Way to develop graphs and model machine learning

luis cdfd492d41 Trying cache(2)		%!s(int64=7) %!d(string=hai) anos
browser	202bede5f0 Registry, tests, coverage	%!s(int64=7) %!d(string=hai) anos
docker	be46042d0d Implemented collaboration and server serialization	%!s(int64=7) %!d(string=hai) anos
go	1a705813be Fixed registry tests	%!s(int64=7) %!d(string=hai) anos
.drone.yml	cdfd492d41 Trying cache(2)	%!s(int64=7) %!d(string=hai) anos
.gitignore	9631547566 Fixing submodule issue	%!s(int64=7) %!d(string=hai) anos
Makefile	0e1e145a1a Fixed a test reporting bad results	%!s(int64=7) %!d(string=hai) anos
readme.md	e0378dd50b Backdev	%!s(int64=7) %!d(string=hai) anos

TODO

Minimal idea

UI to create multipurpose tensorflow alike graphs, upload to the server pass inputs and do training fetch model and use it with new inputs distributed purpose to calculate several nodes across a network

implement common AI methods(funcs) reuse them

Frontend

Deal with svg relative mouse position
do an event from one socket to another highlighting the ones compatible for data type
Create something to add/remove links/nodes as in populating from the server
Colaborative editing through possibly websocket, by sharing states
const/var editor with multiple props
add types to sockets (litle label aside)

Backend

create http server that serializes and inspect run graphs (step by step)
Serve registry with information about inputs/outputs
Protocol to build and run a graph from frontend

readme.md

TODO

Minimal idea

Frontend

Backend