返回首页
最新
Each moving arrow represents a real bike ride. There are 291 million rides in total, covering 12 years of history from June 2013 to December 2025, based on public data published by Lyft.<p>If you've ever taken a Citi Bike ride before, you are included in this massive visualization! You can search for your ride using Cmd + K and your Citi Bike receipt, which should give you the time of your ride and start/end station.<p>Some technical details:<p>- No backend! Processed data is stored in parquet files on a CDN, and queried directly by DuckDB WASM<p>- deck.gl w/ Mapbox for GPU-accelerated rendering of thousands of concurrent animated bikes<p>- Web Workers decode polyline routes and do as much precomputation as possible off the main thread<p>- Since only (start, end) station pairs are provided, routes are generated by querying OSRM for the shortest path between all 2,400+ station pairs<p>Legend:<p>- Blue = E-Bike<p>- Purple = Classic Bike<p>- Red = Bike docked<p>- Green = Bike unlocked
Hi HN,<p>I am a CTO/Founder usually stuck in "development hell" on long-term projects. To fix my perfectionism, I started a challenge on Jan 1st: Ship 12 startups in 12 months.<p>This is Project #01: TierWise.<p>The Problem: Most SaaS founders price for the US/EU market. Charging a flat $49/mo excludes huge segments of users in LATAM, India, or Southeast Asia where purchasing power is lower. Building a custom GeoIP/PPP logic for every side project is a distraction.<p>The Solution: I built a drop-in JS widget that detects the visitor's country and calculates a discount based on their local Purchasing Power Parity (PPP).<p>The Stack (The "Speed Run" setup):<p>Backend: Laravel 11 (API).<p>Frontend: Nuxt 3 (SSR).<p>Logic: Custom Middleware + Redis (for request throttling) + MaxMind GeoIP.<p>Design: Soft Brutalism (trying to avoid the generic "AI dark mode" look).<p>Business Model: It’s an open SaaS.<p>Free: 500 adjustments/month (no credit card).<p>Paid ($9/mo): Unlimited + White-label.<p>I’d love your feedback on the implementation and the "Soft Brutalism" UI approach.<p>Link: <a href="https://tierwise.dev/" rel="nofollow">https://tierwise.dev/</a>
Long time lurker here. My family had a pen-and-paper game we'd play on long drives to visit my great-grandmother. After she passed, I spent the holidays recreating it:
<a href="https://a26z.fun" rel="nofollow">https://a26z.fun</a><p>How it works:<p>Find 15 words from a category (like "Stone Fruits," "US States," or "Dog Breeds") as fast as you can. Once you meet the 15 word minimum, you can play for as long as you want.<p>Each letter shows how many target words start with it (A¹ = one word starts with A, N² = two words start with N)<p>That small ² in the bottom-right? Multi-word answers allowed. For "US States" with N², both "NEW YORK" and "NORTH DAKOTA" count<p>Unlimited guesses, 2 hints, and a shuffle button to reorder by frequency.<p>Example:
Category: US States | Letters: A¹ M¹ N² S²
Answers: ALABAMA, MONTANA, NEW MEXICO, SOUTH DAKOTA<p>If you're into Connections or Strands, this scratches a similar itch but with a deduction twist.
During the holidays, 50k people were dropped from one AI training project without the story reaching the front page. That tells you how invisible this expert workforce still is.<p>Right before the new year, the AI training community absorbed one of its biggest shockwaves: 50k contributors waking up to sudden removal and a one-line “quality requirements changed” message, with no real path to recover. For many, it meant losing time, momentum, and income.<p>This isn’t a post against AI training, just more of a defense for experts’ contributions. RLHF and data annotation help make models reliable, effective, and safe in the real world, and scaling it will demand deep expertise across industries, languages, and edge cases.<p>If we’re serious about scaling it, we need to start elevating the expert workforce that shapes AI across domains. We can’t treat them as disposable or erase them overnight.