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- ⏰ Featured AIs: AWS Introduces SWE-PolyBench & Meta AI Releases Web-SSL...
⏰ Featured AIs: AWS Introduces SWE-PolyBench & Meta AI Releases Web-SSL...
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Dive into the hottest AI breakthroughs of the week—handpicked just for you!
Agentic AI
AWS Introduces SWE-PolyBench: A New Open-Source Multilingual Benchmark for Evaluating AI Coding Agents
AWS AI Labs has introduced SWE-PolyBench, a multilingual, repository-level benchmark designed for execution-based evaluation of AI coding agents. The benchmark spans 21 GitHub repositories across four widely-used programming languages—Java, JavaScript, TypeScript, and Python—comprising 2,110 tasks that include bug fixes, feature implementations, and code refactorings.
SWE-PolyBench adopts an execution-based evaluation pipeline. Each task includes a repository snapshot and a problem statement derived from a GitHub issue. The system applies the associated ground truth patch in a containerized test environment configured for the respective language ecosystem (e.g., Maven for Java, npm for JS/TS, etc.). The benchmark then measures outcomes using two types of unit tests: fail-to-pass (F2P) and pass-to-pass (P2P).....
⇧ 1,249 Likes
Robotics & Open Source AI
NVIDIA AI Releases HOVER: A Breakthrough AI for Versatile Humanoid Control in Robotics (Promoted)
Researchers from NVIDIA, Carnegie Mellon University, UC Berkeley, UT Austin, and UC San Diego introduced HOVER, a unified neural controller aimed at enhancing humanoid robot capabilities. This research proposes a multi-mode policy distillation framework, integrating different control strategies into one cohesive policy, thereby making a notable advancement in humanoid robotics.
HOVER is a paradigm shift. It’s a “generalist policy”—a single neural network that harmonizes diverse control modes, enabling seamless transitions and unprecedented versatility. HOVER supports diverse control modes, including over 15 useful configurations for real-world applications on a 19-DOF humanoid robot. This versatile command space encompasses most of the modes used in previous research......
⇧ 1,749 Likes
Important AI News 🔥 🔥 🔥
Agentic AI & MCP
🧵 Atla AI Introduces the Atla MCP Server: A Local Interface of Purpose-Built LLM Judges via Model Context Protocol (MCP) (promoted)
⇧2,500 Likes
Computer Vision
📢 NVIDIA AI Releases Describe Anything 3B: A Multimodal LLM for Fine-Grained Image and Video Captioning
⇧2,300 Likes
Agentic AI
🚨 Meet Rowboat: An Open-Source IDE for Building Complex Multi-Agent Systems
⇧2,100 Likes
Image Generation
💡 OpenAI Launches gpt-image-1 API: Bringing High-Quality Image Generation to Developers
⇧1,800 Likes
LLM Evaluation
🧲 Sequential-NIAH: A Benchmark for Evaluating LLMs in Extracting Sequential Information from Long Texts
⇧1,500 Likes
Agentic AI & Open Source
🧵 Meet Xata Agent: An Open Source Agent for Proactive PostgreSQL Monitoring, Automated Troubleshooting, and Seamless DevOps Integration
⇧1,300 Likes
Computer Vision
Meta AI Releases Web-SSL: A Scalable and Language-Free Approach to Visual Representation Learning
⇧ 4,500 Likes
To explore the capabilities of language-free visual learning at scale, Meta has released the Web-SSL family of DINO and Vision Transformer (ViT) models, ranging from 300 million to 7 billion parameters, now publicly available via Hugging Face. These models are trained exclusively on the image subset of the MetaCLIP dataset (MC-2B)—a web-scale dataset comprising two billion images. This controlled setup enables a direct comparison between WebSSL and CLIP, both trained on identical data, isolating the effect of language supervision.
WebSSL encompasses two visual SSL paradigms: joint-embedding learning (via DINOv2) and masked modeling (via MAE). Each model follows a standardized training protocol using 224×224 resolution images and maintains a frozen vision encoder during downstream evaluation to ensure that observed differences are attributable solely to pretraining......
⇧3,200 Likes
Agentic AI & C
Hands-on-Coding </>
🖥️ A Coding Guide to Build an Agentic AI‑Powered Asynchronous Ticketing Assistant Using PydanticAI Agents, Pydantic v2, and SQLite Database
In this tutorial, we’ll build an end‑to‑end ticketing assistant powered by Agentic AI using the PydanticAI library. We’ll define our data rules with Pydantic v2 models, store tickets in an in‑memory SQLite database, and generate unique identifiers with Python’s uuid module. Behind the scenes, two agents, one for creating tickets and one for checking status, leverage Google Gemini (via PydanticAI’s google-gla provider) to interpret your natural‑language prompts and call our custom database functions. The result is a clean, type‑safe workflow you can run immediately in Colab...….
!pip install --upgrade pip
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