<h2>
 
 
 🍔 Callbacks, Promises &amp; Async-Await Explained! Using Swiggy, Zomato &amp; Food Orders
</h2>

<blockquote>
JavaScript runs on a single thread, yet it somehow manages to fetch APIs, read files, hit databases, and return responses without freezing your app.

How? 
With Callbacks → Promises → Async–Await.

And today, we’ll understand these three using something we ALL can relate to: 👉 Ordering food from Swiggy/Zomato.
</blockquote>

<h2>
 
 
 🥡 1. Callbacks - “Call me back when the food arrives”
</h2>

Think of this situation:

You order food online. 
You can't stand in front of the restaurant and wait. 
So you tell them:

<blockquote>
“When the food is ready, call me back.”
</blockquote>

This is a callback - you pass a function that should run later.

PS: If you don't have node js installed in your local computer, feel free to use replit in your browser to run the codes snippets mentioned in this blog XD.

📌 Callback Example in Code 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>function orderFood(callback) {
 console.log("Order placed... waiting for restaurant 🕒");

 // Simulating a delay of 2 seconds to cook the order (just for example, otherwise we've to wait for few minutes practically hehe)
 setTimeout(() =&gt; {
 console.log("Food is ready! 🍱");
 callback();
 }, 2000);
}

orderFood(() =&gt; {
 console.log("Food delivered! Enjoy 😋");
});
</code></pre>

</div>



⭐ Problem

Callbacks seem okay… but as tasks grow:

<ul>
<li>Order food</li>
<li>Track order</li>
<li>Notify delivery boy</li>
<li>Update payment</li>
<li>Send invoice</li>
<li>etc…</li>
</ul>

It becomes Callback Hell → nested, messy code.

<h2>
 
 
 🧩 2. Promises - “Your order is confirmed &amp; will be delivered!”
</h2>

Promises were created to fix callback hell.

A promise is like the Swiggy app telling you:

<blockquote>
“Your food will be delivered. 
You don’t need to ask again. 
Just use <code>.then()</code> when it arrives.”
</blockquote>

📌 Promise Example 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>function orderFood() {
 return new Promise((resolve, reject) =&gt; {
 console.log("Order placed... 🕒");

 setTimeout(() =&gt; {
 const cooked = true;
 if (cooked) resolve("Food delivered! 😋");
 else reject("Restaurant closed 😭");
 }, 2000);
 });
}

orderFood()
 .then(msg =&gt; console.log(msg))
 .catch(err =&gt; console.log(err));
</code></pre>

</div>



⭐ Promise Advantages

<ul>
<li>Cleaner than callbacks</li>
<li>Errors handled using .catch()</li>
<li>No nested pyramids</li>
<li>Values flow beautifully</li>
</ul>

<h2>
 
 
 ⚡ 3. Async–Await - “Track your order like a King 😎”
</h2>

Async–Await is the most beautiful layer on top of Promises.

When Swiggy shows:

<blockquote>
“Food is being prepared… 
Delivery partner assigned… 
Arriving in 10 mins…”
</blockquote>

You can wait for the order with clean readable steps.

Async–await lets JavaScript pause a function until the promise settles.

📌 Async–Await Example 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>function orderFood() {
 return new Promise((resolve) =&gt; {
 console.log("Order placed... 🕒");
 setTimeout(() =&gt; resolve("Food delivered! 😋"), 2000);
 });
}

async function trackOrder() {
 console.log("Tracking started… 👀");

 const result = await orderFood(); // waits here
 console.log(result);

 console.log("Order completed!");
}

trackOrder();
</code></pre>

</div>



⭐ Why async–await is better?

<ul>
<li>Looks like synchronous code</li>
<li>No <code>.then()</code> chaining</li>
<li>Error handling becomes cleaner with <code>try–catch</code>
</li>
</ul>

<h2>
 
 
 🧠 4. Under the Hood - Async–Await is just Promises
</h2>

This is the part most beginners miss:

<blockquote>
Async-Await does NOT replace Promises. 
It is built ON TOP OF Promises.
</blockquote>

Internally:

<ul>
<li>await pauses the function</li>
<li>JavaScript lets other tasks run</li>
<li>When the promise resolves → execution resumes</li>
</ul>

Same as:

<blockquote>
“Wait for the delivery update while doing other things.”
</blockquote>

📌 Proof (async function returns a Promise) 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>async function hello() {
 return "Hi!";
}

hello().then(console.log); // prints "Hi!"

</code></pre>

</div>



Even when you don’t write a promise, async makes one.

<h2>
 
 
 🛠 5. Real-world Example - Fetching API
</h2>

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F12wyhac0wtc4gpqgj08x.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F12wyhac0wtc4gpqgj08x.png" alt="An infographic for Async functions in Js" width="800" height="533"></a>

Using Callback (old &amp; ugly) 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>getUser((data) =&gt; {
 getPosts(data.id, (posts) =&gt; {
 getComments(posts[0], (comments) =&gt; {
 console.log(comments);
 });
 });
});
</code></pre>

</div>



Using Promises 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>getUser()
 .then(user =&gt; getPosts(user.id))
 .then(posts =&gt; getComments(posts[0]))
 .then(console.log);

</code></pre>

</div>



Using Async–Await (cleanest) 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>async function showComments() {
 const user = await getUser();
 const posts = await getPosts(user.id);
 const comments = await getComments(posts[0]);
 console.log(comments);
}

showComments();

</code></pre>

</div>



This is why async–await dominates modern JavaScript.

<div class="table-wrapper-paragraph"><table>
 <thead>
 <tr>
 <th>JS Feature</th>
 <th>Food Analogy</th>
 <th>What it Solves</th>
 </tr>
 </thead>
 <tbody>
 <tr>
 <td>Callbacks</td>
 <td>“Call me when ready”</td>
 <td>Basic async</td>
 </tr>
 <tr>
 <td>Promises</td>
 <td>“Order confirmed → Track in app”</td>
 <td>Avoid callback hell</td>
 </tr>
 <tr>
 <td>Async–Await</td>
 <td>“Clean step-by-step tracking”</td>
 <td>Most readable async code</td>
 </tr>
 </tbody>
</table></div>

<blockquote>
Mastering this trio makes backend dev, API-fetching, and Express work MUCH easier.
</blockquote>

❤️ Written By

<blockquote>
Applexity
</blockquote>

يشرح المقال جافا سكريبت غير المتزامن باستخدام سياق مألوف يتمثل في طلب الطعام من سويغي وزوماتو. يبرز كيف تعمل جافا سكريبت على خيط واحد، لكنها قادرة على التعامل مع مهام متعددة مثل استرجاع واجهات برمجة التطبيقات وقراءة الملفات دون تجميد التطبيقات. يتم تقديم مفاهيم الاستدعاءات، والوعود، وasync-await، مع تعريف الاستدعاءات على أنها وظائف تُمرر إلى وظائف أخرى ليتم تنفيذها لاحقًا. يهدف المقال إلى توضيح هذه المفاهيم من خلال تشبيه توصيل الطعام، مما يسهل على القراء فهم البرمجة غير المتزامنة في جافا سكريبت.

El artículo explica el JavaScript asíncrono utilizando el contexto familiar de pedir comida a través de Swiggy y Zomato. Destaca cómo JavaScript opera en un solo hilo, pero puede manejar múltiples tareas como la recuperación de APIs y la lectura de archivos sin congelar las aplicaciones. Se introducen los conceptos de callbacks, promesas y async-await, definiendo los callbacks como funciones que se pasan a otras funciones para ser ejecutadas más tarde. El artículo tiene como objetivo aclarar estos conceptos a través de la analogía de la entrega de comida, facilitando así la comprensión de la programación asíncrona en JavaScript.

L'article explique le JavaScript asynchrone en utilisant le contexte familier de la commande de nourriture via Swiggy et Zomato. Il souligne comment JavaScript fonctionne sur un seul fil tout en pouvant gérer plusieurs tâches comme la récupération d'APIs et la lecture de fichiers sans bloquer les applications. Les concepts de callbacks, promesses et async-await sont introduits, avec les callbacks définis comme des fonctions passées à d'autres fonctions pour être exécutées plus tard. L'article vise à clarifier ces concepts à travers l'analogie de la livraison de nourriture, facilitant ainsi la compréhension de la programmation asynchrone en JavaScript.

The article explains asynchronous JavaScript using the relatable context of ordering food from Swiggy and Zomato. It highlights how JavaScript operates on a single thread yet can handle multiple tasks like fetching APIs and reading files without freezing applications. The concepts of callbacks, promises, and async-await are introduced, with callbacks defined as functions passed to other functions to be executed later. The article aims to clarify these concepts through the analogy of food delivery, making it easier for readers to understand asynchronous programming in JavaScript.

Async JS Explained Using Swiggy, Zomato & Food Orders 😋🍴

arXiv:1906.07172v5 Announce Type: replace-cross 
Abstract: Equivariant neural networks are a class of neural networks designed to preserve symmetries inherent in the data. In this paper, we introduce a general method for modifying a neural network to enforce equivariance, a process we refer to as equivarification. We further show that group convolutional neural networks (G-CNNs) arise as a special case of our framework.

Equivariant neural networks and equivarification

arXiv:2311.13745v4 Announce Type: replace-cross 
Abstract: Diffusion models have become the most popular approach to deep generative modeling of images, largely due to their empirical performance and reliability. From a theoretical standpoint, a number of recent works have studied the iteration complexity of sampling, assuming access to an accurate diffusion model. In this work, we focus on understanding the sample complexity of training such a model; how many samples are needed to learn an accurate diffusion model using a sufficiently expressive neural network? Prior work showed bounds polynomial in the dimension, desired Total Variation error, and Wasserstein error. We show an exponential improvement in the dependence on Wasserstein error and depth, along with improved dependencies on other relevant parameters.

Improved Sample Complexity Bounds for Diffusion Model Training

arXiv:2410.22995v2 Announce Type: replace-cross 
Abstract: A hallmark of advanced artificial intelligence is the capacity to progress from passive visual perception to the strategic modification of visual information to facilitate complex reasoning. This advanced capability, however, remains critically underdeveloped in current Large Multi-modal Models (LMMs). The deficiency is often masked by evaluation metrics that prioritize final-answer accuracy, creating an illusion of competence where genuine reasoning is absent. Using the domain of geometric problem-solving as a precise instrument, we probe this issue through tasks that require constructing visual aids. To this end, we introduce \textbf{VisAidMath}, a challenging benchmark, and our novel Three-Layered Funnel Evaluation Framework. This framework moves beyond simple accuracy (ACCU) to scrutinize the generation of valid visual aids (PVA) and the soundness of subsequent reasoning steps (SPRS). Our extensive experiments on state-of-the-art models, including Doubao-Seed-1.6 and o4, reveal a profound ``Reasoning Illusion''. We observe that high surface-level accuracy conceals a catastrophic failure in the models' ability to produce valid visual aids or to reason from them. Our findings expose a fundamental schism between visual perception and logical deduction in modern LMMs. We host an evaluation platform at CodaBench for testing publicly. Homepage: https://nlp2ct.github.io/VisAidMathHomepage/ Evaluation: https://www.codabench.org/competitions/7634/

VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning

arXiv:2508.01249v3 Announce Type: replace-cross 
Abstract: Large Language Model (LLM) agents offer a powerful new paradigm for solving various problems by combining natural language reasoning with the execution of external tools. However, their dynamic and non-transparent behavior introduces critical security risks, particularly in the presence of prompt injection attacks. In this work, we propose a novel insight that treats the agent runtime traces as structured programs with analyzable semantics. Thus, we present AgentArmor, a program analysis framework that converts agent traces into graph intermediate representation-based structured program dependency representations (e.g., CFG, DFG, and PDG) and enforces security policies via a type system. AgentArmor consists of three key components: (1) a graph constructor that reconstructs the agent's runtime traces as graph-based intermediate representations with control and data flow described within; (2) a property registry that attaches security-relevant metadata of interacted tools \& data, and (3) a type system that performs static inference and checking over the intermediate representation. By representing agent behavior as structured programs, AgentArmor enables program analysis for sensitive data flow, trust boundaries, and policy violations. We evaluate AgentArmor on the AgentDojo benchmark, the results show that AgentArmor can reduce the ASR to 3\%, with the utility drop only 1\%.

AgentArmor: Enforcing Program Analysis on Agent Runtime Trace to Defend Against Prompt Injection

arXiv:2511.14691v1 Announce Type: cross 
Abstract: Attention is the brain's ability to selectively focus on a few specific aspects while ignoring irrelevant ones. This biological principle inspired the attention mechanism in modern Transformers. Transformers now underpin large language models (LLMs) such as GPT, but at the cost of massive training and inference energy, leading to a large carbon footprint. While brain attention emerges from neural circuits, Transformer attention relies on dot-product similarity to weight elements in the input sequence. Neuromorphic computing, especially spiking neural networks (SNNs), offers a brain-inspired path to energy-efficient intelligence. Despite recent work on attention-based spiking Transformers, the core attention layer remains non-neuromorphic. Current spiking attention (i) relies on dot-product or element-wise similarity suited to floating-point operations, not event-driven spikes; (ii) keeps attention matrices that suffer from the von Neumann bottleneck, limiting in-memory computing; and (iii) still diverges from brain-like computation. To address these issues, we propose the Spiking STDP Transformer (S$^{2}$TDPT), a neuromorphic Transformer that implements self-attention through spike-timing-dependent plasticity (STDP), embedding query--key correlations in synaptic weights. STDP, a core mechanism of memory and learning in the brain and widely studied in neuromorphic devices, naturally enables in-memory computing and supports non-von Neumann hardware. On CIFAR-10 and CIFAR-100, our model achieves 94.35\% and 78.08\% accuracy with only four timesteps and 0.49 mJ on CIFAR-100, an 88.47\% energy reduction compared to a standard ANN Transformer. Grad-CAM shows that the model attends to semantically relevant regions, enhancing interpretability. Overall, S$^{2}$TDPT illustrates how biologically inspired attention can yield energy-efficient, hardware-friendly, and explainable neuromorphic models.

Attention via Synaptic Plasticity is All You Need: A Biologically Inspired Spiking Neuromorphic Transformer

arXiv:2511.08955v2 Announce Type: replace-cross 
Abstract: Simulating microstructure evolution (MicroEvo) is vital for materials design but demands high numerical accuracy, efficiency, and physical fidelity. Although recent studies on deep learning (DL) offer a promising alternative to traditional solvers, the field lacks standardized benchmarks. Existing studies are flawed due to a lack of comparing specialized MicroEvo DL models with state-of-the-art spatio-temporal architectures, an overemphasis on numerical accuracy over physical fidelity, and a failure to analyze error propagation over time. To address these gaps, we introduce MicroEvoEval, the first comprehensive benchmark for image-based microstructure evolution prediction. We evaluate 14 models, encompassing both domain-specific and general-purpose architectures, across four representative MicroEvo tasks with datasets specifically structured for both short- and long-term assessment. Our multi-faceted evaluation framework goes beyond numerical accuracy and computational cost, incorporating a curated set of structure-preserving metrics to assess physical fidelity. Our extensive evaluations yield several key insights. Notably, we find that modern architectures (e.g., VMamba), not only achieve superior long-term stability and physical fidelity but also operate with an order-of-magnitude greater computational efficiency. The results highlight the necessity of holistic evaluation and identify these modern architectures as a highly promising direction for developing efficient and reliable surrogate models in data-driven materials science.

MicroEvoEval: A Systematic Evaluation Framework for Image-Based Microstructure Evolution Prediction

Vous utilisez les Conventional Commits et vous commencez à apprécier la clarté de l’historique Git (si ce n’est pas le cas, <a href="https://dev.to/jpiazzal/conventional-commits-un-premier-pas-vers-lautomatisation-3l5d">voici un article pour le découvrir</a>).

Mais vous continuez à gérer vos versions à la main, écrire le changelog, créer les tags, publier les nouvelles versions…

Et si tout ça se faisait tout seul ? C’est la promesse de <a href="https://github.com/semantic-release/semantic-release" rel="noopener noreferrer">semantic-release</a>.

<blockquote>
Par défaut, semantic-release est étroitement lié à l’écosystème npm, puisqu’il est développé en JavaScript et pensé à l’origine pour les projets Node.js mais certains plugins peuvent exister dans d’autres contextes.
</blockquote>

<h2>
 
 
 Pourquoi semantic-release ?
</h2>

Versionner à la main peut être une source d’erreurs multiple :

<ul>
<li>on oublie d’incrémenter le numéro de version</li>
<li>on modifie le changelog une fois sur deux</li>
<li>on ne sait plus si on doit passer en <code>1.4.2</code> ou <code>1.5.0</code>
</li>
<li>et parfois on publie sans tag</li>
</ul>

semantic-release résout tout ça automatiquement en s’appuyant sur les messages de commit (suivant la norme Conventional Commits).

En clair : vous écrivez votre code, vous faites vos commits correctement, vous mergez et le reste se fait tout seul.

<h2>
 
 
 Comment ça fonctionne ?
</h2>

Tout part de l’analyse des commits depuis la dernière version publiée (ex : <code>1.0.0</code>) :

<ul>
<li>un <code>fix:</code> déclenche une version patch (ex : <code>1.0.1</code>)</li>
<li>un <code>feat:</code> déclenche une version mineure (ex : <code>1.1.0</code>)</li>
<li>un <code>!</code> ou un <code>BREAKING CHANGE</code> déclenche une version majeure (ex : <code>2.0.0</code>)</li>
</ul>

Une fois la version déterminée, semantic-release :

<ol>
<li>met à jour le changelog automatiquement</li>
<li>crée un tag Git</li>
<li>et publie la nouvelle version (sur npm, GitHub, GitLab, etc.)</li>
</ol>

Chaque étapes peut être supprimée ou configurée.

En somme : un cycle de release entièrement piloté par votre historique Git.

<h2>
 
 
 L’écosystème autour de semantic-release
</h2>

semantic-release ne travaille pas seul. Il s’intègre parfaitement avec :

<ul>
<li>
commitlint et Husky, pour vérifier la structure des commits</li>
<li>
GitHub Actions, GitLab CI, ou n’importe quelle pipeline CI/CD</li>
</ul>

Une fois en place, il s’impose comme un maillon essentiel du processus d’intégration continue.

<h2>
 
 
 Mise en place
</h2>

Si vous souhaitez l'intégrer sur votre projet, <a href="https://semantic-release.gitbook.io/semantic-release/usage/getting-started" rel="noopener noreferrer">la documentation officielle</a> est relativement bien faite.

<h2>
 
 
 En résumé
</h2>

Avec semantic-release, vous éliminez toute la partie fastidieuse du versioning et vous rendez votre cycle de release fiable, prévisible et sans effort manuel.

En un mot : publie sans y penser.

يتناول المقال فوائد استخدام semantic-release، وهي أداة مصممة لأتمتة إدارة الإصدارات في تطوير البرمجيات. يبرز التحديات المرتبطة بالنسخ اليدوي، والتي يمكن أن تؤدي إلى أخطاء متعددة، ويؤكد على الوضوح الذي توفره Conventional Commits في تاريخ Git. تم تطوير semantic-release في الأصل لمشاريع Node.js بلغة JavaScript، وتعد بتبسيط العملية من خلال التعامل تلقائيًا مع زيادة الإصدارات، وتوليد سجلات التغييرات، ونشر الإصدارات الجديدة، مما يقلل من احتمال حدوث أخطاء بشرية.

El artículo discute los beneficios de usar semantic-release, una herramienta diseñada para automatizar la gestión de versiones en el desarrollo de software. Destaca los desafíos del versionado manual, que pueden llevar a múltiples errores, y enfatiza la claridad proporcionada por los Conventional Commits en el historial de Git. Semantic-release, desarrollado originalmente para proyectos de Node.js en JavaScript, promete simplificar el proceso al manejar automáticamente los incrementos de versión, la generación de changelogs y la publicación de nuevas versiones, reduciendo así el potencial de error humano.

L'article traite des avantages de l'utilisation de semantic-release, un outil conçu pour automatiser la gestion des versions dans le développement logiciel. Il met en lumière les défis du versionnement manuel, qui peuvent entraîner de multiples erreurs, et souligne la clarté apportée par les Conventional Commits dans l'historique Git. Semantic-release, développé à l'origine pour les projets Node.js en JavaScript, promet de rationaliser le processus en gérant automatiquement les incréments de version, la génération de changelogs et la publication de nouvelles versions, réduisant ainsi le potentiel d'erreur humaine.

The article discusses the benefits of using semantic-release, a tool designed to automate version management in software development. It highlights the challenges of manual versioning, which can lead to multiple errors, and emphasizes the clarity provided by Conventional Commits in Git history. Semantic-release, originally developed for Node.js projects in JavaScript, promises to streamline the process by automatically handling version increments, changelog generation, and publishing new releases, thus reducing the potential for human error.

semantic-release : quand la CI gère nos versions à notre place

Most of us hit this at some point:

<blockquote>
“I need to generate a PDF from HTML… and it has to look exactly like the page.”
</blockquote>

So you pick a CLI tool or library, wire it up, and it kind of works… 
until you have:

<ul>
<li>heavy JavaScript
</li>
<li>charts and dashboards in iframes
</li>
<li>
React / SPA pages that render mostly on the client</li>
<li>and a production server that now runs some fragile headless setup</li>
</ul>

I’ve been through this a few times, so I ended up building a service I wish I had earlier: 
a hosted HTML → PDF/PNG API called <a href="https://gopdfgenie.com" rel="noopener noreferrer">GoPDFGenie</a>.

This post is about:

<ul>
<li>why I built it,</li>
<li>how the async API works (jobs),</li>
<li>how to call it from Node/Python,</li>
<li>and how you can run it hosted or self-hosted.</li>
</ul>




<h2>
 
 
 The pain with “just use a PDF library”
</h2>

Tools like <code>wkhtmltopdf</code> and many HTML→PDF wrappers are fine when:

<ul>
<li>the HTML is simple,</li>
<li>there’s almost no JS,</li>
<li>and you don’t care too much about pixel-perfect layout.</li>
</ul>

In real products I kept seeing:

<ul>
<li>PDFs that didn’t match the live UI</li>
<li>things breaking when the frontend team changed layout or added JS</li>
<li>weird CSS/JS incompatibilities with older engines</li>
<li>and the ongoing cost of maintaining your own headless rendering stack</li>
</ul>

Once SPAs, dashboards, and iframes show up, the “just install X and call it a day” story falls apart.




<h2>
 
 
 What I wanted instead
</h2>

For my own projects, I wanted a service that:

<ol>
<li>Renders pages in a real modern browser engine
</li>
<li>Handles SPAs, iframes, dashboards, charts
</li>
<li>Is hosted, so I don’t maintain headless infra again</li>
<li>Has simple, usage-based pricing
</li>
<li>Still allows self-hosting/on-prem if a team needs that</li>
</ol>

So I built GoPDFGenie.

👉 Live site: <a href="https://gopdfgenie.com" rel="noopener noreferrer">https://gopdfgenie.com</a> 
👉 API docs (Swagger UI): <a href="https://gopdfgenie.com/swagger-ui/index.html" rel="noopener noreferrer">https://gopdfgenie.com/swagger-ui/index.html</a> 
👉 GitHub examples: <a href="https://github.com/gopdfgenie/html-to-pdf-api" rel="noopener noreferrer">https://github.com/gopdfgenie/html-to-pdf-api</a> :contentReference[oaicite:2]{index=2} 




<h2>
 
 
 What GoPDFGenie does (and how the async API works)
</h2>

At a high level, GoPDFGenie is:

<blockquote>
“Give me a URL or HTML → I’ll render it in a headless browser → you fetch the PDF/PNG when it’s ready.”
</blockquote>

The API is asynchronous:

<ol>
<li>You create a conversion job.</li>
<li>The API returns a <code>jobId</code>.</li>
<li>You poll the job status.</li>
<li>When it’s <code>COMPLETED</code>, you fetch the result file as PDF or PNG.</li>
</ol>

Base URL:



<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>
text
https://gopdfgenie.com/api/v1
</code></pre>

</div>

يتناول المقال التحديات المتعلقة بإنشاء ملفات PDF من HTML التي تعكس بدقة الصفحة الأصلية، خاصةً للتطبيقات المعقدة مثل SPAs ولوحات المعلومات. يقدم المؤلف حلاً قام بتطويره، وهو GoPDFGenie، واجهة برمجة تطبيقات مستضافة لتحويل HTML إلى PDF/PNG مصممة للتعامل مع هذه التعقيدات. يوضح المنشور أسباب إنشائها، وكيفية عمل واجهة برمجة التطبيقات غير المتزامنة، وكيفية استدعائها باستخدام Node أو Python، سواء كانت مستضافة أو ذاتية الاستضافة.

El artículo trata sobre los desafíos de generar PDFs a partir de HTML que reflejen con precisión la página web original, especialmente para aplicaciones complejas como SPAs y paneles de control. El autor presenta una solución que desarrolló, GoPDFGenie, una API alojada de HTML a PDF/PNG diseñada para manejar estas complejidades. La publicación describe las razones de su creación, cómo funciona su API asíncrona y cómo implementarla utilizando Node o Python, ya sea alojada o autoalojada.

L'article aborde les défis de la génération de PDF à partir de HTML qui reflètent fidèlement la page web d'origine, en particulier pour des applications complexes comme les SPAs et les tableaux de bord. L'auteur présente une solution qu'il a développée, GoPDFGenie, une API hébergée HTML vers PDF/PNG conçue pour gérer ces complexités. Le post décrit les raisons de sa création, le fonctionnement de son API asynchrone et comment l'implémenter en utilisant Node ou Python, soit hébergé, soit auto-hébergé.

The article discusses the challenges of generating PDFs from HTML that accurately reflect the original web page, particularly for complex applications like SPAs and dashboards. The author introduces a solution they developed, GoPDFGenie, a hosted HTML to PDF/PNG API designed to handle these complexities. The post outlines the reasons for its creation, its asynchronous API functionality, and how to implement it using Node or Python, either hosted or self-hosted.

I built an HTML PDF API for real-world web apps (SPAs, iframes, dashboards)

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 AI Amnesia: Erasing Knowledge Without a Trace
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Imagine your AI model accidentally learned something it shouldn't have – sensitive customer data, for example. Current methods for deleting this information often require retraining the entire model, an expensive and time-consuming process. What if we could surgically remove that knowledge without starting from scratch?

The key lies in a novel approach: creating artificial "forgetting cues." We're teaching the model to unlearn specific data patterns by exposing it to carefully crafted synthetic examples. These examples are designed to strongly contradict the information we want the model to forget, effectively overwriting the problematic associations in its memory. This works even if you don’t have access to the original data you need to erase.

Think of it like this: you're trying to forget a bad song stuck in your head. Instead of trying to actively suppress it (which rarely works), you blast an even more catchy song. The new song overwrites the old one, effectively erasing it from your mental playlist.

Benefits of Selective Forgetting:

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<li> Enhanced Data Privacy: Remove sensitive data without compromising the overall model's performance.</li>
<li> Reduced Retraining Costs: Avoid full model retraining, saving significant time and resources.</li>
<li> Improved Model Security: Eliminate vulnerabilities introduced by unintentionally learned patterns.</li>
<li> Adaptable Learning: Enables continuous refinement of AI models based on evolving data landscapes.</li>
<li> Compliance Ready: Supports compliance with data privacy regulations like GDPR.</li>
<li> Scalable Solutions: Works efficiently even with limited access to training data.</li>
</ul>

Practical Tip: One challenge is ensuring the synthetic data accurately targets the information you want to remove without negatively impacting the model's ability to generalize. Rigorous testing and validation with holdout datasets are crucial.

The promise of AI that can truly 'forget' opens exciting possibilities for responsible AI development. By enabling precise data deletion, we pave the way for more secure, compliant, and adaptable machine learning systems. Imagine AI models that can adapt to changing ethical guidelines or quickly unlearn incorrect information, all without massive retraining efforts. This is a crucial step towards trustworthy and responsible AI that respects data privacy and aligns with societal values. Future exploration could include extending this to different data modalities and model architectures.

Related Keywords: machine unlearning, data privacy, few-shot learning, zero-shot learning, synthetic data, model editing, catastrophic forgetting, incremental learning, continual learning, deep learning, neural networks, data security, algorithmic fairness, responsible ai, ethical ai, federated unlearning, privacy-preserving ai, model retraining, AI governance, data deletion, GDPR compliance

يتناول المقال نهجًا جديدًا لمعالجة مشكلة نسيان الذكاء الاصطناعي، حيث قد تحتفظ نماذج الذكاء الاصطناعي عن غير قصد بمعلومات حساسة. تتطلب الطرق التقليدية لحذف هذه البيانات غالبًا إعادة تدريب كاملة للنموذج، وهو ما يعد مكلفًا ويستغرق وقتًا. تتضمن التقنية الجديدة إنشاء 'إشارات نسيان' اصطناعية تساعد النموذج على نسيان أنماط بيانات معينة من خلال تعريضه لأمثلة صناعية تتناقض مع المعلومات غير المرغوب فيها، مما يسمح بإزالة المعرفة المستهدفة دون الحاجة للوصول إلى البيانات الأصلية.

El artículo aborda un nuevo enfoque para tratar el problema de la amnesia de la IA, donde los modelos de IA pueden retener inadvertidamente información sensible. Los métodos tradicionales para eliminar estos datos a menudo requieren un reentrenamiento completo del modelo, lo que es costoso y lleva tiempo. La nueva técnica implica crear 'señales de olvido' artificiales que ayudan al modelo a desaprender patrones de datos específicos al exponerlo a ejemplos sintéticos que contradicen la información no deseada, permitiendo así la eliminación selectiva de conocimientos sin necesidad de acceder a l…

L'article traite d'une nouvelle approche pour résoudre le problème de l'amnésie de l'IA, où les modèles d'IA peuvent involontairement conserver des informations sensibles. Les méthodes traditionnelles pour supprimer ces données nécessitent souvent un réentraînement complet du modèle, ce qui est coûteux et long. La nouvelle technique consiste à créer des 'cues d'oubli' artificiels qui aident le modèle à désapprendre des schémas de données spécifiques en lui présentant des exemples synthétiques qui contredisent les informations indésirables, permettant ainsi une suppression ciblée des connaissan…

The article discusses a novel approach to address the issue of AI amnesia, where AI models may inadvertently retain sensitive information. Traditional methods for deleting such data often require complete retraining of the model, which is costly and time-consuming. The new technique involves creating artificial 'forgetting cues' that help the model unlearn specific data patterns by presenting it with synthetic examples that contradict the unwanted information, allowing for targeted knowledge removal without needing access to the original data.

Async JS Explained Using Swiggy, Zomato & Food Orders 😋🍴

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