AI’s next bottleneck is not just compute. It is time. Time lost moving data.
Time lost coordinating chips.
Time lost waiting on memory, interconnects, and software layers to catch up. That shift changes how we should think about AI infrastructure. A thread… #HuaweiPartner
AI
-
AI’s next bottleneck is time lost on data, chips, and memory
By
–
-
Frustration with explaining ML for tabular data
By
–
Every time someone tries to explain to me ML for tabular data. @trainxgb @tabul_ai
-
AI Wardrobe App Picks Outfit Based on Weather and Schedule
By
–
#AI Wardrobe App Picks Your Outfit Based on Weather and Schedule
— Ronald van Loon (@Ronald_vanLoon) 17 juin 2026
by @jayhxmo#ArtificialIntelligence #MachineLearning #ML pic.twitter.com/zxv2Cm6IU0#AI Wardrobe App Picks Your Outfit Based on Weather and Schedule
by @jayhxmo #ArtificialIntelligence #MachineLearning #ML -
AI does not replace humans: the low human added value
By
–
The big problem with all those who say that AI does not replace humans is that their argument relies on the fact that the remaining human part in a production process has value. That's a mistake: the human part actually has low added value.
-
NVIDIA’s LocateAnything speeds object detection 10x by fixing coordinate bottleneck
By
–
🚨 @NVIDIA just dropped LocateAnything, making object detection ~10x faster by fixing one core bottleneck:
— Charly Wargnier (@DataChaz) 17 juin 2026
How the model writes coordinates.
Standard AI models do visual grounding the slow way.
They predict coordinates piece by piece: Token 1, Token 2, Token 3, Token 4 etc.… pic.twitter.com/GAyr0taCbX@NVIDIA just dropped LocateAnything, making object detection ~10x faster by fixing one core bottleneck: How the model writes coordinates. Standard AI models do visual grounding the slow way. They predict coordinates piece by piece: Token 1, Token 2, Token 3, Token 4 etc.
-
HarnessAudit Framework Audits LLM Agent Safety Beyond Correct Answers
By
–
Are your LLM agents really safe even when they return the correct answer? Researchers from UCSB, UC Berkeley, Stanford, UW-Madison, and Microsoft Research introduce HarnessAudit—a framework that audits full execution trajectories for boundary compliance, resource access, and
-
Kimi K2.7 Release Advances Long Context Reasoning and LLM Efficiency
By
–
Kimi K2.7 Release: Advancing Long Context Reasoning and LLM Efficiency! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux
-
Roadmap to Becoming an LLM Engineer in 2026
By
–
The Roadmap to Becoming an LLM Engineer in 2026! @kdnuggets #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding
-
A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference
By
–
A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using CityGraph, OSMnx, and PyTorch Geometric! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #GeoSpatial #Mathematics #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java