EnvisEdge

Deploy recommendation engines with Edge Computing (by NimbleEdge)

EnvisEdge Alternatives

Similar projects and alternatives to EnvisEdge

  1. rtl-sdr-blog

    Modified Osmocom drivers with enhancements for RTL-SDR Blog V3 and V4 units.

  2. SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

    SaaSHub logo
  3. Scala.js

    Scala.js, the Scala to JavaScript compiler

  4. Quill

    15 EnvisEdge VS Quill

    Compile-time Language Integrated Queries for Scala

  5. Converter

    Typescript to Scala.js converter

  6. privacy

    2 EnvisEdge VS privacy

    Library for training machine learning models with privacy for training data

  7. oakestra

    A Lightweight Hierarchical Orchestration Framework for Edge Computing

  8. vision_ui

    This is a vision-based 3d model manipulation and control UI

  9. exodus

    4 EnvisEdge VS exodus

    Platform to audit trackers used by Android application (by Exodus-Privacy)

  10. spotlight

    0 EnvisEdge VS spotlight

    Deep recommender models using PyTorch. (by maciejkula)

  11. py-striq

    Discontinued Python bindings for STRIQ — queryable time-series compression. Sub-µs aggregates on compressed sensor data. [GET https://api.github.com/repos/NahumResearch/py-striq: 404 - Not Found // See: https://docs.github.com/rest/repos/repos#get-a-repository]

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better EnvisEdge alternative or higher similarity.

EnvisEdge discussion

Log in or Post with

EnvisEdge reviews and mentions

Posts with mentions or reviews of EnvisEdge. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-12-25.
  • A new way to build decentralised recommendation engines for the creator economy
    1 project | news.ycombinator.com | 25 Dec 2021
    Hear me out on what I think a truly decentralised content curation.

    Twitter, FB (Meta), Youtube everyone harvests user data and train their recommendation engines which are then monetised by them (often unfairly).

    In the future, the data stays on the users' devices and anyone can train their models by asking the user for the consent. THe data never leaves the device and ML models get trained on user device itself. The users get to choose from a host of recommendation choices and can ask for payment in return for using their data. So no one party can build a monopoly over the platform.

    Check out a cool project I have been working on to solve this https://github.com/NimbleEdge/RecoEdge

  • Ask HN: What cutting-edge technology do you use?
    5 projects | news.ycombinator.com | 25 Dec 2021
    Edge computing for machine learning. Instead of running ML models on the cloud, I train them on user's device, ask these devices to offload computation between each other and give me the best performance out there. I have my own local cloud formed by my laptop, smartphone and ipad.

    I built out the library for these myself, checkout https://github.com/NimbleEdge/RecoEdge

Stats

Basic EnvisEdge repo stats
2
135
3.5
almost 3 years ago

Sponsored
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com

Did you know that Python is
the 1st most popular programming language
based on number of references?