Vals using hono
Description from the NPM package:
Web framework built on Web Standards
dthyresson
whatsThatInHotDogs
HTTP
What's that in Hot Dogs? Inspired by the 2014 Boston Baseball Hackday submission, this val shows how many hot dogs a baseball player can buy in a year based on his annual salary and the average price of a hot dog sold at their home team's stadium. Back then, the team spent most of the hackday time collecting data, building s javascript app from scratch and then deployed to Heroku. It took some time. It had much nicer design, artwork, styling, etc to be sure. I wanted to see how quickly a similar app could be created and made available on Val.town 10 years later. I asked Chat GPT to build the app: You are a javascript web developer and a baseball fan. Build a web app using Hono called "What's that in hot dogs?". The app should let you select a baseball player. Based on 1) the player annual salary calculate the number of hot dogs they could buy based on the average price of a hot dog sold at their stadium. For example, Mike Trout played for the Los Angeles Angels and makes $35M per year. The Los Angeles Angels play at Angel Stadium a hot dog costs $8. That means he can buy 35,000,000/8 = 4.375M hot dogs. The app lets you pick a player and then will display an emoji icon of a hot dog. One emoji hot dog is worth 100,000 hot dogs. So picking Mike Trout will show 43.75 (or 44) hot dog icons. It worked! But, it only had one player's worth of data. The app needs more player salary and hot dog price data. Find the top players in each MLB team, their salary, and the price of a hot dog at their home stadium. I then asked for a small improvement: Improve the app by also showing the team the play plays for, the stadium, salary and price of the hot dog. Include it in a nice table below the hot dog emoji list. Within 10 minutes, I had a working "What's that in Hot Dogs?". I then re-worked the headings and added a legend and it matched what I remember seeing back in 2014 ... but built and deployed in minutes versus hours. Note: Much of the 2014 team's time was spent collecting data manually by looking up players' salaries and finding the cost of a hot dog at stadiums. In this example, I've not validated that ChaptGPT correctly identified these figures.
0
yawnxyz
ai
HTTP
An http and class wrapper for Vercel's AI SDK Usage: Groq: https://yawnxyz-ai.web.val.run/generate?prompt="tell me a beer joke"&provider=groq&model=llama3-8b-8192 Perplexity: https://yawnxyz-ai.web.val.run/generate?prompt="what's the latest phage directory capsid & tail article about?"&provider=perplexity Mistral: https://yawnxyz-ai.web.val.run/generate?prompt="tell me a joke?"&provider=mistral&model="mistral-small-latest" async function calculateEmbeddings(text) {
const url = `https://yawnxyz-ai.web.val.run/generate?embed=true&value=${encodeURIComponent(text)}`;
try {
const response = await fetch(url);
const data = await response.json();
return data;
} catch (error) {
console.error('Error calculating embeddings:', error);
return null;
}
}
0
