Automate Notion updates with n8n template for data pipeline

Over the past few weeks, I’ve been building a small but exciting project — MLflow Experiment Analysis Agent. The goal is simple: make it easier to analyze MLflow experiment runs using LLMs. Instead of manually browsing through Ml flow's UI or digging into logs, this agent takes experiment metadata, formats it, and lets an LLM summarise performance, highlight the best runs, spot anomalies, and even suggest improvements. ⚡ Key highlights: Automatic conversion of MLflow experiment logs into JSON (mlflow_experiments.json) Two analysis modes: 1️⃣ LangChain Agent – Uses Qwen2-1.5B-Instruct (currently under testing 🚧) 2️⃣ Direct Transformer – Directly processes JSON logs with the model Clean and extensible structure with logging, MLflow utilities, and agent modules 💡 Why this matters? Experiment tracking is at the heart of ML workflows, but interpreting results quickly is still a pain point. Automating this step with an LLM can save data scientists time and bring consistency in evaluating experiments. 🚀 Next steps I’m currently testing the LangChain pipeline to handle larger experiments and more complex prompts. My vision is to integrate this into a continuous evaluation loop where every new run is automatically analyzed and reported. ✨ Thoughts This project scratches the surface of how LLMs can complement MLOps. Instead of replacing dashboards, they can serve as smart copilots that translate metrics into insights. I believe this kind of workflow will become common in ML teams, especially where experiments run frequently. 👉 Curious to hear from the community: Would you trust an LLM-generated summary of experiment runs? What’s the biggest challenge you face while analyzing MLflow experiments? Github link: https://lnkd.in/djuFCnux

🚀 𝗔 𝘀𝗺𝗮𝗹𝗹 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 𝘁𝗵𝗮𝘁 𝗳𝗲𝗹𝘁 𝗹𝗶𝗸𝗲 𝗮 𝗯𝗶𝗴 𝘀𝗵𝗶𝗳𝘁 — 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿 + 𝗟𝗟𝗠 𝗳𝗼𝗿 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 I tried something simple: I connected the 𝗠𝗦𝗦𝗤𝗟 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿 to 𝗖𝗹𝗮𝘂𝗱𝗲 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 & 𝗩𝗦 𝗖𝗼𝗱𝗲 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗖𝗵𝗮𝘁, pointed it to my local 𝗔𝗱𝘃𝗲𝗻𝘁𝘂𝗿𝗲𝗪𝗼𝗿𝗸𝘀𝗗𝗪 database… and instead of writing a single SQL query or designing a dashboard, I 𝗷𝘂𝘀𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝗮𝘀𝗸𝗶𝗻𝗴 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗽𝗹𝗮𝗶𝗻 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲. Something interesting happened.  • Instead of giving raw tables, it 𝘀𝘂𝗺𝗺𝗮𝗿𝗶𝘀𝗲𝗱 𝗹𝗶𝗸𝗲 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗿𝗲𝗽𝗼𝗿𝘁  • It didn’t just return numbers — it 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝗲𝗱 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 like revenue drop patterns, strong markets, attach-rate potential, VIP customers, seasonality curves…  • And the best part — I didn’t shift between tools. The conversation itself became the analytics workspace. 𝗧𝗵𝗶𝘀 𝗠𝗖𝗣 + 𝗟𝗟𝗠 𝗰𝗼𝗺𝗯𝗼 𝗳𝗲𝗲𝗹𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲:  • No manual querying, no drag-drop visuals — just ask and go deeper with follow-up questions  • SQL becomes just one tool behind the server — the LLM handles the thinking and narration  • It’s not limited to “show me the data” — it naturally flows into “tell me what matters here”  • And because it's MCP, I can swap models later (Claude, GPT, Copilot Chat…) without rebuilding anything 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 It didn’t feel like working with a database. It felt like having a business conversation with my data. Not dashboards. Not queries. Just insight-ready conversations. I'm calling this — for myself at least — a shift from 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 → 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀+𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀. FYI, the entire PDF was generated by LLM+MCP Server with just 1 prompt, added only 5 pages here.. Check out this Github Repo for more MCP Servers: https://lnkd.in/g_YkScW2 Thanks Pawel Potasinski for your post https://lnkd.in/gJPKrgjB #MCP #LLM #Claude #VSCopilotChat #SQL #ConversationalAnalytics #DataInsights #AzureData #AIEngineering #DataTeams #AdventureWorksDW #NextGenBI

💻 GUI tools look intuitive. CLI tools look intimidating. But the productivity gap between them is only getting wider. This year, I finally decided to dive deep into the command line. And here's what I learned 👇 🚀 They're faster. Much faster. While Jupyter notebooks shine for exploration, CLI tools dominate at focused, repeatable tasks. They're lightweight, easy to automate, and integrate seamlessly into any data pipeline. The "Core Four" that completely transformed my workflow: 🔸 curl → Effortless API requests & data retrieval 🔸 jq → JSON processing that feels like Pandas for the shell 🔸 awk / sed → Powerful text manipulation that saves hours 🔸 git → Version control that keeps chaos in check Once you're comfortable with these, explore more advanced tools: • csvkit for seamless CSV transformations • parallel to distribute workloads across cores • ripgrep for lightning-fast searches • datamash for quick statistical operations 🎯 Yes, there's a learning curve. But the payoff is massive: ✅ Speed ✅ Control ✅ True automation Start small. Pick one tool. Practice it for a week. Your future self will thank you. 👉 Which command-line tool has boosted your productivity the most? #DataScience #CommandLine #Productivity #Automation #DevTools #Efficiency #TechTips 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/dwCyzG9E

Tired of manually hunting down API docs and copying endpoints into spreadsheets? I built an n8n template that automates the full pipeline: discover API docs, scrape pages, classify and chunk content, embed and store documents in a vector DB, extract REST operations with an LLM, and produce a clean JSON schema and sheet of endpoints — end to end. What it includes: - Automated Google search + Apify web scraping to fetch documentation pages - Deduplication, content chunking and storage in Qdrant (vector store) - Google Gemini embeddings and LLMs to detect docs and extract GET/POST/PATCH/DELETE operations - Results appended to Google Sheets and exported as a JSON schema uploaded to Google Drive - Built-in stages for Research → Extract → Generate, plus error handling and retry logic Why this matters: you can scale API discovery across hundreds of services, speed up SDK or integration work, and keep a reproducible source of truth for API operations. Try importing the template into n8n, wire up your Apify/Google/Qdrant credentials, and point it at a service. Curious to see which services you run it on — or how you’d adapt it for internal developer portals. Send feedback or questions and I’ll help you get started. Template link in the comments section. #n8n #Automation #WorkflowAutomation #APIDocumentation #APIDiscovery #WebScraping #DataExtraction #GoogleSheets #LLM #AI #JSON #DataPipeline #ETL #LowCode #OpenSource #DeveloperTools #KnowledgeManagement #TechnicalWriting #APISchema #ErrorHandling

Automate Notion updates end-to-end with this n8n template — from incoming webhooks to RAG-powered, context-aware results. How the workflow works - Webhook Trigger: Receive POST requests to kick off the flow. - Text Splitter + Embeddings: Chunk incoming content and create OpenAI embeddings for semantic search. - Supabase Vector Store: Persist embeddings and run similarity queries to provide context. - Memory & RAG Agent: Use a windowed memory plus a chat-based RAG agent (Anthropic) to produce context-aware outputs. - Logging & Alerts: Append agent results to Google Sheets for auditing and send Slack alerts on errors. Why this matters - Turn manual Notion edits into automated, reproducible updates. - Keep a searchable vectorized record of content for richer, context-aware responses. - Maintain an audit trail (Google Sheets) and immediate alerts (Slack) for reliability. Who this is for - Product teams, docs engineers, and ops teams who want reliable, traceable Notion updates and richer automation that leverages retrieval-augmented generation. Customize it - Swap models, change your vector store, add transformers, or extend the RAG agent prompts to fit your workflow. Try it out — link in the first comment. If you want a walkthrough or a quick customization suggestion for your use case, ask below. Template link in the comments section. #n8n #Notion #NotionAPI #APIAutomation #WorkflowAutomation #Automation #Webhook #OpenAI #Embeddings #RAG #GenerativeAI #Supabase #VectorDatabase #LLM #GoogleSheets #Slack #NoCode #LowCode #DataAutomation #Integration

🚀 Q-Explorator !!! Quickly and instantly generate the syntax for: LOAD <Field 1>, <Field 2>, <Field n> FROM... LOAD * FROM... LOAD DISTINCT <Field 1>, <Field 2>, <Field n>/* FROM... Include the WHERE clause using the applied filters if desired. Also, create your LOAD INLINE from the filtered data or the LOAD MAPPING of the unique values in a column. Export the desired syntax to the clipboard or a QVS (Qlik Scripting) file. Download by visiting the site: https://lnkd.in/ebHyNZ6w #QFabrica #QExplorator #Qlik #QVD #QlikSense #Qlikview #DataAnalytics #DataEngineering #TechTips #Tools

Web Scraping at Scale with Claude Desktop + MCP Servers From manual data collection to automated intelligence—transform any website into structured, analyzable data. In this tutorial, see how Samir builds an MCP server that gives Claude Desktop web scraping superpowers: crawling entire sites, extracting specific data, and mapping content architectures that would typically require expensive enterprise tools. A user asks Claude to analyze a website. Claude crawls up to 150 pages, following links and respecting depth limits. Results return as structured data with SEO insights, orphaned pages, and linking patterns. This transforms what used to be hours of manual auditing into instant, conversational analysis. 🔗 Watch the tutorial & get the code: https://lnkd.in/ejuUPXUW — I'm Pierre Rizzitas, founder and CEO of PR Consulting (London). We support retail and supply chain businesses in aligning operations, sustainability, and profitability through simulation and analytics. #webscraping #mcp #claudedesktop #python #dataextraction #supplychain #automation #aiagents

"Vibe-Coding" Your Enterprise Apps?Try Spec-Driven Development with AI. We all love the speed of Azure OpenAI and GitHub Copilot or a Cursor, but are you asking them to do too much at once? With "Vibe-Coding" we are letting the AI define requirements, make minor architectural choices, and generate the code in a single, massive prompt. It feels fast, but on enterprise systems, it’s a recipe for some anti patterns: The Vibe-Coding View: * Context Overload: The AI’s context window is consumed inefficiently. * Invisible Architecture: Key architectural decisions vanish between sessions. * Inconsistent Code: Shifting assumptions lead to brittle, hard-to-maintain code. * Team Disconnect: Scope and direction are lost when there are no shared artifacts. The Spec-Driven View: Separate Design from Build. Guide the AI step-by-step to create traceable, reusable, and team-friendly artifacts before generating code. * Define Requirements * Generate System & Data Design * Break Down Tasks The Prompt Difference: Procurement Workflow on Azure/.NET Imagine building a new Procurement Workflow system with supplier onboarding, approvals, and invoice tracking VIBE-CODING PROMPT : "Build me a procurement workflow system with supplier onboarding, purchase requests, approvals, and invoice tracking using .NET 8 and Azure Functions.” SPEC-DRIVEN PROMPT : "Generate requirements, high-level design, and task breakdown for a procurement workflow system using .NET 8, Azure Functions, and Azure SQL. Save results as requirements.md, design.md, and tasks.md. " The Spec-Driven approach ensures your architecture stays intact and your teams remain aligned. It gives the AI clarity and gives your team structure, clarity, and scale. The question is: How are you managing AI-assisted development on your team? Lets together, learn and explore more on Spec-Driven development. #AI #specdrivendevelopment #Azure #dotnet #GitHubCopilot #Architecture

🚀 Understanding the Push Operation in Stack Data Structure! In Data Structures, a Stack is a linear structure that follows the LIFO (Last In, First Out) principle — meaning the last element added is the first one to be removed. 🧩 In this image, you can see how the Push Operation works: Initially, the stack contains elements A, B, C, D. When we push a new element E, it gets added to the top of the stack. The new top element becomes E, while previous elements move one position below. 💡 Key Concept: The push() operation adds a new element to the top of the stack, increasing its size by one. Stacks are widely used in programming for: ✅ Expression evaluation ✅ Function call management (recursion) ✅ Undo/Redo operations ✅ Browser backtracking #DataStructures #Stack #ProgrammingConcepts #Learning #CodingJourney #PushOperation #TechEducation

#AIShowAndTell at #GitHubHQ Pol Peiffer Sierra created an open source simulation framework for evaluating customer service agents across various domains. https://lnkd.in/g6nHk5xk