Oilnote.codefolio.dev VS Data Analytic System

Oilnote.codefolio.dev's answer

Most car log apps either charge a subscription or have stopped being updated, and people have lost years of records when one shuts down. Oilnote keeps your records on your iPhone and in your own iCloud, exports to CSV whenever you like, and charges once. Logging for one vehicle is free for good, including fuel economy, the history PDF and backups.

Data Analytic System's answer:

Price trackers like CoinGecko or CoinMarketCap show numbers; Data Analytic System shows the numbers together with their source, date and context, and adds what trackers do not: an ESMA-based MiCA/CASP status list for EU exchanges, sourced documentary studies, a free glossary and learning modules, and multilingual e-books (EN, HU, ES). The free tier has no delay and the same data as the paid tier. We do not sell trading calls or recommendations of any kind.

Oilnote.codefolio.dev's answer

You scan a garage receipt and Oilnote fills in the date, cost and work done. The receipt is read on your iPhone and never uploaded. It also imports the CSV from the app you are leaving, such as Fuelly, and shows every row before saving so nothing is lost. It costs one price, paid once, with no subscription and no account.

Data Analytic System's answer:

Every data panel and every article names its source and date. The platform is run by a small Budapest-based publisher (Data Analytic Investments Kft.) and combines live market data for crypto, equity indices, FX and commodities with an EU MiCA/CASP authorisation tracker, a free glossary and learning modules, and documentary studies such as the RIPPLE book, in which every factual claim carries a verifiable reference. Content is produced with a documented human+AI verification workflow (Uncle Sunny method): a human editor closes every item. Educational content only, not investment advice.

Oilnote.codefolio.dev's answer

iPhone owners who look after their own cars, trucks, EVs and equipment such as mowers, generators and boats. Many are moving from Fuelly or another logging app and want to bring their history with them.

Data Analytic System's answer:

People who want to understand crypto and macro markets rather than be told what to do: self-directed readers, students, journalists and compliance-minded professionals in the EU, plus Hungarian- and Spanish-speaking readers who lack sourced material in their language. Typical use: checking a data point with its source, reading the MiCA status of an exchange, or working through a learning module.

Oilnote.codefolio.dev's answer

Fuelly's own forum filled up with users asking where to go next, and other logging apps moved to subscriptions. I build small apps on my own, and I wanted a log that does not depend on a server staying online: the data stays with the owner and moves in and out as CSV. Oilnote came out of that, built as a solo project and released on the App Store in October 2026.

Data Analytic System's answer:

Data Analytic Investments Kft. was founded in Budapest in 2026 by Janos Szabo. The starting point was frustration with crypto content that mixes facts, opinion and promotion without references. The answer was a publishing method (Uncle Sunny) in which nothing is published before it is challenged and sourced, and a site built around that rule. The first flagship product was the RIPPLE documentary study (ISBN 978-615-83207 series, HU/EN/ES); the market-data dashboards, the MiCA tracker and the learning sections followed.

Oilnote.codefolio.dev's answer

Swift and SwiftUI, SwiftData with sync through the user's private iCloud database, VisionKit and on-device Apple frameworks for reading receipts, WidgetKit for Home Screen widgets, App Intents for Siri and Control Center, PDFKit for the service history PDF, and StoreKit for the one-time purchase.

Data Analytic System's answer:

A TypeScript/React web application with server-side rendering, a MySQL database, and Stripe for payments. Market data comes from public exchange and data-provider APIs; regulatory data from ESMA registers. The editorial workflow uses several AI systems under human review, with every published claim tied to a named source.