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Google DeepMind’s CaMeL: A Breakthrough in Stopping AI’s "Prompt Injection" Problem? For years, prompt injection attacks (where hidden instructions trick AI models into bypassing safeguards) have haunted developers. Despite countless fixes, no solut

Tata Harrier EV Officially Launched in India -Starting at ₹21.49 Lakh Experience Supercar-like performance Underpinned by the acti.ev + architecture, the Harrier.ev delivers unmatched acceleration, control, and confidence across varied terrains. Fr

🚀 Breaking: Quash AI Secures Pre-Seed Funding Led by Arali Ventures! Thrilled to see another powerful player in India's AI ecosystem! Quash AI is revolutionizing workplace automation with their innovative agentic AI platform. What makes Quash stan

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

AI Recap Gemini 2.5 Flash Image (“nano-banana”) dominates image editing Model reveal, capabilities, availability: The anonymous “nano-banana” on community arenas was confirmed as Gemini‑2.5‑Flash‑Image‑Preview by Google DeepMind. It delivers s

MCP is getting attention, but it’s just one piece of the puzzle If you’re developing Agentic AI systems, it’s crucial to understand more than just MCP. There are 5 key protocols shaping how AI agents communicate, collaborate, and scale intelligence

What Are Some Real Examples of Large Language Models, and How Are They Used? Large Language Models (LLMs) are revolutionizing the way we interact with artificial intelligence. From chatbots to code generators, these models are reshaping industries b

"A Survey on Post-Training of Large Language Models" This paper systematically categorizes post-training into five major paradigms: 1. Fine-Tuning 2. Alignment 3. Reasoning Enhancement 4. Efficiency Optimization 5. Integration & Adaptation 1️⃣ Fin

Top 10 AI Research Papers Since 2015 🧠 1. Attention Is All You Need (Vaswani et al., 2017) Impact: Introduced the Transformer architecture, revolutionizing natural language processing (NLP). Key contribution: Attention mechanism, enabling models