Article URL: https://journals.sagepub.com/doi/full/10.1177/14771535211014792 Comments URL: https://news.ycombinator.com/item?id=49956932 Points: 3 # Comments: 0
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I am working on a PHP-based worksheet generation system and recently ran into a performance problem. The application generates printable PDF files based on user-selected options. The workflow is: User selects options ↓ Server generates data ↓ PHP creates PDF ↓ User downloads the file The problem is that the generation time increases significantly when the document contains many items. For example: 100+ exercises multiple pages answer pages custom layouts Current implementation The frontend sends a POST request to the PDF generation endpoint: $result = http_post( https://www.wativate.com/?api/p
Hi everyone 👋 I'm Andrea, a full-stack developer from Italy, living in Tenerife. Yes, that one, the island with the big Teide, a volcano, which is maybe why I ended up building this. I have been building Vue apps in production for about four years: a blockchain explorer, a marketplace, editors, dashboards, data visualisation pages. What I had never built is a map. No GIS, no projections, no OpenLayers.
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass Last Frost is an offline garden planner. You type your ZIP code and what you want to grow; it looks up your local first fall frost and last spring frost dates, then a small open-weight model running on your own machine tells you what to plant this week, what to hold back, and why — based on the actual number of frost-free days you have left. It's for anyone whose gardening app experience is: open app → get generic advice → still not know if it's too late to plant tomatoes. Last Frost replaces "generic" with
Small decision models have two bad options today: generate text token-by-token (slow, fragile to parse), or "introspect" their confidence verbally (systematically miscalibrated). Jev-LCT is an open-source System-One decision engine that takes a third route — extracting calibrated probabilities directly from a transformer's internal recurrent dynamics. Parallel Looped Prefill: recurrently recomputes only the top k=2 layers of a causal transformer, preserving full representation fidelity. Endogenous Trajectory Confidence: reads calibrated probabilities from hidden-state convergence dynamics (cos
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