ciflow/vllm/179439
update vllm commit hash
Could not retrieve the full article text.
Read on PyTorch Releases →Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
update
The Senior Engineer's Guide to CLAUDE.md: From Generic to Actionable
Transform your CLAUDE.md from a vague wishlist into a precise, hierarchical configuration file that gives Claude Code the context it needs to execute complex tasks autonomously. The Senior Engineer's Guide to CLAUDE.md: From Generic to Actionable Claude Code is not a junior developer you manage. It's a force multiplier for senior engineers who know how to direct it. The difference between a productive and frustrating experience almost always comes down to configuration, specifically your CLAUDE.md files. The CLAUDE.md Hierarchy You're Probably Missing Most developers drop a single CLAUDE.md in their project root and call it a day. That's leaving power on the table. Claude Code reads a hierarchy of these files, and understanding this is your first leverage point. Global: ~/.claude/CLAUDE.md

Only 20% of MCP Servers Are 'A-Grade' Secure — Here's How to Vet Them Before Installing
Most MCP servers lack documentation or contain security flags. Use specific tools and criteria to install only vetted, safe servers. The Security Problem Nobody Was Tracking The Model Context Protocol (MCP) ecosystem has exploded, crossing 20,000 servers. This growth solved the tooling problem for AI agents but created a massive, unmonitored security surface. When you run claude code with an MCP server, that code executes with your permissions—accessing your shell, filesystem, and environment variables. A malicious or poorly written server is a direct supply chain attack on your development environment. A new analysis from Loaditout scanned the entire public MCP ecosystem and assigned security grades. The results are stark: only 20.5% of servers (4,230 out of 20,652) earned an 'A' grade ,

Why AI-Powered Ecommerce Website Development Is the New Competitive Edge in 2026
The rules of online retail have changed. If your business is still relying on static product pages, manual inventory updates, and generic checkout flows, you are already falling behind. In 2026, ecommerce website development services have moved far beyond just coding a shopping cart — they now integrate artificial intelligence at every layer of the buying experience. What Has Changed in Ecommerce Development? A few years ago, building an online store meant choosing a template, uploading products, and setting up a payment gateway. Today, an eCommerce Development Company is expected to deliver AI-driven personalization engines, real-time inventory prediction, dynamic pricing algorithms, and voice-search-optimized storefronts. The shift is not cosmetic — it is architectural. AI Features Now S
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models

How to build custom key-value extraction (similar to Azure Document Intelligence)?
Hi everyone, I’m trying to build a custom document understanding system and could use some guidance. Currently, I’m using Azure Document Intelligence, where we can define specific fields and train a model by annotating documents. The trained model then extracts only the required key-value pairs from new documents. I’m interested in building a similar solution using open-source models available on Hugging Face, but I’m not sure where to begin. Could anyone suggest: Suitable models or approaches for extracting specific fields from documents Recommended workflows for training such a system Thanks in advance for your help! 1 post - 1 participant Read full topic

Grokking Beyond Addition
Hi everyone, I’m excited to share my research paper: “Grokking Beyond Addition: Circuit-Level Analysis of Algebraic Learning in Transformers” Paper: https://zenodo.org/records/19256207 This work explores grokking across multiple algebraic structures and shows a clear result: At small model scale (d_model = 64), transformers reliably grok abelian tasks but fail to generalize on non-abelian groups , even with 100% training accuracy. It also highlights: Early circuit formation before generalization Evidence for discrete-log structure in multiplication Strong embedding similarity across different tasks (CKA) I’m opening this project for collaboration and contributions: Scaling experiments (d_model = 128 / 256) Extending to more algebraic structures Interpretability improvements Reproduction an



Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!