ReactJS Emoji Keyboard | Playground An emoji keyboard component for ReactJS. Installation npm install reactjs-emoji-keyboard or yarn add reactjs-emoji-keyboard Usage import { EmojiKeyboard } from "reactjs-emoji-keyboard"; function App() { return ( <EmojiKeyboard height={320} width={350} theme="dark" searchLabel="Procurar emoji" searchDisabled={false} onEmojiSelect={(emoji) =>console.log(emoji)} categoryDisabled={false} /> ); } Features 🌙 Dark theme 🎨 Customizable 🖱️ Customizable click handler Props Prop Type Default Description searchDisabled boolean false Show/hide emoji search bar categoryDisabled boolean false Show/hide emoji categories searchLabel string Procurar emoji Change the emoji search input label onEmojiSelect function false Function executed when an emoji is selected theme “ligh”, “dark” “light” Sets the emoji keyboard theme width number 320 Sets the emoji keyboard width height number 350 Sets the emoji keyboard height containerStyle style – Applies the style to the main keyboard container searchStyle style – Applies the style to the search container Learn More To learn more about React, take a look at the following resources:……

Lektor is a new static site generator which was developed by Armin Ronacher. Armin has written a lot of Python software I really love, particularly Flask. I’ve always been impressed with his careful eye for API design and the excellent quality of his code, so when he released Lektor a few days ago, I was really interested. I wanted to use Lektor for the website you’re reading right now, but I host it on S3, which isn’t natively supported by Lektor as a deployment target. Fortunately, there was a clear way forward: Lektor comes with a plugin system which is intended to help developers add functionality to Lektor without requiring the core codebase to sprawl too much. It’s a good model and it’s worked well with Flask; I think it’ll serve Lektor quite well too. I wrote lektor-s3, which to my knowledge is the first published third-party plugin for Lektor.……

I got an email recently asking something along these lines: I’m a smart ex-engineer who likes stats. I want to be a data scientist. How difficult will it be for me to find a job doing data science work at a startup? I think a lot of people have this question. It’s interesting that it’s common given how hard it is to find a quality data scientist. I sent back an email which looked more or less like the following post - these are my thoughts on how to get hired, as seen from the eyes of someone who actually really wants to find more talented data scientists to hire. Quantitative intuition The first thing that I’m looking for is general quantitative sense. This is a really low bar. It’s not really important whether someone knows the quirks of Cauchy distributions or whatever.……

Web apps seem to graduate through a series of stages with regards to analytics. Stage 1: We should probably keep track of page views, you guys The “is this thing on?” phase. Most people start with Google Analytics or something like it. Nobody is looking for a lot of depth in the numbers - all you want to know is whether people are using your adorable little web app or reading your blog or whatever. Basically, this is about seeing whether you need to do a better job promoting yourself. There’s probably somebody in the company who compulsively checks on how many visitors you’ve got. Nobody really knows what they’re looking for, but 10,000-visitor days from being on the front of Hacker News get everyone excited. Stage 2: We should probably track signups, too, you guys The “okay we’re ready for the users now” phase.……

You can find gobs of chatter out there about what makes a good data scientist. Most focus on technical aspects: know statistics, write scripts, build machine-learning tools. Sure, these are all important, but the list is incomplete. The most valuable skill for a data scientist is the ability to shape the culture around them, to convince others to use data to inform their decisions. The best data scientists are the ones who transform their company. They motivate an entire organization to make decisions by measurement and numbers. They do not do all analysis themselves - they turn those around them into analysts. People who have machine learning skills, know statistics, and can write code are terrific, yes. But those alone are not transformative. why do companies want data scientists? When companies hire data scientists, they rarely want a wizard who can dazzle with highly technical presentations but who works alone.……