
Research
Two Malicious Rust Crates Impersonate Popular Logger to Steal Wallet Keys
Socket uncovers malicious Rust crates impersonating fast_log to steal Solana and Ethereum wallet keys from source code.
A universal AI wrapper for JavaScript & TypeScript.
Generate text, images, and voice from anywhere—servers, browsers and apps. AIWrapper works in anything that runs JavaScript.
:warning: It's in early WIP stage and the API may change.
Install with npm or import in Deno by URL.
npm install aiwrapper
import * as aiwrapper from "https://deno.land/x/aiwrapper/mod.ts";
import { Lang } from "aiwrapper";
const lang = Lang.openai({ apiKey: "YOUR KEY" });
const result = await lang.ask("Say hi!");
console.log(result);
import { Img } from "aiwrapper";
const img = Img.openai({ apiKey: "YOUR KEY" });
const image = await img.ask('A portrait of a cute cat');
console.log(image);
import { Speech } from "aiwrapper";
const speech = Speech.elevenlabs({ apiKey: "YOUR KEY" });
const audio = speech.ask('Hello, world!');
console.log(audio.length);
import { Lang } from "aiwrapper";
const lang = Lang.openai({ apiKey: "YOUR KEY" }); // or Lang.anthropic
await lang.ask('Hello, AI!', streamingResult => {vs
console.log(streamingResult.answer);
});
// In most cases - a prompt template should be just a function that returns a string
function getPrompt(product) {
return `You are a naming consultant for new companies. What is a good name for a company that makes ${product}?
Write just the name. Nothing else aside from the name - no extra comments or characters that are not part of the name.`;
}
const prompt = getPrompt("colorful socks");
await lang.ask(prompt, streamingResult => {
console.log(streamingResult.answer);
});
async function askForCompanyNames() {
// We can ask for an object with a particular schema. In that case - an array with company names as strings.
const product = "colorful socks";
const numberOfNames = 3;
const result = await lang.askForObject({
instructions: [
`You are a naming consultant for new companies. What is a good name for a company that makes ${product}?`,
`Return ${numberOfNames} names.`
],
objectExamples: [
["Name A", "Name B", "Name C"]
]
}, streamingResult => {
console.log(streamingResult.answer);
});
return result.answerObj;
}
const names = await askForCompanyNames();
async function askForStoriesBehindTheNames() {
// We can use an answer in other prompts. Here we ask to come up with stores for all of the names we've got.
const names = await askForCompanyNames();
const stories = [];
for (const name of names) {
const story = await lang.askForObject({
instructions: [
`You are a professional writer and a storiteller.`,
`Look at the name "${name}" carefully and reason step-by-step about the meaning of the name and what is the potential story behing it.`,
`Write a short story. Don't include any comments or characters that are not part of the story.`,
],
objectExamples: [
{
"name": "Name A",
"reasoning": "Reasoning about Name A",
"story": "Story about Name A"
}
]
}, streamingResult => {
console.log(streamingResult.answer);
});
stories.push(story);
}
return stories;
}
const namesWithStories = await askForStoriesBehindTheNames();
// When you work with complex objects it's better to define them as classes or types.
class Task {
constructor(name, description, tasks) {
this.name = name;
this.description = description;
this.tasks = tasks;
}
}
async function getTask() {
// In this case we represent the schema. You may also treat it
// as a few shot example.
const exampleTask = new Task("Root Task", "This is the task that has subtasks", [
new Task("Task A1", "This is task A1", []),
new Task("Task A2", "This is task A2", []),
]);
const taskPrompt = {
instructions: [
"Reflect on the objective and tasks (from the Objective section) step by step. Ensure that you understand them; identify any ambiguities or gaps in information. The Context section offers relevant information. Feel free to add critique or insights about the objective.",
"Create a tree of tasks. If the task is complex, break it down into subtasks, following the KISS principle. Each task should have a clear, actionable title, and a reasoning. If there are ambiguities or gaps in information, start by posing follow-up questions.",
],
outputExamples: [
exampleTask,
],
content: {
"Objective":
"Make me $1 000 000 in 3 years. I have $10000 to spare and can live without income for 18 months. I only want to do it by starting a business. Be my CEO.",
"Context": "I'm a software developer and a digital nomad",
},
};
const result = await lang.askForObject(taskPrompt, res => {
console.log(res.answer);
});
return result.answerObject
}
const task = await getTask();
// We can get the cost of using models from result.totalCost
const result = await lang.ask('Say a nice hello in about 200 characters');
console.log(result.totalCost);
FAQs
A Universal AI Wrapper for JavaScript & TypeScript
The npm package aiwrapper receives a total of 114 weekly downloads. As such, aiwrapper popularity was classified as not popular.
We found that aiwrapper demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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