Sultan_AI

What is an automation you actually pay for? A lot of automation tools float around promising to save time, but I’m curious about the ones people actually find worth paying for. For example: Some marketers swear by email drip automations. Devs might pay for CI/CD pipelines or error monitoring. Ops teams sometimes invest in invoice/payment automations.

@femke_plantinga I am diving into RAG world, thanks for the thread

Is naive RAG hitting a wall for your use cases? RAG has become super popular (and for good reason!), but let's be honest - sometimes the traditional approach just isn't cutting it anymore… While 𝗥𝗔𝗚 dominated 2023, we're seeing tons of cases where traditional vector search just doesn't capture the full picture. The fundamental issue? Each entry in naive RAG is completely independent - it only knows about proximity in vector space, not actual relationships between data points. Let’s take the diagram below for example: Traditional RAG sees isolated facts about PSG, Messi, and Neymar. But GraphRAG understands that Messi and Neymar both played for the same teams, creating insights like 'players who shared teammates' that basic search would miss entirely. This is where 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚 comes in 👇 GraphRAG takes a completely different approach by understanding relationships between entities. Instead of just semantic similarity, it extracts entities, builds knowledge graphs, and generates community summaries that span multiple relationships and nodes. The result? Way more nuanced context that captures the interconnected nature of your data. The hybrid approach is where things get super interesting. You get: • Semantic search capabilities from vector databases like @weaviate_io • Relationship intelligence from graph databases like @neo4j • Entity-centric indexing that provides richer descriptions Yet... GraphRAG isn't perfect either. It faces challenges with static LLM-generated summaries that require full reindexing when new data comes in, plus substantial token costs. Traditional RAG also reindexes, but the updates are limited to embedding and storing new/modified chunks, which is way lighter than regenerating graph summaries. 𝘚𝘰 𝘸𝘩𝘢𝘵'𝘴 𝘵𝘩𝘦 𝘴𝘰𝘭𝘶𝘵𝘪𝘰𝘯? It's not about choosing one over the other - it's about understanding your data. If your information is well-structured and self-contained, naive RAG works great. But if your data is rich in connections and interdependencies (contracts, research papers, organizational records), GraphRAG delivers more accurate and insightful responses. The future is hybrid systems that leverage both semantic similarity and structural insights. Blog: weaviate.io/blog/graph-rag… Notebook: github.com/neo4j-contrib/… Free advanced RAG ebook: weaviate.io/ebooks/advance…

@AndrewBolis the best illustration i have ever seen

Learning how to build AI agents isn't difficult. Here’s a roadmap divided into 3 levels: [ 🔖 bookmark this post for later ] Level 1: GenAI & RAG Basics 1. Introduction to GenAI: Discover what Generative AI is and how it's used. 2. LLM Basics: Learn how large language models are trained and applied. 3. Prompt Engineering: Master writing good prompts for better LLM outputs. 4. LLM Parameters: Adjust outputs using temperature, top-p, and token limits. 5. Data Preprocessing: Format, clean, and split data for AI systems. 6. RAG Fundamentals: Mix LLMs with search to retrieve accurate information. 7. Vector Databases: Save & find embeddings with Pinecone, Chroma, etc. 8. API Wrappers: Connect to LLMs via LangChain, LlamaIndex, or direct APIs. 9. Tool Integration: Enable LLMs to access search, code, or external APIs. Level 2: AI Agent Essentials 10. What Are AI Agents? Discover how agents plan, think, and work autonomously. 11. Agent Frameworks: Learn LangChain, CrewAI, AutoGen, and similar tools. 12. Build Your First Agent: Build a basic AI agent that handles real-world tasks. 13. Agent Workflows: Plan how agents process, decide, and execute work. 14. Agent Memory: Give agents memory to remember past interactions. 15. Agent Evaluation: Measure agent accuracy, reliability, and performance. 16. Multi-Step Reasoning: Teach agents to follow logical thinking sequences. 17. Multi-Agent Systems: Enable agents to collaborate on complex tasks. 18. Agentic RAG: Build RAG into an autonomous agent system. 19. Action Planning: Make agents plan, adapt, retry, and optimize decisions. 20. Guardrails & Safety: Add filters to ensure agents stay factual and safe. Level 3: Advanced Agent Skills 21. Real-World Integration: Link agents with apps like Gmail, Slack, or Notion. 22. Autonomous Loops: Build agents that manage and execute tasks on their own. 23. Custom Toolkits: Give agents custom APIs or Python tools. 24. Performance Optimization: Boost agent speed, reduce costs, and fix errors. 25. Production Deployment: Launch your AI agent for actual users to use. 📌 Get Advanced ChatGPT Guide (free): bit.ly/3StIB3z 👉 Follow me @AndrewBolis for more and 🔄 Repost this to help others learn AI

Maybe automate this and forget?

@Python_Dv The orchestrator was always a challenge for me.

5 Ways to Use Agentic AI amzn.to/4pnW3Fa 1. Prompt Routing: Dynamically decide whether to respond, search internal data, or hit the internetbased on the user’s prompt. 2. Query Writing: Write its own query functions with filters, sorting, and parameters to get exactly the right results. 3. Data Processing: Clean, transform, enrich, and summarize raw inputs so they’re ready for reasoning or downstream tasks. 4. Tool Orchestration: Pick the right tools/APIs, chain them together, handle fallbacks, and adapt workflows in real time. 5. Decision Support & Planning: Break down goals into steps, compare options, prioritize, and recommend next actions.

@n8n_io cool

Manual runs just got faster in n8n. ⏱️ We revamped the canvas so you can test and iterate without the wait; big workflows now run way smoother. Frontend-first improvements you’ll feel instantly. Watch the before/after video. What heavy workflows will you try? Released in n8n@1.110.0

@Hesamation @langchain Context engineering and MCP is the new space for me.

Context Engineering explained simply in just 14 minutes by @langchain 90% of building agents is how to optimize their context. Do not sleep on this skill.

Saw this on X. AI receptionist that answers calls. What do you think about?

@langchain Thanks Langchain. Parsing websites is what I need the most right now.

🤖🌐 ParserGPT: Smart Web Scraping Transform messy websites into clean CSV data using LLMs and deterministic rules. Powered by LangChain and LangGraph, ParserGPT learns website structures once to enable efficient, repeated data extraction. Learn more about ParserGPT: @ayush.shrivastava016/parsergpt-public-beta-coming-soon-turn-messy-websites-into-clean-csvs-dd8c7199ca97" target="_blank" rel="nofollow noopener">medium.com/@ayush.shrivas…

@Selinaliyy Google pixel has that thing that I do not like. They renew their batteries and the old ones become weaker.

just played w google pixel at a best buy and questioning myself why im still using an iPhone 🥲

The myth of ‘AI will solve everything’ is dangerous. AI won’t fix bad processes. It just make them faster. A broken customer journey + AI = angry customers at large scale. Before you deploy an agent, ask Yourself: Does this solve a real problem. or just make my dashboard look cool? I think in today's time there is two type of people: one is who use ai to get better and become more productive. They use ai as a tool and on the other hand their are some people who ignoring it and they have fear that ai will replace them or like ai can't do anything or like ai is a garbage but I think these people soon replaced by the one who use ai to maximize his output and increase productivity.

@NoahEpstein_ this is a must read

Been building in the AI automation space for 1 year. Yes I'm new, but that's exactly why you should listen, the mistakes are fresh and the lessons still sting. Made every mistake possible. Lost money, burned clients, built trash that broke. Here's what I wish someone told me when I started. The biggest lie is that technical skills matter most. They don't. I watched developers with 10 years experience fail while a hairdresser built a $30K/month agency. The difference? She understood business problems. They understood code. I fell for this trap hard - spent months deep in n8n tutorials thinking technical knowledge would equal sales. It doesn't. Your first 10 automations will be garbage. Accept it. Mine were 200-node monsters that broke if someone typed their name wrong. Now? Most problems need 15 nodes max. Complexity is usually incompetence disguised as sophistication. Pricing is where everyone fucks up. Stop charging hourly. Stop charging per project. The only sustainable model is value-based with recurring. If your automation saves them $10K/month, charge $2K/month. They're still up $8K. You get predictable revenue. Or hit them with a fat upfront fee then add monthly for usage and "maintenance" - whatever keeps cash flowing. Client acquisition is stupidly simple once you understand one thing: businesses don't buy automation. They buy outcomes. "I'll automate your invoicing" loses to "I'll get you paid 15 days faster" every time. The tools don't matter as much as you think. I've seen people make fortunes with Zapier, n8n, Make, or just Google Sheets and Apps Script. Pick one, understand it deeply, stop tool-hopping. The best automation is the one that actually gets built. Your first client should be yourself. Automate your own business first. It's free practice, you understand the problem deeply, and you can show real results. My customer support bot and stock tracking system for my ecom brand became my first proper build and first $6K invoice because I had real data proving it worked. Error handling will make or break you. Every automation needs to assume users are drunk, data is wrong, and APIs will fail. Build for chaos. Your 3am self will thank you when nothing breaks. Niches print money but not how gurus tell you. Don't pick "dentists" or "lawyers". Pick "dental practices losing patients to no-shows" or "immigration lawyers drowning in paperwork". Specific problems, not broad industries. Maintenance isn't optional. Budget 20% of your time for keeping things running. Charge for it. Position it as insurance, not a burden. "For $500/month, you never think about this again" sells itself. The best automations are boring. Invoice processing, lead routing, appointment booking. The stuff nobody wants to do manually. Stop trying to build AI-powered revolutionary solutions. Build things that save 2 hours a day. Competition is irrelevant if you understand this: every business has unique stupidity. What works for one company is useless for another. There are 30 million small businesses. You need 10 clients. Do the math. Learning resources are mostly worthless. YouTube tutorials teach you to build demos, not production systems. The only way to learn is to build real things for real businesses with real consequences. Your imposter syndrome is justified. You don't know enough. Neither does anyone else. The difference between you and successful builders is they started anyway. Perfect knowledge doesn't exist. Good enough does. Most valuable skill isn't technical. It's translating between business and tech. CEOs don't care about webhooks. They care about results. Learn to speak money, not modules. The market is so big it's stupid. Every pizza shop, dentist, accountant, and lawn care company runs on manual processes. While you're reading this, someone just charged $8K to automate appointment reminders. Could've been you. Final reality: this is the easiest time in history to build a profitable automation business. Tools are accessible, businesses are desperate, and most consultants are overpriced dinosaurs. You don't need permission, funding, or a computer science degree. You need to start. Stop consuming, start building. Your first automation will suck. Your tenth will be decent. Your fiftieth will print money. But only if there's a first.

@PythonPr on point

Agentic AI Roadmap 2025

@dashboardlim AI Assistant

Just Recorded a Tutorial on How to Build Your Own AI Assistant in WhatsApp (step-by-step) You can now have a personal assistant living inside WhatsApp managing your info, calendar, reminders, and meetings with AI Like + Comment "AI assistant" to get the FULL Guide + n8n workflow (Must be following)

@SeroneyAi I guess the first one is the best. I have tried the second option, but it created more problems

@Sultan_The_AI Client could give you access to their workspace that has all their credentials or ask you to build the workflow and send the Json file to them

Do you build automations on your agency or clients software? I was wondering whether you guys typically build out agents and automations using your clients accounts or whether you use your own businesses accounts to create and run automations?

@EXM7777 many use claude now.

how to automate content creation on X: you can't automate tweet writing, but you can automate the research: - mine subreddits and forums for audience pain points and language patterns - use Grok to analyze competitor content and identify what drives engagement - create a Claude project loaded with your best content to generate new ideas you never want to post AI slop, but you can use AI to never run out of ideas

@aiwithlady Steal

HOLY SH*T… This AI Agent does everything 🤯 Built in n8n : 🔁 Clones viral TikToks ✍️ Rewrites w/ GPT-4o 🎥 Auto creates avatar videos 🎬 Adds captions & edits 📤 Posts to 9 platforms (TikTok, IG, YT, X…) 🔁 Like+RT ✅ Reply “Steal” 🤝 Follow me & I’ll DM you workflow FREE

@godofprompt @OpenAI fire tool

How to Build AI Agents by @OpenAI 🔖 Bookmark for later

@_avichawla great illustration. me as a beginner in MCP can understand. thanks

The power of MCP explained in one picture! Without MCP: - Every LLM app wrote its own tool integration - M apps & N tools = M×N integrations With MCP: - Create an MCP server for your tool and plug it into an LLM app - You go from M×N integrations to M+N integrations