Transform conventional chatbots into sophisticated AI assistants with Prem’s No Code–Tool Calling. This solution seamlessly integrates with services such as Slack, Google Calendar, and GitHub, automating scheduling, messaging, and data updates while eliminating complex coding and OAuth challenges.

Small Language Models (SLMs) deployed on edge devices overcome cloud dependency by reducing latency, bandwidth, and privacy risks. Explores quantization, pruning, model optimization, and efficient inference for edge computing and energy efficiency.

Chatbots vs. AI agents: Which is the best choice for customer support? This article explores key differences, workflow automation, real-time AI responses, and decision-making criteria to help businesses implement the most efficient AI-powered solution for enhancing customer experience.

In 2025, enterprises will accelerate Edge AI adoption, leverage multimodal AI for enhanced analytics, and implement Multi-Agent Systems for efficient automation, emphasizing sustainability, governance, explainability, and workforce readiness in AI deployment strategies.

Open-source agentic frameworks like LangGraph, SmolAgents, CrewAI, PhiData, and Composio enable multi-agent AI systems with scalable, modular architectures. Key features include graph-based workflows, retrieval-augmented generation, hierarchical planning, and collaborative task allocation.

Transformer inference powers tasks in NLP and vision, but is computationally intense, requiring optimizations. Large models like GPT-3 need extensive memory and FLOPs, with techniques like KV caching, quantization, and parallelism reducing costs.

Open-source CodeLLMs like DeepSeek-Coder and Qwen2.5-Coder revolutionize code intelligence by offering repository-level training, multilingual support, and advanced features. These models rival proprietary solutions, fostering transparency, collaboration, and customization.

Chunking enhances Retrieval-Augmented Generation (RAG) by splitting large texts into manageable parts for efficient processing in language models. This technique supports accurate responses, maintains context, and enables fast, parallel processing.

As AI technology expands, so does its carbon footprint. This underscores the challenge: while AI accelerates digital transformation, it risks becoming a significant contributor to global CO₂ emissions.