
Analytics Vidhya
Vasu Deo Sankrityayan writes about artificial intelligence and machine learning. He covers topics like building AI voice apps, structuring Claude projects, and engineering deep agents.
Profile generated by AI for Anything

Complete Guide to Thinking Machines Inkling
1d

The original title is "10 Trending GitHub AI Agent Repositories — July 2026"
2d

Connect MCP servers to Claude Desktop and Claude Code
4d

The original title is "GPT-5.6 Sol vs Claude Fable 5: Benchmarks, Pricing & Hands-On"
5d

What is Meta Prompting and How does it work?
7d

The original title is: "How to Measure Video Similarity: 6 Techniques I Tested (and the One I Shipped)"
8d

The original title is "Handling Imbalanced Classification: What Works Better Than SMOTE"
9d

The original title is "RAG Evaluation Frameworks Compared: RAGAS vs TruLens vs DeepEval"
10d

OpenAI Launches GPT-5.6 Models (Sol, Terra, Luna) with Free Public Access
11d

The original title is about "Loop Engineering" and persistent AI agents for repeated workflow tasks. Let me rewrite this for a mobile feed.
12d

DeepSeek DSpark: Speculative Decoding Boosts DeepSeek-V4 Generation Speed by 60-85%
13d

OKF: Redefining Knowledge Bases for AI Agents
14d

The original title is "Modern VLMs Explained: How GPT-4o, Gemini, Claude Vision, and Qwen-VL Work"
15d

The original title is "YOLO26 Tutorial: Object Detection, Pose Estimation & More"
17d

The original title is "Large Action Models (LAMs) vs Agentic LLMs: What's the Real Difference?"
18d

Claude Sonnet 5: The Fable 5 at Home
20d

Comparing ChatGPT Plus, Claude Pro, and Gemini Pro at $20/month
21d

The original title is "GraphRAG vs Vector RAG: Which Retrieval Method is Best?"
22d

Using AI When You Don’t Trust AI
25d

The Self-Improving Loop in AI Agents: Architecture, Benefits, and How it Outperforms Traditional Agent Workflows
26d

Harness-1: 20B Retrieval Subagent Simplifies Search Agent Architecture
27d

Sakana Fugu: Multi-Agent System as a Model
28d

How Claude Generates SVG Illustrations With Code
29d

System Design for ML Interviews: 10 Real Problems Walked Through
32d