Storing user-uploaded files directly on application server local disk works fine in development, but completely breaks horizontal scaling. The moment your cloud infrastructure auto-scales to 3 instances, a user who uploads an avatar on Instance 1 will get a 404 Not Found whenever their subsequent request lands on Instance 2 or 3.

Every service storing AI model API keys is becoming a potential target Railway’s security incident is a clear warning Attackers are going after AI API keys, potentially to use them for model distillation This won’t be the last attack of its kind

Spreadsheets are great for storing numbers. They’re terrible for showing them off. 📈👀 Lately, the Stitch community has been using their design partner to turn raw data dumps into sleek, production-ready dashboards: 🔹 Paste in CSVs, analytics exports, or workout logs 🔹 Ask Stitch for a specific vibe, layout, or design system 🔹 Instantly explore high-fidelity charts, stat callouts, and clean data hierarchies 🤖 Automation Hack: Pair Gemini Spark or Antigravity with the Stitch MCP Server. You can automate your daily tracking so a fresh, tailored dashboard is generated before your first cup of coffee. Check out the demo to see how we turn raw data into eye candy! 🔊👇

InterLink ID verifies humans without storing the face it scans InterLink ID starts with a face scan, and @inter_link runs an AI deepfake and liveness check before anything is stored. The face becomes a feature vector, a string of numbers describing facial geometry rather than an image. Those numbers are scrambled and hashed so the original face cannot be rebuilt. What gets stored is a cryptographic commitment to that hash, a sealed record that can be checked against but not read, held across a decentralized node pool per InterLink's whitepaper. Later verification means proving a fresh scan matches the sealed record without revealing either. The algorithm behind the matching was submitted to NIST's face recognition evaluation in November 2024.