How I Turned an Ugly Spreadsheet into an AI Assisted App with Antigravity

**I have a confession to make.** Up until now, I wasn’t that much into “vibe coding.” I used AI all the time for Python coding, but I never really built a whole app from scratch in a language I knew nothing about. That changed today. I encountered a really annoying problem: I had to review a massive amount of talk submissions for a conference. We’re talking about a massive spreadsheet. Staring at those tiny cells was literally making my eyes hurt. My initial thought was, “Hey, let’s create a really sharp UI for the submission review.” But then I thought, why stop there? Why not let AI provide me valuable inputs from social media to help me with the review itself? So, I decided to build **TalkScout**. And since I wanted to test drive [Google Antigravity](https://antigravity.google/docs/home) (Google’s new AI-powered coding agent), I figured this was the perfect opportunity.  <center><small>Talkscout Dashboard (synthetic data)</small></center> Here is how I went from a painful CSV to a fully deployed [Cloud Run](https://docs.cloud.google.com/run/docs?utm_campaign=CDR_0x91b1edb5_default_b473111509&utm_medium=external&utm_source=blog) app-without writing a single line of React code myself. ## Step 1: The “Meta-Prompt” (Asking Gemini to Talk to Antigravity) I didn’t start by coding; I started by chatting. I used **meta-prompting** to get started. So, what is meta-prompting, you may ask? It’s actually when you go to Gemini 3 and ask it to write the prompt for the coding agent. I explained my problem to **Gemini 3** in simple words. Gemini 3 acted as my architect. It turned my “brain dump” requirements into a technical spec, defining the component structure and data model. I didn’t have to guess the right words, I just pasted that polished spec into Antigravity. ## Step 2: Ditching the Spreadsheet for a Dashboard With that prompt, Antigravity built the app of my dreams. It allowed me to: - Upload the CSV with all the conference talks. - Get a dashboard showing the status of each talk. - See a beautiful, high-contrast UI to review abstracts and demo plans without squinting at cells.  <center><small>TalkScout submission review page with high contrast UI</small></center> **The “Vibe” Fix:** It wasn’t all smooth sailing — I actually hit a nasty React hydration error. This can take hours to debug, especially if you’re not a frontend developer… But I simply provided the error message to Antigravity and the coding agent pinpointed the mismatch in the DOM and fixed it in minutes. ## Step 3: Integrating Grounded Intelligence I didn’t just want a UI; I wanted to overcome my own bias. How do I know if a niche topic is actually hot? I added a button to get an **AI Assessment**. But I didn’t want hallucinations. I used **Google Search Grounding** so the AI could search through Reddit, X (Twitter), and LinkedIn for real-world developer signals. That provided me inputs based on the current developer audience mindshare.  <center><small>TalkScout submission review page with AI social media analysis</small></center> ## Step 4: Calibrating the “Strict” Reviewer Initially, the AI was way too nice. It was giving high scores to anything with trendy keywords. I used what’s called **few-shot prompting** to calibrate it. I gave examples of my scores vs. its scores and introduced what I call the **“Marketing Fluff Penalty”**. - If a submission reads like a documentation/marketing page? Points docked. - If the submission was way too short? We capped the score at a hard 2. - If it includes war stories and actual learnings — increase rating. After a few examples, it became more calibrated to my taste. ## Step 5: The Pivot to Batch Mode I realized it was taking me too long to ask the AI to evaluate each talk individually while I reviewed it. So, I asked Antigravity to refactor the backend for **Batch Mode**. Now, TalkScout processes the entire submission pool in the background. By the time I grab a coffee, the “AI Draft” column is full of insights, allowing me to focus only on the final decisions. ## Step 6: Sharing the Goodness (Deploy to [Cloud Run](https://docs.cloud.google.com/run/docs?utm_campaign=CDR_0x91b1edb5_default_b473111509&utm_medium=external&utm_source=blog)) TalkScout was working great for me, but I thought, “It would be great to share this with the other reviewers.” This is where Antigravity really showed off. I simply asked it to deploy the app. It automatically recognized my Google Cloud Project ID, handled the containerization, generated the exact deployment commands, and deployed it to [Cloud Run](https://docs.cloud.google.com/run/docs?utm_campaign=CDR_0x91b1edb5_default_b473111509&utm_medium=external&utm_source=blog). One simple ask, and minutes later, I had a URL to share with the team. ## It Was Pretty Fun! It was pretty fun to actually solve a real problem I had using Antigravity and vibe coding. I built a tool that handles ingestion, provides a distraction-free rating interface, and provides valuable inputs for my reviews. I would love to hear from you all - have you recently solved a problem using vibe coding? If you haven’t already - try playing around with [Antigravity](https://antigravity.google/docs/home) and easily deploy your apps to [Cloud Run](https://docs.cloud.google.com/run/docs?utm_campaign=CDR_0x91b1edb5_default_b473111509&utm_medium=external&utm_source=blog).