tracking
tracking on Beyond Market Intelligence: a running collection of 6 stories we have gathered and hand-picked because they are worth your time. Every post here touches on tracking in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around tracking, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
What is the best way to 'hide' calculation cells or numbers in Excel while keeping same end result?
Protecting sensitive data, like profit margins, within Excel spreadsheets is a common challenge. For users facing interference from colleagues altering calculations – as detailed in a recent community post – the most effective approach isn’t simply hiding columns. Instead, consider embedding the 'margin' calculation within a formula applied to other cells. This obscures the direct input while maintaining the final result. Explore advanced Excel functions to automate this process, ensuring data integrity and preventing unauthorized modifications.
How to get a table to match the number of rows, and row order, of a parent table
Need to streamline your data management? When adding new expense types to your parent table, automatically populate corresponding rows in linked tables—saving valuable time. This approach ensures consistent data structure and eliminates manual row insertion. It’s a powerful way to maintain data integrity and workflow efficiency. Discover how to configure this feature, mirroring your parent table's growth across related sheets. For troubleshooting formula errors that might arise, see our article, "Pls help - Need to fix formula with Spill Error," for helpful guidance.
Google takes on AirTag with the new $29 Pixel Tag
Google enters the item-tracking arena with the new Pixel Tag, priced at $29. Leveraging Google’s expansive Find Hub network, this device offers a reliable way to locate misplaced keys, bags, and other essentials. It represents a direct challenge to Apple’s AirTag, providing a familiar solution with Google’s ecosystem advantages. For a comprehensive overview of Google’s recent product announcements, including the Pixel Tag and other innovations, explore our detailed coverage of "Everything announced at Made by Google ’26."
Google’s top hacker hunter explains why hacking groups get codenames
Understanding why cybersecurity firms assign codenames to hacking groups reveals a strategic approach to threat management. Google’s leading hacker hunter recently explained this practice to TechCrunch, highlighting how these identifiers streamline tracking and communication within security teams. Rather than focusing on individual actors, codenames represent broader campaigns and associated risk. This allows for more efficient analysis and response. For example, recent research uncovered vulnerabilities across critical infrastructure, as detailed in our article on risks to Polish institutions.
Android app developers may be unwittingly sharing their users’ location data with advertisers
Android app developers should be aware of a potential privacy risk: third-party code embedded within their apps may be surreptitiously collecting user location data, even when permission is granted only to the app itself. New research from the Electronic Frontier Foundation highlights this critical issue, urging developers to carefully vet their dependencies. This underscores the ongoing need for vigilance regarding data security. For further context on related privacy concerns, explore our coverage of Apple’s recent challenge to UK government demands regarding iCloud access.
Are Your ML Experiments a Mess? Here’s the Fix
Are your machine learning experiments feeling disorganized? Reproducibility and efficient tracking are critical for progress, yet often overlooked. This hands-on guide delivers a practical fix: MLflow. Discover how to streamline experiment tracking, meticulously log models, and reliably reproduce results, empowering your data science workflows. Learn to navigate the complexities of ML development with clarity and confidence. For a deeper dive into related challenges, explore "Yelp Unifies ML Model Training with Training Orchestrator" and unlock further insights.