SensorFlow reading list: event counts, identity, and feature adoption

I work on SensorFlow. This is an AI-assisted summary of three tutorials we have already published, collected here for people checking their event-tracking definitions. It is a reading path, not a new product release.

Before trusting an analytics dashboard, I would separate three questions: which records qualify, what the identifiers represent, and which population had an opportunity to act. Fixing one of those does not automatically fix the other two.

1. Event count is not user count

A repeated delivery, a repeated action, and a second identifier are different things. Start by specifying the event filter, duplicate-delivery key, time window, and identity key. A distinct identifier count should not quietly become a claim about distinct people.

The tutorial includes an offline fixture that rejects conflicting duplicates and compares qualifying event records with identifiers:
https://sensorflowsite.blogspot.com/2026/10/event-count-is-not-user-count-define.html

2. Anonymous-to-signed-in linking needs evidence and scope

A shared browser is not sufficient evidence to merge two accounts. Conversely, the same verified account can appear on multiple devices. The useful acceptance cases are an account switch on one device, the same account on two devices, and a visitor who must remain unresolved.

This example uses explicit, supplied linking evidence and bounded segments. It does not infer real authentication or reproduce a vendor's identity graph:
https://sensorflowsite.blogspot.com/2026/10/anonymous-to-signed-in-event-tracking.html

3. Feature adoption needs an eligibility denominator

For an account-level question, define which accounts could use the feature before counting which completed the action. Button clicks, failed attempts, internal accounts, and completions outside the reporting window should not silently enter the numerator.

This tutorial uses a fixed window-start eligibility snapshot. That is one stated policy, not a universal definition; changing permissions during the window needs a different, explicit treatment:
https://sensorflowsite.blogspot.com/2026/10/feature-adoption-needs-eligibility.html

All three use synthetic data and runnable local examples. They are teaching fixtures, not customer evidence, production SDK tests, or claims that SensorFlow automatically implements these policies.

A practical review order is: write down the metric's unit; state its identity and population rules; run a small acceptance fixture; then verify actual delivery and stored fields separately. Passing the fixture does not prove the real event stream is complete.

Which edge case would you add to this acceptance set before using a signup or feature-adoption report to make a product decision? The identity scope and expected result are more useful than a dashboard screenshot alone.