Hey folks. Looking to do a quick proof of concept and could use some advice, assistance, mentoring. I want to use a form as a trigger to verify identity and look a balance for that user and provide it to them. For the form I need to collect: Company name (dropdown box) First name Last name Employee# I need to query a sql database to verify the user is in the database. If they are then I need to find their balance and display it for them to see. Ideally this will look somewhat professional so I’m guessing Google forms is out since they have their text at the bottom. Anyone willing to guide me in the right direction? Am I trying to recreate the wheel and this has already been done and I don’t know about it? Many thanks in advance.

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Hey guys does anyone have any suggestions for reference material on how to link to API’s in program that uses SQL databases? Looking to create a form that will reference the database for a company, an employee first and last name, employee number, balance in their “account”. This would then be displayed back to them. Eventually I want to use this balance to go into a “store” where they can purchase items the company allows but that will be a phase2 or phase3 project. Thanks for any help pointing me in the right direction.

Client called at 11 PM: "The workflow is taking 45 SECONDS per PDF. We have 10,000 to process by Monday." Here's how I optimized it to 3 seconds (and saved the weekend): THE EMERGENCY: Law firm acquisition - needed to process 10,000 contracts by Monday morning for due diligence. Current speed: 45 seconds each = 125 hours = impossible THE DIAGNOSIS (1 hour of profiling): Where time was wasted: - PDF download: 2 seconds ✓ (fine) - Generic text extraction: 38 seconds ❌ (disaster) - Table parsing: 4 seconds ⚠️ (could improve) - Database write: 1 second ✓ (fine) The killer: Using basic extraction on 300-page contracts when we only needed 5 pages. THE SURGERY (3 AM - 5 AM): Fix #1: Intelligent Page Detection - Preview first page, find table of contents - Jump directly to signature pages and payment terms - Skip the 280 pages of boilerplate - Time saved: 35 seconds Fix #2: Parallel Processing - Split into 10 parallel n8n workflows - Each handles 1,000 documents - Load balanced across CPU cores - Time saved: 80% overall Fix #3: Better Extraction - Switched from generic to PDF Vector's LLM-enhanced mode - Understands context, not just text patterns - Handles their weird merged cells perfectly - Accuracy actually IMPROVED to 99.4% Fix #4: Caching - MD5 hash of each document - Skip if already processed - 1,100 were duplicates - Time saved: 55 minutes THE RESULT: Saturday 6 AM test run: - 10,000 contracts - Total time: 8 hours 23 minutes - Average per document: 3 seconds - Accuracy: 99.4% - Client reaction: "You're a f***ing wizard" Sunday: I slept for 14 hours THE TOOLS: - n8n with SplitInBatches for parallel processing - Redis for caching - PDF Vector API (handles huge docs without memory issues) - 1 very large coffee Lesson learned: Most "slow" workflows are doing unnecessary work. Find what you actually need and skip everything else. What's your worst performance nightmare? Let's optimize it together ⚡

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