Google launched its Vibe Coding tool inside AI Studio a few weeks ago, and people made thousands of videos on it. The reason it’s being so popular among vibe coders is that it’s completely free. You don’t even have to integrate the Gemini API key with it because it’s integrated automatically inside AI Studio. But here are two reasons you should avoid using it right now: 1. No Support for Environment Variables AI Studio won’t let you create env vars while vibe coding. So if you are building a web app that uses APIs (e.g., OpenAI, Anthropic), you have to use them right inside your code, which will expose your API keys. 2. No Testing URL AI Studio doesn’t provide a testing URL, which is very important for setting up Google authentication, Supabase, etc. But wait! If you still want to build web Apps using Gemini, you can use Gemini CLI, which has a versatile free tier. You can install Gemini CLI inside your Device using Node.js. It means you can store your API keys inside .env.local file while building apps, and you will get a static testing URL like localhost:5000

Anyone from Europe here? Has anyone here had practical experience with OpenAI's EU Data Residency? I'm currently building a tool based on Whisper/GPT-4o Mini Transcribe and want to make sure all data processing stays entirely within Europe (so no US region). I'm curious if anyone has successfully set up a project or organization with EU Data Residency enabled (like through an Enterprise or Team account) — and whether the audio models (Transcribe) actually work properly there. Are there any experiences or pitfalls you can share?

Client paid an agency $8k to "optimize" their workflow. I rebuilt it from scratch in 11 minutes. It's 3x faster now. Here's what happens when agencies prioritize billable hours over actual solutions: THE SETUP Client: "Our invoice processing is too slow" Agency: "We'll optimize your existing workflow" Me: "Can I see what they're optimizing?" Client: *shares their n8n workflow* Me: "This is... impressively overcomplicated" THE AGENCY'S "OPTIMIZATION" What they quoted: - Workflow audit: 1 week - Optimization plan: 3 days - Implementation: 2 weeks - Testing phase: 1 week - Total timeline: 6 weeks - Investment: $8,000 Their audit findings (22-page PDF): - "Suboptimal node placement" - "Inefficient data transformations" - "Missing error handling protocols" - "Requires architectural restructuring" Translation: They made it sound worse to justify the price. WHAT THEY ACTUALLY DID Week 1-2: Meetings and documentation Week 3-4: Moved some nodes around Week 5: Added 3 more nodes "for resilience" Week 6: Testing (aka fixing what they broke) Result: - Original processing time: 45 seconds - "Optimized" processing time: 38 seconds - Cost: $8,000 - Improvement: 15% Client: "Is this worth $8k?" Agency: "Enterprise optimization is an investment" THE REAL PROBLEM I looked at their "optimized" workflow. 67 nodes. For invoice processing. It had: - 4 different parsing methods (only needed 1) - Redundant validation steps - Multiple database calls that could be batched - Error handling that created more errors - Comments like "legacy node - don't remove" This wasn't optimization. This was justification for billable hours. MY APPROACH Client: "Can you actually fix this?" Me: "I'm not going to fix it" Client: "Oh..." Me: "I'm going to rebuild it properly" Opened Skada AI Described what they actually needed: "Extract data from incoming invoices, validate required fields, check for duplicates, update accounting system, send confirmation email, flag exceptions for review"

If you've built a chat automation using ManyChat, Insta, and n8n, you might be familiar with this problem: 1. When the chatbot responds too slowly—for example, because Supabase was slow or for some other reason—the HTTP request, which are supposed to be sent back to ManyChat, times out. 2. As a result, the user sometimes doesn't receive any reply at all, which is obviously frustrating and requires manual intervention. Here's a solution for this: Solution Steps: 1. Once the AI has generated a response, save that message in a Google Sheet, a Data Table, or Airtable (I bet you do it anyways) 2. If an error occurs and the answer is returned to ManyChat too late: - ManyChat detects the error message and triggers a small workflow (e.g. a Condition "response is null" sends one more http request to n8n) - IMPORTANT : Before starting the second workflow, ManyChat has to wait ~20 seconds to ensure the response has already been saved in the Google Sheet. 3. In this smaller n8n workflow, the saved response is retrieved from the Google Sheet and sent to the user again. This creates a loop with the last message, ensuring that the user always receives a response—even if the original automation was too slow.

Hey, fam! What is your experience in this niche? Is there a demand for it in your country? Especially for voice agents. Is anyone in Europe here, who's building this? If so, which voice agents are you using? Do you connect the back end to an R-Keeper-like terminal, and is this actually necessary, or do you just enter data into an Airtable? I'm excited to read your answers!