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This is a submission for Weekend Challenge: Passion Edition What I Built Every developer has a graveyard of side projects — started with fire, abandoned quietly on a Tuesday. They deserved better than an empty GitHub repo gathering digital dust. DevGraveyard is a gothic memorial platform where developers give their abandoned passion projects a proper burial. Connect your GitHub, pick a dead repo, carve its epitaph — and watch Gemini AI write a dramatic breakup letter from you to the project. Here's what it does: ⚰️ Bury a project — 3-step burial wizard: pick a repo → choose cause of death ( "Never Made it Past Localhost" , "Ran Out of Weekend" , "It Was Complicated" ...) → write an epitaph 🪦 Real tombstone data — pulls your actual commit history: peak obsession streak, most commits in a single day, last commit message ( your final words ) 🤖 AI Eulogy — Google Gemini writes a dramatic breakup letter from you to the project, referencing your real commit data 🕯️ Community mourning — light candles, leave RIP messages, vote to resurrect projects 🌐 3D Graveyard — a full Three.js interactive cemetery: bare trees, fireflies, flickering candles, soul wisps, resurrection pulse rings. Click any tombstone to interact My own ARweave repo had 56 commits, a 2-day peak streak, 30 commits on its best day. Cause of death: "Never Made it Past Localhost." Last words: "feat: overlay plane in 3D builder — drag/scale image on marker, position saved to DB and restored in AR viewer." It worked until it worked. Demo 🔗 Live → devgraveyard.varshithvhegde.in Code Varshithvhegde / devgraveyard Give your abandoned passion projects a proper burial. A gothic graveyard for dead side projects. ⚰️ DevGraveyard A memorial for your abandoned side projects. They deserved better than an empty GitHub repo gathering digital dust. Live → devgraveyard.varshithvhegde.in What is this? Every developer has a graveyard of passion projects — started with fire, abandoned quietly on a Tuesday. DevGraveyard gives them

This is a submission for Weekend Challenge: Passion Edition What I Built Heirloom AI preserves family recipes & skills using Gemini multimodal AI. Upload handwritten cards, cooking photos, voice recordings, or gesture videos — it generates structured archive entries with poetic memory cards, evidence-based ingredients (with confidence levels), physical-cue step guides, and prominently flagged "speculative gaps" for family verification. Includes AI illustration generation and image enhancement. Full-stack React + Express app, persists in localStorage, exports Markdown. Demo heirloom-ai.ai.studio Code gxobst / Heirloom-AI Transform messy family recipe cards, verbal kitchen instructions, or raw cooking photos into beautifully structured, editable archive entries with Gemini. Heirloom AI 🌾 Preserve the recipes, rituals, and skills that live in family memory. Heirloom AI is a web application designed to transform messy personal materials—such as raw photographs, chaotic scribbles, handwritten notes, verbal stories, videos, and voice recordings—into beautiful, structured, editable, and shareable archival entries. The first MVP focuses heavily on family recipes , because culinary traditions are deeply emotional, practical, highly visual, and uniquely susceptible to being lost across generations. 📖 What It Does Rather than generating generic, standardized recipes off the web, Heirloom AI acts as a warm oral-history and preservation assistant. It processes your specific memory context and images to draft custom archives containing: Evocative Memory Cards : A warm, poetic summary and quote summarizing the tradition. Personal Narrative & Lore : Captures the emotional voice and regional background. Evidence-based Ingredients : Checklists tracking what ingredients are visible or described. Physical Cue Guides : Instruction steps… View on GitHub How I Built It Single Node server running Express + Vite SPA (no CORS issues). Two Gemini models: gemini-3.5-flash with schema-constrain

This is a submission for Weekend Challenge: Passion Edition Target categories: Best use of Snowflake...

I'm a experienced/senior developer which is frequently using ai, guiding coding agents, etc. I wonder, how does my job look like in a few years? Which skills might be the best ones to have? Currently, having business knowledge, development experience helps greatly with guiding coding agents, creating MVPs/PoCs in "no time", improving code, etc. But what if coding agents/ai would overtake this job?

Every logistics and field-sales team runs the same expensive process: a driver photographs a receipt into a WhatsApp group, and a back-office clerk manually types the invoice number, total, and date into a spreadsheet. Hundreds of receipts a week = transcription errors and thousands of wasted hours. AI vision models kill that bottleneck. Here's the pipeline that turns a blurry field photo into clean structured data in seconds. Why vision models beat traditional OCR OCR reads characters. Modern vision models (Claude Vision, Gemini Vision, GPT-4 Vision) read structure — they distinguish a tax ID from a total, and a date from an amount, even on crumpled, angled, or poorly lit receipts. No brittle per-vendor parsers. The pipeline (3–8 seconds end to end) WhatsApp image → Apps Script doPost → forward to vision model → model returns JSON { InvoiceNumber, TotalAmount, VendorName, Date, Category, confidence_score } → confidence routing: > 90 → auto-append to ledger 70–90 → flag for human review < 70 → ask driver to re-photo → write row to Google Sheet (+ link to original image) → auto WhatsApp confirmation to driver The confidence_score is the whole trick — it's what stops bad extractions from silently polluting your ledger. Model selection (this drives your bill) Gemini Vision — cost-efficient default, strong multilingual OCR, great on clean receipts. Claude Vision — highest accuracy on degraded receipts; use for high-stakes flows. GPT-4o Vision — competitive, strong structured extraction. Pattern: Gemini for the first pass, escalate only low-confidence cases to Claude / GPT-4o. The economics ~500 receipts/week: vision API $10–40 + WhatsApp API $30–60 + Apps Script free = ~$40–100/month . Versus a clerk at ~25 hrs/week = $2,000–4,000/month in loaded labor. Per-receipt cost: $0.005–0.02 (compress images to ~1024px to cut it further). Accuracy: 92–97% on legible receipts, 75–85% on handwritten/damaged — hence the confidence routing. Pitfalls to avoid Auto-appending with no c