Revolutionizing Animal Welfare: Indian Startups Lead With AI Technology - The Logical Indian
Revolutionizing Animal Welfare: Indian Startups Lead With AI Technology The Logical Indian
Could not retrieve the full article text.
Read on GNews AI welfare →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
startupindia
Defining and creating a basic Design System based on any website (in Figma and React) using Claude
It's 2026, and MCP and design tooling are constantly changing with various improvements in the AI space. Arguably, one of the most long-awaited features was Figma MCP supporting agents writing to its canvas. The funny thing is, I created a particular workflow 2 weeks ago, the lack of the write to canvas was a bit of a bottleneck but could be worked around since Claude Code was pretty good at generating plugins to create and clean up tokens (variables), as well as components and their props. Thankfully, that gap didn't hang around for long enough, and now there's native support for writing directly to canvas. Firstly, we'll look at how we can set up the correct tooling for the following use case: You've joined a startup, they have a website or a web page, but lack any reusable design system

The convergence of FinTech and artificial intelligence: Driving efficiency and trust in financial services - cio.economictimes.indiatimes.com
The convergence of FinTech and artificial intelligence: Driving efficiency and trust in financial services cio.economictimes.indiatimes.com
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products

A Reduction-Driven Local Search for the Generalized Independent Set Problem
arXiv:2505.21052v2 Announce Type: replace Abstract: The Generalized Independent Set (GIS) problem extends the classical maximum independent set problem by incorporating profits for vertices and penalties for edges. This generalized problem has been identified in diverse applications in fields such as forest harvest planning, competitive facility location, social network analysis, and even machine learning. However, solving the GIS problem in large-scale, real-world networks remains computationally challenging. In this paper, we explore data reduction techniques to address this challenge. We first propose 14 reduction rules that can reduce the input graph with rigorous optimality guarantees. We then present a reduction-driven local search (RLS) algorithm that integrates these reduction rule

MBGR: Multi-Business Prediction for Generative Recommendation at Meituan
arXiv:2604.02684v1 Announce Type: new Abstract: Generative recommendation (GR) has recently emerged as a promising paradigm for industrial recommendations. GR leverages Semantic IDs (SIDs) to reduce the encoding-decoding space and employs the Next Token Prediction (NTP) framework to explore scaling laws. However, existing GR methods suffer from two critical issues: (1) a \textbf{seesaw phenomenon} in multi-business scenarios arises due to NTP's inability to capture complex cross-business behavioral patterns; and (2) a unified SID space causes \textbf{representation confusion} by failing to distinguish distinct semantic information across businesses. To address these issues, we propose Multi-Business Generative Recommendation (MBGR), the first GR framework tailored for multi-business scenar




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