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machine learning programming school work

No More Software Left to Write

TLDR: Traditional software engineering is becoming commoditized. Infrastructure, deployment, and development have become incredibly easy thanks to modern tools and platforms. While this means utility software (business applications) will likely be automated away, there’s still room for creativity and personal impact. Even though most software has already been written or will be handled by AI, developers should focus on writing software that matters to them or makes a difference to others, rather than waiting for even better tools.

The world of software engineering and development is changing at a breakneck pace. For someone who’s been in SWE for nearly 40 years (since I was 6 years old) and professionally for nearly 25 years (since I started getting paid for SWE work), I am concerned, but still hopeful.

What do I mean by “no more software left to write”? It means a few things:

  • Software infrastructure has become so widely developed that writing new applications today — by hand, we’ll get to AI later — is 100x easier, faster, secure and optimized than just 5 years ago, and this rate of development is constant, meaning that in 5 years it would be 100x faster / easier / better than today.
  • The amount of software written has risen dramatically. Just the sheer volume of applications and projects has increased, and within those – open-source projects that are easily copiable or integrable. It’s nearly impossible today to find an alcove of human pursuit that has been untouched by software or digitization.
  • Software (and software engineering) has become as much like LEGO as it has ever been, where engineers (builders) can piece together an application in minutes! With advanced features and production-ready backends with just lines of code.
  • Software deployment has become automated to immense degrees, where an app (web, mobile, desktop) can be packaged and served online (or dished out as download executable) with a single line of terminal. All backend services, all testing and integrations fully managed by someone else.
  • AI coding has made it so full applications can be “written” ad-hoc to the user’s needs almost instantaneously, making the need for bespoke engineering obsolete – apps can be generated on the fly for a single use! The age of disposable food containers has arrived in software engineering.
  • Hardware platforms are more generous than ever before! With memory, compute and disk capabilities that really make runtime optimization a thing of the past. You can brute force your way to software success and deal with consequences later, if at all it will become an issue.

All this means is that it has never been a better time to be a software engineer. And it has never been a worse time to start as a software engineer. If you’re starting out today, take note of the rate of change in the field – it is exponential. Tools and paradigms used today will be obsolete or woefully outdated in just 2-3 years. Except for the deepest of technologies, engineering applications has been commoditized to a pulp.

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cmake code graphics gui qt Stream video

URL/API Source OBS Plugin: Fetch Live Data in your Stream

If you’re a fan of OBS (Open Broadcaster Software), you may already be familiar with its vast library of plugins that enhance its functionality and provide added features. One such plugin that I recently developed is the URL API source plugin. This plugin allows you to fetch information from a URL and display it in your OBS stream. In this blog post, we will take a closer look at the source code for this plugin and understand how it works.

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code machine learning programming Stream video

CleanStream OBS Plugin: Remove Filler Words with Whisper CPP

CleanStream OBS Plugin is a powerful tool that helps clean live audio streams from unwanted words, filler words, and profanities. Created in C++, this plugin can improve the quality of live streams while saving time and effort in post-processing. In this blog post, we will take a detailed walk-through of the code for my CleanStream OBS plugin, explaining how it is built and its core functionalities.

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code graphics machine learning opencv opengl programming video vision

Building an OBS Background Removal Plugin: A Walkthrough

In this blog post, we will take a closer look at the development of the OBS Background Removal Plugin, discussing its key components, functionalities, and the process behind building it. The plugin was created to address the need for virtual green screen and background removal capabilities in OBS (Open Broadcaster Software), a popular live streaming and recording software. With over 500,000 downloads and ongoing contributions from various developers, the OBS Background Removal Plugin has gained significant traction in the streaming community. Whether you’re interested in understanding how this plugin works or considering building a similar plugin yourself, this walkthrough will provide valuable insights.

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cloud code javascript programming Web

AWS Lambda NodeJS Telegram Bot with Typescript, Serverless and DynamoDB

Sharing a bit of experience building a telegram bot with Serverless, AWS Lambda and TypeScript.

In this tutorial, we will explore how to build a simple Telegram bot using serverless with TypeScript and AWS Lambda. We’ll leverage the power of AWS services such as API Gateway and DynamoDB to create a highly scalable and efficient bot. While there are various tutorials available online, this guide aims to provide a more comprehensive and detailed approach. So, let’s dive in!

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code ffmpeg python video

Transcribing Videos with Google Cloud Speech-to-Text

Got an hour-long video and not really into manually creating subtitles? not plans to put it on YouTube for their automated transcription services? then – try Google Cloud Speech-to-Text! In this post I’ll share some scripts for automating the process and creating an .str file to go along your video for displaying the subtitles.

Categories
opencv programming vision

Mastering OpenCV 4 – my new book!

mastering opencv4

I’m very excited to announce the publication of my latest Mastering OpenCV book!
With many new chapters and all the others re-written practically from scratch, this edition is by far the best ever.
The excellent David Millán Escrivá and I go deep and wide across the range of capabilities of OpenCV, explaining the theory and implementing recent real-world vision tasks from the ground up.
It’s been baking for many months in the oven, rising slowly, and finally ready for consumption… yum!
The sources are free to grab: https://github.com/PacktPublishing/Mastering-OpenCV-4-Third-Edition
And copies are available on
Amazon: https://amzn.to/2Ff1mmE
Packt: https://www.packtpub.com/application-development/mastering-opencv-4-third-edition?utm_source=github&utm_medium=repository&utm_campaign=9781789533576
Enjoy reading!

Categories
graphics opencv programming python vision

Cylindrical Image Warping for Panorama Stitching


Hey-o
Just sharing a code snippet to warp images to cylindrical coordinates, in case you’re stitching panoramas in Python OpenCV…
This is an improved version from what I had in class some time ago…
It runs VERY fast. No loops involved, all matrix operations. In C++ this code would look gnarly.. Thanks Numpy!
Enjoy!
Roy

Categories
code machine learning python

Take a SWIG out of the Gesture Recognition Toolkit (GRT)

Reporting on a project I worked on for the last few weeks – porting the excellent Gesture Recognition Toolkit (GRT) to Python.
Right now it’s still a pull request: https://github.com/nickgillian/grt/pull/151.
Not exactly porting, rather I’ve simply added Python bindings to GRT that allow you to access the GRT C++ APIs from Python.
Did it using the wonderful SWIG project. Such a wondrous tool, SWIG is. Magical.
Here are the deets

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code machine learning opencv programming python vision

Aligning faces with py opencv-dlib combo

This is my first trial at using Jupyter notebook to write a post, hope it makes sense.
I’ve recently taught a class on generative models: http://hi.cs.stonybrook.edu/teaching/cdt450
In class we’ve manipulated face images with neural networks.
One important thing I found that helped is to align the images so the facial features overlap.
It helps the nets learn the variance in faces better, rather than waste their “representation power” on the shift between faces.
The following is some code to align face images using the excellent Dlib (python bindings) http://dlib.net. First I’m just using a standard face detector, and then using the facial fatures extractor I’m using that information for a complete alignment of the face.
After the alignment – I’m just having fun with the aligned dataset 🙂