Analytics Vidhya

Anthropic's Claude Haiku 4.5 is here, and it's a powerhouse of efficiency. [https://lnkd.in/gmcRXxs8] Here’s what it delivers: ⚡ Blazing Speed: Over twice the speed of previous balanced models for lightning-fast responses. 💰 Radical Cost-Efficiency: Achieves near-top-tier performance at approximately one-third the cost. 🧠 Near-Frontier Intelligence: Coding and reasoning capabilities that rival Anthropic's larger Sonnet 4 model. 🤔 Extended Thinking (New): Shows its work for complex problem-solving, a first for the Haiku family. 🌐 Context Awareness (New): Intelligently manages conversation space for smoother, longer multi-step tasks. � Full Tool Use: Robust support for all Claude tools, including code execution, web search, and browser automation. ✅ Enhanced Safety: Rated as Anthropic's safest model to date with a lower rate of misaligned behavior. Want to see our real-world tests and benchmarks? 👉 Read the full analysis on Aayush Tyagi's blog: https://lnkd.in/gmcRXxs8 #ClaudeHaiku #Anthropic #AI #GenAI #Efficiency #TechInnovation #DeveloperTools

This Ultimate Cursor Cheat Sheet gives you instant access to: https://lnkd.in/gFYhVwNV 🤖 How to use the Cursor Agent 🎯 Common prompts for debugging, DB ops, and refactoring 💡 Pro tips like using @web for docs Bookmark this for your next project! If you are just getting started with Cursor, we have the perfect FREE course for you: https://lnkd.in/gFYhVwNV #CheatSheet #AIAssistant #TechResources #DevCommunity #CursorAI #CodeFaster

Build Your First AI Agent – Free Course https://lnkd.in/gBAcjX-s Learn how to design and configure intelligent AI agents using the Strands framework and AWS tools. In this course, you’ll: ✔️ Build custom AI agents with prompts ✔️ Integrate with AWS services like S3, DynamoDB & Bedrock ✔️ Work with Anthropic, LiteLLM, Ollama & more ✔️ Explore agent-to-agent communication and observability Beginner-friendly | 2 hours | 4.6 rating | Free certificate 📌 Enroll now and start your AI Agent journey: https://lnkd.in/gBAcjX-s A big thank you to Laura Salinas and Elizabeth Fuentes Leone for leading this course and making AI agent development simple, practical, and accessible for learners everywhere. 🙏 #AI #AIagents #Strands #AWS #FreeCourse #DataScience

We've moved from AI that writes to AI that creates. 🎬 Our latest project, "Time Capsule," provides the answer. We built a functional web app that uses a multi-model AI stack including - RunwayML, Fal.ai, and OpenAI API to turn a user's photo into a custom historical video. [https://lnkd.in/gmNcs8Z6] We believe in sharing knowledge to drive the industry forward. That's why we've documented the entire build process, covering: 🔹 The end-to-end architecture 🔹 Key technical challenges & solutions 🔹 The code to get you started Learn how to guild WebApps like Time Capsule and more in our FREE Course: https://lnkd.in/gmNcs8Z6 #AITutorial #TechEducation #SoftwareDevelopment #AI #Innovation

Bigger isn't always better. 🤯 A new research paper introduces Tiny Recursive Models (TRMs): an approach where a 7M-parameter model is beating billion-parameter giants like Claude & GPT-4 on logical reasoning. It scored 87% on hard Sudoku, while the big players scored 0%. This could redefine efficient AI. We've broken down the complex paper into a simple explainer. Simplified Guide by Vipin Vashisth: https://lnkd.in/gFPgKxFW 📜 Original Research: https://lnkd.in/dja_bY6c #AIResearch #MachineLearning #LLM #ArtificialIntelligence #Reasoning #TechInnovation

The way you chunk your data can make or break performance. From Fixed Size and Recursive Chunking to Semantic, LLM-based, and even Late Chunking—each technique has its own strengths and tradeoffs. Knowing when to use which can drastically improve retrieval accuracy and efficiency. We broke down the major approaches: ✅ Fixed Size Chunking ✅ Recursive Chunking ✅ Document-Based Chunking ✅ Semantic Chunking ✅ LLM-Based Chunking ✅ Late Chunking If you’re working with RAG systems or search-augmented LLMs, this is your go-to guide. 💡 Bonus: We’re offering a Free GenAI Program to help you deepen your expertise. Don’t miss it 👉 https://lnkd.in/dfhwme-A #AnalyticsVidhya #GenAI #Chunking

Large Language Models , like GPT-4, have transformed the way we approach tasks that require language understanding, generation, and interaction. From drafting creative content to solving complex problems, the potential of LLMs seems boundless. However, the true power of these models is not just in their architecture but in how effectively we communicate with them. This is where prompting techniques become the game changer. The quality of the prompt directly influences the quality of the output. Think of prompting as a conversation with the model the more structured, clear, and nuanced your instructions are, the better the model’s responses will be. While basic prompting can generate useful answers, advanced prompting techniques can transform the outputs from generic to insightful, from vague to precise, and from uninspired to highly creative. In this post, we will explore 17 advanced prompting techniques that go beyond the basics, diving into methods that allow users to extract the best possible responses from LLMs. #AnalyticsVidhya #Prompting #Techniques

[Free AI Agents Program: https://lnkd.in/gFDy4mVU] Agentic AI isn’t just a buzzword - it’s a shift. From reasoning and planning to autonomy and collaboration, these are the key concepts shaping how AI systems think, act, and work together. Here’s your cheat sheet: - Agentic AI - LLMs - Autonomous Agents - Multi-Agent Systems - MCP (Model Context Protocol) - RAG (Retrieval-Augmented Generation) - A2A (Agent-to-Agent Protocol) - Tool Use Agents - Action Orchestration - Memory-Augmented Agents - Reasoning & Planning Agents - Autonomous Decision Making - Human-in-the-Loop - Agent Framework - Guardrails - Tool Calling We’re entering the era where AI doesn’t just respond it reasons, collaborates, and acts. If you work in AI, product, or data, it’s time to get fluent in this new language.

📚 15 Papers That Defined NLP & LLMs 🎓 The Ultimate Reading Stack (2017–2025) 1️⃣ Attention Is All You Need (2017): https://lnkd.in/gh84Xb-C The transformer paper that started it all. • ⚙️ No recurrence • 🚀 Massive parallelism • 🧠 Backbone for GPT, BERT, and beyond 2️⃣ BERT (2018): https://lnkd.in/gvCbb2jy Transfer learning comes to NLP. • 📦 Pretrain + fine-tune • 🔄 Bidirectional context • 🧰 Inspired dozens of spin-offs 3️⃣ GPT-3: Language Models Are Few-Shot Learners (2020): https://lnkd.in/gdn3D6gg Proved that scale changes everything. • 📈 175B parameters • 🧠 Emergent reasoning • 🪄 Prompting becomes an interface 4️⃣ T5 (2020): https://lnkd.in/gDNU-XSF Unified NLP as a text-to-text problem. • 🔤 One framework for many tasks • 💡 Clean, elegant idea 5️⃣ Scaling Laws (2020): https://lnkd.in/gswH6-3v Mapped how performance grows with size. • 📊 Predictable improvement • 🧭 Playbook for building big models 6️⃣ RAG (2020): https://lnkd.in/gyu_ZiJy Merged retrieval with generation. • 🔍 External knowledge • 📚 Better factual grounding 7️⃣ LoRA (2021): https://lnkd.in/gYREMpEA Made fine-tuning efficient again. • 💸 Low-cost adaptation • 🧰 Easy enterprise adoption 8️⃣ Chain-of-Thought Prompting (2022): https://lnkd.in/gvwt8TJZ Prompting for reasoning. • 🧠 Multi-step thinking • ✏️ Handles complex tasks better 9️⃣ Self-Consistency (2022): https://lnkd.in/gG_R2NHa Voting your way to better reasoning. • 🔄 More reliable results • 📈 Improved logical accuracy 🔟 In-Context Learning & Induction Heads (2022): https://lnkd.in/gm9JCBWy How LLMs actually learn from context. • 🧩 Mechanistic insights • 🔍 Foundation for interpretability 1️⃣1️⃣ Instruction Tuning (2022): https://lnkd.in/gNaknD4F Taught LLMs to follow directions. • 🧑🏫 Conversational behavior • 🧰 Works without retraining 1️⃣2️⃣ Toolformer (2023): https://lnkd.in/gMXePE6P Models that teach themselves to use tools. • 🛠️ API calling • 🤖 Early signs of planning 1️⃣3️⃣ ColBERTv2 (2022): https://lnkd.in/g_N2tT3g Late interaction for efficient retrieval. • 🔎 High-accuracy search • ⚙️ Scales to billions 1️⃣4️⃣ LLMs as a Judge (2023): https://lnkd.in/g25MdgT2 LLMs evaluating other LLMs. • 🧑⚖️ 85% human-level agreement • 📊 Automates evaluation pipelines 1️⃣5️⃣ DeepSeek-R1 (2025): https://lnkd.in/gPHh3URb RL-driven reasoning at scale. • 🧠 Structured, step-by-step thought • 🔭 A look at LLM 2.0 #AnalyticsVidhya #ReserachPapers #NLP #LLMs