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const getFileData = (fileName = "fileName.json") => ({ files }) => files[fileName];

const getContent = ({ content }) => content;

const getData = ({ data }) => data;

const mapper = fn => array => array.map(fn);

const getPubkeyAndAddresses = ({ pubkey = { key: "" }, address = "" }) => ({

address,

pubkey: {

key: pubkey.key,

const compose = (...fns) => (initialValue) => fns.reduceRight((acc, val) => val(acc), initialValue);

const getFileContents = (fileName = "") =>

class BufferResult {

* @member {Uint8Array}

buffer: Uint8Array;

* @member {boolean}

isError = false;

* Create a Buffer Result.

* @param {Uint8Array} buffer - the payload.

* @param {boolean} isError - is the result an error

constructor(buffer: Uint8Array, isError: boolean) {

export default async function main(req: Request): Promise<Response> {

return new Response("Hello world");

export default async function main(req: Request): Promise<Response> {

return new Response("Hello, world!");

* This val creates a simple hacker news clone with 30 fake sample stories.

* It uses blob storage to store story data, including the ability to submit

* new stories and upvote existing stories.

interface Story {

id: number;

title: string;

url: string;

points: number;

createdAt: string;

const DEFAULT_STORIES: Story[] = Array.from({ length: 30 }).map((_, idx) => ({

export default function badgeMiddleware(handler, url: string) {

return async function(req: Request): Promise<Response> {

if (req.method !== "GET") return await handler(req);

let res = await handler(req);

if (res.headers.get("Content-Type")?.includes("text/html")) {

let body = await res.text();

const badge = valTownBadge(url);

body += `<div style="position:fixed;right:0;bottom:0;margin:8px">${badge}</div>`;

res = new Response(body, res);

return res;

Protect your vals behind a password. Use session cookies to persist authentication.


See @pomdtr/password_auth_test

type Session = {

id: string;

expiresAt: number;

async function createSessionTable(tableName: string) {

Example webhook evaluator (see docs in http://docs.agenta.ai/evaluation/evaluators/webhook-evaluator)

export default async function server(request: Request): Promise<Response> {

// Parse the request body

let body: { inputs: Record<string, string>, output: string, correct_answer: string };

body = await request.json();

const score = body.output.toLowerCase() === body.correct_answer.toLowerCase() ? 1.0 : 0.0;

const responseBody = { score };

return new Response(JSON.stringify(responseBody), {

status: 200,

headers: { 'Content-Type': 'application/json' }

export function t(tag: string, args: any = {}, children: any[] = []) {

const func = (...argsOrChildren: any) => {

if (Array.isArray(argsOrChildren[0]))

return func(...argsOrChildren[0], ...argsOrChildren.slice(1));

const args2 = { ...args },

ch2 = [...children];

for (const c of argsOrChildren) {

if (typeof c === "string") ch2.push(c);

else if (typeof c.html === "string") ch2.push(c.html);

else for (const k in c) args2[k] = c[k];

const basePrompt = [

`Generate an image generation model prompt (description) for the previous image, in nightmare before christmas chibi 3D anime style.`,

`The objective is to try to replicate the image as closely as possible,`,

`but in the new style. To do that, you should be very detailed in including`,

`relevant information necessary to reproduce the image, e.g. colors, poses,`,

`facial expressions, background objects, etc. Ensure that the entire description `,

`is consistent with the nightmare before christmas chibi 3D anime style, especially`,

`the 'nightmare before christmas 3D' part. Output only the prompt and nothing else.`,

].join(" ");

const settings = {

const replacements = [

[/\*/g, "\\*"],

[/#/g, "\\#"],

[/\//g, "\\/"],

[/\(/g, "\\("],

[/\)/g, "\\)"],

[/\[/g, "\\["],

[/\]/g, "\\]"],

[/</g, "&lt;"],

[/>/g, "&gt;"],

import * as squint_core from "npm:squint-cljs/core.js";

globalThis.squint_core = squint_core;

export function compileCljsToES6(cljs: string) {

return compileString(cljs, { repl: true, "elide-exports": true }).replace(

"squint-cljs",

"npm:squint-cljs",

const basePrompt = [

`Generate an image generation model prompt (description) for the previous image, in nightmare before christmas chibi 3D anime style.`,

`The objective is to try to replicate the image as closely as possible,`,

`but in the new style. To do that, you should be very detailed in including`,

`relevant information necessary to reproduce the image, e.g. colors, poses,`,

`facial expressions, background objects, etc. Ensure that the entire description `,

`is consistent with the nightmare before christmas chibi 3D anime style, especially`,

`the 'nightmare before christmas 3D' part. Output only the prompt and nothing else.`,

].join(" ");

export default serve({

export let aqi = async () => {

let pm25 = (

await fetchJSON(

"https://api.openaq.org/v2/latest?" +

new URLSearchParams({

limit: "10",

page: "1",

location: "Geneva",

offset: "0",

sort: "desc",

export async function living(req: Request) {

let name = decodeURI(new URL(req.url).pathname.replace("/", ""));

let alive, error;

try {

alive = await alive2(name);

catch (e) {

error = e;

return html(

`<body style="color: white; font-family: sans-serif; background: linear-gradient(to right, #fbc21d 0%, #f79622 30%, #ff6a00 60%, #ff2b04 100%)">

<div style="font-size:2em; padding:0.5em">