I can relate the vice of envy most closely with my own writing, because that’s my profession, and I’ve longed to be a professional novelist since I was in elementary school

Writing for the Common Good - Front Porch Republic

U.S. Department of the Treasury Withdraws from the Network of Central Banks and Supervisors for Greening the Financial System (NGFS)

Front page | U.S. Department of the Treasury

U.S. Department of the Treasury Releases Policy Brief on Small Business Financing

BOISE, Idaho & CHICAGO, January 13, 2025--Clearwater Analytics (NYSE: CWAN) ("Clearwater") and Enfusion, Inc. (NYSE: ENFN) ("Enfusion") today announced their entry into a definitive merger agreement for Clearwater to acquire Enfusion, a leader in software-as-a-service (SaaS) solutions for the ...

Clearwater Analytics to Acquire Enfusion for $1.5 Billion Expanding its Investment Management Platform with Front-to-Back Capabilities

Cybersecurity isn’t just for tech experts — it’s a responsibility that affects every person, organization, and even entire nations.

Cybersecurity: The New Front Line and Why Everyone Should Care | by Christi Lee | Dec, 2024 | Medium

Here's the latest from the frontline in Ukraine as Russia's grinding invasion nears its second year.

Russian forces reportedly advance in Ukraine's Avdiivka | Euronews

Front

front

After almost six months of hard work we're super excited to share all of the new features improvements and fixes that we think you're going to love. This is the biggest release of Yumma CSS and we can't wait for you to try it out! Try Yumma CSS v3.0 straight in your browser on Yumma Play. Try Yumma CSS v3.0 straight in your browser on Yumma Play. Yumma Play Get to know what's new in v3.0! We've completely rewritten the code base for Yumma CSS v3 both internally and externally and we're excited to improve the way you deal with CSS. We're making some big changes to keep improving Yumma and take it to the next level. In v3 we're planning to address some major performance issues and add and improve existing utilities in the framework among other things. What's new in Yumma CSS 3.0? — On YouTube What's new in Yumma CSS 3.0? — On YouTube All-new CLI Until recently you had to import a lot of annoying CSS from Yumma CSS which was a real pain. With v3 you won't have to stress about shipping unused CSS to the browser. The new CLI will scan and get rid of them for you automatically. Install Yumma CSS
Add yummacss to your project as dev dependency.
npm install yummacss@latest --save-dev



Add the configuration file
Next add the yummacss.config.js to the root level of your project or run npx yummacss init to create it for you.
 node_modules/
 ├── public/
 │   └── favicon.ico
 ├── src/
 │   ├── globals.css
 │   └── index.html
 ├── .gitignore
 ├── package-lock.json
 ├── package.json
 └── yummacss.config.js <- add this file



Set up the config file
To generate styles using the CLI just set up the source array with the path to your template files and set the output string with the path to the CSS file you want to generate.
module.exports = {
source: ["./src/**/*.html"] // 👈
output: "./src/globals.css" // 👈
  buildOptions: {
    reset: true,
    minify: false,
  },
};



Write CSS
Start using Yumma CSS utilities in your to generate CSS with the CLI.
<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width initial-scale=1.0"/>
    <link rel="stylesheet" href="/src/globals.css" />
  </head>

  <body>
    <div class="b-1 bc-silver-2 bg-white rad-2 bs-sm p-4">
      <h1 class="fw-600 tc-indigo fs-lg">Hello 👋 - name's Renildo.</h1>
      <p class="tc-gray-7">I'm the creator of Yumma CSS! 🖌️</p>

      <button class="bg-indigo fs-sm h:bg-indigo-7 mt-6 px-4 py-1 rad-1 tc-white">
        GitHub
      </button>
    </div>
  </body>
</html>



Compile the SCSS
To compile the source into CSS you can run the following command:
npx yummacss build

When you run the build command the CLI will create a new CSS file and scan project paths based on the yummacss.config.js to purge of any unused styles..rad-1 {
  border-radius: .25rem;
}

.rad-2 {
  border-radius: .5rem;
}

.b-1 {
  border-width: 1px;
}

.mt-6 {
  margin-top: 1.5rem;
}

.p-4 {
  padding: 1rem;
}

.px-4 {
  padding-left: 1rem;
  padding-right: 1rem;
}

.py-1 {
  padding-top: .25rem;
  padding-bottom: .25rem;
}

/* etc */ Install Yumma CSS
Add yummacss to your project as dev dependency.
npm install yummacss@latest --save-dev Install Yumma CSS Install Yumma CSS Add yummacss to your project as dev dependency. yummacss npm install yummacss@latest --save-dev npm install yummacss@latest --save-dev Add the configuration file
Next add the yummacss.config.js to the root level of your project or run npx yummacss init to create it for you.
 node_modules/
 ├── public/
 │   └── favicon.ico
 ├── src/
 │   ├── globals.css
 │   └── index.html
 ├── .gitignore
 ├── package-lock.json
 ├── package.json
 └── yummacss.config.js <- add this file Add the configuration file Add the configuration file Next add the yummacss.config.js to the root level of your project or run npx yummacss init to create it for you. yummacss.config.js npx yummacss init node_modules/
 ├── public/
 │   └── favicon.ico
 ├── src/
 │   ├── globals.css
 │   └── index.html
 ├── .gitignore
 ├── package-lock.json
 ├── package.json
 └── yummacss.config.js <- add this file node_modules/
 ├── public/
 │   └── favicon.ico
 ├── src/
 │   ├── globals.css
 │   └── index.html
 ├── .gitignore
 ├── package-lock.json
 ├── package.json
 └── yummacss.config.js <- add this file Set up the config file
To generate styles using the CLI just set up the source array with the path to your template files and set the output string with the path to the CSS file you want to generate.
module.exports = {
source: ["./src/**/*.html"] // 👈
output: "./src/globals.css" // 👈
  buildOptions: {
    reset: true,
    minify: false,
  },
}; Set up the config file Set up the config file To generate styles using the CLI just set up the source array with the path to your template files and set the output string with the path to the CSS file you want to generate. source output module.exports = {
source: ["./src/**/*.html"] // 👈
output: "./src/globals.css" // 👈
  buildOptions: {
    reset: true,
    minify: false,
  },
}; module.exports = {
source: ["./src/**/*.html"] // 👈
output: "./src/globals.css" // 👈
  buildOptions: {
    reset: true,
    minify: false,
  },
}; Write CSS
Start using Yumma CSS utilities in your to generate CSS with the CLI.
<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width initial-scale=1.0"/>
    <link rel="stylesheet" href="/src/globals.css" />
  </head>

  <body>
    <div class="b-1 bc-silver-2 bg-white rad-2 bs-sm p-4">
      <h1 class="fw-600 tc-indigo fs-lg">Hello 👋 - name's Renildo.</h1>
      <p class="tc-gray-7">I'm the creator of Yumma CSS! 🖌️</p>

      <button class="bg-indigo fs-sm h:bg-indigo-7 mt-6 px-4 py-1 rad-1 tc-white">
        GitHub
      </button>
    </div>
  </body>
</html> Write CSS Write CSS Start using Yumma CSS utilities in your to generate CSS with the CLI. <!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width initial-scale=1.0"/>
    <link rel="stylesheet" href="/src/globals.css" />
  </head>

  <body>
    <div class="b-1 bc-silver-2 bg-white rad-2 bs-sm p-4">
      <h1 class="fw-600 tc-indigo fs-lg">Hello 👋 - name's Renildo.</h1>
      <p class="tc-gray-7">I'm the creator of Yumma CSS! 🖌️</p>

      <button class="bg-indigo fs-sm h:bg-indigo-7 mt-6 px-4 py-1 rad-1 tc-white">
        GitHub
      </button>
    </div>
  </body>
</html> <!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width initial-scale=1.0"/>
    <link rel="stylesheet" href="/src/globals.css" />
  </head>

  <body>
    <div class="b-1 bc-silver-2 bg-white rad-2 bs-sm p-4">
      <h1 class="fw-600 tc-indigo fs-lg">Hello 👋 - name's Renildo.</h1>
      <p class="tc-gray-7">I'm the creator of Yumma CSS! 🖌️</p>

      <button class="bg-indigo fs-sm h:bg-indigo-7 mt-6 px-4 py-1 rad-1 tc-white">
        GitHub
      </button>
    </div>
  </body>
</html> Compile the SCSS
To compile the source into CSS you can run the following command:
npx yummacss build

When you run the build command the CLI will create a new CSS file and scan project paths based on the yummacss.config.js to purge of any unused styles..rad-1 {
  border-radius: .25rem;
}

.rad-2 {
  border-radius: .5rem;
}

.b-1 {
  border-width: 1px;
}

.mt-6 {
  margin-top: 1.5rem;
}

.p-4 {
  padding: 1rem;
}

.px-4 {
  padding-left: 1rem;
  padding-right: 1rem;
}

.py-1 {
  padding-top: .25rem;
  padding-bottom: .25rem;
}

/* etc */ Compile the SCSS Compile the SCSS To compile the source into CSS you can run the following command: npx yummacss build npx yummacss build When you run the build command the CLI will create a new CSS file and scan project paths based on the yummacss.config.js to purge of any unused styles. build yummacss.config.js .rad-1 {
  border-radius: .25rem;
}

.rad-2 {
  border-radius: .5rem;
}

.b-1 {
  border-width: 1px;
}

.mt-6 {
  margin-top: 1.5rem;
}

.p-4 {
  padding: 1rem;
}

.px-4 {
  padding-left: 1rem;
  padding-right: 1rem;
}

.py-1 {
  padding-top: .25rem;
  padding-bottom: .25rem;
}

/* etc */ .rad-1 {
  border-radius: .25rem;
}

.rad-2 {
  border-radius: .5rem;
}

.b-1 {
  border-width: 1px;
}

.mt-6 {
  margin-top: 1.5rem;
}

.p-4 {
  padding: 1rem;
}

.px-4 {
  padding-left: 1rem;
  padding-right: 1rem;
}

.py-1 {
  padding-top: .25rem;
  padding-bottom: .25rem;
}

/* etc */ All-new utilities To make the Yumma CSS framework as complete as possible we're adding support for over 50 utility classes to the core of the framework. Box Shadow


Margin LTR/RTL


Padding LTR/RTL



Interactivity


Field Sizing



Positioning


Isolation


Visibility



Transforms


Rotate


Scale


Skew


Transform Origin Box Shadow


Margin LTR/RTL


Padding LTR/RTL Box Shadow Margin LTR/RTL


Padding LTR/RTL Margin LTR/RTL Margin LTR/RTL Margin LTR/RTL Padding LTR/RTL Padding LTR/RTL Padding LTR/RTL Interactivity


Field Sizing Interactivity Field Sizing Field Sizing Field Sizing Field Sizing Positioning


Isolation


Visibility Positioning Isolation


Visibility Isolation Isolation Isolation Visibility Visibility Visibility Transforms


Rotate


Scale


Skew


Transform Origin Transforms Rotate


Scale


Skew


Transform Origin Rotate Rotate Rotate Scale Scale Scale Skew Skew Skew Transform Origin Transform Origin Transform Origin And a lot more! Check out the blog for more details. blog Build performance We completely overhauled the entire codebase to get better performance in build times and overall file size. We changing the way utilities and modifiers are generated, to eliminate any potential for duplicated or unnecessary data in the /dist folder. /dist Color utility changes In v3, both the light (l-) and dark (d-) characters are being removed across all color utilities. As a result, the range used to determine a color's shade was also adjusted. l- d- See what the bg-* background utility looks like compared to v2.1 bg-* Also, the color hue is increasing from 10% shade modification to 14%. This means that light colors will become lighter, and dark colors will become darker. 10% 14% Conclusion There is a LOT MORE going on with Yumma CSS v3 than what you see above. You can find out more about it in the documentation. LOT MORE documentation If you're planning on migrating your project to yummacss@3.0 you can follow our migration guide in the documentation. yummacss@3.0 migration guide Thanks for reading If you enjoyed this post, you can follow Yumma CSS on Twitter and GitHub to get the latest updates on Yumma CSS. Twitter GitHub

What's New in Yumma CSS 3.0?

Next.js v15 — What’s new under the hood

Building user interfaces is hard. Building nice interfaces is harder, and building exceptional ones is only achievable by the teams that have the best tooling. Today, we will talk about Storybook, one of the most essential tools in modern front-end development, and see how it helps us in building interfaces. Storybook UI Development Lifecycle Let’s do a small exercise and think about how to build the following interface: a to-do list page. It should allow users to view, create, modify and delete their to-dos. First, set things up: create an empty component and add it to the routing. Then wire the component to the API to be able to manage the to-dos. At this point, we can run the dev server, go to the new page, and finally start building it. First, set things up Now, build the thing: implement various component states. Every time we want to build a different state, we need to update the backend so that it returns correct data to the UI. For example, after building an empty state for the list, we need to create some todos so that we get them from the API and can build a non-empty state. Now, build the thing 🔍 Review Time After all the components are ready and tested, we either deploy our changes to a preview environment or merge and deploy to dev/prod under a feature flag to show to our team. Let’s say we got some feedback from the designers. They asked us to make some visual changes to both the empty and non-empty states of the todo list. Now, we delete all todos, tweak the empty state, create todos, and update the non-empty state. The design feedback is incorporated. We deployed the new version of the page and… found that we caused a regression in one of the states. What do we do? You got it. We will update the backend to return the necessary data and fix the issue. And we’re done - everything works this time. We ship our change and roll out the feature to customers. regression 🤔 Was There Anything to Improve? What we’ve done to build that page was reasonable - setup routing, manipulate the backend state to display what’s needed, review work with the team, incorporate the feedback. But along the way, we had to constantly use the backend API to manipulate the data. Want to work with an empty state? Delete all items. Want to get a non-empty state? Create items. Want to get a state with particular items on the list? Same story. We also introduced a regression in our simple dual-state page. Imagine what happens when you have not two but 10 states of your component. When you have to support every single edge case that can happen. Will you constantly update the data to switch to every state? How long will it take? Thankfully, Storybook helps with exactly that! Let’s see how. See it. Build it. Sorted. Storybook is a frontend development environment. It allows us to build components in isolation, mock hard-to-reach edge cases, write visual & interaction tests, and much more. Storybook 🏖️ Building in Isolation & API Mocking When building a component in storybook, we don’t have to add it to our application routing. In fact, we don’t have to run the app at all, bypassing the authorization and getting to the right page that renders the component. It saves seconds, but those seconds add up to a big waiting time. Since there is no app running, we don’t need to rely on a backend API. Instead, we create a set of mocked responses, such as when there is no data, when there is data, when there is a network error, etc. Then, for each state of the page we want to support, we render a component as a separate storybook page (also known as “story”) and specify what response that particular story should get. mocked responses (also known as “story”) It allows us to develop various states simultaneously without making any changes to the underlying data - everything is mocked and defined in the story itself. 🦾 Automated Testing Feels Much Easier If you tried to write tests for your UI components three years ago, it was probably quite a frustrating experience. Test runners like Vitest or Jest are great for business logic tests, but testing the visual components is a nightmare. Just compare these two screenshots of how the test error looks in Vitest and Storybook when an element you tried to query wasn't found. Vitest Storybook The difference is clear - it’s much harder for a human to parse 100 lines of HTML and understand why the query failed compared to seeing how the component was rendered in the real browser and finding the root cause. Additionally, with tools like storybook-addon-test-codegen, the time to write such tests goes down by an order of magnitude. Simply interact with your story, and the addon generates the test code for you. Copy-paste the generated code and the test is done! Now, if anyone breaks the component, they will know it immediately and won’t ship this broken component to customers. storybook-addon-test-codegen storybook-addon-test-codegen makes writing tests 20x faster! I wrote more about it here: https://maecapozzi.com/newsletter/82 20x faster! https://maecapozzi.com/newsletter/82 💅 Easy Peasy Design Reviews Does this sound familiar: building something for a long time, only to be asked to change a bunch of things? You wish you had shown the result of your work to stakeholders earlier to get some early feedback and keep the number of changes small. It’s easily achievable with Storybook. As you finish working on the stories, say, for one of the page components, put together a PR and deploy your storybook. Then, let your team see the piece that you build and comment solely on that. It will keep your PRs smaller (which is always a great thing), make incorporating the feedback faster, and reduce the mental complexity that you would otherwise have when shipping a big change. deploy your storybook 🕵️ Visual Tests With Chromatic Chromatic is a paid solution developed by the Storybook team. It screenshots your stories and compares them to their baselines (stories captured in the main branch). Whenever there is a change in any of the components, Chromatic highlights it and notifies the engineers. Chromatic main A person responsible for making the PR checks the diff to see if the change is intended or if it’s a UI regression. If the change is expected, they can accept it. If it’s not expected, they can safely fix it before the PR lands. Visual testing catches a lot of UI issues that would otherwise go to production unnoticed. not expected Chromatic snapshots are different from HTML snapshots. An HTML snapshot generated by expect(element).toMatchSnapshot() is a raw HTML that is not parseable by a human eye. Whereas a Chromatic snapshot is a screenshot of the component that preserves all the styles and truly displays what your users will see. Chromatic snapshots are different from HTML snapshots expect(element).toMatchSnapshot() Let’s summarize! Storybook improves UI development by allowing us to build components in isolation, eliminating the need to manipulate backend data for different states. It makes testing intuitive with visual comparisons instead of cryptic errors, streamlines design reviews through easy sharing, and catches regressions with tools like Chromatic. Storybook transforms UI development from a tedious process into an efficient workflow, helping teams create exceptional interfaces with less effort and frustration. Useful Resources: Storybook & their Component Tests Overview
Great guide on how to write component tests from Storybook team
Storybook Test Codegen Storybook & their Component Tests Overview Storybook Component Tests Overview Great guide on how to write component tests from Storybook team how to write component tests Storybook Test Codegen Storybook Test Codegen

Why Every Front-End Team Needs Storybook

What if artificial intelligence could step into a company’s codebase, understand its intricacies, and churn out production-ready front-end code in minutes—without human oversight? That’s the question AutonomyAI aims to answer as it emerges from stealth mode in New York, backed by $4 million in pre-seed funding. Investors like Inbound Capital, Gilad Shany of IoN Partners, and Vikram Makhiija from Google Cloud Security have placed their bets on this startup, which promises to shift how businesses approach software development. AutonomyAI At a time when AI’s role in coding is expanding, AutonomyAI’s launch sparks curiosity about whether autonomous agents can truly transform an industry long reliance on human precision. The company’s focus is niche and ambitious: front-end development, the part of software engineering that shapes what users see and interact with. Unlike existing AI tools that assist individual developers, AutonomyAI claims its technology integrates directly into a company’s workflow, delivering results that match the output of seasoned professionals. With research firm IDC forecasting a $749 billion market for AI-supporting technologies by 2028—and $227 billion slated for 2025 alone—the timing seems ripe for such innovation. But can AutonomyAI deliver on its bold vision, or is this another case of tech hype outpacing reality? What Sets AutonomyAI Apart in the AI Coding Landscape? AutonomyAI’s core innovation lies in its Agentic Context Engine (ACE), a system designed to decode a company’s unique codebase and operational standards. This engine powers a suite of AI agents that don’t just suggest code snippets—they execute tasks end-to-end. “Until now, AI solutions for code development operated in silos, lacking context about the organization and its goals, helping individual developers complete their tasks rather than perform the tasks for them in accordance with the company’s sprint cycles,” said Adir Ben-Yehuda, CEO of AutonomyAI, in a statement on April 3, 2025. The company argues that this contextual awareness allows its agents to fetch external data, like UI designs, and pull components from existing codebases without needing constant human input. Compare this to tools like GitHub Copilot, which developers use to autocomplete lines of code based on patterns. Copilot boosts productivity but stops short of being autonomous—it’s a helper, not a doer. AutonomyAI’s agents, by contrast, aim to handle entire workflows, from conception to deployment, in minutes. The company cites the precision of a senior developer as its benchmark, a claim that raises eyebrows given the complexity of front-end tasks like responsive design and cross-browser compatibility. If successful, this could free engineers to focus on broader business logic rather than repetitive coding chores. Skeptics might point to past AI promises that fizzled—tools that generated buggy code or required heavy refactoring. AutonomyAI counters this by emphasizing its agents’ ability to align with a company’s specific needs, not just generic coding patterns. Still, the proof will lie in real-world results, which the company has yet to publicly demonstrate beyond its initial announcement. The $4 million infusion suggests investors see potential, but the road from stealth to scale is littered with challenges. Who’s Behind Autonomy AI, and Why Does It Matter? The company’s leadership offers clues to its credibility. Co-founder and Chairman Arik Faingold brings experience from founding Pentera, a cybersecurity firm, and Comm-IT, a tech services company. Co-founder and CTO Tammuz Dubnov leads an engineering team that includes four former CTOs, a detail AutonomyAI highlights to underscore its technical chops. “Autonomy AI is the first AI coding solution that truly comprehends and adapts to the unique context of a business, empowering engineers to dramatically increase their efficiency while allowing organizations to put more resources into business logic and high-impact projects,” Faingold said. The team’s pedigree suggests a deep understanding of both software and organizational dynamics. CEO Adir Ben-Yehuda, who steers the company’s vision, frames this as “pragmatic AI.” His perspective aligns with a broader industry shift, exemplified by Salesforce CEO Marc Benioff’s comments at Davos 2025: deploying agentic AI has allowed him to redistribute resources more effectively within his company. AutonomyAI’s leaders seem to share this belief—that AI’s value lies not in replacing humans but in amplifying their capacity. The involvement of investors such as Gilad Shany, who called the approach “a fundamental shift” in how organizations use AI, adds weight to this narrative. AutonomyAI Yet expertise alone doesn’t guarantee success. The team’s focus on front-end development—a field notorious for its blend of creativity and technical precision—could be a double-edged sword. Success hinges on whether ACE can handle the nuances of real-world projects, from legacy codebases to shifting design trends. The engineering team’s CTO-heavy roster might signal strength, but it also raises questions about scalability and practical execution beyond the lab. Why Is AutonomyAI Betting on Autonomous Agents Now? The timing of AutonomyAI’s launch taps into a swelling wave of interest in agentic AI—systems that act independently to achieve goals. IDC’s 2025 projection of $227 billion in AI investments, with 67% targeting business integrations like AI agents, underscores this trend. Companies across industries are exploring how autonomous systems can streamline operations, a sentiment echoed by Benioff’s resource-shifting experiment at Salesforce. AutonomyAI positions itself as a pioneer in this space, arguing that plugging AI into organizational contexts unlocks efficiency previous tools couldn’t touch. Investor confidence reflects this momentum. “AutonomyAI’s leadership team’s vision represents a fundamental shift in how organizations use AI, which will soon become the new standard,” said Gilad Shany, Managing Partner at ION Crossover, on April 3, 2025. The $4 million pre-seed round, while modest compared to some AI startups, signals early belief in the concept. For context, the broader AI coding market has seen players like Cognition, behind the autonomous coding agent Devin, raise $175 million by April 2024. AutonomyAI’s narrower focus on front-end development might give it an edge in a niche yet critical domain. But timing cuts both ways. The market is crowded with AI coding startups, from Poolside to Cursor, each vying to solve development bottlenecks. AutonomyAI’s bet on autonomy over assistance could either set it apart or expose it to risks—like reliability issues that have plagued other agentic systems. As businesses weigh AI’s promise against its pitfalls, AutonomyAI must prove its agents can deliver consistent, production-grade output without the “hallucinations” or errors that have tripped up less contextual AI models. Opinion and Final Thoughts AutonomyAI’s vision intrigues me. The idea of AI agents that don’t just assist but fully execute front-end tasks feels like a logical next step in a world where developers are stretched thin. If ACE lives up to its promise, it could shift how companies allocate talent, letting engineers tackle strategic challenges while agents handle the grunt work. I see parallels to how automation transformed manufacturing—repetitive tasks faded, and human focus sharpened on innovation. Ben-Yehuda’s “pragmatic AI” pitch resonates because it prioritizes real-world utility over flashy demos. That said, I’m cautious. Front-end development isn’t just code—it’s art, logic, and user experience rolled into one. Can an AI truly grasp that mix without human oversight? The lack of public evidence so far—beyond funding and quotes—leaves me skeptical about immediate impact. Competitors with broader scopes or proven track records might outpace AutonomyAI if it stumbles on execution. Still, the concept excites me more than it worries me. If the company can bridge the gap between hype and results, it might redefine efficiency in software development. For now, I’ll watch closely as AutonomyAI moves from stealth to spotlight. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: business blogging program business blogging program

How Does AutonomyAI Plan to Revolutionize Front-End Development with $4M in Funding?

Як AutonomyAI плануе рэвалюцыянізаваць інтэрфейсную распрацоўку з фінансаваннем у 4 мільёны долараў?

¿Cómo planea AutonomyAI revolucionar el desarrollo front-end con una financiación de 4 millones de dólares?

Comment AutonomyAI prévoit-il de révolutionner le développement front-end avec un financement de 4 millions de dollars ?

Kijan AutonomyAI planifye pou revolisyone devlopman front-end ak $4M nan finansman?

AutonomyAIは4百万ドルの資金調達でフロントエンド開発を革命化する方法をどのように計画していますか?

AutonomyAI ວາງແຜນທີ່ຈະປະຕິວັດການພັດທະນາ Front-End ດ້ວຍເງິນທຶນ 4 ລ້ານໂດລາສະຫະລັດແນວໃດ?

Kaip „AutonomyAI“ planuoja pakeisti „Front-End“ plėtrą su 4 mln. USD finansavimu?

AutonomyAI 4 сая долларын санхүүжилтээр Front-End хөгжилд хэрхэн хувьсгал хийхээр төлөвлөж байна вэ?

Bagaimanakah AutonomyAI Merancang untuk Merevolusikan Pembangunan Front-End dengan Pembiayaan $4M?

ඩොලර් මිලියන 4 ක අරමුදල් සමඟින් ඉදිරිපස සංවර්ධනය විප්ලවීය කිරීමට AutonomyAI සැලසුම් කරන්නේ කෙසේද?

Si planifikon AutonomyAI të revolucionarizojë zhvillimin e front-end me 4 milionë dollarë financim?

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Frontend Testing for Busy Engineers: A Guide

Artificial Intelligence Optimization (AIO) is one of the newest developments in search engine optimization. AIO combines AI technologies with traditional SEO techniques which improves the processes. This concept, which is frequently called AI SEO or SEO using AI, uses sophisticated algorithms and machine learning models to analyze and optimize web content. The purpose of employing these new technologies shifts toward increasing the possibilities of using AI information and automation to transform the digital marketing industry. Adopting AI SEO techniques and automation increases the accuracy of reaching the target audience and increases user engagement, thus solidifying AIO as a breakthrough in digital marketing. digital marketing digital marketing Evolution From Traditional SEO to Artificial Intelligence Optimization As with any technology, the efficacy of AI tools hinges on the operator’s skill. Every output is reliant on the input given to the AI. Hence, for the best results, ensure the queries placed within the AI tools are clear, accurate, and direct. As important as creativity is, placing AI output alongside modern-day algorithms of search engines and E-E-A-T – Experience, Expertise, Authority, Trustworthiness- is important. Following these standards helps improve the quality of the piece and ensures a higher ranking of the piece in search engines, which means higher visibility and trust. Tuning the parameters of the AI model boosts performance and minimizes bias. For instance, the distillation technique whereby a large teacher model trains a smaller unit called a student model has eased the process of AI development and brought down costs substantially. An example would be DeepSeek which developed a competitive model at $6 million, which goes against the traditional expensive model of AI development. $6 million $6 million It is always a must to ensure proper structuring of AI-generated content to guarantee that the content is easy to index and locate. This knowledge can be used to change the code so that the content is easier to find, helping improve engagement and reducing chances of Zero-Click Searches taking traffic from your site. Zero-Click Searches Zero-Click Searches It is imperative to formulate strategies to address and manage biases as well as ethical AI practices. The trust and integrity of information presented to users must be protected by AI frameworks through the effective management of bias, misinformation, and other evolving issues surrounding technology in today’s society. Core Components of Artificial Intelligence Optimization Rather than simply focusing on interactivity as traditional SEO does, AIO applies complex AI technologies to generate content, including articles, product descriptions, and more. This type of automation was exactly what a New Jersey gown distributor needed. AI assistance with product description generation led to a 60% reduction in time spent on content creation and a 40% decrease in surplus inventory. 40% 40% In classic methods of search engine optimization, the relevance of keywords and backlinks is what raises a site’s rank on the search engine. AIO, however, focuses on content generation along with prompt engineering, AI training refinement, and the ever-evolving demands of the marketplace. This method of content creation enables companies to adapt quickly to volatile market conditions. All the previously mentioned strategies are aimed at increasing a company’s visibility on Google, which directly increases the traffic that the website receives. AIO has taken things a step further and uses SEO AI agents to analyze user interactions on multiple platforms, such as social media, so they can create customized content for every user. Personalization extends even further, with email campaign content tailored to the individual recipient. To optimize a website with SEO, it is essential to monitor algorithms constantly as they influence the site’s ranking. Search engines using AIO technology come with their own challenges, such as misinformation and algorithm manipulation bias, which raised concerns about accurate content vetting. On TikTok, for instance, AI-generated avatars of medical professionals have been known to dispense dangerously false medical information. Both domain authority and site traffic are considered indicators of SEO success, while traffic resulting from an AI algorithm is classified under organic traffic. This type of AIO assessment emphasizes the analysis of user satisfaction benchmarks and participation efficacy. The accuracy of responses and engagement with the AI system plays a central role in determining quality satisfaction. Challenges in Implementing Artificial Intelligence Optimization Misinformation can spread with the use of AI systems because of the unfair biases included within the training data. This is why data checking and monitoring in order to eliminate biases need to be focused on for AI-generated content. Understanding the decision-making processes of AI systems can be straightforward due to their complicated algorithms. ECAT was launched to study the impact brought about by the use of algorithmic systems on the internet and how they affect users. The goal is to help users, together with the regulators, understand how these systems work in order to help them know how to make use of them. ECAT ECAT As much as AI can easily create content within a short period of time, human moderation is still needed to dig deep through the content in order to ensure its accuracy. This can be seen when the US House Judiciary Committee subpoenaed Alphabet for the leaked communication over content moderation policies showing the concerns of AI usage within content management. So long as AI is coupled with human intellect, the issued content can remain useful and trustworthy. US House Judiciary Committee US House Judiciary Committee Every country around the globe is introducing some form of regulation to deal with the problem of content created by AI. In America, an Executive Order released in October 2023 requires AI to ‘promote equity and civil rights’ while also mandating the use of ‘watermarks’ to assist the general public in differentiating between human and AI-generated content. In order to comply with the regulations: Maintain Compliance: Ensure that your activities related to AI practices align with existing policy frameworks. Maintain Compliance: Ensure that your activities related to AI practices align with existing policy frameworks. Maintain Compliance: Use Detection Tools: Use software meant to identify AI-generated content like GPTZero and ensure that all measures are taken to include proper attribution. Use Detection Tools: Use software meant to identify AI-generated content like GPTZero and ensure that all measures are taken to include proper attribution. Use Detection Tools: The Future of AIO in Digital Marketing Let's go over three distinct characteristics of AIO that do or can influence businesses. 1. Ability for Integration with Multimodal AI Functionality Imagine engaging your audience through a captivating presentation composed of text, images, videos, and even audio. Multimodal AI does this. It uses complex video and voice tools to achieve objectives which not only assist but actuate objectives in a whole new way. For instance, Coca-Cola recently had its holiday ads AI-generated. This stood to the wonders of AI in the creative and marketing scope. Alongside Multimodal AI, personalized and rich content tailored to grab attention and strengthen bonds can be made. Coca-Cola 2. Development Alongside Policies for AI Governance as Well as Trust Frameworks The evolution of technologies in the AI sector is unquestionable. These innovations also bring ethical dilemmas and governance policies to the table. Companies such as L’Oréal are crafting in-house policies on forward-thinking, responsible AI to ease implementation. As such policies develop, they protect brand strategies and help build trust with consumers. Thus, strengthening your brand image. 3. Anticipated Outcomes Relating To User Experience and Engagement With the advent of AI, there are many ways to improve user experience by adding personalization and relevancy, including marketing. For example, user engagement improved dramatically after the EdTech startup, Headway, used AI tools which increased ad performance by 40%. 40% Combining AI in advertising with marketing strategy greatly enhances the user experience by predicting user needs and interactions on an unprecedented scale. Conclusion For businesses aiming to keep pace in the digital world, adopting Artificial Intelligence Optimization (AIO) is a must. Companies wishing to adopt these competitive edges can drive their use of AI optimization to enhance their SEO strategies and make seo using AI more productive. This innovation in AI SEO not only starts to automate workflows but also creates possibilities for growth, thus positioning AI SEO optimization as the new frontier in digital marketing.

Artificial Intelligence Optimization (AIO): The Next Frontier in SEO

কৃত্রিম বুদ্ধিমত্তা অপ্টিমাইজেশন (AIO): SEO-তে পরবর্তী সীমানা

Optimierung durch künstliche Intelligenz (AIO): Die nächste Grenze in der SEO

Optimización de Inteligencia Artificial (AIO): La próxima frontera en SEO

Optimisation par intelligence artificielle (AIO) : la prochaine frontière du référencement

आर्टिफिशियल इंटेलिजेंस ऑप्टिमाइजेशन (AIO): SEO में अगला मोर्चा

人工知能最適化 (AIO): SEO の新たなフロンティア

인공 지능 최적화(AIO): SEO의 새로운 전선

Otimização de Inteligência Artificial (AIO): A Próxima Fronteira em SEO

Оптимизация с помощью искусственного интеллекта (AIO): новый рубеж в SEO

Yapay Zeka Optimizasyonu (AIO): SEO'daki Bir Sonraki Sınır

Tối ưu hóa trí tuệ nhân tạo (AIO): Biên giới tiếp theo trong SEO

人工智能优化 (AIO)：SEO 的下一个前沿

Debugging network communication issues is a critical skill for any front-end developer. While tools like Wireshark provide low-level insight into network traffic, modern browsers like Chrome and Firefox offer developer tools with powerful features tailored for web development. In this post, we will discuss using browser-based tools to debug network communication issues effectively. This is a far better approach than using Wireshark for the vast majority of simple cases. https://youtu.be/_DfNti1q6ec?embedable=true As a side note, if you like the content of this and the other posts in this series, check out my Debugging book that covers this subject. If you have friends who are learning to code, I'd appreciate a reference to my Java Basics book. If you want to get back to Java after a while, check out my Java 8 to 21 book. Network Debugging Powerhouse Modern browsers come equipped with developer tools that rival standalone IDE debuggers in capability and convenience. Both Chrome and Firefox have robust network monitoring features that allow developers to observe/analyze requests and responses without leaving the browser. On the basic level, which you’re probably familiar with, these tools include: Network monitors: View all HTTP and HTTPS requests, including their headers, payloads, and responses. Throttling controls: Simulate slower connections to test performance and debug race conditions. Request replay functionality: Modify and resend requests directly from the browser. While this post focuses on debugging techniques, it's worth noting that these tools are invaluable for performance optimization as well, though that topic warrants its own discussion. Re-issuing and Modifying Requests One of the most powerful debugging features is the ability to re-issue requests. Instead of switching to external tools like cURL or Postman, browsers allow us to modify and resend requests directly. This lets us quickly test variations of a failing API call to pinpoint issues without leaving the debugging environment. It’s especially useful when we have hard-to-reproduce issues or deep UI hierarchies. In Firefox, we can right-click any network entry in the Firefox Developer Tools and select "Resend." This opens an editable window where we can change request parameters, such as headers, payloads, or query strings, and resend the request. Chrome provides similar functionality, though its interface for modifying and resending requests is slightly less direct than Firefox's. cURL and Postman Both browsers let you copy a request as a cURL command via the context menu. This is useful for reproducing issues in the terminal or sharing with back-end developers. I use this frequently as part of creating a reproducible issue. If you prefer Postman, you can copy request headers and payloads from the browser and paste them into Postman to replicate requests. Throttling and Debugging Race Conditions Network throttling is a highly underrated feature that can be a game-changer for debugging specific classes of bugs. Both Chrome and Firefox allow developers to simulate various network speeds, from 2G connections to fast 4G. Why Throttling Matters: Some bugs only surface when requests arrive out of their expected order. Slowing down the network can help replicate and analyze these situations. Typical examples would be race conditions and related issues. This is also very useful for simulating real-world conditions. Many users may not have fast or reliable internet connections. Throttling helps you understand how your application behaves in these scenarios. I use this frequently when testing loading indicators which disappear too quickly when running locally. Instead of adding sleep code into the JavaScript or server code, I can simulate slow-loading assets to verify that loading spinners or placeholders appear correctly. How to Use In Chrome, we Open Developer Tools → Network tab. We then use the "No throttling" dropdown to select pre-configured speeds or create a custom profile. In Firefox, we have a similar functionality available under the Network Monitor. Managing State With Storage Tools Local storage, session storage, and indexedDB often hold data critical to reproducing bugs, especially those tied to specific user states or devices. Challenges of State Management Even in incognito mode, the state can persist if multiple private windows are open simultaneously. Persistence across sessions is a big challenge in these situations. Understanding the exact state of a user's local storage can provide insight into seemingly random bugs. Debugging user-specific issues is problematic without control over storage. In Firefox, the dedicated Storage tab in Developer Tools makes it easy to inspect, edit, and delete data from local storage, session storage, cookies, and indexedDB. In Chrome, the Application tab consolidates all storage options, including the ability to clear specific caches or edit entries manually. This functionality has many powerful uses for debugging: Inject Debug Information: Tools like these let us manually add or modify storage data to simulate edge cases or specific user conditions.


Share Local State: Users can export their local storage, cookies, or indexedDB entries, allowing developers to reproduce issues locally.


Clear Cache Strategically: Clear only the relevant entries instead of a blanket cache clear, preserving useful state for debugging. Analyzing Request and Response Headers Request and response headers often hold the key to understanding network issues. We can use the network monitor to inspect: Authorization headers: Check for missing or malformed tokens. CORS headers: Verify that the server allows requests from your domain. These are some of the most painful types of HTTP bugs. Reviewing these headers can be a lifesaver.  If requests fail with CORS errors, inspect the Access-Control-Allow-Origin header in the response. Cache-Control headers: Ensure proper caching behavior for your resources. These tools are especially useful when debugging missing headers: Look for required headers like Content-Type or Authorization. Debugging Authentication: Use the "Copy as cURL" feature to test API calls with modified headers directly in the terminal. Debugging in Incognito Mode: Limitations and Best Practices Incognito mode can help isolate issues by providing a clean slate, however, it has some limitations. Multiple incognito windows share the same state, which can lead to unintentional persistence of local data. I suggest using storage management tools to manually clear or modify local data instead of relying solely on incognito mode. Keep only one incognito window open during testing to avoid unintended state sharing. Connecting the Front End to the Database The front end is often a transition point between user interaction and back-end data processing. While this post focuses on debugging the network layer, it's important to remember that: Network issues often manifest due to back-end problems (e.g., a database error resulting in a 500 Internal Server Error). Front-end developers should collaborate closely with back-end engineers to trace issues across the stack. We can use custom response headers to include diagnostic information from the back end, such as query execution time or error codes. We can leverage server logs in conjunction with front-end debugging to get a complete picture of the issue. Final Word Browser developer tools are indispensable for debugging network communication issues, offering features like request replay, throttling, and storage management that simplify the debugging process. By mastering these tools, front-end developers can efficiently identify and resolve issues, ensuring a smoother user experience. With the techniques and tips outlined in this post, you'll be better equipped to tackle network debugging challenges head-on. As you grow more familiar with these tools, you'll find them invaluable not only for debugging but also for improving your development workflow. Debugging network communication issues is a critical skill for any front-end developer. While tools like Wireshark provide low-level insight into network traffic, modern browsers like Chrome and Firefox offer developer tools with powerful features tailored for web development. In this post, we will discuss using browser-based tools to debug network communication issues effectively. Wireshark This is a far better approach than using Wireshark for the vast majority of simple cases. https://youtu.be/_DfNti1q6ec?embedable=true https://youtu.be/_DfNti1q6ec?embedable=true As a side note, if you like the content of this and the other posts in this series, check out my Debugging book that covers t his subject. If you have friends who are learning to code, I'd appreciate a reference to my Java Basics book. If you want to get back to Java after a while, check out my Java 8 to 21 book . Debugging book t Java Basics book. Java 8 to 21 book . Network Debugging Powerhouse Network Debugging Powerhouse Modern browsers come equipped with developer tools that rival standalone IDE debuggers in capability and convenience. Both Chrome and Firefox have robust network monitoring features that allow developers to observe/analyze requests and responses without leaving the browser. On the basic level, which you’re probably familiar with, these tools include: Network monitors: View all HTTP and HTTPS requests, including their headers, payloads, and responses. Network monitors: View all HTTP and HTTPS requests, including their headers, payloads, and responses. Network monitors: Throttling controls: Simulate slower connections to test performance and debug race conditions. Throttling controls: Simulate slower connections to test performance and debug race conditions. Throttling controls: Request replay functionality: Modify and resend requests directly from the browser. Request replay functionality: Modify and resend requests directly from the browser. Request replay functionality: While this post focuses on debugging techniques, it's worth noting that these tools are invaluable for performance optimization as well, though that topic warrants its own discussion. Re-issuing and Modifying Requests Re-issuing and Modifying Requests One of the most powerful debugging features is the ability to re-issue requests. Instead of switching to external tools like cURL or Postman, browsers allow us to modify and resend requests directly. This lets us quickly test variations of a failing API call to pinpoint issues without leaving the debugging environment. It’s especially useful when we have hard-to-reproduce issues or deep UI hierarchies. In Firefox, we can right-click any network entry in the Firefox Developer Tools and select "Resend." This opens an editable window where we can change request parameters, such as headers, payloads, or query strings, and resend the request. In Firefox, we can Chrome provides similar functionality, though its interface for modifying and resending requests is slightly less direct than Firefox's. cURL and Postman Both browsers let you copy a request as a cURL command via the context menu. This is useful for reproducing issues in the terminal or sharing with back-end developers. I use this frequently as part of creating a reproducible issue. If you prefer Postman, you can copy request headers and payloads from the browser and paste them into Postman to replicate requests. Throttling and Debugging Race Conditions Throttling and Debugging Race Conditions Network throttling is a highly underrated feature that can be a game-changer for debugging specific classes of bugs. Both Chrome and Firefox allow developers to simulate various network speeds, from 2G connections to fast 4G. Why Throttling Matters: Some bugs only surface when requests arrive out of their expected order. Slowing down the network can help replicate and analyze these situations. Typical examples would be race conditions and related issues. This is also very useful for simulating real-world conditions. Many users may not have fast or reliable internet connections. Throttling helps you understand how your application behaves in these scenarios. I use this frequently when testing loading indicators which disappear too quickly when running locally. Instead of adding sleep code into the JavaScript or server code, I can simulate slow-loading assets to verify that loading spinners or placeholders appear correctly. How to Use In Chrome, we Open Developer Tools → Network tab. We then use the "No throttling" dropdown to select pre-configured speeds or create a custom profile. In Firefox, we have a similar functionality available under the Network Monitor. Managing State With Storage Tools Managing State With Storage Tools Local storage, session storage, and indexedDB often hold data critical to reproducing bugs, especially those tied to specific user states or devices. Challenges of State Management Even in incognito mode, the state can persist if multiple private windows are open simultaneously. Persistence across sessions is a big challenge in these situations. Understanding the exact state of a user's local storage can provide insight into seemingly random bugs. Debugging user-specific issues is problematic without control over storage. In Firefox, the dedicated Storage tab in Developer Tools makes it easy to inspect, edit, and delete data from local storage, session storage, cookies, and indexedDB. Storage In Chrome, the Application tab consolidates all storage options, including the ability to clear specific caches or edit entries manually. Application This functionality has many powerful uses for debugging: Inject Debug Information: Tools like these let us manually add or modify storage data to simulate edge cases or specific user conditions. Share Local State: Users can export their local storage, cookies, or indexedDB entries, allowing developers to reproduce issues locally. Clear Cache Strategically: Clear only the relevant entries instead of a blanket cache clear, preserving useful state for debugging. Inject Debug Information: Tools like these let us manually add or modify storage data to simulate edge cases or specific user conditions. Inject Debug Information: Tools like these let us manually add or modify storage data to simulate edge cases or specific user conditions. Inject Debug Information: Share Local State: Users can export their local storage, cookies, or indexedDB entries, allowing developers to reproduce issues locally. Share Local State: Users can export their local storage, cookies, or indexedDB entries, allowing developers to reproduce issues locally. Share Local State: Clear Cache Strategically: Clear only the relevant entries instead of a blanket cache clear, preserving useful state for debugging. Clear Cache Strategically: Clear only the relevant entries instead of a blanket cache clear, preserving useful state for debugging. Clear Cache Strategically: Analyzing Request and Response Headers Analyzing Request and Response Headers Request and response headers often hold the key to understanding network issues. We can use the network monitor to inspect: Authorization headers: Check for missing or malformed tokens. Authorization headers: Check for missing or malformed tokens. Authorization headers: CORS headers: Verify that the server allows requests from your domain. These are some of the most painful types of HTTP bugs. Reviewing these headers can be a lifesaver.  If requests fail with CORS errors, inspect the Access-Control-Allow-Origin header in the response. CORS headers: Verify that the server allows requests from your domain. These are some of the most painful types of HTTP bugs. Reviewing these headers can be a lifesaver.  If requests fail with CORS errors, inspect the Access-Control-Allow-Origin header in the response. CORS headers: Access-Control-Allow-Origin Cache-Control headers: Ensure proper caching behavior for your resources. Cache-Control headers: Ensure proper caching behavior for your resources. Cache-Control headers: These tools are especially useful when debugging missing headers: Look for required headers like Content-Type or Authorization . Content-Type Authorization Debugging Authentication: Use the "Copy as cURL" feature to test API calls with modified headers directly in the terminal. Debugging in Incognito Mode: Limitations and Best Practices Debugging in Incognito Mode: Limitations and Best Practices Incognito mode can help isolate issues by providing a clean slate, however, it has some limitations. Multiple incognito windows share the same state, which can lead to unintentional persistence of local data. I suggest using storage management tools to manually clear or modify local data instead of relying solely on incognito mode. Keep only one incognito window open during testing to avoid unintended state sharing. storage management tools Connecting the Front End to the Database Connecting the Front End to the Database The front end is often a transition point between user interaction and back-end data processing. While this post focuses on debugging the network layer, it's important to remember that: Network issues often manifest due to back-end problems (e.g., a database error resulting in a 500 Internal Server Error). Network issues often manifest due to back-end problems (e.g., a database error resulting in a 500 Internal Server Error). Front-end developers should collaborate closely with back-end engineers to trace issues across the stack. Front-end developers should collaborate closely with back-end engineers to trace issues across the stack. We can use custom response headers to include diagnostic information from the back end, such as query execution time or error codes. We can leverage server logs in conjunction with front-end debugging to get a complete picture of the issue. custom response headers server logs Final Word Browser developer tools are indispensable for debugging network communication issues, offering features like request replay, throttling, and storage management that simplify the debugging process. By mastering these tools, front-end developers can efficiently identify and resolve issues, ensuring a smoother user experience. With the techniques and tips outlined in this post, you'll be better equipped to tackle network debugging challenges head-on. As you grow more familiar with these tools, you'll find them invaluable not only for debugging but also for improving your development workflow.

Front End Debugging Part 3: Debugging Networking Communication Issues

Author:
(1) Subhajit Sahu, IIIT Hyderabad, Hyderabad, Telangana, India (subhajit.sahu@research.iiit.ac.in). Table of Links Abstract and 1 Introduction 2 Related Work 3 Preliminaries 4 Approach 5.1 Experimental Setup 5.2 Performance of Dynamic Frontier PageRank 5.3 Strong Scaling of Dynamic Frontier PageRan 6 Conclusion, Acknowledgments, and References 5. EVALUATION 5.1 Experimental Setup 5.1.1 System used. We conduct experiments on a system equipped with an AMD EPYC-7742 processor, with 64 cores and operating at a frequency of 2.25 GHz. Each core has a 4 MB L1 cache, a 32 MB L2 cache, and shares a 256 MB L3 cache. The server is configured with 512 GB of DDR4 system memory and operates on Ubuntu 20.04. 5.1.3 Dataset. We use four graph classes sourced from the SuiteSparse Matrix Collection [14], as detailed in Table 1. The number of vertices in these graphs range from 3.07 million to 214 million, with edge counts spanning from 37.4 million to 1.98 billion. To address the impact of dead ends (vertices lacking out-links), a global teleport rank computation is needed in each iteration. We mitigate this overhead by adding self-loops to all vertices in the graph [1, 15]. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Author: (1) Subhajit Sahu, IIIT Hyderabad, Hyderabad, Telangana, India (subhajit.sahu@research.iiit.ac.in). Author: Author: (1) Subhajit Sahu, IIIT Hyderabad, Hyderabad, Telangana, India (subhajit.sahu@research.iiit.ac.in). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Preliminaries 3 Preliminaries 4 Approach 4 Approach 5.1 Experimental Setup 5.1 Experimental Setup 5.2 Performance of Dynamic Frontier PageRank 5.2 Performance of Dynamic Frontier PageRank 5.3 Strong Scaling of Dynamic Frontier PageRan 5.3 Strong Scaling of Dynamic Frontier PageRan 6 Conclusion, Acknowledgments, and References 6 Conclusion, Acknowledgments, and References 5. EVALUATION 5.1 Experimental Setup 5.1.1 System used. We conduct experiments on a system equipped with an AMD EPYC-7742 processor, with 64 cores and operating at a frequency of 2.25 GHz. Each core has a 4 MB L1 cache, a 32 MB L2 cache, and shares a 256 MB L3 cache. The server is configured with 512 GB of DDR4 system memory and operates on Ubuntu 20.04. 5.1.1 System used. 5.1.3 Dataset. We use four graph classes sourced from the SuiteSparse Matrix Collection [14], as detailed in Table 1. The number of vertices in these graphs range from 3.07 million to 214 million, with edge counts spanning from 37.4 million to 1.98 billion. To address the impact of dead ends (vertices lacking out-links), a global teleport rank computation is needed in each iteration. We mitigate this overhead by adding self-loops to all vertices in the graph [1, 15]. 5.1.3 Dataset. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Dynamic Frontier PageRank Achieves Efficiency and Accuracy with Batch Updates and Parallel Computing

Author:
(1) Subhajit Sahu, IIIT Hyderabad, Hyderabad, Telangana, India (subhajit.sahu@research.iiit.ac.in). Table of Links Abstract and 1 Introduction 2 Related Work 3 Preliminaries 4 Approach 5.1 Experimental Setup 5.2 Performance of Dynamic Frontier PageRank 5.3 Strong Scaling of Dynamic Frontier PageRan 6 Conclusion, Acknowledgments, and References 4. APPROACH 4.1 Our Dynamic Frontier approach 4.1.2 A simple example. Figure 1 shows an example of the Dynamic Frontier approach. The initial graph, shown in Figure 1(a), comprises 16 vertices and 25 edges. Subsequently, Figure 1(b) shows a batch update applied to the original graph involving the deletion of an edge from vertex 2 to 1 and the insertion of an edge from vertex 4 to 12. Following the batch update, we perform the initial step of the Dynamic Frontier approach, marking outgoing neighbors of 2 and 4 as affected, i.e., 1, 3, 4, 8, and 12 are marked as affected (indicated with a yellow fill). Note that vertex 2 is not affected as it is a source of the change while vertex 4 being a neighbour of 2 is marked as affected. Now, we are ready to execute the first iteration of PageRank algorithm. During the first iteration (see Figure 1(c)), the ranks of affected vertices are updated. It is observed that the rank changes of vertices 1 and 12 surpass the frontier tolerance 𝜏𝑓 (highlighted with a red border). In response to this, we incrementally mark the outgoing neighbors of 1 and 12 as affected, i.e., vertices 3, 5, 11, and 14. During the second iteration (see Figure 1(d)), the ranks of affected vertices are again updated. Here, its is observed that the change in rank of vertices 3, 5, 11, and 14 is greater than frontier tolerance 𝜏𝑓 . Thus, we mark the outgoing neighbors of 3, 5, 11, and 14 as affected, namely vertices 4, 6, and 15. In the subsequent iteration, the ranks of affected vertices are again updated. If the change in rank of each vertex is within iteration tolerance 𝜏, the ranks of vertices have converged, and the algorithm terminates. 4.2 Synchronous vs Asynchronous implementation In a synchronous implementation, separate input and output rank vectors are used, ensuring deterministic results for parallel algorithms through vector swapping at the end of each iteration. In contrast, an asynchronous implementation utilizes a single rank vector, potentially achieving faster convergence and eliminating memory copies for unaffected vertices in dynamic approaches. 4.3 Determination of Frontier tolerance (𝜏𝑓) 4.4 Our Dynamic Frontier PageRank implementation Algorithm 1 shows our implementation of Dynamic Frontier PageRank, which is designed to compute the PageRank of vertices in a graph while efficiently handling dynamic changes in the graph structure over time. The algorithm takes as input the previous and current versions of the graph, edge deletions and insertions in the batch update, and the previous rank vector. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Author: (1) Subhajit Sahu, IIIT Hyderabad, Hyderabad, Telangana, India (subhajit.sahu@research.iiit.ac.in). Author: Author: (1) Subhajit Sahu, IIIT Hyderabad, Hyderabad, Telangana, India (subhajit.sahu@research.iiit.ac.in). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Preliminaries 3 Preliminaries 4 Approach 4 Approach 5.1 Experimental Setup 5.1 Experimental Setup 5.2 Performance of Dynamic Frontier PageRank 5.2 Performance of Dynamic Frontier PageRank 5.3 Strong Scaling of Dynamic Frontier PageRan 5.3 Strong Scaling of Dynamic Frontier PageRan 6 Conclusion, Acknowledgments, and References 6 Conclusion, Acknowledgments, and References 4. APPROACH 4.1 Our Dynamic Frontier approach 4.1.2 A simple example. Figure 1 shows an example of the Dynamic Frontier approach. The initial graph, shown in Figure 1(a), comprises 16 vertices and 25 edges. Subsequently, Figure 1(b) shows a batch update applied to the original graph involving the deletion of an edge from vertex 2 to 1 and the insertion of an edge from vertex 4 to 12. Following the batch update, we perform the initial step of the Dynamic Frontier approach, marking outgoing neighbors of 2 and 4 as affected, i.e., 1, 3, 4, 8, and 12 are marked as affected (indicated with a yellow fill). Note that vertex 2 is not affected as it is a source of the change while vertex 4 being a neighbour of 2 is marked as affected. Now, we are ready to execute the first iteration of PageRank algorithm. 4.1.2 A simple example. During the first iteration (see Figure 1(c)), the ranks of affected vertices are updated. It is observed that the rank changes of vertices 1 and 12 surpass the frontier tolerance 𝜏𝑓 (highlighted with a red border). In response to this, we incrementally mark the outgoing neighbors of 1 and 12 as affected, i.e., vertices 3, 5, 11, and 14. During the second iteration (see Figure 1(d)), the ranks of affected vertices are again updated. Here, its is observed that the change in rank of vertices 3, 5, 11, and 14 is greater than frontier tolerance 𝜏𝑓 . Thus, we mark the outgoing neighbors of 3, 5, 11, and 14 as affected, namely vertices 4, 6, and 15. In the subsequent iteration, the ranks of affected vertices are again updated. If the change in rank of each vertex is within iteration tolerance 𝜏, the ranks of vertices have converged, and the algorithm terminates. 4.2 Synchronous vs Asynchronous implementation In a synchronous implementation, separate input and output rank vectors are used, ensuring deterministic results for parallel algorithms through vector swapping at the end of each iteration. In contrast, an asynchronous implementation utilizes a single rank vector, potentially achieving faster convergence and eliminating memory copies for unaffected vertices in dynamic approaches. 4.3 Determination of Frontier tolerance (𝜏𝑓) 4.4 Our Dynamic Frontier PageRank implementation Algorithm 1 shows our implementation of Dynamic Frontier PageRank, which is designed to compute the PageRank of vertices in a graph while efficiently handling dynamic changes in the graph structure over time. The algorithm takes as input the previous and current versions of the graph, edge deletions and insertions in the batch update, and the previous rank vector. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Dynamic Frontier PageRank Efficiently Updates Ranks on Dynamic Graphs

In the past, I often noticed a common approach where developers (including myself of course) used the same API for both reads and writes on every case. Even more so, we frequently relied on the same data source, such as MySQL/PostgreSQL, to handle both operations. This means writing to the same columns and reading from them, often leading to struggles with optimizing indexes on fields that were heavily queried. So for instance, we’d find ourselves frequently tweaking indexes to accommodate new filters or improve query performance, and fields used with operators like LIKE posed particular challenges due to their impact on performance. These changes often lead to further adjustments to the backend, including modifying APIs to expose the updated functionality, measured times because of additional JOINs and so on... To address the challenge about adding new filters and stuff in the API, there were attempts to optimize the process using tools and standards like Apicalypse and, of course, GraphQL. These solutions aimed to streamline API query generation and reduce the manual effort required to implement new filters and functionalities, offering a more dynamic approach to handling data access, but, they had high learning curve. With the rise of CQRS (Command Query Responsibility Segregation), a new approach began to emerge. This mindset encouraged the use of separate sources for writes and reads. Writes could emit events, and reads could build views from those events in dedicated places. Even if the reads and writes were managed within the same database (but different tables), this separation brought significant benefits, and, of course was able to get rid off of second challenge - JOINs and search queries on domain models, as read models are commonly in a form of denormalized JSONs. However, this raised another problem. With reads, we had to scale writes, meaning the only reason we had to scale instances of our application from X to Y was because of reads. This issue could be partially mitigated with caching, and in the world of microservices, we could have dedicated microservices for reads. But... Still, this was not an ideal solution for other architectural styles like modular monoliths, where such separation might not align well with the system’s design philosophy. Another thing was, when API was down, the whole product was down, and keeping in mind, that most of the products are relying on more reads than writes, it could unnecessary impact the business (Aparat from down API of course ;) ) So, what if we could ask those "views," also known as read models, directly without involving the API and handling loads? This is where solutions like Meilisearch, AppSearch and others come into play, leveraging a pattern called the "Valet Key." By using this pattern, frontends can access read-optimized models directly, reducing the dependency on backend APIs. Of course, frontend still has to "ask" API for "Valet key", but frontend can cache keys, so even when API is down, frontend can still communicate and display content. With this approach, we can focus on the read database and not worry about handling the traffic for reads in our API. The "Valet Key" provided to the frontend via our API is secured in a way that the frontend cannot alter it. It includes predefined filters and indexes. If the frontend requires additional capabilities, it can request them through the API, where API can validate whether to allow them. It's still less calls. Some pros I can see are: Reduced API Load: Offloads the read traffic from the API, allowing it to focus on core operations.
Scalability: Read databases or search services are better optimized to handle high traffic, reducing the need to scale the application backend.
Flexibility: SaaS or self-hosted options allow teams to choose the best fit for their infrastructure.
Security: Predefined filters and indexes ensure the frontend can only access allowed data, minimizing risks. Keys can be invalidate by the API.
Developer Efficiency: Reduces the need for constant API updates for new filters or search capabilities.
Improved Performance: Direct access to read-optimized models provides faster query responses for users. But, there are always cons: Eventual Consistency: Data may appear after some time due to the nature of eventual consistency in read models.
Additional Maintenance: Introduces an extra component that requires monitoring and management.
Schema Complexity: Schemas must be stored in code or a common place, as different teams from different contexts may need to populate the same document (e.g., employee with email, but also with available credits and coupons). While not directly tied to this pattern, it adds complexity.
Cost of SaaS version or self hosted maintenance So, this approach is not a silver bullet and introduces its own set of challenges, but if you're okay with a cons, then a small change on the frontend likely won’t require involving the backend team, streamlining the development process and improving overall agility, and of course scalability should be easier. In the past, I often noticed a common approach where developers (including myself of course) used the same API for both reads and writes on every case. Even more so, we frequently relied on the same data source, such as MySQL/PostgreSQL, to handle both operations. This means writing to the same columns and reading from them, often leading to struggles with optimizing indexes on fields that were heavily queried. So for instance, we’d find ourselves frequently tweaking indexes to accommodate new filters or improve query performance, and fields used with operators like LIKE posed particular challenges due to their impact on performance. These changes often lead to further adjustments to the backend, including modifying APIs to expose the updated functionality, measured times because of additional JOINs and so on... To address the challenge about adding new filters and stuff in the API, there were attempts to optimize the process using tools and standards like Apicalypse and, of course, GraphQL . Apicalypse GraphQL These solutions aimed to streamline API query generation and reduce the manual effort required to implement new filters and functionalities, offering a more dynamic approach to handling data access, but, they had high learning curve. With the rise of CQRS (Command Query Responsibility Segregation), a new approach began to emerge. This mindset encouraged the use of separate sources for writes and reads. Writes could emit events, and reads could build views from those events in dedicated places. Even if the reads and writes were managed within the same database (but different tables), this separation brought significant benefits, and, of course was able to get rid off of second challenge - JOINs and search queries on domain models, as read models are commonly in a form of denormalized JSONs. However, this raised another problem. With reads, we had to scale writes, meaning the only reason we had to scale instances of our application from X to Y was because of reads. This issue could be partially mitigated with caching, and in the world of microservices, we could have dedicated microservices for reads. But... But... Still, this was not an ideal solution for other architectural styles like modular monoliths, where such separation might not align well with the system’s design philosophy. Another thing was, when API was down, the whole product was down, and keeping in mind, that most of the products are relying on more reads than writes, it could unnecessary impact the business (Aparat from down API of course ;) ) So, what if we could ask those "views," also known as read models, directly without involving the API and handling loads? This is where solutions like Meilisearch , AppSearch and others come into play, leveraging a pattern called the "Valet Key." By using this pattern, frontends can access read-optimized models directly, reducing the dependency on backend APIs. Of course, frontend still has to "ask" API for "Valet key", but frontend can cache keys, so even when API is down, frontend can still communicate and display content. Meilisearch AppSearch With this approach, we can focus on the read database and not worry about handling the traffic for reads in our API. The "Valet Key" provided to the frontend via our API is secured in a way that the frontend cannot alter it. It includes predefined filters and indexes. If the frontend requires additional capabilities, it can request them through the API, where API can validate whether to allow them. It's still less calls. Some pros I can see are: Some pros I can see are: Reduced API Load: Offloads the read traffic from the API, allowing it to focus on core operations. Scalability: Read databases or search services are better optimized to handle high traffic, reducing the need to scale the application backend. Flexibility: SaaS or self-hosted options allow teams to choose the best fit for their infrastructure. Security: Predefined filters and indexes ensure the frontend can only access allowed data, minimizing risks. Keys can be invalidate by the API. Developer Efficiency: Reduces the need for constant API updates for new filters or search capabilities. Improved Performance: Direct access to read-optimized models provides faster query responses for users. Reduced API Load : Offloads the read traffic from the API, allowing it to focus on core operations. Reduced API Load Scalability : Read databases or search services are better optimized to handle high traffic, reducing the need to scale the application backend. Scalability Flexibility : SaaS or self-hosted options allow teams to choose the best fit for their infrastructure. Flexibility Security : Predefined filters and indexes ensure the frontend can only access allowed data, minimizing risks. Keys can be invalidate by the API. Security Developer Efficiency : Reduces the need for constant API updates for new filters or search capabilities. Developer Efficiency Improved Performance : Direct access to read-optimized models provides faster query responses for users. Improved Performance But, there are always cons: But, there are always cons: Eventual Consistency: Data may appear after some time due to the nature of eventual consistency in read models. Additional Maintenance: Introduces an extra component that requires monitoring and management. Schema Complexity: Schemas must be stored in code or a common place, as different teams from different contexts may need to populate the same document (e.g., employee with email, but also with available credits and coupons). While not directly tied to this pattern, it adds complexity. Cost of SaaS version or self hosted maintenance Eventual Consistency : Data may appear after some time due to the nature of eventual consistency in read models. Eventual Consistency Additional Maintenance: Introduces an extra component that requires monitoring and management. Schema Complexity : Schemas must be stored in code or a common place, as different teams from different contexts may need to populate the same document (e.g., employee with email, but also with available credits and coupons). While not directly tied to this pattern, it adds complexity. Schema Complexity Cost of SaaS version or self hosted maintenance So, this approach is not a silver bullet and introduces its own set of challenges, but if you're okay with a cons, then a small change on the frontend likely won’t require involving the backend team, streamlining the development process and improving overall agility, and of course scalability should be easier.

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