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A practical guide to what changed, the benchmarks that matter, the cost controls developers should not miss, and one hands-on demo worth building. Anthropic has just released Claude Sonnet 5.5. It is the middle child of the Claude family, and the one most people will actually use. It is quick, capable, cheap to run, and […] The post Claude Sonnet 5.5 Review: Faster Agentic Coding & Visual QA appeared first on Analytics Vidhya.

AI agents can handle large context windows, yet still forget what happened after a session ends. Memory systems close that gap by preserving useful facts, preferences, relationships, and task state, giving agents continuity beyond a single prompt. Open-source projects now approach memory through APIs, graphs, benchmarks, and portable agent state. In this article, we look […] The post 7 Open-Source Memory GitHub Projects on AI Memory appeared first on Analytics Vidhya.

For the first time, an OCR model reads Indian languages as fluently as English documents. Sarvam Vision 2.1 breaks a long-standing trade-off. Indian businesses had to choose between structural document parsing and script recognition. Never both. Finance teams automating invoices faced this. Insurance companies processing handwritten claims in multiple states faced this. Organizations digitizing regional […] The post Sarvam Vision 2.1: The OCR Model Built for the Documents India Actually Has appeared first on Analytics Vidhya.

Agentic Context Learning or ACE is a learning paradigm that lets an AI agent improve across tasks by editing the context it reads, while leaving model weights unchanged. The paper outlining the techniques show why full rewrites fail, how the playbook update works, and where the measured gains hold up. This article explores how ACE […] The post Agentic Context Engineering (ACE): Self-Improving Language Models appeared first on Analytics Vidhya.

Projects are the bridge between learning and becoming a professional. While theory builds fundamentals, recruiters value candidates who can solve real problems. A strong, diverse portfolio showcases practical skills, technical range, and problem-solving ability. This guide compiles 10 solved projects across AI domains, from basic machine learning to advanced generative AI system. The tools and […] The post 10 Solved Generative AI Projects to Boost your Profile appeared first on Analytics Vidhya.

What happens when a frontier model’s abilities get packed into cheaper, faster versions? That’s what OpenAI did with GPT-6 Sol and GPT-6 Luna, two new models built with methods similar to GPT-6 Astra. OpenAI is cutting API prices for both models by 50% compared with their GPT-5.6 versions, and says they bring Astra’s gains in […] The post GPT-6 Sol and Luna: Near-Astra Performance at Half the Price? appeared first on Analytics Vidhya.

What happens when an AI model gets better at reasoning, faster at responding, and cheaper to run at the same time? That is the promise behind Claude Opus 5.5, Anthropic’s latest flagship model and the first release in the Claude 5.5 family. Opus 5.5 brings several notable changes. It now reasons on every request, generates […] The post Claude Opus 5.5 Tested: What’s New and How Good is it? appeared first on Analytics Vidhya.

If you follow trends in the AI world, chances are you have already come across Jev, a new AI model by TypeSafe AI. It is trending on X, and once you understand the reason behind it, you will want to try it out for yourself. TypeSafe AI came out of two years in stealth on […] The post Jev Explained: The AI Model That Never Generates a Word of Text appeared first on Analytics Vidhya.

What would an AI agent do when a required file is missing or an API refuses access? The expected response is to explain the limitation… essentially, coming out with it. OpenAI’s latest disclosures shed light in another direction. Models sometimes take another route: hiding failures, using credentials without permission, or publishing files to finish the […] The post OpenAI Model Misalignment Explained Through Six Real Incidents appeared first on Analytics Vidhya.

RSI or Recursive Self-improvement has been the talk of the town lately. The term came into surface when it was emphasized as the next step in the LLM evolution cycle by pioneers of the field like Sam Altman, Dario Amodei, and Elon musk. But also, via a research paper outlining the method titled: The Last AI Built by Humans. These two alone should help you realize […] The post Recursive Self-Improvement: The Last AI Built by Humans appeared first on Analytics Vidhya.

Putting AI into production now takes more than deploying a model and tracking accuracy. MLOps made traditional ML manageable, while LLMOps added concerns around prompts, retrieval, evaluation, latency, and cost. AgentOps adds another layer for systems that decide, call tools, and complete multi-step tasks. These shifts change what teams monitor and control. In this article, we compare MLOps, LLMOps, and AgentOps, and explain how observability evolves as AI systems move to action. […] The post LLMOps vs MLOps vs AgentOps: What Changes When You’re Operating Language Models at Scale appeared first on Analytics Vidhya.

A year ago, terminal AI mostly meant asking a model to explain errors, generate commands, or edit small functions. In 2026, leading coding CLIs have become full agent runtimes that can inspect repositories, plan work, modify files, run tests, use external tools, and verify results. In this article, we compare five standouts agentic coding CLIs: Claude Code, Codex CLI, […] The post Top 5 Agentic Coding CLI Tools Developers Should Know in 2026 appeared first on Analytics Vidhya.

OpenAI has released ChatGPT Images 2.5, its latest image-generation model, with a greater emphasis on controlled editing than simply producing prettier images. The update promises sharper details, more natural lighting and textures, stronger reference-image preservation, more reliable multi-turn editing, and up to 50% lower generation latency than Images 2.0. But marketing claims only tell part […] The post 5 ChatGPT 2.5 Features to Try Today! appeared first on Analytics Vidhya.

A Hindi lesson can mix English terms (loan words), scanned tables and handwritten equations. Making that content searchable, translating it and reading it aloud requires several kinds of AI. Bodhan AI and AI4Bharat’s four new models target those jobs across Indian languages. Released in September 2026, the models cover document parsing, translation, speech recognition and speech generation, with support for mixed languages and scripts. In this […] The post Bodhan AI Releases Four Indic Models for OCR, Translation and Speech appeared first on Analytics Vidhya.

As LLM applications grow more complex, inference cost and latency become increasingly important. This article breaks down four caching techniques used in LLM serving—KV caching, prefix caching, prompt caching, and semantic caching—and explains how each one avoids repeated computation at a different layer of the stack.

Local LLM model names like 'Qwen3.8-27B-A3B-It-2507-gguf-q2ks-mixed-AutoRound' look cryptic but each segment conveys crucial details. This guide breaks down meaning of parameter count, MoE architecture, active parameters, Base/Instruct tuning, weight precision (FP16/BF16), quantization (Q4/Q8), quantization variants (e.g., Q4_K_M), and file format (GGUF) to help you choose the right model.

Proprietary models are amazing! But sometimes what is of importance is configurability rather than raw power. This has led to the emergence of locally hosted models. The Mac mini has emerged as a surprisingly capable machine for running AI locally. With Apple Silicon, enough unified memory, and tools like Ollama and LM Studio, users can now run capable models entirely on-device. But […] The post 5 Best Local LLMs You Can Run on a Mac mini in 2026 appeared first on Analytics Vidhya.

You don’t need to know anything about AI automation to get started. There are plenty of free courses that can take you from the basics to building your own automations, even if you’re starting from zero. Some are completely beginner-friendly, while others are better once you’re comfortable with the basics. We’ve picked courses covering popular […] The post Top 7 Free AI Automation Courses with Certificates appeared first on Analytics Vidhya.

AI agents are everywhere right now. You hear terms like tool calling, agent loops, MCP, guardrails thrown around as if its common language… it isn’t! But that is about to change. Agentic AI isn’t nearly as complicated as it sounds once you understand the few core ideas that actually matter. Here are 10 agentic AI concepts […] The post 10 Essential Agentic AI Concepts Explained Simply appeared first on Analytics Vidhya.

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 […] The post Mastering the AI Project Cycle: From Concept to Production appeared first on Analytics Vidhya.

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. MIT’s NANDA […] The post What is a Forward Deployed Engineer? Role, Skills & Salary appeared first on Analytics Vidhya.

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. In […] The post How to Use Kimi K3: Moonshot AI’s 2.8T Open-Weight Model appeared first on Analytics Vidhya.

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 […] The post Spec-Driven Development with Claude Code: Writing Bulletproof Specs appeared first on Analytics Vidhya.

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 […] The post Top 5 Agentic AI Research Papers of 2026 appeared first on Analytics Vidhya.

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. Together, these techniques make […] The post PagedAttention vs. RadixAttention: Optimizing LLM KV Cache Management appeared first on Analytics Vidhya.

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 […] The post How to Remove Claude Watermarks from Text, Code, and Files appeared first on Analytics Vidhya.

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 […] The post How to Add Skills in Agents using LangChain appeared first on Analytics Vidhya.

Developers launch new models every week, but most barely change how you work. Kimi K3 is different—not 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 […] The post 7 Kimi K3 Features That Make Every Other Model Feel Outdated appeared first on Analytics Vidhya.

NVIDIA Nemotron 3.5 Lightning is an open-weight reasoning and instruction model built for the execution layer of AI agents. With 30B total parameters but only 3B active, a hybrid Mamba-2 + MoE + selective attention architecture, and up to 1M token context, it is designed to offload high-volume tool calls, file reads, validations, and other routine agent work from expensive frontier models. Official benchmarks show NVFP4 quantization stays close to BF16, and the model is available through NVIDIA's free API, OpenRouter, Fireworks, Ollama, and self-hosted vLLM.

About a month ago, Baidu (often called the “Google of China”) introduced Unlimited-OCR, an advancement over DeepSeek OCR. The model was designed to transcribe long, multi-page documents with high accuracy while delivering fast and stable inference. Unlike conventional vision-language OCR systems, Unlimited-OCR addresses a major bottleneck in long-document transcription: the rapidly growing Key-Value (KV) cache, […] The post How Baidu Unlimited-OCR Works: Solving Long-Document Transcription appeared first on Analytics Vidhya.