The point of the AnnotifyAPP is to advance the cause of making Science more social … it’s being done; it’s really not that hard to envision or contemplate any more; now it’s really more about a cleaner, more pragmatic and user-friendly foundational architecture for building the next architecture of learning.

This architecture of learning is partly about making Science more engaging, even more fun – but the point of it all is to generally make it possible for everyone to learn faster … we learn faster by learning both about the topic and the processes of learning faster from others who are learning faster … learning faster is not just about observability engineering and applied social network telemetry … it’s also about the structure of knowledge graphs and the importance of that in graph database engineering … and since it’s necessarily visual, this involves citation metrology and applied scientometery.

And the beauty of all this jargon is that it’s not hype … PEOPLE ARE ALREADY DOING THIS … there are plenty of open source communities and even products out there right now. The point of AnnotifyAPP is to participate in an already ongoing revolution in how Science is being done.

Dogfooding our dogfood

Learning about applied scientometery means that our “app” or learning and research toolchain will be blatantly stolen from what is working best … since we rely primarily upon FREE open source tools, that means that we use the toolchains with an aim toward to building something better … approaching every tool with dogfooding mentality.

Although, we cannot emphasize the dogfooding nature of this enough … the point of the Annotify project is to be more aware of developments that impact what we are trying to develop – we are not interested in buzz or hype, but instead we are interested in major themes and significant developments that are indeed practically changing the landscape … real change, positive improvement, things that are PROVING to be significant, genuine, permanent advances … it’s not about those hyped products with claim to be major advances.

The intention of our little Sci Ops knowledge graph project is to develop something that might eventually be useful enough mature into a community project … maybe that will never happen … but we aim to to use our annotifiable, recursively neural, knowledge graph analyic toolchain enough so that it’s valuable … at first, the value is just for us learning and dogfooding a toolchain and contributing to those learning open source projects which are most efficaciously advancing the learning cause.

The general philosophical approach to learning is fundamentally about using millions of internetworked developers who are developing for billions of internetworked power users … crowdliness is about learning at scale … we learn faster and better by learning from the best of the best 10X-ers or even 100X-ers or 1000X-ers who are learning faster and better while distilling our understanding so that we are able to share our skills for how we ARE learning. …

Learning at scale … or crowdliness … is effectively a matter of using rising tides and larger waves from gigantic meta-RNN ocean of continually updating models … trying to hold back the sea is impossible; it’s necessary to master the sea.