Large Language Models are improving exponentially: https://
spectrum.ieee.org/large-language
-model-performance
… via @IEEESpectrum #AI #GenAI #LLMs #GenerativeAI #MachineLearning Quote:
"Even we had very, very clever AIs, this pace of progress could still end up bottlenecked on things like hardware and robotics."
MACHINE LEARNING
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Large Language Models Performance Improving Exponentially
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MIT Uses Machine Learning to Design Ocean-Monitoring Glider Vehicles
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AVs called "gliders" drift thru the ocean to collect data about aquatic environments.
— MIT CSAIL (@MIT_CSAIL) 10 juillet 2025
MIT approach uses machine learning to explore new glider designs, testing & refining designs in simulation. It could help scientists monitor marine life: https://t.co/FPLiOQkPDX pic.twitter.com/kwKROZjydfAVs called "gliders" drift thru the ocean to collect data about aquatic environments. MIT approach uses machine learning to explore new glider designs, testing & refining designs in simulation. It could help scientists monitor marine life: https://
bit.ly/40NZkmv -
Vibe Modeling Accelerates Data Science Experimental Workflows
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Vibe Modeling is here—and it’s changing how data scientists work. We break down how to speed up the experimental loop with control. Check out our blog and Git repository to get started: https://
domino.buzz/4eLqtfN #DataScience #MLOps #GenAI #AIProductivity -
Adopting Small Language Models Without Rebuilding Your Tech Stack
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Adopt SLMs without rearchitecting your entire stack: → Start by auditing where LLMs are used and which tasks can be offloaded
→ Use distillation and fine-tuning to train SLMs on those subtasks
→ Gradually replace LLMs with SLMs in your pipeline, monitor, and optimize -
SLMs Rapid Improvement and Easy Migration from LLMs
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A few takeaways: 1. SLMs are improving rapidly, even at the same parameter size
2. Migrating from LLMs to SLMs is easier than expected -
Devstral Small Medium 2507: Improved AI for Coding Tasks
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Introducing Devstral Small and Medium 2507! This latest update offers improved performance and cost efficiency, perfectly suited for coding agents and software engineering tasks.
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Fine-tuning tiny AI models with Colab notebooks tutorial
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Note that these models are tiny, so I would always recommend fine-tuning them for your use case. My colleague @EdoardMosca created two Colab notebooks to help you in this adventure. You can find them in the model cards. More tutorials to come! 🙂
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LFM2-1.2B outperforms larger Qwen3 model through efficient training
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This speed should come at a cost but, thanks to efficient training, we managed to squeeze incredible performance in these tiny models. LFM2-1.2B is competitive with Qwen3-1.7B, a model 47% larger!
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Algorithms on Trees and Graphs with Python Code
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#Algorithms on Trees and Graphs (with #Python Code): http://
amzn.to/3IrmKWs ——————
#AI #MachineLearning #BigData #DataScience #RDF #LinkedData #GraphDB #GraphAnalytics #Semantics #KnowledgeGraphs #RAG #LLMs #DataStructures #DataScientist #Coding -
Hybrid Model with Multiplicative Gates and Short Convolutions
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The result is a hybrid model with multiplicative gates and short convolutions: – 10 double-gated short-range LIV convolution blocks
– 6 grouped query attention (GQA) blocks It's REALLY fast, especially on CPU!