Programmer's Corner

Welcome to the revolutionary world of ChatPDF, a cutting-edge tool that transforms the way you interact with any PDF document. Designed to cater to students, researchers, and professionals alike, ChatPDF leverages powerful AI technology to make understanding and navigating through complex PDFs as easy as having a conversation. Whether it’s a challenging academic paper, a detailed legal contract, or a technical manual, ChatPDF allows you to ask questions directly and get answers instantly.

Features

ChatPDF boasts a range of features that make it an indispensable tool for anyone dealing with PDF documents:

1. AI-Powered Understanding: Simply upload your PDF and start chatting with it as you would with a human expert.
2. Multi-File Chats: Organize your PDFs into folders and interact with multiple documents in a single conversation for comparative analysis and more comprehensive insights.
3. Cited Sources: Every answer provided by ChatPDF includes references to the source location in the original document, ensuring you can verify and explore the context further.
4. Any Language Capability: ChatPDF supports PDFs in any language, making it a globally accessible tool that breaks down language barriers.
5. User-Friendly Interface: Easy to navigate interface allows for straightforward uploading and interaction with documents.

How It Works

Engaging with ChatPDF is a straightforward process:

1. Upload Your PDF: Drag and drop your PDF file into the ChatPDF platform.
2. Ask Questions: Once your PDF is uploaded, simply type in your questions to start the conversation.
3. Receive Answers: ChatPDF quickly analyzes the content and provides you with answers and summaries directly from the text.

Benefits

Using ChatPDF provides numerous advantages:

1. Enhanced Productivity: Saves time by providing quick answers to specific questions without the need for manual searching.
2. Improved Research Efficiency: Ideal for researchers and students who need to extract information and gather insights from lengthy documents.
3. Accessibility: Makes information more accessible for non-native speakers and professionals dealing with foreign language documents.
4. Educational Support: Assists students in studying for exams and understanding complex topics effortlessly.
5. Professional Aid: Helps professionals in understanding and navigating through dense and intricate documents like contracts and reports.

Pricing

ChatPDF offers its services under a freemium model:

• Free Version: Users can enjoy basic features without any cost, suitable for casual or light users.
• Premium Version: Advanced features and capabilities are available for a subscription fee, details of which can be found directly on the ChatPDF website.

Review

ChatPDF has received widespread acclaim for its innovative approach to handling PDF documents. Users around the globe praise the tool for its ability to simplify complex information and make educational and professional materials more accessible. The ease of use and the ability to interact with documents in any language are frequently highlighted as some of the app’s strongest points.

Conclusion

In conclusion, ChatPDF represents a significant advancement in how individuals interact with PDF documents. By combining AI technology with a user-friendly interface, it offers a unique solution that enhances learning, research, and professional activities. Whether you are a student, a researcher, or a professional, ChatPDF provides a powerful tool to streamline your workflow and improve your understanding of complex documents.

The post ChatPDF appeared first on AI Parabellum • Your Go-To AI Tools Directory for Success.

The last few decades have witnessed a remarkable evolution in the field of computing power. From the early days of room-sized computers with minimal processing capabilities to the modern era of pocket-sized devices with immense computational power, the progress has been exponential. This exponential growth in computing power is often attributed to Moore’s Law, a principle that has shaped the technology industry for over five decades. Understanding Moore’s Law is crucial in comprehending the rapid advancements in computing and predicting the future of this ever-evolving field.

Understanding Moore’s Law: Definition and Origin

Moore’s Law, formulated by Gordon Moore, one of the founders of Intel, is a principle that states the number of transistors on a microchip doubles approximately every two years, while the cost is halved. This law has been a driving force behind the rapid advancement of technology, as it sets the pace for the development of faster, smaller, and more efficient electronic devices. Moore initially observed this trend in 1965, and his prediction has held remarkably true for several decades, guiding the industry in its pursuit of ever-increasing computing power.

The Mathematical Equation Behind Moore’s Law

While Moore’s Law is often discussed in terms of its observations and implications, there is also a mathematical equation that underlies this phenomenon. The equation that describes Moore’s Law can be expressed as follows: N = N₀ * 2^(t/T), where N represents the number of transistors on a microchip at a given time, N₀ is the initial number of transistors, t is the time elapsed, and T is the doubling time. This equation demonstrates the exponential growth of transistors on a chip, as the number of transistors doubles every T years. It provides a quantitative understanding of the rapid advancement of computing power and allows for predictions about future technological developments.

An Exploration of Technology Scaling and Transistors

To comprehend the implications of Moore’s Law, it is essential to delve into the concepts of technology scaling and transistors. Technology scaling refers to the process of shrinking the size of transistors on a microchip, allowing for more transistors to be packed into the same space. This scaling leads to increased computational power and improved performance, as smaller transistors enable faster switching speeds and reduced power consumption. Transistors, the fundamental building blocks of electronic devices, are responsible for controlling the flow of electrical current within a circuit. As the number of transistors increases, more complex computations can be performed, leading to enhanced processing capabilities and the ability to handle more data. The continuous advancement in the scaling of transistors has been a crucial factor in the exponential growth of computing power.

Implications of Moore’s Law on Computing Industry

The impact of Moore’s Law on the computing industry cannot be overstated. It has provided a roadmap for technological progress, shaping the strategies and investments of companies in the development of new products. The doubling of transistors every two years has led to the creation of smaller and more powerful electronic devices, such as smartphones, laptops, and high-performance computing systems. This increased computing power has revolutionized various sectors, including healthcare, finance, education, and entertainment, enabling the development of innovative applications and solutions. Moore’s Law has also driven competition among technology companies, as they strive to stay ahead by constantly improving their products and pushing the boundaries of computing power.

Challenges to the Continuation of Moore’s Law

Despite its remarkable track record, Moore’s Law is facing challenges that threaten its continuation. One of the major obstacles is the physical limitations of semiconductor technology. As transistors become increasingly small, quantum effects and other physical phenomena start to affect their performance. Additionally, the cost of research and development required to keep up with Moore’s Law is escalating, making it more difficult for companies to invest in new technologies. The limitations of traditional silicon-based technology and the increasing complexity of chip manufacturing pose significant hurdles to sustaining the historical rate of progress. Overcoming these challenges will require innovations in materials, manufacturing techniques, and alternative computing architectures.

Alternatives to Moore’s Law: Post-Moore Computing

As the limitations of Moore’s Law become more apparent, researchers are exploring alternative approaches to continue the trend of increasing computing power. Post-Moore Computing encompasses a range of technologies and concepts that aim to overcome the physical limitations of traditional transistor scaling. This includes innovations such as new materials like graphene and carbon nanotubes, novel computing architectures like neuromorphic and quantum computing, and advancements in software optimization techniques. These alternative paths offer the potential for continued progress in computing power beyond the limitations of Moore’s Law. While these technologies are still in their early stages, they hold the promise of ushering in a new era of computing and enabling further advancements in various fields.

The Future of Computing Power

The future of computing power is both exciting and uncertain. While the challenges to sustaining Moore’s Law are significant, the industry is continuously pushing the boundaries of technology to find new solutions. Whether through advancements in traditional semiconductor technology or the adoption of post-Moore computing paradigms, the quest for greater computing power will likely persist. The evolution of computing power has transformed the world we live in, and it will continue to shape our lives in ways we cannot yet fully comprehend. As we embark on this journey into the future, one thing is certain: the law of computing power will remain a driving force behind technological progress for years to come.

The post The Law of Computing Power: Moore’s Law appeared first on AI Parabellum • Your Go-To AI Tools Directory for Success.

Comparing visual artifacts can be a powerful, if fickle, approach to automated testing. Playwright makes this seem simple for websites, but the details might take a little finessing.

Recent downtime prompted me to scratch an itch that had been plaguing me for a while: The style sheet of a website I maintain has grown just a little unwieldy as we’ve been adding code while exploring new features. Now that we have a better idea of the requirements, it’s time for internal CSS refactoring to pay down some of our technical debt, taking advantage of modern CSS features (like using CSS nesting for more obvious structure). More importantly, a cleaner foundation should make it easier to introduce that dark mode feature we’re sorely lacking so we can finally respect users’ preferred color scheme.

However, being of the apprehensive persuasion, I was reluctant to make large changes for fear of unwittingly introducing bugs. I needed something to guard against visual regressions while refactoring — except that means snapshot testing, which is notoriously slow and brittle.

In this context, snapshot testing means taking screenshots to establish a reliable baseline against which we can compare future results. As we’ll see, those artifacts are influenced by a multitude of factors that might not always be fully controllable (e.g. timing, variable hardware resources, or randomized content). We also have to maintain state between test runs, i.e. save those screenshots, which complicates the setup and means our test code alone doesn’t fully describe expectations.

Having procrastinated without a more agreeable solution revealing itself, I finally set out to create what I assumed would be a quick spike. After all, this wouldn’t be part of the regular test suite; just a one-off utility for this particular refactoring task.

Fortunately, I had vague recollections of past research and quickly rediscovered Playwright’s built-in visual comparison feature. Because I try to select dependencies carefully, I was glad to see that Playwright seems not to rely on many external packages.

Setup

The recommended setup with npm init playwright@latest does a decent job, but my minimalist taste had me set everything up from scratch instead. This do-it-yourself approach also helped me understand how the different pieces fit together.

Given that I expect snapshot testing to only be used on rare occasions, I wanted to isolate everything in a dedicated subdirectory, called test/visual; that will be our working directory from here on out. We’ll start with package.json to declare our dependencies, adding a few helper scripts (spoiler!) while we’re at it:

{
  "scripts": {
    "test": "playwright test",
    "report": "playwright show-report",
    "update": "playwright test --update-snapshots",
    "reset": "rm -r ./playwright-report ./test-results ./viz.test.js-snapshots || true"
  },
  "devDependencies": {
    "@playwright/test": "^1.49.1"
  }
}

If you don’t want node_modules hidden in some subdirectory but also don’t want to burden the root project with this rarely-used dependency, you might resort to manually invoking npm install --no-save @playwright/test in the root directory when needed.

With that in place, npm install downloads Playwright. Afterwards, npx playwright install downloads a range of headless browsers. (We’ll use npm here, but you might prefer a different package manager and task runner.)

We define our test environment via playwright.config.js with about a dozen basic Playwright settings:

import { defineConfig, devices } from "@playwright/test";

let BROWSERS = ["Desktop Firefox", "Desktop Chrome", "Desktop Safari"];
let BASE_URL = "http://localhost:8000";
let SERVER = "cd ../../dist && python3 -m http.server";

let IS_CI = !!process.env.CI;

export default defineConfig({
  testDir: "./",
  fullyParallel: true,
  forbidOnly: IS_CI,
  retries: 2,
  workers: IS_CI ? 1 : undefined,
  reporter: "html",
  webServer: {
    command: SERVER,
    url: BASE_URL,
    reuseExistingServer: !IS_CI
  },
  use: {
    baseURL: BASE_URL,
    trace: "on-first-retry"
  },
  projects: BROWSERS.map(ua => ({
    name: ua.toLowerCase().replaceAll(" ", "-"),
    use: { ...devices[ua] }
  }))
});

Here we expect our static website to already reside within the root directory’s dist folder and to be served at localhost:8000 (see SERVER; I prefer Python there because it’s widely available). I’ve included multiple browsers for illustration purposes. Still, we might reduce that number to speed things up (thus our simple BROWSERS list, which we then map to Playwright’s more elaborate projects data structure). Similarly, continuous integration is YAGNI for my particular scenario, so that whole IS_CI dance could be discarded.

Capture and compare

Let’s turn to the actual tests, starting with a minimal sample.test.js file:

import { test, expect } from "@playwright/test";

test("home page", async ({ page }) => {
  await page.goto("/");
  await expect(page).toHaveScreenshot();
});

npm test executes this little test suite (based on file-name conventions). The initial run always fails because it first needs to create baseline snapshots against which subsequent runs compare their results. Invoking npm test once more should report a passing test.

Changing our site, e.g. by recklessly messing with build artifacts in dist, should make the test fail again. Such failures will offer various options to compare expected and actual visuals:

We can also inspect those baseline snapshots directly: Playwright creates a folder for screenshots named after the test file (sample.test.js-snapshots in this case), with file names derived from the respective test’s title (e.g. home-page-desktop-firefox.png).

Generating tests

Getting back to our original motivation, what we want is a test for every page. Instead of arduously writing and maintaining repetitive tests, we’ll create a simple web crawler for our website and have tests generated automatically; one for each URL we’ve identified.

Playwright’s global setup enables us to perform preparatory work before test discovery begins: Determine those URLs and write them to a file. Afterward, we can dynamically generate our tests at runtime.

While there are other ways to pass data between the setup and test-discovery phases, having a file on disk makes it easy to modify the list of URLs before test runs (e.g. temporarily ignoring irrelevant pages).

Site map

The first step is to extend playwright.config.js by inserting globalSetup and exporting two of our configuration values:

export let BROWSERS = ["Desktop Firefox", "Desktop Chrome", "Desktop Safari"];
export let BASE_URL = "http://localhost:8000";

// etc.

export default defineConfig({
  // etc.
  globalSetup: require.resolve("./setup.js")
});

Although we’re using ES modules here, we can still rely on CommonJS-specific APIs like require.resolve and __dirname. It appears there’s some Babel transpilation happening in the background, so what’s actually being executed is probably CommonJS? Such nuances sometimes confuse me because it isn’t always obvious what’s being executed where.

We can now reuse those exported values within a newly created setup.js, which spins up a headless browser to crawl our site (just because that’s easier here than using a separate HTML parser):

import { BASE_URL, BROWSERS } from "./playwright.config.js";
import { createSiteMap, readSiteMap } from "./sitemap.js";
import playwright from "@playwright/test";

export default async function globalSetup(config) {
  // only create site map if it doesn't already exist
  try {
    readSiteMap();
    return;
  } catch(err) {}

  // launch browser and initiate crawler
  let browser = playwright.devices[BROWSERS[0]].defaultBrowserType;
  browser = await playwright[browser].launch();
  let page = await browser.newPage();
  await createSiteMap(BASE_URL, page);
  await browser.close();
}

This is fairly boring glue code; the actual crawling is happening within sitemap.js:

• createSiteMap determines URLs and writes them to disk.
• readSiteMap merely reads any previously created site map from disk. This will be our foundation for dynamically generating tests. (We’ll see later why this needs to be synchronous.)

Fortunately, the website in question provides a comprehensive index of all pages, so my crawler only needs to collect unique local URLs from that index page:

function extractLocalLinks(baseURL) {
  let urls = new Set();
  let offset = baseURL.length;
  for(let { href } of document.links) {
    if(href.startsWith(baseURL)) {
      let path = href.slice(offset);
      urls.add(path);
    }
  }
  return Array.from(urls);
}

Wrapping that in a more boring glue code gives us our sitemap.js:

import { readFileSync, writeFileSync } from "node:fs";
import { join } from "node:path";

let ENTRY_POINT = "/topics";
let SITEMAP = join(__dirname, "./sitemap.json");

export async function createSiteMap(baseURL, page) {
  await page.goto(baseURL + ENTRY_POINT);
  let urls = await page.evaluate(extractLocalLinks, baseURL);
  let data = JSON.stringify(urls, null, 4);
  writeFileSync(SITEMAP, data, { encoding: "utf-8" });
}

export function readSiteMap() {
  try {
    var data = readFileSync(SITEMAP, { encoding: "utf-8" });
  } catch(err) {
    if(err.code === "ENOENT") {
      throw new Error("missing site map");
    }
    throw err;
  }
  return JSON.parse(data);
}

function extractLocalLinks(baseURL) {
  // etc.
}

The interesting bit here is that extractLocalLinks is evaluated within the browser context — thus we can rely on DOM APIs, notably document.links — while the rest is executed within the Playwright environment (i.e. Node).

Tests

Now that we have our list of URLs, we basically just need a test file with a simple loop to dynamically generate corresponding tests:

for(let url of readSiteMap()) {
  test(`page at ${url}`, async ({ page }) => {
    await page.goto(url);
    await expect(page).toHaveScreenshot();
  });
}

This is why readSiteMap had to be synchronous above: Playwright doesn’t currently support top-level await within test files.

In practice, we’ll want better error reporting for when the site map doesn’t exist yet. Let’s call our actual test file viz.test.js:

import { readSiteMap } from "./sitemap.js";
import { test, expect } from "@playwright/test";

let sitemap = [];
try {
  sitemap = readSiteMap();
} catch(err) {
  test("site map", ({ page }) => {
    throw new Error("missing site map");
  });
}

for(let url of sitemap) {
  test(`page at ${url}`, async ({ page }) => {
    await page.goto(url);
    await expect(page).toHaveScreenshot();
  });
}

Getting here was a bit of a journey, but we’re pretty much done… unless we have to deal with reality, which typically takes a bit more tweaking.

Exceptions

Because visual testing is inherently flaky, we sometimes need to compensate via special casing. Playwright lets us inject custom CSS, which is often the easiest and most effective approach. Tweaking viz.test.js…

// etc.
import { join } from "node:path";

let OPTIONS = {
  stylePath: join(__dirname, "./viz.tweaks.css")
};

// etc.
  await expect(page).toHaveScreenshot(OPTIONS);
// etc.

… allows us to define exceptions in viz.tweaks.css:

/* suppress state */
main a:visited {
  color: var(--color-link);
}

/* suppress randomness */
iframe[src$="/articles/signals-reactivity/demo.html"] {
  visibility: hidden;
}

/* suppress flakiness */
body:has(h1 a[href="/wip/unicode-symbols/"]) {
  main tbody > tr:last-child > td:first-child {
    font-size: 0;
    visibility: hidden;
  }
}

:has() strikes again!

Page vs. viewport

At this point, everything seemed hunky-dory to me, until I realized that my tests didn’t actually fail after I had changed some styling. That’s not good! What I hadn’t taken into account is that .toHaveScreenshot only captures the viewport rather than the entire page. We can rectify that by further extending playwright.config.js.

export let WIDTH = 800;
export let HEIGHT = WIDTH;

// etc.

  projects: BROWSERS.map(ua => ({
    name: ua.toLowerCase().replaceAll(" ", "-"),
    use: {
      ...devices[ua],
      viewport: {
        width: WIDTH,
        height: HEIGHT
      }
    }
  }))

…and then by adjusting viz.test.js‘s test-generating loop:

import { WIDTH, HEIGHT } from "./playwright.config.js";

// etc.

for(let url of sitemap) {
  test(`page at ${url}`, async ({ page }) => {
    checkSnapshot(url, page);
  });
}

async function checkSnapshot(url, page) {
  // determine page height with default viewport
  await page.setViewportSize({
    width: WIDTH,
    height: HEIGHT
  });
  await page.goto(url);
  await page.waitForLoadState("networkidle");
  let height = await page.evaluate(getFullHeight);

  // resize viewport for before snapshotting
  await page.setViewportSize({
    width: WIDTH,
    height: Math.ceil(height)
  });
  await page.waitForLoadState("networkidle");
  await expect(page).toHaveScreenshot(OPTIONS);
}

function getFullHeight() {
  return document.documentElement.getBoundingClientRect().height;
}

Note that we’ve also introduced a waiting condition, holding until there’s no network traffic for a while in a crude attempt to account for stuff like lazy-loading images.

Be aware that capturing the entire page is more resource-intensive and doesn’t always work reliably: You might have to deal with layout shifts or run into timeouts for long or asset-heavy pages. In other words: This risks exacerbating flakiness.

Conclusion

So much for that quick spike. While it took more effort than expected (I believe that’s called “software development”), this might actually solve my original problem now (not a common feature of software these days). Of course, shaving this yak still leaves me itchy, as I have yet to do the actual work of scratching CSS without breaking anything. Then comes the real challenge: Retrofitting dark mode to an existing website. I just might need more downtime.

Automated Visual Regression Testing With Playwright originally published on CSS-Tricks, which is part of the DigitalOcean family. You should get the newsletter.