Meet Kosmos: An AI Scientist that Automates Data-Driven Discovery
[ad_1] Kosmos, built by Edison Scientific, is an autonomous discovery system that runs long research campaigns on a single goal....
Comparing Memory Systems for LLM Agents: Vector, Graph, and Event Logs
[ad_1] Reliable multi-agent systems are mostly a memory design problem. Once agents call tools, collaborate, and run long workflows, you...
Gelato-30B-A3B: A State-of-the-Art Grounding Model for GUI Computer-Use Tasks, Surpassing Computer Grounding Models like GTA1-32B
[ad_1] How do we teach AI agents to reliably find and click the exact on screen element we mean when...
Moonshot AI Releases Kosong: The LLM Abstraction Layer that Powers Kimi CLI
[ad_1] Modern agentic applications rarely talk to a single model or a single tool, so how do you keep that...
A Coding Implementation to Build and Train Advanced Architectures with Residual Connections, Self-Attention, and Adaptive Optimization Using JAX, Flax, and Optax
[ad_1] In this tutorial, we explore how to build and train an advanced neural network using JAX, Flax, and Optax...
Meta AI Releases Omnilingual ASR: A Suite of Open-Source Multilingual Speech Recognition Models for 1600+ Languages
[ad_1] How do you build a single speech recognition system that can understand 1,000’s of languages including many that never...
Maya1: A New Open Source 3B Voice Model For Expressive Text To Speech On A Single GPU
[ad_1] Maya Research has released Maya1, a 3B parameter text to speech model that turns text plus a short description...
How to Reduce Cost and Latency of Your RAG Application Using Semantic LLM Caching
[ad_1] Semantic caching in LLM (Large Language Model) applications optimizes performance by storing and reusing responses based on semantic similarity...
Baidu Releases ERNIE-4.5-VL-28B-A3B-Thinking: An Open-Source and Compact Multimodal Reasoning Model Under the ERNIE-4.5 Family
[ad_1] How can we get large model level multimodal reasoning for documents, charts and videos while running only a 3B...
How to Build an End-to-End Interactive Analytics Dashboard Using PyGWalker Features for Insightful Data Exploration
[ad_1] def generate_advanced_dataset(): np.random.seed(42) start_date = datetime(2022, 1, 1) dates = [start_date + timedelta(days=x) for x in range(730)] categories =...