Building a Screen-Aware AI with ScreenEnv and Tesseract
Learn how to build screen-aware AI using ScreenEnv and Tesseract for dynamic, real-time screen content
ADaSci Expert
Sachin Tripathi
Sachin Tripathi is the Manager of AI Research at AIM, with over a decade of experience in AI and Machine Learning. An expert in generative AI and large language models (LLMs), Sachin excels in education, delivering effective training programs. His expertise also includes programming, big data analytics, and cybersecurity. Known for simplifying complex concepts, Sachin is a leading figure in AI education and professional development.
The CXO’s Role in Building Agentic AI-Ready Talent
CXOs must lead talent transformation to build Agentic AI-ready teams through upskilling, mentoring, and applied
Redefining Governance, Risk and Compliance for AI Systems with GRC 2.0
As AI systems become more autonomous, organizations face new governance and compliance challenges. This article
Chartered Data Scientist (CDS™)
The highest distinction in the data science profession. Not just earn a charter, but use it as a designation.
Implementing RAG over Excel Sheets through LlamaIndex
Using LlamaIndex and LlamaParse for RAG implementation by preparing Excel data for LLM applications.
Stability.ai’s Stable Audio Open: A Text-to-Audio Generation Model
Stability.ai’s new Stable Audio Open generates audio from text prompts, enhancing creative possibilities in sound
Hands-On Guide to Running LLMs Locally using Ollama
Explore how Ollama enables local execution of large language models for enhanced privacy and cost
Thought-Augmented Reasoning through Buffer of Thoughts (BoT)
Enhance the robustness and accuracy of LLM through thought-augmented reasoning based on the Buffer of
Observing and Examining AI Agents through AgentOps
Explore how AgentOps monitors, debugs, and tracks costs for LLM-based AI agents in various contexts.
Self-Organising File Management Through LlamaFS
Implement LlamaFS, an AI-driven file management system, based on Llama3 and Groq.
GNN-RAG: Enhancing the Reasoning Capabilities of LLMs using GNNs
GNN-RAG, a recent development, synergises GNNs and LLMs to excel in KGQA.
Implementing RAG-as-a-Service using Vectara
Discover Vectara and simplify RAG-as-a-Service for seamless generative AI application building.
RAG Reproducibility and Research using FlashRAG
FlashRAG: An open-source toolkit for standardised comparison and reproduction of RAG methods.
No-Code Approach for Building Agentic RAG using RAGapp
Implement Agentic RAG with RAGapp: no-code, an agent-based framework for multi-step reasoning using Docker.
