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Anthropic has unveiled Claude 3.7 Sonnet, its most advanced AI yet and the first hybrid reasoning model. It combines rapid responses with deep, step-by-step reasoning, redefining AI problem-solving.

there is no point building indian own AI like ChatGPT Grok or Claude , they are the best LLM , just think someone creates an LLM from India what will be the difference nothing , the mainstream Models will dominate no doubt , instead Builders from Ind

How Flcit Works? Ask any question related to startup validation, market analysis, finance, risk and Flict runs a multi-agent debate and presents detailed verdicts with confidence scores while Finnaly judges the perspectives with strong reasoning and

Why Grok AI Outperformed ChatGPT & Gemini — Without Spending Billions In 2025, leading AI companies invested heavily in R&D: ChatGPT: $75B Gemini: $80B Meta: $65B Grok AI, developed by Elon Musk's xAI, raised just $10B yet topped global benchmar

I have DeepSeek , Grok , Qwen , ChatGPT , Gemini , perplexity and Claude apps. They all possess " similar " capabilities more or less and even many of them have given access to their top tier models for free. ( Of course with some limitations) So t

I asked multiple AI platforms to suggest the best laptop around 1 Lakh and shared 10-12 points on minimum specifications I wanted. I tried to make my prompt better and to the point. But they all suggested different companies and models.. ChatGPT ->

New and Improved AI Models: Inflection-2: Google AI introduced Inflection-2, a large language model excelling at reasoning and following instructions. This could pave the way for AI assistants that can understand and complete complex tasks. Claude

Anthropic Launches Claude 3.7 Sonnet: Anthropic unveiled Claude 3.7 Sonnet, a hybrid reasoning AI model offering faster, more nuanced responses. The model features "extended thinking mode" on paid plans, improving performance in mathematics, coding,

LLM Post-Training: A Deep Dive into Reasoning LLMs This survey paper provides an in-depth examination of post-training methodologies in Large Language Models (LLMs) focusing on improving reasoning capabilities. While LLMs achieve strong performance

Most people studying AI agents never deploy one real system people actually use. Because they stop at prompts. Prompting is practice. Building is different. Production systems require architecture, workflows, evaluation, and real operational think