Carlos Ferreira (fe01134)

fe01134

Geek Repo

Company:ibm

Location:boston

Home Page:http://bluemixstirred.wordpress.com/

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Carlos Ferreira's repositories

AMO-Tools-Desktop

AMO-Tools-Desktop is an energy efficiency calculation application for use with industrial equipment such as pumps, furnaces, fans, and motors, as well as for industrial systems such as steam. Currently in beta.

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awesome-selfhosted

This is a list of Free Software network services and web applications which can be hosted locally. Selfhosting is the process of locally hosting and managing applications instead of renting from SaaS providers.

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CodeEngine

Samples for using Code Engine

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dtb_nextjs

dtb_nextjs app to deploy on Amplify on AWS

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get-started-node

Sample and tutorial to help you get started with Express, REST API and a database.

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gitignore

A collection of useful .gitignore templates

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hello-world-nextjs-tailwind-css

Hello, World! with Next.js and Tailwind CSS

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html5-phone

Monitor v2

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hugo-elate-theme

A one page parallax theme with animation for Hugo

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invoke_studio_window

Allows you to score a model deployed on IBM Machine Learning with a sliding window.

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iot-analytics-anomaly

Invoke Custom ML models from Watson IoT Analytics

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llama3

The official Meta Llama 3 GitHub site

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mmfunctions

iotfunctions playground

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monitor-hands-on-lab

This is an MkDocs web site to open source the documentation for Hands On Labl for Maximo Asset Monitor. This will enable multiple contributors including partners and customer to help submit issues and maintain the documentation of the Hands On Lab

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nextjs-complete-guide-course-resources

Course resources for my "NextJS - The Complete Guide" course (https://acad.link/nextjs)

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OpenAPI-Specification

The OpenAPI Specification Repository

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paho.mqtt.python

paho.mqtt.python

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product-management

An app for the product management based on Carbon Design System tutorial

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pythonsp500-plotly-dash

Repository with all files and data needed for Dash by Plotly post.

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visualize-data-with-python

A Jupyter notebook using some standard techniques for data science and data engineering to analyze data for the 2017 flooding in Houston, TX.

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