeMulti-agents collaborations are among the most interesting agent behaviors right now! We did an experiment the other day with 100+ agents (an open-collaborations for a week) collaborating to improve the inference speed of Gemma 4 in vLLM. Got a 5x final improvement in speed but
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Omar Souaidi builds AI verbatim analyzer on Domino
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@bnpp_cardif
's Omar Souaidi at #Rev26: they built a production-ready AI app — a verbatim analyzer using generative AI and statistical modeling — and deployed it on Domino. From concept to value, faster. -
Step By Step Guide To Powering Your Application With LLM
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Step By Step Guide To Powering Your Application With LLM! #BigData #Analytics #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Guide-to-App-L
LM
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Deep Dive into LSTM and xLSTM
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Deep Dive into LSTM and xLSTM! #BigData #Analytics #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Deep-Dive-into
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Platt Scaling for Model Calibration: A Visual Guide
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Platt – Scaling for Model Calibration: A Visual Guide! – John Platt #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming