And take control of your recommendation feed …

The villain ScreenSlayer takes over people’s mind and agency in Incredibles 2 (2018)

Recommendation engines for news feeds, videos, tweets, movies, music and many others create your worldview from which there is no escape. The manipulation and deception to individual lives and the perversion of norms is no longer in question. And, it happens without the consumer’s knowledge or understanding.

Recommendation engines have all the control

Thingy pops your filter bubble by giving you control.

Recommendation engines are not dynamic

Static machine learning

Predictions remain fresh until new data arrives. But, two problems arise: the current model becomes out-of-date and the length of time to re-train it is proportional to the volume of data. The industry solution is to re-train the model as fast as possible.

Modern software systems don’t work like this. For example, with a database (dbms) you can add, update and delete data at any time while people continue to query the database. A dbms is a dynamic system whereas an AI is a regularly re-trained static model.

Dynamic machine learning

Static vs dynamic AI in the enterprise

It is straightforward to extract information from a SoR layer to employ in an AI service running independently. When data changes in the SoR layer it notifies the AI service to collect the new data and begin the model re-training process. The two layers are separate and independent though the AI will always be out of sync and must be continually retrained to catch up with changing data.

But, joined-at-the-hip integration between systems-of-record and systems-of intelligence layers need to carry a large flashing “caveat emptor” neon sign as the former is a dynamic system and the latter a static model.

Machine learning “training” is a workaround

Imagine if databases could not update in real-time or search engines could not show results of current events immediately. We live in a dynamic world and yet ML today is built atop static models which have to be continually re-trained.

Thingy is a dynamic recommendation engine

  1. Automatic generalization: Given one or more items as a query, Thingy learns in realtime to discover other relevant items. There is no machine learning ‘model training’ phase.
  2. Interactive: Engage with Thingy in different and unpredictable ways with various query options. Gives consumers the control to go where they want and when.
  3. Dynamic: Update and add new data at any time.
Thingy’s three parts working together dynamically

Automatic generalization

Automatic generalization

Items (“things”) can be any datatype including images, audio, video, db records, documents, genes and composite data.


Is something similar possible today? Yes.

Interact directly with Thingy to add and remove items from the query; find more of things; bring things to the foreground while pushing others into the background.

Add and remove items from a query

Add and remove items from a query

More like this or these

More like this or these

More of these, less of the others

More of these, less of the others

“I am the query” personalization

“I am the query” personalization is a powerful way to discover things of personal relevance which also minimizes getting stuck in a filter bubble and preserves privacy. Your fingerprint is your doppelganger — and, you can have more than one — to interact with the digital world securely and safely on your behalf. Thingy safeguards privacy implicitly.


Adding new data at any time is a litmus test of a dynamic recommendation engine. The steps to demonstrate adding unknown images to Thingy are:

  1. Query with an unknown image. If the image is known to Thingy then its duplicate will show as the first result. In this case, it doesn’t.
  2. Add a copy of the unknown image to the Thingy system.
  3. Query with the unknown image again. If the image is known to Thingy then its duplicate will show as the first result. This time, it does.

The slide deck shows adding a single new image and 3 new images.

Litmus test for a dynamic recommendation engine

Thingy’s purpose is trusted discovery

Thingy is a trusted public discovery space and a platform for developers to build private spaces. Data privacy, security and transparency is enforced by the Thingy technology and policy.


(2) The official Minority Report trailer —

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Maker of Thingy, a recommendation engine which learns in realtime, is dynamic and interactive. Founder, architect and engineer at