This weekend I experimented with a different way to build RAG. Instead of the typical setup: - Generate embeddings - Store in Pinecone / Supabase - Manage vector DB infra - Handle indexing + costs I tested Gemini File Search API directly inside n8n. And honestly… it simplified the entire pipeline. 🔧 What I Actually Built Inside n8n, I used just 4 HTTP requests: 1. Create a file store 2. Upload a document 3. Move the file into the store 4. Query Gemini That’s it. Gemini handled: - Chunking - Embeddings - Indexing - Retrieval No external vector database.No embedding model setup. 💰 Why This Is Interesting - Storage is free - No hourly DB cost - Indexing is $0.15 per 1M tokens For small projects, internal tools, or MVPs — this is extremely cost-efficient. ⚠️ Important Limitations I Noticed This is not magic. - No automatic version control (re-upload = duplicate data) - Chunk-based retrieval struggles with full-document reasoning - OCR works, but messy documents still need preprocessing - Data is processed on Google servers (privacy considerations apply) So architecture thinking still matters. My Take For: - Internal AI assistants - Automation workflows - Startup prototypes - Personal tools This is a powerful alternative to traditional vector DB setups. I wouldn’t blindly replace enterprise-grade systems yet — but for builders, this is very interesting. If anyone here is experimenting with Gemini File Search or building RAG in n8n, I’d love to compare notes 👇 Happy to share the workflow structure if there’s interest.

While everyone debated chatbots…Sarvam AI focused on documents. And that focus matters. Recent benchmarks suggest the Sarvam Vision OCR model outperforming ChatGPT and Gemini on Indian language OCR tasks — especially on low-quality scans and real-world documents. >Why This Is Important India doesn’t run on clean PDFs. We deal with: - Blurry mobile scans - Government forms - Aadhaar copies - Regional-language invoices - Mixed Hindi + English documents Sarvam Vision is built for: - Indian languages & scripts - Noisy, real-world formats - Enterprise & public-sector workflows - Bharat-scale document processing >What This Means for Builders If you're using n8n, Power Automate, or Make, this unlocks: ✅ Hindi doc → OCR → JSON → database ✅ GST invoice → extract → auto-validate ✅ Land records → digitize → searchable Instead of making global models fit India,you use a model built for India. That means higher accuracy, fewer corrections, and more reliable automation. Sometimes the advantage isn’t being the biggest model. It’s being the most relevant one. What’s your take — will India-first AI dominate applied workflows? 👇

If you’re learning n8n and feel stuck, it’s probably not the tool. It’s the order you’re learning in. Here’s the roadmap that actually works: Phase 1: Learn Automation Basics Before n8n, understand: - What triggers are - What actions do - How webhooks work - What APIs actually mean If you understand data flow, workflows stop feeling confusing. Phase 2: Understand How n8n Works Think in simple logic: Input → Process → Output Learn: - Nodes - Expressions - Credentials That’s enough to remove 80% of beginner confusion. Phase 3: Build Simple Workflows Start small: - Form → Google Sheets - Lead → CRM - Email → Auto-reply - Slack alert Simple builds create real confidence. Phase 4: Add Logic Now learn control: - IF conditions - Loops - HTTP requests - Data formatting At this stage, you can automate almost anything. Phase 5: Then Add AI Now integrate AI models. Use them to: - Summarize - Classify - Enrich data AI should enhance your workflow — not replace your logic. The Goal Not flashy demos. But systems that: - Save time - Remove manual work - Scale operations Build boring first.Then build powerful. 👇 What workflow are you building this week?

For years, automation meant dragging nodes in tools like n8n, Make, and Zapier. Connect this → map that → handle errors → pray nothing breaks. It worked.But it was fragile. One API change. One unexpected response. And your entire workflow collapses. That’s traditional automation. Now we’re entering something different: 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 Instead of wiring every step manually, you define the goal and the agent figures out the steps. Recently, I built an autonomous AI News Agent using Antigravity, leveraging Claude Opus 4.6 for natural language agent control and Gemini 2.0 Flash for automated news summarization within structured pipelines.The difference was obvious. I didn’t: • Manually define every integration • Hard-code every edge case • Write defensive logic for every possible failure Instead, I defined the outcome: “Every morning at 9AM, fetch important AI news and send me a clean briefing to my Gmail inbox” The agent handled: Research Filtering Formatting Error handling 𝐖𝐡𝐲 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐖𝐢𝐧 Here’s what makes them fundamentally different: • Outcome-driven, not step-driven: You define what needs to happen. The system decides how. • Self-adaptive: If something changes (API, format, response), the agent can adjust instead of crashing. • Less manual debugging: The agent can interpret errors and fix issues without you rewriting the entire flow. • Faster to build: No more wiring 20 nodes. One directive can replace an entire visual workflow. • Scales better: As complexity grows, you don’t get a spaghetti mess of connections. • You focus on thinking, not plumbing: Your value shifts from wiring tools to designing intelligent systems. Traditional tools (n8n, Make, Zapier):You design the steps. Agentic workflows:You design the outcome. That shift changes everything. Instead of being a builder wiring nodes,you become an architect defining intent. Automation was about workflows. Agentic systems are about intelligence. And this shift is just getting started.

After building multiple MVPs with Lovable, I realized something important: Building MVPs is no longer limited by coding skills. It’s limited by how clearly you define the product. AI can build almost anything. But only if you give it the right structure. Here’s the exact framework I follow: 1. Start with real problems I don’t start with ideas. I start with problems. I explore Reddit, Discord, and YouTube comments to see what people are struggling with. This helps ensure I’m building something useful and relevant, not just something interesting to me. When the problem is clear, everything else becomes easier. 2. Create a clear blueprint using a PRD prompt Before building, I use Lovable to generate a PRD (Product Requirements Document). This helps define: - Pages and routes - User flow - Core features - Product structure This step is critical. It acts like a roadmap and removes confusion during development. Without structure, AI produces inconsistent results. With structure, it produces production-ready output. 3. Build the skeleton first Next, I generate the basic UI structure: - Layout - Navigation - Pages I don’t worry about features yet. The goal is to create a clean foundation that can be extended easily. 4. Add features incrementally I add one feature at a time using focused prompts. This keeps the system stable and easier to debug. It also helps Lovable produce more accurate results. Trying to build everything at once usually creates messy output. Incremental building works much better. 5. Add authentication Once the core product works, I add authentication using Supabase. This includes: - Login and registration - Password reset - Email verification - Protected routes This makes the product ready for real users. 6. Deploy and iterate Finally, I deploy the product. You can deploy directly from Lovable or connect GitHub and deploy via Vercel or Netlify. Once live, I improve the product based on real usage and feedback.