Search

"ranks": [[1689676050115, 8], [1689679650435, 9], [1689683250422, 9], [1689686850141, 9], [1689690449649, 9], [1689694050066, 10]]

"https://www.nytimes.com/2023/07/18/technology/openai-chatgpt-facial-recognition.html": {

"title": "OpenAI Worries About What Its Chatbot Will Say About People’s Faces",

"url": "https://www.nytimes.com/2023/07/18/technology/openai-chatgpt-facial-recognition.html",

"section": "technology",

import { ai } from "https://esm.town/v/yawnxyz/ai";

console.log(await ai("tell me a reddit joke", {

provider: "anthropic",

model: "claude-3-haiku-20240307",

const pipeline = new Pipeline("task", "model");

const result = await pipeline.run(inputs);

[exampleTranslation](https://www.val.town/v/iamseeley/exampleTranslation)

[exampleTextClassification](https://esm.town/v/iamseeley/exampleTextClassification)

const HUGGING_FACE_API_URL = "https://api-inference.huggingface.co/models";

const HUGGING_FACE_API_KEY = Deno.env.get("HUGGING_FACE_API_KEY");

const defaultModels = {

"feature-extraction": "sentence-transformers/all-MiniLM-L6-v2",

import { Oura } from "https://deno.land/x/oura_api@0.3.1/mod.ts";

export const oura = (ouraPersonalAccessToken) => new Oura(ouraPersonalAccessToken);

import { ModelProvider, modelProvider } from "https://esm.town/v/yawnxyz/ai";

let response = await modelProvider.gen({

prompt: 'hello, who am I speaking to?',

provider: 'google',

console.log('res:', response);

Demo of adding an Embeddings column to Airtable (which calls the embeddings endpoint at yawnxyz/v/ai) and stores embeddings in an Airtable column, then performing search against it

Usage:

- https://yawnxyz-airtablesemanticsearch.web.val.run/search?query=your+search+query&similarity_threshold=0.8&max_results=5&base_id=your_base_id&table_name=your_table_name&content_column=your_content_column&embedding_column=your_embedding_column

- https://yawnxyz-airtablesemanticsearch.web.val.run/search?query=cars

const app = new Hono();

app.use('*', cors({

origin: '*',

allowMethods: ['GET', 'POST'],

allowHeaders: ['Content-Type'],

const airtableApiKey = Deno.env.get("AIRTABLE_API_KEY");

export default async (req) => {

const { readable, writable } = new TransformStream();

const writer = writable.getWriter();

const textEncoder = new TextEncoder();

// Get the prompt from the URL query

const url = new URL(req.url);

const prompt = url.searchParams.get("prompt") || "tell me a joke";

if (!prompt || prompt.trim() === "") {

return new Response("Please provide a prompt in the URL query.", {

headers: {

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

if (req.method === "OPTIONS") {

return new Response("ok");

const searchParams = new URL(req.url).searchParams;

console.log(searchParams);

const topic = searchParams.get("topic");

console.log(topic);

try {

const apiKey = Deno.env.get("GEMINI_API_KEY");

const url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro-latest:generateContent";

Quick AI web search via email (useful for apple watch)

export default async function(e: Email) {

if (!e.from.endsWith("<brian@sarriaking.com>")) {

console.error("unauthorized!", e.from);

return;

const resp = await fetch("https://api.perplexity.ai/chat/completions", {

method: "POST",

headers: {

"accept": "application/json",

import { Hono } from "npm:hono";

const app = new Hono();

Using Vercel AI SDK

import { ModelProvider, modelProvider } from "https://esm.town/v/yawnxyz/ai";

let response = await modelProvider.gen({

prompt: "hello, who am I speaking to?",

provider: "google",

} as any);

console.log("res:", response);

link to val - https://www.val.town/v/fal/creative_upscaler

const upscaledImage = @fal.creative_upscaler("an owl", "https://storage.googleapis.com/falserverless/model_tests/upscale/owl.png")

export let creativeUpscaler = async (

prompt: string,

image_url: string,

creativity: number = 0.5,

Allows for automatic generation of Hono API comatible with GPTs. Endpoints' inputs and outputs need to be specified via types from which the Open API spec is generated automatically and available via `/gpt/schema` endpoint.

Usage example:

* COMMON TYPES

const ResponseCommandSchema = z.object({

feedback: z.string().describe("Feedback regarding submitted action"),

export interface ApiInfo {

url: string;

title: string;

description: string;

https://www.fal.ai/models/stable-diffusion-xl

link to val - https://www.val.town/v/fal/sdxl

const result = await fal.subscribe("fal-ai/fast-sdxl", {

input: {

export let sdxl = async (

prompt: string,

negative_prompt: string = "cartoon, illustration, animation. face. male, female",

image_size: string = "square_hd",

import { Oura } from "https://deno.land/x/oura_api@0.3.1/mod.ts";

export const getReadinessScore = async (ouraToken) => {

const oura = new Oura(ouraToken);

const yesterday = (d => (d.setDate(d.getDate() - 1), d))(new Date()).toISOString().split("T")[0];

const today = new Date().toISOString().split("T")[0];

const docs = await oura.getDailyReadinessDocuments(yesterday, today);

return docs.data.reverse()[0].score;