china just double-dropped Kimi K2.6 and DeepSeek V4. Same week. Both rad. Both open-source. Gemini, GPT and Claude right now:
LLMS
-
LLaTiSA: Difficulty-Stratified Time Series Reasoning Visual Perception
By
–
LLaTiSA
— AK (@_akhaliq) 24 avril 2026
Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics
paper: https://t.co/ODOruVboIu pic.twitter.com/ZM5G3vY3IKLLaTiSA Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics paper: https://
huggingface.co/papers/2604.17
295
… -
Claude Dispatch with Computer Use for Codex Integration
By
–
anyone else using claude dispatch w/ computer use to use codex?
-
DeepSeek’s Efficiency Gains and Future with Advanced Chips
By
–
DeepSeek doing more with less is impressive. The more important question is what happens when they get the better chips anyway.
-
LLMs vs Vision Systems: Neural Nets and Architecture Differences
By
–
Absolutely nothing. They both use neural nets and backprop. But LLMs are generative architectures trained on sequences of discrete symbols. Vision systems used in AEBS and other applications use ConvNets or ViTs trained to detect and classify from labelled samples.
-
Anthropic Accelerates Product Development Timelines with Latest Models
By
–
My biggest takeaways from Claude Code's Head of Product @_catwu
: 1. Anthropic’s product development timelines have gone from six months to one month, sometimes one week, sometimes one day. Part of this acceleration is access to the latest models (i.e. Mythos). Another is -
LLMs Dominated by Industry Limits Academic Research Contribution
By
–
I didn't say that. I said that LLM is in the hands of industry, is largely engineering, and requires too much computing resources for academics to contribute significantly. More importantly, LLMs are today's technology. Academic researchers, particularly PhD students, should be
-
Foundations of Large Language Models eBook
By
–
Free eBook: Foundations of Large Language Model! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #Books
-
Joint Embedding Architectures: Vision Encoders Beyond LLMs
By
–
Through 1. Vision encoders that are not LLMs. They are actually Joint Embedding Architectures that embed images and text description in the same space
2. Painfully exhaustive training on enormous amounts of declarative facts about the physical world. You can train them to answer -
Why LLMs fail at physical world understanding compared to JEPA
By
–
We still don't have AI systems that understand the physical world as well as a cat. JEPA are getting there. LLMs and other generative architectures are not. Generative architectures, particularly those that produce discrete tokens like LLMs simply ***DO NOT WORK*** for