LinkFindr vs Quick ML

LinkFindr is your intelligent link manager — save, organize, and rediscover every link that matters. Powered by on-device AI, LinkFindr automatically categorizes your bookmarks, generates summaries, and keeps everything searchable across all your devices. SAVE LINKS FROM ANYWHERE Copy a link in Safari, Chrome, or any app — LinkFindr instantly detects it and offers a one-tap save. Use the Share Extension to save directly from any browser without switching apps. LinkFindr auto-fetches the page title, favicon, thumbnail, and description so you don't have to type anything. AI-POWERED ORGANIZATION LinkFindr uses Apple Intelligence to understand your links and organize them for you: - Auto-categorization into projects based on content - Smart tagging with relevant keywords - AI-generated summaries so you remember why you saved it - Intelligent search suggestions based on your collection Works on every device — on older iPhones, LinkFindr uses a smart heuristic engine that categorizes links by domain and content patterns. SMART COLLECTIONS Go beyond folders with dynamic collections that update automatically: - Unread links waiting for your attention - Links added this week or this month - AI-summarized content - Articles available offline - Links connected to tasks - And more — all without manual sorting READ OFFLINE Download articles for distraction-free reading anywhere. LinkFindr's built-in reader strips away ads and clutter, showing just the content with adjustable text size. Reading time estimates help you pick the right article for your commute or break. FIND EVERYTHING INSTANTLY Your links are indexed in Spotlight — swipe down on your home screen and search to find any saved link. Inside the app, search across titles, URLs, projects, tags, and descriptions. AI-powered suggestions help you discover links you forgot you saved. SYNCED ACROSS ALL DEVICES iCloud keeps your entire link collection in sync across iPhone, iPad, and Mac. Save a link on your phone at lunch, read it on your iPad at home. BUILT FOR PRODUCTIVITY - Bulk operations — select multiple links to move, tag, or delete at once - Project grouping with collapsible sections - Task linking — connect links to your project tasks - Import and export as JSON for backup or migration - Duplicate URL detection warns you before saving twice - Statistics dashboard with charts showing your link habits SIRI & SHORTCUTS "Hey Siri, save a link in LinkFindr" — use voice commands to save and search your links. Create custom Shortcuts automations for your workflow. PRIVACY FIRST All AI processing happens on-device. Your links, summaries, and browsing habits never leave your phone. No accounts, no tracking, no cloud AI. Terms of Use (EULA): https://www.opticell-limited.com/linkr#terms Privacy Policy: https://www.opticell-limited.com/linkr#privacy

Quick ML is a complete data-science studio for iPhone and iPad. Import real datasets, explore and clean them properly, train machine-learning models, and make predictions - the whole workflow, entirely on device. Built for data scientists and students of data science who don't always have a laptop with Python to hand. FREE TO TRY, ONE PRICE TO OWN The free version is a real studio, not a demo: import files of any size (CSV, Excel, parquet, JSON, XML), shape local JSON and XML visually, combine two projects on a key, explore and clean without limits, train the classic algorithm for each task (linear regression, logistic regression, k-means, and the image classifier), evaluate honestly, and predict. When you want more, Quick ML Pro is a single purchase that unlocks all 19 algorithms, auto-tuning, imports from web pages, APIs, GraphQL, Kaggle, and PostgreSQL, refresh and lookup joins, dashboards, Core ML and Python export, project sharing, and applying anonymisation. No subscription, no account. Yours forever. NO LAPTOP? NO PROBLEM On the train, in a lecture, at a client's site, on the sofa: open a dataset the moment you get it and have a trained, evaluated model before you're anywhere near a computer. A million-row CSV is fine - data streams into a local store and never loads into memory at once. PRIVATE BY ARCHITECTURE Everything computes on your device. No accounts, no uploads, no analytics - your data never touches a server, which also makes it an easy answer when the dataset is sensitive. Projects sync privately between your own devices through your own iCloud. IMPORT ANYTHING CSV and TSV, Excel workbooks, Word and PDF documents, parquet, ZIP archives, web pages with tables, JSON, XML and GraphQL APIs, Kaggle datasets, even a PostgreSQL database. Sources can refresh later: re-import, append, merge new rows on a key, or keep a history of every fetch as a time series. EXPLORE AND CLEAN PROPERLY Full descriptive statistics, histograms, box and violin plots, correlation heat maps, pair plots, and nine statistical tests with plain-English verdicts. A one-tap Data Health Check profiles every column and suggests cleaning steps, each with its reason. Every change is a recorded recipe step - fills, filters, tidy dates, dedupe, outlier trims, derived columns (date parts, bins, ratios, lags, rolling windows, text features) - replayable, synced, and individually undoable, like a pandas script you can swipe. TRAIN REAL MODELS Regression, classification, and clustering: linear and logistic regression, decision trees, random forests, boosted trees, neural networks you design visually, k-nearest neighbours, SVM, Naive Bayes, k-means, DBSCAN, Gaussian mixtures. Honest seeded or time-ordered splits, cross-validated auto-tuning, class balancing, and full evaluation: R2 and RMSE, confusion matrices, ROC and AUC, permutation feature importance, learning curves. STUDYING DATA SCIENCE? IT SHOWS ITS WORKING Every screen explains what the numbers mean and why each step matters, from quartiles to overfitting. Then How to Do This in Python exports your exact pipeline as a Jupyter notebook - every step as pandas and scikit-learn code with the reasoning in markdown so what you did on the sofa becomes the coursework, and the concepts transfer straight to the tools you're learning. WORKING DATA SCIENTIST? TAKE THE RESULTS WITH YOU Export any trained model as a Core ML .mlmodel and drop it into an Xcode project or Core ML pipeline. Run batch predictions over a whole file, score held-out validation sets, compare model versions, build live dashboards, and generate a client-ready PDF report of the entire project in one tap. If you have data and a phone, you have a data-science workstation.