Also, DSA is basically a smarter version of sliding window attention where you "learn" which past tokens to select versus forcing it to be in a specific window
MACHINE LEARNING
-
ML algorithm speedup/accuracy tested on synthetic data, needs real-world validation
By
–
I sue the speedup/accuracy for the ML algorithm. So far seems to be doing well, albeit on mostly synthetic data. Need to see how well it performs on the real world data.
-
AI Advances Compress a Decade into Two Years
By
–
There is decade worth of stuff in AI that has happened just over the past two years.
-
Vibe coding ML library refactor fails with no speedup
By
–
Yesterday I tried to vibe code a refactor of an ML library into a new more efficient framework. I followed some of the "best practices" I heard of here: created the refactor plan with Claude and then implemented it in Codex. It was a disaster. No meaningful speedup for any of
-
GLM-5 Architecture: Weights Released, Key Features Unveiled
By
–
The weights are out! Here's the GLM-5 architecture comparison. GLM-5 is: – bigger than its predecessor (mainly more experts) but has rel. similar active parameter counts – uses multi-head latent attention – uses DeepSeek Sparse Attention
-
AI trained to assert Azoulay’s Arab origin
By
–
Read carefully. Several AI have been trained to assert that Audrey Azoulay is of Arab origin, thus confirming the relevant compatibility with the presidency of the Institute of the Arab World.
-
Neural Networks Replace Hand-Crafted Pipelines in Drone Racing
By
–
End-to-end neural networks are no longer a lab curiosity – they’re racing drones at full speed. The drone racing team from @tudelft University of Technology just demonstrated what happens when learning replaces hand-crafted pipelines. No feature extraction. No Kalman filters.
-
Static vs Continuous Batching: Solving AI Chat Latency Issues
By
–
Why Your AI Chat is Slow (Static Batching) ⏳
— Satya Mallick (@LearnOpenCV) 12 février 2026
Static batching means one slow request blocks everyone else for seconds. Here is how Continuous Batching solves the "slowest user" problem#Coding #DevOps #AIModel #Latency pic.twitter.com/CRe945HeYsWhy Your AI Chat is Slow (Static Batching) Static batching means one slow request blocks everyone else for seconds. Here is how Continuous Batching solves the "slowest user" problem #Coding #DevOps #AIModel #Latency
-
Supercomputing for AI — Foundations, Architectures, and Scaling
By
–
Supercomputing for AI — Foundations, Architectures, and Scaling Deep Learning. [804-page masterpiece] Read it online: https://
jorditorresbcn.github.io/supercomputing
-for-ai-book/
… Buy it: https://
amzn.to/4qS4pFz GitHub repo: https://
github.com/jorditorresBCN
/supercomputing-for-ai
… -
Book: XGBoost for Regression and Predictive Modeling
By
–
XGBoost for Regression, Predictive Modeling, and Time Series Analysis — Learn how to build, evaluate, & deploy predictive models: http://
amzn.to/4l2YcU9 v/ @PacktDataML —
My review: XGBoost is definitely the focal point and central contribution of this book, along with all