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Small Language Models are set to transform AI agents-but only if implemented the right way. By 2027, Gartner predicts task-specific SLMs will be used three times more than general-purpose LLMs. NVIDIA agrees, calling them “the future of AI agents.”

Small language models (SLMs) are compact NLP models optimized for edge devices (smartphones, IoT, embedded systems). With fewer parameters they enable faster, low‑latency on‑device inference, improving privacy by avoiding cloud transfers, reducing en

Hiii! everyone I am working in Robots Robotics blends engineering, computer science, and AI to create machines that can perform tasks autonomously or semi-autonomously. Industrial robots revolutionize manufacturing by handling repetitive tasks wit

India should focus on fine-tuning existing AI models and building applications rather than investing heavily in foundational models or AI chips, says Groq CEO Jonathan Ross. Is this the right strategy for India to lead in AI innovation? Thoughts?

i made an AI News Aggregator in a hackathon one month ago posting it here now. An autonomous AI agent that crawls, summarizes, and publishes news articles. Overview The AI News Aggregator is designed to autonomously gather news articles from variou

here is something new with ai technology which help other to make progress and upgrade there productivity inviting for collaboration using base technologies python , pre fine-tuned models , etc

one of the best articles that I read on deepseek and it's affect on the nvidia stock, it explains in details of how the models is trained and fine tuned to have such strong logical thinking.

LLMs as Agents Large Language Models (LLMs) like *GPT*, *Claude*, or *Gemini* can act as intelligent. *What does it mean to be an agent?* An *LLM agent* can: • Understand a goal • Break it into steps • Use tools or APIs • Adapt based on c

💡 5 Things You Need to Master for learn for integrating AI into your project 1️⃣ Retrieval-Augmented Generation (RAG): Combine search with AI for precise and context-aware outputs. 2️⃣ Vector Databases: Learn how to store and query embeddings for e

"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