🚀 Medial Secures Investment on Shark Tank India - Fueling the Future of Professional Social Networking. 🔥

News

Messages

Try our Valuation Calculator →

Back

Rahul Agarwal

Founder | Agentic AI... • 2m

If AI’s rapid pace feels overwhelming, trust me-everyone feels it. New models, new papers, new frameworks… it’s impossible to keep up with everything. And the good news is-you don’t have to. What actually helps is a clear path, not more noise. So I organized a 10-level roadmap for learning AI Agents-built to take you from the basics to real production systems without burning out. *Tip: Spend about 2–3 weeks per level. Build small projects, test ideas, and let concepts sink in. Go slower if you need. Go faster if you can. And when something new launches? Treat it as Level 11 and keep moving. *Your AI Agents Learning Roadmap Level 1: Foundations of GenAI & Transformers How tokens, embeddings, attention, and inference actually work. Level 2: Prompting & Model Behaviors CoT, ReAct, ToT, context design, prompting patterns, and jailbreak resistance. Level 3: Retrieval-Augmented Generation (RAG) Chunking, vector stores, retrieval pipelines, and what makes RAG good—or terrible. Level 4: LLMOps & Tooling LangChain, LangGraph, Dust, CrewAI, synthetic data, tools, and function calling. Level 5: Agents & Agent Frameworks Planning, memory, agent loops, LangGraph agents, CrewAI agents, and evaluations. Level 6: Memory, State & Orchestration Symbolic vs vector memory, persistent state, compression, and long-term context. Level 7: Multi-Agent Systems Decentralized systems, collaboration patterns, message passing, and agent teams. Level 8: Evaluation & RL LLM-as-a-Judge, RLHF, reward models, and self-improving agent loops. Level 9: Protocols & Safety MCP, agent-to-agent protocols, alignment, guardrails, and traceable autonomy. Level 10: Building & Deploying FastAPI, Streamlit, QLoRA, GGUF, caching, and monitoring with LangSmith/Arize/TruLens. *Save this. Build after every level. If you want tools to explore along the way: Start with Hugging Face (to explore LLMs/SLMs), use Ollama to run SLMs locally (Phi-4, TinyLlama), or try Fireworks AI to run bigger LLMs via API (Qwen 3, Kimi K2, DeepSeek R1). Then dive into LangChain and LangGraph (they’ll teach you 80% of the ecosystem), and later check out agentic frameworks like CrewAI or AutoGen. *Pro tip: Start with cookbooks-they’ll get you building faster than any tutorial.

Recommendations from Medial

Rahul Agarwal

Founder | Agentic AI... • 10d

Stop guessing which agentic AI tool fits your workflow. If you’re building agents, automations, or AI-powered systems in 2026, this guide will help you pick the right tool for the job. Top 10 Agentic AI Tools and Their Strengths: 1. n8n – Ideal for

Vinay Khedkar

Make an impact that ... • 7m

𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻: 𝗧𝗵𝗲 𝗙𝗮𝘀𝘁𝗲𝘀𝘁-𝗚𝗿𝗼𝘄𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 LangChain is quickly emerging as the go to framework for developing AI agents and intelligent LLM-powered applications. In a short

Aparna Pradhan

fullstack dev specia... • 7m

Hey friends, I’ve been building something close to my heart — a *portfolio project* that reimagines how AI can work as your startup team. Introducing *AgentFlow* — a *virtual office of autonomous AI agents* that think, plan, and collaborate like a le

Kimiko

Startups | AI | info... • 9m

AI Agents now have muscle memory. This Python SDK records agent tool-calling patterns, replays them for repeated tasks, and falls back to agent mode for edge cases. 100% Opensource. Read more here: https://www.theunwindai.com/p/muscle-memory-for-a

1 Reply

Rahul Agarwal

Founder | Agentic AI... • 20d

Small Language Models are set to transform AI agents-but only if implemented the right way. By 2027, Gartner predicts task-specific SLMs will be used three times more than general-purpose LLMs. NVIDIA agrees, calling them “the future of AI agents.”

Rahul Agarwal

Founder | Agentic AI... • 2m

Everyone wants to build AI agents these days. But very few actually understand what sits beneath the surface. Here’s the part most people ignore: AI agents are mostly software engineering - about 95%. The “AI” part is just the remaining 5%. All the

Chetan Sable

Turning ambitious id... • 10m

🧠💻 Say Hello to Google's Agent Development Kit (ADK)! 🚀 Unveiled at Google Cloud Next '25, the Agent Development Kit (ADK) is an open-source framework designed to simplify the creation of intelligent, modular, and production-ready AI agents. 🔹

Dan Sukhov

@den_point • 3m

Sharing a selection of AI courses I've mastered some of them, and others are in the backlog. HuggingFace Agent Course - https://huggingface.co/learn/agents-course/en/unit0/introduction MCP with Anthropic - https://www.deeplearning.ai/short-course

1 Reply

AIGuts

Exploring AI's poten... • 9m

What Are Agentic AI Frameworks and How Do They Power Autonomous Systems? Agentic AI frameworks are at the forefront of next-gen AI, enabling systems to act autonomously with decision-making capabilities similar to humans. These frameworks are design

Rahul Agarwal

Founder | Agentic AI... • 2m

If you’re building AI agents today, here’s the reality: Calling an LLM isn’t enough anymore. Modern agents need a full system, a framework of interconnected components that help them think, reason, act, adapt, and collaborate autonomously. Here are