Naive Bayes ran real spam filters for years, and it's the rare ML model whose "training" is just counting . No gradient descent, no iterations — count words, apply Bayes' rule, multiply. I built one from scratch and visualised exactly which words push a message toward spam. 📨 Interactive demo (type a message): https://dev48v.infy.uk/ml/day6-naive-bayes.html This is Day 6 of MachineLearningFromZero — algorithms from scratch, no scikit-learn. 1. Bag of words — order doesn't matter Naive Bayes treats a message as a set of words. "free cash now" and "now cash free" look identical to it. That throws away grammar, but for spam detection the words present matter far more than their order — and it makes the math tiny. 2. Training = counting For every word, how often does it appear in spam vs ham? for ( const { text , label } of trainingData ) for ( const w of tokenize ( text )) counts [ label ][ w ] = ( counts [ label ][ w ] || 0 ) + 1 ; free and click flood spam; meeting and tomorrow live in ham. One pass over the data, done. 3. Bayes' rule flips the question You measured P(words | spam) , but you want P(spam | words) . Bayes flips it: P(spam | words) ∝ P(spam) × P(words | spam) P(spam) is the prior (how common spam is); the likelihood multiplies in the word evidence. 4. "Naive" = pretend words are independent The trick that makes it fast: assume each word is independent given the class, so the likelihood is just a product: P(words | spam) = P(w1|spam) × P(w2|spam) × ... Real words aren't independent ("credit" and "card" co-occur), so it's a naive lie — but the classification still lands right astonishingly often. 5. Smoothing + logs keep it stable Two practical fixes. Add 1 to every count (Laplace smoothing) so an unseen word doesn't zero out the whole product. And add logarithms instead of multiplying tiny probabilities, which would underflow to 0: score [ label ] = Math . log ( prior [ label ]); for ( const w of words ) score [ label ] += Math . log (( counts [ label ][

You type "k8s deployment troubleshooting" into your documentation search. The top result is a page about Kubernetes architecture that never mentions the word "troubleshooting." It is exactly what you need. BM25 would have missed it entirely. This is the promise of vector search: finding documents by meaning, not just matching words. In 2025 and 2026, vector search has moved from niche ML engineering to a core Elasticsearch capability. If you are building search for AI applications - RAG pipelines, semantic Q&A, recommendation systems - understanding how Elasticsearch handles vectors is no longer optional. I have spent the past year building RAG pipelines at Cloudera, and I have learned that vector search is powerful but easy to misuse. This post covers what works, what does not, and how to implement it in production. Why Vector Search Matters (And When It Does Not) BM25, which we covered in a previous post, is brilliant at matching exact terms. But it is fundamentally lexical. It does not understand that: "k8s" and "kubernetes" are the same thing "docker container" and "containerization" are related concepts "out of memory error" and "heap exhaustion" describe the same problem Vector search solves this by converting text into high-dimensional numerical vectors (embeddings) where semantically similar content lives close together in vector space. A query for "k8s deployment troubleshooting" gets embedded into a vector, and Elasticsearch finds the nearest document vectors - even if they do not share a single keyword. But vector search is not a replacement for BM25. It is a complement. BM25 is faster, requires no ML infrastructure, and excels at exact-term matching. Vector search is slower, requires embedding models, and shines at conceptual similarity. The best search systems in 2026 use both. How Elasticsearch Stores and Indexes Vectors Elasticsearch introduced the dense_vector field type in version 7.x and has dramatically improved it through 8.x and into 2026. Here

Most "compress your image" websites upload your photo to a server. You don't need one. The browser's own canvas can re-encode an image at any quality — I built a drag-and-drop compressor in about 30 lines , and your photo never leaves your machine. 🗜️ Try it (drop a photo): https://dev48v.infy.uk/solve/day9-image-compressor.html 1. Catch the dropped file — locally drop . addEventListener ( " drop " , e => { e . preventDefault (); const file = e . dataTransfer . files [ 0 ]; // stays in the tab, 0 bytes uploaded loadImage ( file ); }); For sensitive images (IDs, screenshots), "never uploaded" is a real feature, not just a nicety. 2. Decode it into an <img> A dropped file is just bytes. Load it via a local blob: URL: const img = new Image (); img . src = URL . createObjectURL ( file ); await img . decode (); 3. Draw it onto a canvas Now the browser holds the raw pixels, detached from the original file format: canvas . width = img . naturalWidth ; canvas . height = img . naturalHeight ; canvas . getContext ( " 2d " ). drawImage ( img , 0 , 0 ); 4. Re-encode at a quality (this IS the compression) canvas . toBlob ( blob => { preview . src = URL . createObjectURL ( blob ); showSize ( blob . size ); }, " image/jpeg " , 0.7 ); // 0.7 = 70% quality JPEG and WebP are lossy — they discard detail the eye barely notices. That third argument is the entire compression dial; a small quality drop often halves the file size. 5. Hand the result back as a download link . href = URL . createObjectURL ( blob ); link . download = " compressed.jpg " ; // the browser saves it, no server The takeaway FileReader → Image → Canvas → toBlob is a surprisingly powerful local image pipeline. The same four steps do resizing, format conversion, cropping, watermarking — all client-side, all private. A whole category of "image tools" needs no backend at all. Open it and drop a photo.

Why Your Chinese-Tenured Faculty Resume Won’t Work in US Industry US data scientist hiring managers scan a resume in 7–15 seconds looking for one thing: evidence you can solve business problems with data. A Chinese faculty resume often leads with tenure status, publication counts, and grant amounts—none of which translate to industry value. Worse, the CV-style length and Chinese-specific qualifications (e.g., “Professor of Record,” “National Natural Science Foundation PI”) confuse HR software and recruiters unfamiliar with that system. You need to strip the academic frame and rebuild around what a US data scientist does: clean messy data, build predictive models, deploy to production, and communicate results to non-technical stakeholders. Think of every faculty achievement as raw material you must reframe. Core Rewriting Rules: From Academic to Industry Rule 1: Replace Tenure Rank with a US-Equivalent Data Science Title Do not list “Tenured Associate Professor” unless it is your most recent position at a well-known university (e.g., Peking University, Tsinghua). Instead, use a title that reveals your function: “Senior Data Scientist – Research Computing” or “Lead Data Scientist – Machine Learning Research Lab.” The point is to signal the job function, not the academic rank. Example: Before: “Tenured Associate Professor, School of Computer Science, Fudan University” After: “Senior Data Scientist / Research Lead, Fudan University AI Lab” Rule 2: Translate Every Accomplishment into a Business-Relevant Metric Chinese faculty resumes often say “published 15 papers in top-tier journals” or “secured ¥3M in research funding.” That means nothing to a hiring manager at a fintech startup. Instead, describe what you did with the data and the outcome. Concrete example – before and after: BEFORE (faculty bullet): “Led research project on deep learning for medical image segmentation; published 3 papers in IEEE TMI.” AFTER (industry data scientist bullet): “Built and validated a co

In recent years, Angular has taken an important step toward a simpler and more declarative reactivity model with the introduction of Signals . NgRx, which has long been the de facto standard for state management in complex Angular applications, followed this evolution by introducing Signal Store . The goal is not to completely replace @ngrx/store , but to offer a lighter and more local alternative, designed for use cases where the classic Actions → Reducers → Selectors pattern feels excessive. In this article, we'll see how to use NgRx Signal Store to build a reactive, typed store that integrates seamlessly with Angular components, drastically reducing boilerplate and improving code readability. This tutorial is aimed at Angular developers who are already familiar with Signals and "classic" NgRx. What is NgRx Signal Store NgRx Signal Store introduces a different way of thinking about state compared to classic @ngrx/store . A Signal Store : is not based on Redux does not use actions or reducers does not require explicit selectors Instead, the model revolves around three main concepts: 🧩 State State is defined as a set of signals , typically using withState . Each state property is immediately reactive and can be read directly by components. 🧠 Derived state Derived state is defined using withComputed . It is the conceptual equivalent of selectors, but with a more direct syntax and better integration with Angular's Signals system. 🔧 Methods State changes and side effects (such as HTTP calls) are encapsulated in methods declared with withMethods . This keeps the store logic in a single place, without having to orchestrate multiple files as in the traditional NgRx pattern. In other words, a Signal Store resembles a strongly structured reactive service more than a pure Redux store. This approach makes Signal Stores particularly suitable for: local or feature state small to medium-sized applications reducing complexity in contexts where Redux would be overkill Creating the

SanDisk has announced an expensive way to boost the PlayStation 5's storage capacity. The company's new Optimus GX PRO 850P NVMe SSD is an officially licensed PS5 accessory in capacities ranging from 1TB to 8TB. The largest option can store up to 200 PS5 games (based on average installation sizes) SanDisk claims, but thanks to […]

There's a brand new ariaNotify() method — defined by the WAI-ARIA 1.3 Specification — that provides a means of programmatically triggering narration in a screen reader. The Siren Song of ariaNotify() originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

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