Aici VS Code Extension updated with AI features

Check out this cool AI-assisted dashboard workflow I just built, powered by n8n and designed with Lovable AI! " This project is a great example of how to automate a process and visualize the data without writing a lot of code." Tools used : Backend : n8n UI : Lovable AI DB : Google Sheets GitHub : https://lnkd.in/gaKPizaJ #AiAgent #n8n #LovableAI #googlesheets #Dashboard #AI #Workflow #Automation Thankyou.

Just wrapped up a fun side project where I built a Trail Recommendation Agent using n8n 🌲 The idea was simple: Pull weather data from OpenWeatherMap Check my Google Calendar for free time Look up trails from a Google Sheet (miles, elevation, shade, time) Use an AI Agent to combine all this info and suggest the best trails for the day. Finally, send myself a daily email with recommendations via Gmail. What I loved about this: 1) n8n made it easy to connect multiple services without writing heavy glue code. 2) I got to see how triggers, APIs, and LLM prompts work together in a flow. 3) It reinforced the power of “agent thinking” → role, task, input, tools, constraints, output. For me, the most important takeaway was understanding the orchestration layer: n8n isn’t just automation, it’s a way to prototype AI agents with real-world tools quickly. #productiveSundays #n8n #GenerativeAI #LLM #NoCode #cohere #commandr

A complete guide from someone who went from zero to building real MCP servers (me☝🏽). Hope it helps anyone diving into the world of MCP servers! Grateful for the chance to publish and put my words out there. #ai #mcp #beginner #guide #python #internship

🚀 Production-Ready #AI #Agent with #LangGraph! AI Agents might break down in #production on edge cases—missing tool calls, invalid inputs, or implausible outputs. In this #prototype, I built a layered #validation and #repair pipeline that checks every step, corrects when needed, and executes reliably—paving the way for consistent and trustworthy AI in production. 🔍 In my design, I applied validations at multiple levels: ✅ Tool call: ensure that a valid tool call is always present. ✅ Tool name: accept only recognized tools to prevent unintended execution. ✅ Arguments: validate all inputs with Pydantic for strict consistency. ✅ Execution: wrap every HTTP request in try/except to handle timeouts and errors gracefully. ✅ Result: validate outputs with Pydantic to confirm they fall within realistic bounds. By layering validation + repair loops, the agent doesn’t just “work when everything is perfect” — it recovers gracefully when things go wrong. ⚡ Reliability is critical if we want AI agents to be trusted in real-world systems, especially beyond toy demos. 🔗 Full code on GitHub: https://lnkd.in/ewrj8ZYh #AI #LangGraph #LLMOps #AIagents #Reliability #Validation

Here’s a technical snapshot from our “How to Build AI Features in 2025” blog. Notes: 🔹 Unlimited context windows remove the need for manual chunking 🔹 Emerging competitors and open‑weight models offer flexible deployment 🔹 Specialized models on-task now beat generic monoliths 🔹 Text extraction use cases gain traction A read on architecture and model strategy this year. https://lnkd.in/d_cZ6Dzu

To people wrapping the entire world with MCP: if a tool already has a good, self-documented command-line interface, you probably DON'T need to turn it into an MCP server. If the CLI of a tool makes it difficult for an agent to use it, please do not write an MCP interface to make it smoother for AI agents. Write a *better* command-line interface to make it smoother for AI agents *and* humans *and* scripts. If something can just run locally and serialize its state to disk, it should be a regular CLI tool, simple as that. Thus, example of a questionnable MCP use-case: https://gitmcp.io/

What happens if an AI call goes unanswered? Until recently: Nothing. The call just failed. Now: It waits, retries, and logs everything. I recently upgraded one of my client’s AI voice agents (built with n8n + Retell AI) to make it: Smarter (knows when to retry) More reliable (safe exits, no loops) Fully trackable (logs every call & outcome) Here’s what changed 👇 ✅ Google Sheets Logging Every call—answered, missed, failed, or skipped—is stored automatically. No more digging through console logs. ✅ Retry Logic If unanswered, the system: Waits 30 minutes Redials once Tracks if the retry worked ✅ Safe Exit Path No infinite loops. A call is never retried more than once. ✅ Skipped Calls Handled Even missing info (like no phone number in a calendar invite) gets logged with clear reasoning. ✅ Future-Ready Built modular, so new logic can be plugged in anytime without breaking things. 💡 Why this matters: It’s no longer “just an AI call.” It’s a trackable, testable, and dependable system—the kind businesses can actually rely on. 🚀 If you want your AI workflows to behave like this, let’s talk. Tasknova Aarav Varma Rajpal Rathore #nocode #automation #voiceAI #RetellAI #n8n #workflowautomation #productops #retrylogic #googleapps #aiintegration #builders

Durable Agents in Pydantic AI – now with DBOS support! Pydantic AI 🤝 DBOS Run agents that: - Survive restarts, failures, and deploys - Handle long-running async + human-in-the-loop tasks - Autosave progress with your database (SQLite/Postgres) Agent main loop → DBOS workflows MCP & model calls → DBOS steps (with retries) Custom tools → your choice: step, workflow, or enqueued tasks Production-grade resilience in just a few lines of code.

If you’re wondering what I’ve been building lately for Razroo | Synthetic Data & AI Simulations : This past week I’ve been integrating tools in ways I’ve never done before for makebind .com. It’s been an incredibly arduous process but also seamless because it has to be done in a way the user never leaves the chat. Things like web sockets, jwt tokens, mini frontends, dedicated vms, dedicated AI agents, specialized webhooks for 3rd party integrations, and AI assistant switch case statements in frontend to trigger events if need be. db tables to integrate them all with each other in a secure, scalable and safe fashion(which architected db table its own thing). However, once complete, which it seems like will, it opens up the door for a lot more people to join the process. Combine that with data, well you can do some pretty incredible things in the enterprise space. Keep an eye out