Just a reminder: you don’t need special permission, very specific hardware, an online account, or any other gimmick in order to train XGBoost. @trainxgb
COMPUTING
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Discussion on Frontier AI Model Capabilities and Compute Allocation
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Seedance 2.0 has been out for a while now and is SOTA. Strangely, there's been little word from US Frontier Labs to counter it. Either they're foregoing it because they need and are reserving the compute resources (as with OpenAI) for the development and research of upcoming
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Fear of NVIDIA Dropping CUDA Support for GPUs
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GPU I am scared of the day NVIDIA drops the CUDA support for it
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Graph Databases and Linked Data Impact on Knowledge Systems
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This book had a big impact on my thinking (graph databases, linked data, semantic knowledgebases, contextual metadata, connected IoT, context engineering, etc.): http://
amzn.to/4cBYJIi "Linked: How Everything is Connected to Everything Else — What It Means for Business, -
Master Python Fundamentals: Ultimate Beginner’s Guide with Practice Questions
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"Master #Python Fundamentals: Ultimate Guide for Beginners" — The Number #1 Python Book For Beginners with Extra 300+ Hands-on Practice Questions Get this brilliant book at http://
amzn.to/3QCQCDn by @RealBenjizo ————
#DataScience #DataScientist #ComputationalScience -
NaLaFormer: Faster AI Models with Less Memory
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What if your AI models could process massive data faster, with far less memory, and achieve state-of-the-art accuracy? Researchers from Harbin Institute of Technology, Pengcheng Laboratory, and UQMM Lab introduce NaLaFormer. Their NaLaFormer method uses a clever
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Mastering PyTorch: Build and Deploy Deep Learning Models, from CNNs to LLMs
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"Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond" – http://amzn.to/40IFEQR via @PacktDataML #AI #ML #MachineLearning #DataScience #DataScientist #GenAI
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Advice against wasting money and time on LLMs and local AI
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please don't waste your money or time on this for LLMs / local AI
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TT-Boltz runs on Tenstorrent Blackhole Galaxy protein prediction server
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TT-Boltz now runs on the @tenstorrent Blackhole Galaxy server.
— Moritz Thüning (@moritzthuening) 28 mars 2026
32 Blackhole processors predicting protein structures in parallel. 32x the throughput of a single Blackhole card. Each protein can contain more than 3k amino acids. For small proteins, a single Blackhole card runs… pic.twitter.com/50ZibE0uEJTT-Boltz now runs on the @tenstorrent Blackhole Galaxy server. 32 Blackhole processors predicting protein structures in parallel. 32x the throughput of a single Blackhole card. Each protein can contain more than 3k amino acids. For small proteins, a single Blackhole card runs Boltz 2 faster than an RTX 5090, at a fraction of the cost. It’s beautiful. The Galaxy server has just become the best product you can slide into your rack to predict protein structures at scale. This is still just the beginning. Very soon, it will run across multiple Galaxy servers and one day, I want to walk into a pharmacy and see a drug that was designed using our hardware. We won’t stop until that is the case. We just don’t stop.
→ View original post on X — @tenstorrent, 2026-03-28 13:06 UTC
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Edge Computing Ensures Real-Time Manufacturing Production Line Health
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Keeping a production line healthy happens in real time, in hundreds of milliseconds. Sandeep Pandya, TDK SensEI CEO, explains why edge computing is fundamental to manufacturing uptime. #PaidPartnership with TDK SensEI. #tdk_iiot pic.twitter.com/iXEEe4NrfV
— Lucian Fogoros (@fogoros) 28 mars 2026Keeping a production line healthy happens in real time, in hundreds of milliseconds. Sandeep Pandya, TDK SensEI CEO, explains why edge computing is fundamental to manufacturing uptime. #PaidPartnership with TDK SensEI. #tdk_iiot