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For those of you, who are wondering what is “Analytics Vidhya”, “Analytics” can be defined as the science of extracting insights from raw data. The spectrum of analytics starts from capturing data and evolves into using insights / trends from this data to make informed decisions. “Vidhya” on the other hand is a Sanskrit noun meaning “Knowledge” or “Clarity on a subject”. Knowledge, which has been gained through reading literature or through self practice / experimentation.
Analytics Vidhya provides a community based knowledge portal for Analytics and Data Science professionals. The aim of the platform is to become a complete portal serving all knowledge and career needs of Data Science Professionals. Source
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Recent Articles
Search Articles10 Essential Agentic AI Concepts Explained Simply
AI agents are everywhere right now. You hear terms like tool calling, agent loops, MCP, guardrails thrown around as if its common languageโฆ it isnโt! But that is about to change. Agentic AI isnโt nearly as complicated as it sounds once you understand the few core ideas that actually matter. Here are 10 agentic AI concepts that everyone should know about, ranked by what people are searching for and using daily.
Mastering the AI Project Cycle: From Concept to Production
Reading list In fact, AI projects are not built by simply choosing a model and feeding it data. Furthermore, a successful AI system goes through multiple stages, starting with identifying the right problem and ending with deployment, monitoring, and continuous improvement. This structured journey is known as the AI Project Cycle. It helps teams move from an initial idea to a reliable, production-ready AI solution while reducing risks and improving outcomes.
What is a Forward Deployed Engineer? Role, Skills & Salary
Reading list A forward deployed engineer (FDE) is a software engineer who embeds directly inside a customer’s team and infrastructure to build, integrate, and run production systems, instead of building a generic product from headquarters. Consultants deliver recommendations. An FDE delivers working code that stays in production. Therefore, the reason this job exists is uncomfortable.
How to Use Kimi K3: Moonshot AI’s 2.8T Open-Weight Model
Moonshot AI’s Kimi K3 is a 2.8-trillion-parameter open-weight model built with a Mixture-of-Experts architecture. It activates only a small fraction of its parameters per token, helping reduce inference costs while delivering strong coding and agentic performance. K3 combines near-frontier capabilities, open weights, and lower API pricing, making it an interesting alternative to proprietary models.
Spec-Driven Development with Claude Code: Writing Bulletproof Specs
I have written enough specs for Claude Code now to have hit the failure mode nobody warns you about. The spec was fine. The plan was fine. Claude worked through the tasks, ran the test suite, and reported everything passing. I looked at the diff properly the next morning and found it had converted a flaky test from an assertion into a skip. The suite was green. The requirement was not met. The test could no longer fail, because it either passed or quietly skipped. That is not a bug in Claude Code.
Top 5 Agentic AI Research Papers of 2026
Agentic AI research in 2026 has moved past the basic question of whether a model can be called a tool. The harder questions are whether an agent can finish long workflows, survive live websites, verify its own work, recover from failure, and improve its process over time. The five papers below map that shift well enough. Two focus on evaluation, one reframes code as the infrastructure around an agent, and two explore research systems that iterate on their own work.
PagedAttention vs. RadixAttention: Optimizing LLM KV Cache Management
Reading list Modern LLMs rely on quantization, pruning, distillation, and faster attention kernels, but production performance often depends most on KV cache management. As context windows grow, the cache consumes significant GPU memory, limiting concurrency, throughput, and latency. Two breakthroughs transformed this challenge: PagedAttention improves memory allocation, while RadixAttention enables efficient prefix reuse.
How to Remove Claude Watermarks from Text, Code, and Files
Claude now marks AI-generated content. But it does not mark everything the same way. Anthropic currently uses embedded watermarks for text and signed C2PA provenance metadata for supported files. Code sits somewhere in between: it is still text, but its structure gives the watermark fewer places to work. I went into detail about Claude’s watermarks in my article how Claude’s watermarking works, and here Iโd answer the obvious question: How do you remove the watermark?
How to Add Skills in Agents using LangChain
Reading list Ever wondered how ChatGPT, Gemini, and other chat interfaces generate PDFs, PowerPoints, and more when all they have under the hood is an LLM? The trick isn’t a smarter model. It’s something simpler: skills which are instructions an agent loads only when needed. Next, let’s explore how skills work using LangChain and how they can make your own agents more capable, flexible, and efficient.
7 Kimi K3 Features That Make Every Other Model Feel Outdated
Reading list Developers launch new models every week, but most barely change how you work. Kimi K3 is differentânot because of benchmark charts, but because of a few small API changes that fundamentally affect how you use it. The first is reasoning_effort, which defaults to maximum, alongside 131,072 max_completion_tokens. Ask K3 to rename a variable, and it may reason through race conditions. The key lesson: K3 features come with settings.