20211003 Development R11 M3 Use Cases - orbitalfoundation/wiki GitHub Wiki

Use Cases

  • Bespoke computer vision and display synthesis. We see pedestrians having compute platforms similar to vehicles. Actual use cases are serendipitous discovery of friends nearby, or ambient contextual matching (an opportunity exists to help a new friend solve a problem that you're good at). Another use case is simply navigation and routing (especially as street signs or street lighting may be obsolete with the rise of AR glasses). It's note-worthy that these services are running all the time in the background (we discuss this in more detail here https://orbitalweb.github.io/philosophy/2021/02/10/persistent-web-apps.html ). These services may even be downloaded and run on device without explicit consent, for some classes of applications. For example a group of friends may want to put a funny hat on all their friends who have a given NFT. This also has several implications about segmentation and semantic intent and "right sized grammars" for expressing intent in an efficient way. See https://github.com/orbitalweb/orbitalweb.github.io/wiki/20210529-Dev-M2-R6-Parts and https://github.com/orbitalweb/orbitalweb.github.io/wiki/Development-R10-M3-Flow-Language-Scripting .

  • Bespoke farmbot. A farmer may have a fine-grained management of a microbiome ecosystem with fairly specialized arrangements for tree cover, nitrogen fixing and so on. And they may have a fairly complex scheme for what kinds of bugs or plants they want to encourage or discourage. They train a farmbot to keep an eye out for certain kinds of flora or fauna, and they share this with other nearby farmers in the same biome (see OpenCV and https://github.com/opencv/opencv_zoo ).

  • Creative and or Social work with friends. A group of people may want to create or share a model of a project together to do a barn-raising. This means rendering that model in 3d, between the participants, and letting participants all work together and especially see each other working together, to learn and refine the work. (See Figma and Miro).

  • Live Programming. In some venues such as music venues or live art performances it may be helpful for a programmer to be able to safely and quickly make live changes to an application. This could be as simple as a visual shader or a more complex set of effects (see Ableton and MaxMSP for example). This also suggests visual models of programming.

  • Just in time agents and expertise. An exopaleontologist on Mars may want to bring tools to their device to analyze a fossilized leaf pattern. This means pulling down computation from the cloud, probably running it on device, having that digital agent be able to advise the researcher on where to look for more fossils or provide other real time contextual advice.

  • Political Cartoons. Humorists or meme makers may want to make and share small apps and humor (rather than just images) and will want ways to quickly and easily deploy this humor to their network. An example is a parody face mask that they or others can wear.

  • Complex Systems Modeling. Environmentalists or other groups will need better tools to predictively digitally simulate or model the near term behavior of whole systems such as watersheds, small towns, estuaries, factory economic and ecological concerns, the impacts of new laws or policies on indigenous communities. Importantly there will be some pressure for these tools to be civic, accessible and editable.

  • Secure Computing. There will be increasing pressure for technology users to feel safe, that as their data is increasingly in their devices, that those devices are difficult to compromise.

  • Dynamically Load Balanced Computing. Apps today typically run on device or run remote. Bridges between parts of apps are explicitly and painstakingly engineered. There appears to be some pressure to automate RPC calls and provide tools that seamlessly straddle between client and server. That implies apps themselves will have their internal components or "services" running on different devices simultaneously, and that will be dynamically balanced by some kind of hypervisor based on whatever is most efficient and meets latency requirements. In some cases (such as querying a medical database) the volume of data may preclude local computation on device, or in other cases latency may be critical (such as a game).