EliteSaaS - Launch in Hours

<p>Take your cloud knowledge to the next level at AWS re:Invent this December. Gain exclusive insights through interactive labs, technical sessions, and workshops while receiving direct mentorship from AWS experts. Connect with peers who share your technical interests at the Developer Pavilion, and enjoy live music and amazing food at re:Play, the ultimate tech celebration. Take advantage of a 50% discount voucher for any AWS Certification exam when attending in person, and get one free pass for every 10 passes purchased when bringing your team*.</p><div><a href="https://www.vpdae.com/redirect/bnandvwgqwvvgcvvpkq23cwqw9e">2025 event catalog is now available. Learn more and register</a></div>

Level up your skills at AWS re:Invent Las Vegas

<div>EliteSaaS is more than just a boilerplate — it’s a full-stack SaaS launchpad built for indie hackers, solo founders, and developers who want to skip the boring scaffolding and get to market fast.<br><br></div><div>Built on Next.js, Tailwind, Supabase, Stripe, and Resend, EliteSaaS gives you <b>authentication, billing, multi-tenant support, and even AI-powered marketing automation out of the box</b>.<br><br></div><div>Whether you’re launching a side project, a client MVP, or your next startup — EliteSaaS helps you ship a polished product and grow it.<br><br></div><div>You don’t just deploy your SaaS with EliteSaaS — it promotes itself.</div>

Launch Your SaaS Product in Hours — Not Weeks

This article discusses the feasibility and benefits of building web applications without relying on frontend frameworks, suggesting alternative approaches for developers.

You can and should build one yourself, but you would benefit from using a framework in production. Modern JavaScript and Browser APIs Today you can achieve most of the features with " simple " JavaScript (TS) to update your state, have routing, a bundler with code splitting, maybe JSX, templating and even getting different components like a calendar or emoji picker you can do in many ways like copy an example from the web and make it your own or use an OSS web component. Browsers and the JavaScript community have came pretty far from the old days of IE6. You have many ways to implement UIs and a ton of libs to do the small thing you want. JQuery and other major APIs were added to the library and our bundlers are plentiful with many features out of the box for all your CSS, HTML and JS needs. Issues Developers complain about speed, JS bundle size, react-dom weighs too much. Well if you think of these frameworks and their components as abstractions, you should try to understand the payoff. These known frameworks have been tested on many edge cases your own code wasn ' t even ran that many times. So many bugs were fixed and though you might not have the edge cases their fixes are in your framework. Is this a good thing? Depends on your opinion, like best practices, would you want them implemented from the start for the chance you ' ll need them and enable extending your apps or prefer to keep it small and to the point. A good reminder from OSS and from work is that everything you write will one day be legacy and will be passed to someone else to maintain potentially and in the worse case it will be you. IDK about you but I don ' t remember every line I wrote 5 years ago, who am I kidding, 3 months ago . My point is that someone will have to deal with what you write and they will need docs and examples like StackOverflow or ChatGPT to maintain that shit you called the best thing ever. For me TypeScript helps with keeping things maintainable when paired with tests to ensure I don ' t break everything. I recently updated my library Jstates and had to refactor the majority of the code after 2 years of not touching it, and it ' s a super simple library with 1 function. Having tests and types are a huge benefit, and I did go to many documentations for rollup, vite, vitest and other tools I used to update my code base. Build your own Vanillajs framework I like to breakdown tools to understand how they work to see how I could build the same and then understand the choices the author made and how things work underneath. I always try to create examples and enjoy the challenge (when it works). For the example, I worked on creating a frontend app without any framework, I used my Jstates library to have updating values and a lib for JSX and vite for bundling and the rest was pretty ok without a framework but with all the base features I need from any framework. The repo Including TypeScript, JSX, State updates, Templating, Routing, code splitting. . What else do you need? Ok. . Go ahead and add tailwind or another CSS lib, but IMO modern CSS is enough  I would recommend trying to build one yourself for learning, and maybe you ' ll find a use case to actually use it. For example, if you ' re unfamiliar with SPA routing, doing something like this could give you insights into what you need to do to trick the browser to do what you want and not redirect and refresh your frontend app on every click. There are also vanillajs libs for this just like anything else, the point it you can try and make things different and make your own. I know there are many frameworks, some say too many, but I think it ' s a good thing. There are many use cases out there and it ' s great to find a battle tested, production ready framework that fits your needs that someone else is maintaining for free. The more we share our tooling the better they usually are by having more people contributing to the ecosystem with plugins, complimentary tooling, design libraries on top, creative ideas, articles and " How to " guides and more. . Frameworks are helpful to abstract away a lot and get something production tested very quickly off the ground if you know them already. Animations, pre built components and more and more features and capabilities. More issues Lots of people say they don ' t need all the new features in react because they don ' t have the needs of fb, well they might need all the goodies from the react community or they might need a new developer to get up and running fast in the new job, and they know react because it ' s the hottest thing right now. That ' s how the abstractions helps. Also in the job market, it ' s hard to find positions that don ' t require you to know one of the frontend frameworks for a frontend role. This standardization, also around the frameworks features are there to help us abstract noise away and make us more productive. Many junior developers that I worked with had an easier time starting with a framework than Vanillajs, I started with jQuery. The issue for many is added features on top which they feel they don ' t need, and they might be right, even though you often don ' t have to use them in order to use the framework. Back in the early days of react, I met teams which used react for rendering and and older framework for their state management, this was before redux even and I think it was Backbone or something, anyway. . The real issue for many developers are the other developers on their team that would want to use these new features and abstract the code even more. I believe this is actually good as it would bring up the discussion of the benefit of the features and weighing their benefits. I was in some teams that were not using library features because of a bug or because the team decided not to. Only issue there is you go away from the main usage of the library, which might be harder in the future. Final works (before everyone attacks ) Go build your own Vanillajs framework, learn from it, understand the frameworks you use better as a result and share the knowledge. And remember, if you think of open sourcing your framework or library, don ' t expect people to use it, and if they do, now you ' ll have to maintain it and hear their complaints like the lovely framework maintainers  Some great examples are Solidjs and preact

You don't need a frontend framework

This article explores the execution of the KittenTTS text-to-speech model in the browser using WebAssembly (WASM) and the ONNX framework, detailing the integration process and technologies involved.

Running AI models in the browser used to be a pipe dream. Neural networks required powerful GPUs, gigabytes of memory, and server-side processing. But what if I told you we ' re now running a complete text-to-speech AI model entirely in your browser, with no server communication whatsoever? This is the technical story of how we built our Text to Speech tool using KittenTTS, ONNX Runtime, and WebAssembly—creating a privacy-first, unlimited AI voice synthesis system that runs completely client-side. The Technical Challenge Traditional text-to-speech systems rely on server-side processing for good reason: Model size: Neural TTS models can be hundreds of megabytes Computational complexity: Voice synthesis requires intensive matrix operations Memory usage: Audio generation consumes significant RAM Browser limitations: JavaScript wasn ' t designed for heavy numerical computing Yet here we are, doing exactly that. Let ' s dive into how we solved each challenge. Architecture Overview Our browser-based TTS system consists of four main components: ┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ Text Input │───&gt; │ Text Cleaner │───&gt; │ Phonemizer │ └─────────────────┘ └──────────────────┘ └─────────────────┘ │ ┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ Audio Output │&lt; ───│ KittenTTS ONNX │&lt; ───│ Token Converter │ └─────────────────┘ └──────────────────┘ └─────────────────┘ Each component runs entirely in the browser, with no external dependencies once loaded. KittenTTS: The Neural Voice Engine At the heart of our system is KittenTTS, a neural text-to-speech model that balances quality with efficiency. Unlike massive models like Tacotron 2 or FastSpeech, KittenTTS is designed to be lightweight while still producing natural-sounding speech. Model Architecture KittenTTS uses a transformer-based architecture with: Text encoder: Converts phonemes to hidden representations Style embeddings: Define voice characteristics (8 distinct voices) Decoder: Generates mel-spectrograms from text representations Vocoder: Converts spectrograms to raw audio waveforms The entire pipeline from text to audio happens in a single ONNX model file, making it perfect for browser deployment. ONNX Runtime Web: Bringing ML to Browsers ONNX (Open Neural Network Exchange) Runtime is Microsoft ' s cross-platform ML inference engine. The Web version brings near-native performance to browsers through WebAssembly. Why ONNX Runtime? // Initialize ONNX Runtime with WebAssembly backend const sessionOptions = { executionProviders: [ ' wasm ' ], graphOptimizationLevel: ' disabled ' , enableCpuMemArena: false, enableMemPattern: false, logSeverityLevel: 3 }; this. Model = await InferenceSession. Create(modelBuffer, sessionOptions); ONNX Runtime Web offers several advantages: Performance: WebAssembly execution is 10-20x faster than pure JavaScript Memory efficiency: Optimized tensor operations minimize memory allocation Cross-platform: Works consistently across all modern browsers GPU acceleration: Can leverage WebGL when available Loading the Model One of our biggest challenges was loading a 25MB ONNX model efficiently in the browser: async load(): Promise&lt; void&gt; { // Check IndexedDB cache first let modelBuffer = await this. LoadModelFromCache(); if (! ModelBuffer) { // Load from embedded assets or fetch if (this. Config. UseEmbeddedAssets &amp; &amp; hasEmbeddedAssets()) { modelBuffer = getEmbeddedModel(); } else { const response = await fetch(this. Config. ModelPath); modelBuffer = await response. ArrayBuffer(); } // Cache for future sessions await this. SaveModelToCache(modelBuffer); } this. Model = await InferenceSession. Create(new Uint8Array(modelBuffer)); } This approach provides progressive loading: first-time users download the model, while returning users load instantly from IndexedDB. WebAssembly: JavaScript ' s Numerical Computing Engine WebAssembly (WASM) is the secret sauce that makes browser-based AI possible. It provides near-native performance for computationally intensive operations. WASM Configuration configureWasmPaths(wasmPaths: Record&lt; string, string&gt;) { (env. Wasm as any). WasmPaths = wasmPaths; // Optimize for browser environment env. Wasm. NumThreads = 1; // Single-threaded for compatibility env. Wasm. Simd = true; // Enable SIMD if available env. LogLevel = ' warning ' ; } Key WASM optimizations: Single-threaded execution: Avoids SharedArrayBuffer requirements SIMD support: Vectorized operations when browser supports it Memory management: Efficient allocation for tensor operations Performance Characteristics Our benchmarks show impressive performance across different browsers: Browser First Load Cached Load Generation (100 chars) Chrome 120+ 8-12 seconds 2-3 seconds 3-5 seconds Firefox 119+ 10-15 seconds 3-4 seconds 4-6 seconds Safari 17+ 12-18 seconds 4-5 seconds 5-7 seconds Text Processing Pipeline Converting text to audio requires several preprocessing steps that we ' ve optimized for browser execution. Text Cleaning and Normalization export function cleanTextForTTS(text: string): string { // Remove emojis using Unicode ranges const emojiRegex = /[\u{1F600}-\u{1F64F}]|[\u{1F300}-\u{1F5FF}]/gu; return text. Replace(emojiRegex, ' '). Replace(/\b\/\b/, ' slash ' ). Replace(/[\/\\()ˉ]/g, ' '). Replace(/[ " " " ]/g, ' '). Replace(/\s—/g, ' . ' ). Replace(/[^\u0000-\u024F]/g, ' ') // Keep only Latin characters. Trim(); } Phonemization Converting text to phonemes is crucial for natural speech. We use the phonemizer. Js library: // Convert text to phonemes const phonemesList = await phonemize(text, ' en-us ' ); const phonemes = phonemesList. Join( ' '); // Add boundary markers const tokensWithBoundaries = `${phonemes}`; // Convert to token IDs const tokens = this. TextCleaner. Call(tokensWithBoundaries); tokens. Unshift(0); // Add start token tokens. Push(0); // Add end token Token Encoding Our TextCleaner converts phonemes to numerical tokens that the neural network understands: export class TextCleaner { private wordIndexDictionary: Record&lt; string, number&gt; ; constructor() { const _pad = " $ " ; const _punctuation = ' ; : , . ! ? ??—… " ?? " " ' ; const _letters = ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz ' ; const _letters_ipa = " ɑ?????β?????e??????????ɡ??????????????????????????θ??????????????????????χ??????????∥???????????????↓↑→↗↘ ' ? ' ? " ; const symbols = [_pad, . . . Array. From(_punctuation), . . . Array. From(_letters), . . . Array. From(_letters_ipa)]; this. WordIndexDictionary = {}; for (let i = 0; i &lt; symbols. Length; i++) { this. WordIndexDictionary[symbols[i]] = i; } } call(text: string): number[] { const indexes: number[] = []; for (const char of text) { if (this. WordIndexDictionary[char]! == undefined) { indexes. Push(this. WordIndexDictionary[char]); } } return indexes; } } Neural Network Inference The actual voice synthesis happens through ONNX model inference with carefully prepared inputs. Model Input Preparation private async prepareInputs(text: string, options: GenerateOptions) { const { voice = ' expr-voice-2-m ' , speed = 1. 0 } = options; // Get phonemes and convert to tokens const phonemes = await phonemize(text, ' en-us ' ); const tokens = this. TextCleaner. Call(phonemes. Join( ' ')); // Add start/end tokens tokens. Unshift(0); tokens. Push(0); const tokenIds = new BigInt64Array(tokens. Map(id =&gt; BigInt(id))); const voiceEmbedding = this. Voices[voice]; return { ' input_ids ' : new Tensor( ' int64 ' , tokenIds, [1, tokenIds. Length]), ' style ' : new Tensor( ' float32 ' , voiceEmbedding, [1, voiceEmbedding. Length]), ' speed ' : new Tensor( ' float32 ' , new Float32Array([speed]), [1]) }; } Running Inference async generateSingle(text: string, options: GenerateOptions) { const inputs = await this. PrepareInputs(text, options); // Run neural network inference const results = await this. Model! . Run(inputs); // Extract audio tensor (usually the largest output) let audioTensor = null; for (const [name, tensor] of Object. Entries(results)) { if (! AudioTensor || tensor. Size &gt; audioTensor. Size) { audioTensor = tensor; } } // Convert to Float32Array and post-process const audioData = new Float32Array(audioTensor. Data); return this. PostProcessAudio(audioData); } Audio Post-Processing Raw neural network output requires several post-processing steps to create clean, playable audio. Cleaning and Normalization private postProcessAudio(audioData: Float32Array): Float32Array { // Clean NaN values for (let i = 0; i &lt; audioData. Length; i++) { if (isNaN(audioData[i])) { audioData[i] = 0; } } // Trim silence let startIdx = 0, endIdx = audioData. Length - 1; const threshold = 0. 001; for (let i = 0; i &lt; audioData. Length; i++) { if (Math. Abs(audioData[i]) &gt; threshold) { startIdx = i; break; } } for (let i = audioData. Length - 1; i &gt; = 0; i--) { if (Math. Abs(audioData[i]) &gt; threshold) { endIdx = i; break; } } const trimmedAudio = audioData. Slice(startIdx, endIdx + 1); // Normalize volume let maxAmplitude = 0; for (const sample of trimmedAudio) { maxAmplitude = Math. Max(maxAmplitude, Math. Abs(sample)); } if (maxAmplitude &gt; 0) { const normalizationFactor = 0. 8 / maxAmplitude; for (let i = 0; i &lt; trimmedAudio. Length; i++) { trimmedAudio[i] *= normalizationFactor; } } return trimmedAudio; } Memory Management and Optimization Running large neural networks in browsers requires careful memory management. Chunked Processing For long texts, we automatically split content into manageable chunks: export function chunkText(text: string): string[] { const MAX_CHUNK_LENGTH = 500; const sentences = text. Split(/(? &lt; =[. ! ?])(? =\s+[A-Z]|$)/); const chunks = []; let currentChunk = ' '; for (const sentence of sentences) { const potentialChunk = currentChunk + (currentChunk? ' ': ' ') + sentence. Trim(); if (potentialChunk. Length &gt; MAX_CHUNK_LENGTH) { if (currentChunk) chunks. Push(currentChunk); currentChunk = sentence. Trim(); } else { currentChunk = potentialChunk; } } if (currentChunk) chunks. Push(currentChunk); return chunks; } IndexedDB Caching We implement a sophisticated caching system using IndexedDB: class ModelCache { async set(key: string, data: ArrayBuffer): Promise&lt; void&gt; { const db = await this. Init(); return new Promise((resolve, reject) =&gt; { const transaction = db. Transaction([this. StoreName], ' readwrite ' ); const store = transaction. ObjectStore(this. StoreName); const request = store. Put({ key, data, timestamp: Date. Now() }); request. Onsuccess = () =&gt; resolve(); request. Onerror = () =&gt; reject(request. Error); }); } async get(key: string): Promise&lt; ArrayBuffer | null&gt; { const db = await this. Init(); return new Promise((resolve) =&gt; { const transaction = db. Transaction([this. StoreName], ' readonly ' ); const store = transaction. ObjectStore(this. StoreName); const request = store. Get(key); request. Onsuccess = () =&gt; { const result = request. Result; if (result &amp; &amp; Date. Now() - result. Timestamp &lt; 7 * 24 * 60 * 60 * 1000) { resolve(result. Data); } else { resolve(null); } }; }); } } Audio Format Conversion Converting Float32Array audio data to playable WAV format happens entirely in JavaScript: export function createWavBlob(audioData: Float32Array, sampleRate: number): Blob { const buffer = new ArrayBuffer(44 + audioData. Length * 2); const view = new DataView(buffer); // Write WAV header const writeString = (offset: number, string: string) =&gt; { for (let i = 0; i &lt; string. Length; i++) { view. SetUint8(offset + i, string. CharCodeAt(i)); } }; writeString(0, ' RIFF ' ); view. SetUint32(4, 36 + audioData. Length * 2, true); writeString(8, ' WAVE ' ); writeString(12, ' fmt ' ); view. SetUint32(16, 16, true); view. SetUint16(20, 1, true); view. SetUint16(22, 1, true); view. SetUint32(24, sampleRate, true); view. SetUint32(28, sampleRate * 2, true); view. SetUint16(32, 2, true); view. SetUint16(34, 16, true); writeString(36, ' data ' ); view. SetUint32(40, audioData. Length * 2, true); // Convert float to 16-bit PCM let offset = 44; for (let i = 0; i &lt; audioData. Length; i++) { const sample = Math. Max(-1, Math. Min(1, audioData[i])); view. SetInt16(offset, sample &lt; 0? Sample * 0x8000: sample * 0x7FFF, true); offset += 2; } return new Blob([buffer], { type: ' audio/wav ' }); } Performance Optimizations Progressive Loading We ' ve implemented several strategies to improve perceived performance: Embedded assets: Critical model files bundled with the application IndexedDB caching: Models persist across browser sessions Lazy initialization: ONNX Runtime loads only when needed Background processing: Model loading doesn ' t block the UI Browser Compatibility Our implementation gracefully handles different browser capabilities: // Try WebAssembly backend first, fallback to CPU try { this. Model = await InferenceSession. Create(modelBuffer, { executionProviders: [ ' wasm ' ], graphOptimizationLevel: ' disabled ' }); } catch (wasmError) { console. Warn( ' WebAssembly failed, using CPU backend ' ); this. Model = await InferenceSession. Create(modelBuffer, { executionProviders: [ ' cpu ' ], graphOptimizationLevel: ' basic ' }); } Security and Privacy Implications Running AI models client-side has significant privacy advantages: Zero data transmission: Text never leaves the user ' s device No server logs: No record of what users synthesize Offline capability: Works without internet after initial load No API keys: No authentication or usage tracking Content Security Policy WebAssembly requires specific CSP headers: // netlify. Toml [[headers]] for = " /* " [headers. Values] Cross-Origin-Embedder-Policy = " require-corp " Cross-Origin-Opener-Policy = " same-origin " Challenges and Limitations Current Limitations Initial load time: First-time users wait 8-15 seconds for model download Memory usage: Neural networks consume 100-200MB RAM during inference Browser compatibility: Requires modern browsers with WebAssembly support Mobile performance: Slower generation on resource-constrained devices Future Optimizations Several improvements are on our roadmap: Model quantization: Reduce model size through 8-bit precision WebGL acceleration: Leverage GPU when available Streaming inference: Generate audio progressively for long texts Service Worker caching: More aggressive asset caching strategies The Bigger Picture Our KittenTTS implementation represents a broader trend toward edge AI computing. By running neural networks in browsers, we ' re enabling: Privacy-preserving AI: No data leaves the user ' s device Reduced infrastructure costs: No expensive GPU servers required Better user experience: No network latency or rate limits Democratized AI: Advanced capabilities accessible to everyone Technical Takeaways If you ' re building browser-based AI applications, here are key lessons from our implementation: ONNX Runtime Web is production-ready for neural network inference in browsers WebAssembly provides significant performance gains for numerical computing Careful memory management is crucial for large model deployment Progressive loading strategies improve perceived performance IndexedDB caching is essential for models larger than a few megabytes Try It Yourself Want to experience browser-based AI voice synthesis? Try our Text to Speech tool and see KittenTTS, ONNX Runtime, and WebAssembly working together in real-time. For developers interested in implementing similar systems, our KittenTTS JavaScript package (@quickeditvideo/kittentts) provides a clean API for browser-based neural text-to-speech synthesis. (Coming shortly) The future of AI is moving to the edge—and that includes the browser edge. Privacy-preserving, unlimited, client-side AI is not just possible, it ' s here today.

Running KittenTTS in the Browser: A Deep Dive into WASM and ONNX

This article discusses a favorite front-end interview question used by the author to assess candidates, providing insights into the reasons behind its effectiveness in evaluating technical and problem-solving skills.

My Favourite Front-End Interview Question to Ask Candidates and Why

This article provides a comprehensive overview of different types of loops in JavaScript, detailing their use cases and performance implications. It includes practical examples and tips on selecting the most suitable loop type for various coding scenarios.

Every programming language has loops. Loops perform an operation (i. E. , a chunk of work) a number of times, usually once for every item in an array or list, or to simply repeat an operation until a certain condition is met.  All About JavaScript Loops originally published on CSS-Tricks, which is part of the DigitalOcean family. You should get the newsletter.

All About JavaScript Loops

This article discusses the process of binding events to the Document Object Model (DOM) elements, providing insights and coding techniques as part of a UI development series.

Table of Contents Introduction Bindings for events Callback Params Event Data Model Target Element Event String Parent Data Object More information Conclusion Introduction Today we are going to dive a layer deeper into the Peasy-UI library. We are going to do a deep dive into binding strategies for user events in the DOM. User events in the DOM are particularly targeted towards how the user of the app will interact with it. This can be touch interfaces, mouse pointers and drag, typing into fields, etc. . . If you missed the introduction article for Peasy-UI, you can read it here, and it can provide the overview for the library, and even introduces upcoming topics in the series. Bindings for events Let ' s dig into the mechanics of capturing user events from the DOM. If want to render a drop down select input, or a button? Peasy-UI gives you access to mapping the event listeners automatically and tying them into the data model. BINDING: ${ event @=&gt; callbackName} Buttons provide easy examples for this, but all DOM events can be used. Const model = { clickHandler: () =&gt; { window. Alert ( " I got clicked " ); }, }; const template = ` &lt; div&gt; &lt; button \$ {click @=&gt; clickHandler}&gt; Click Me! &lt; /button&gt; &lt; /div&gt; `; You can bind any HTML DOM event, such as change, input, click, mouseenter, etc. . . As an event binding, and then you provide the ' handler ' callback which exists IN the data model. Callback Params Peasy passes 5 standard parameters into the handler for your convenience. Event The first parameter is the HTMLEvent that actually is fired. Const model = { clickHandler: (evt: HTMLEvent) =&gt; { console. Log (evt); }, }; const template = ` &lt; div&gt; &lt; button \$ {click @=&gt; clickHandler}&gt; Click Me! &lt; /button&gt; &lt; /div&gt; `; Data Model The second parameter is the ' localized ' data model that is bound to the element. This CAN be tricky, because depending on your nesting of elements, it maybe be a localized data model for just that element, or if it is a top level binding, it will be the standard data model. When we dig into lists and arrays and how Peasy renders them, we will discuss this localized data model and how it can be used. Const model = { isA: true; numClicks: 0; clickHandler: (evt: HTMLEvent, model: any) =&gt; { console. Log (model); }, }; const template = ` &lt; div&gt; &lt; button \$ {click @=&gt; clickHandler}&gt; Click Me&lt; /button&gt; &lt; /div&gt; `; // will log --&gt; &gt; {isA: true, numclicks: 0, clickHandler: f} Target Element The third parameter is the target element that fired the event, so you can access any attributes nested in the element or its value. Const model = { clickHandler: (evt: HTMLEvent, model: any, elem: HTMLElement) =&gt; { console. Log (elem); }, }; const template = ` &lt; div&gt; &lt; button \$ {click @=&gt; clickHandler}&gt; Click Me&lt; /button&gt; &lt; /div&gt; `; Event String The fourth element is the string representation of the event, such as ' click ' or ' change ' and this helps in case you bind multiple events to the same callback, you can distinguish which event triggered this callback. Const model = { clickHandler: (evt: HTMLEvent, model: any, elem: HTMLElement, eStr: string) =&gt; { console. Log (eStr); }, }; const template = ` &lt; div&gt; &lt; button \$ {click @=&gt; clickHandler} \$ {mouseenter @=&gt; clickHandler}&gt; Click Me&lt; /button&gt; &lt; /div&gt; `; Parent Data Object The final parameter is the overarching data model. Inside this object, regardless of how nested your binding is, will be an object that contains the model property, and you can navigate that object to have access to ANY property in the data model. This becomes very important when you start using arrays and lists for rendering elements. Const model = { clickHandler: (evt: HTMLEvent, model: any, elem: HTMLElement, eStr: string, obj: any) =&gt; { console. Log (obj); }, }; const template = ` &lt; div&gt; &lt; button \$ {click @=&gt; clickHandler}&gt; Click Me&lt; /button&gt; &lt; /div&gt; `; // will log out --&gt; &gt; {$model: {. . . }} More information More information can be found in the GitHub repo for Peasy-Lib, you also will find all the other companion packages there too. Also, Peasy has a Discord server where we hang out and discuss Peasy and help each other out. The author ' s twitter: Here The author ' s itch: Here Github Repo: Here Discord Server: Here Conclusion In this entry of the Peasy-UI library breakdown we dove deeper into how user input DOM events can be bound into the data model so Peasy-UI can create and manage the event listeners for you. One unique aspect of the event handlers in the data model is the callback parameters that are pulled into the callback by default, including: the event object, the data models, the event string, and the HTML element itself. This gives the developer a unique set of tools to perform very customizable events, but with a favorable developer experience. Our next article will address some of the helper CSS classes that come with Peasy-UI out of the box.

Binding Events for the DOM: Part 3 of the Peasy-UI Series

The article discusses the results of the 2024 State of CSS survey, highlighting the current trends, challenges, and advancements within the CSS community.

The results from this year ' s survey are fairly fresh off the presses. We took a little time to sit with them and jot down some things we noticed and found interesting. State of CSS 2024 Results originally published on CSS-Tricks, which is part of the DigitalOcean family. You should get the newsletter.

State of CSS 2024 Results

Web developers seeking to improve their resumes can find valuable advice and examples on crafting effective CVs that highlight key skills and experiences.

Explore 10 tailored web developer resume examples for various specializations and career levels. Learn how to craft the perfect resume. Continue reading 10 Amazing Web Developer Resume Examples for Different Web Dev Specializations on SitePoint.

10 Amazing Web Developer Resume Examples for Different Web Dev Specializations

This article explains the mechanisms behind how ChatGPT maintains context in ongoing conversations, focusing on its architectural design and underlying technology.

The concept of context in conversation is crucial, as it allows for coherent and meaningful exchanges. But how does ChatGPT keep context? Continue reading Understanding How ChatGPT Maintains Context on SitePoint.

Understanding How ChatGPT Maintains Context

Frontend Gems: Framework-Free & JavaScript Wonders

This issue of Frontend Weekly brings a diverse selection of articles that are essential for modern web development. Firstly, there's an insightful piece on 'You don't need a frontend framework,' exploring how developers can build web applications without relying on frameworks and suggesting valuable alternative approaches. Dive into 'Running KittenTTS in the Browser' to learn about integrating the KittenTTS text-to-speech model using WASM and ONNX, offering a technical deep dive into browser-based execution. For those involved in recruitment or preparing for interviews, 'My Favourite Front-End Interview Question' is a must-read, as it shares a compelling question aimed at assessing candidate's skills effectively. 'All About JavaScript Loops' provides an in-depth understanding of various loop types in JavaScript, crucial for optimizing code performance. Part of the Peasy-UI series, 'Binding Events for the DOM' covers advanced event binding techniques. Also, the 'State of CSS 2024 Results' reveals exciting trends and challenges faced by the CSS community today. Enhance your professional profile with '10 Amazing Web Developer Resume Examples,' which offers tailored advice for different web specializations. Lastly, understand how AI keeps conversations contextually relevant in 'Understanding How ChatGPT Maintains Context.' Each article in this issue provides valuable insights and practical knowledge for developers at all levels.

Explore building apps without frameworks, dive into KittenTTS in-browser with WASM/ONNX, and learn a top front-end interview question. Discover JavaScript loops, DOM event binding, CSS 2024 trends, amazing web developer resumes, and ChatGPT context maintenance in this issue of Frontend Weekly.

Issue %%issue_number%%

I'm creating this article for a newsletter, give me a 2 sentence summary of the article.  Make it factual and professional.  Do not mention the article title or source, just the summary of the article.

The best articles, links and news related to Frontend Development delivered once a week to your inbox.

国内精品久久久久影院日本,日本中文字幕视频,99久久精品99999久久,又粗又大又黄又硬又爽毛片