36 related articles

Cursor launches its Start plan for India at ₹649/month (~$7.70), featuring Grok 4.5 and Composer Agent. A deep dive into the PPP pricing strategy and its market implications.

A fresh grad interviewing for a GenAI Trainer role faced prime number coding and activation function questions while the interviewer used Gemini to generate questions live — exposing AI hiring chaos.

A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.

Context engineering is the core methodology for building efficient AI Agents, covering query enhancement, RAG retrieval, prompt design, memory management, and tool invocation. Master Write, Select, Compress, and Isolate to solve LLM hallucination at its root.
Human-Centered AI: Real-World Implemen…
An MSR workshop reveals the truth about AI deployment: from a $20 corneal diagnostic device to expert-in-the-loop chatbots, researchers share real-world experiences of AI in healthcare and design within resource-scarce environments.

What is an AI agent? How does it differ from a large language model? Learn the core concepts, the Agent formula (LLM + Workflow + Knowledge Base), and how to choose between Dify, LangChain, and LlamaIndex.

Traditional Java roles are shrinking while AI demand surges. Learn the three paths into AI for developers, and why RAG knowledge bases are the highest-ROI entry point for Java engineers.

At the Microsoft Research India summit, top experts explore the real progress of multimodal AI and embodied intelligence: fusing classical robotics with large models, healthcare AI deployment challenges, perceptual bottlenecks in reasoning, and possibilities beyond scaling.
Three Core Gaps in Multimodal LLMs: Fr…
Microsoft Research India reveals three core gaps in multimodal LLMs: visual perception blindspots, cognitive hallucination, and architectural limitations. Explores Faithful GRPO, behavior modeling, and model alignment breakthroughs.

Confused by scattered LLM resources and unclear learning paths? This guide maps a complete roadmap from basics to advanced, covering Karpathy, Stanford CS224N, DeepLearning.AI, Hugging Face, plus RAG, fine-tuning, and Agent deep dives.

Knowing how to call an API doesn't make you an AI engineer. This article breaks down the complete skill structure of an AI application engineer, covering Python fundamentals, LLM fine-tuning, Agent development, and enterprise projects.

India's AI/data science postings hit 11,557 this week, down 5% from last week, but the skill demand structure barely changed. Python, ML, and SQL remain top skills while GenAI/LLM demand keeps rising.

A clear, in-depth guide to how AI Agents work: the paradigm shift from traditional programs, the perception-decision-action loop, and the four pillars—LLMs, tool calling, memory, and RAG.

A complete AI Agent learning roadmap covering BDI theory, core components (Perception/Planning/Execution), AutoGen multi-agent frameworks, and DeepSeek RAG projects for beginners.

A complete AI Agent learning roadmap covering agent principles, prompt engineering, RAG, multi-agent systems, and hands-on projects — from zero to real-world deployment.

A deep dive into AI Agent development: real architecture, entry barriers, and learning paths. From ReAct to multi-agent systems and LangChain — cut through the hype.

Want to break into AI application development? This guide covers the full learning path — from Agents and RAG to Prompt Engineering — helping you master LLM engineering skills and land the job.

A systematic guide to Coze's core positioning, its differences from Dify/n8n, and its full capability system covering agents, workflows, and multi-agent modes—helping beginners get started fast.

A systematic breakdown of the complete AI Agent learning roadmap, covering prompt engineering, the ReAct paradigm, memory mechanisms, and multi-agent collaboration, with hands-on project advice.

An exclusive look at the AI Engineer Summit dress rehearsals, decoding the paradigm shift from research to production. A deep dive into AI Engineer challenges, RAG, agent systems, and AI engineering as a distinct discipline.