apps-script-samples VS examples

One of the things that stifles openness is noise. Sifting through our public repos, some are very obviously redundant and just distracting. So we started by archiving a whole bunch of them - over half, in fact. We wrote some Google Apps Script in a spreadsheet to import and analyze the state of all our public repos:

I am not a JS developer, but in some tech areas there are niches with relatively fewer developers competing for the same position.

I don't know if this is actually a niche, but since I don't see it mentioned frequently on HN, perhaps you could have a look at Google Apps Script [0], which is basically JS for automating and extending Google Docs (Docs, Sheets, Forms, Slides), and integrating them into GCP. Tons of companies have whole departments living inside Google Docs, so I imagine they would pay for software making their lives easier. I would but I currently don't have the budget for it, so I am writing short scripts myself. Curiously, Bard and ChatGPT produce really bad Apps Scripts code, which would be to your advantage.

When I occasionally hire people, I do look at their repos to see how good they are in a specific language. Specifically, how easy to follow and idiomatic their code is, what the quality of their comments and documentation is, are there any tests, even if very rudimentary. I particularly enjoy original projects like home automation, custom keyboard firmware, an alternative Netflix UI, a ggplot extension, a basic Python wrapper for some overly-complicated API vs. your typical to-do app or yet another analysis of the Bike Sharing dataset. I don't expect all people to have public repos or FLOSS contributions because not everyone has the time for that. However, if you wanted to demonstrate skills in a technology you haven't used professionally, I think it is a good idea.

0. https://developers.google.com/apps-script

I have a function in Apps Script that returns the Document Position, and I currently have it set to be called every second. However, I'm concerned about potential limitations or quotas that I might reach by making such frequent function calls. I got the idea from their own samples.

Here is the documentation link to help you: https://developers.google.com/apps-script/

Before we get started with ChatGPT, you'll need to set up Google Apps Script. If you're not already familiar with Google Apps Script, you can get started with the documentation here.

Since a few years I had a Google Apps Script that prefixed some events in my calendar with an emoji. This helps me spot certain events or categories of events. This blogpost explains very well why I like to add emoji's to my calendar events.

From my current employer I discovered that you can get surprisingly high mileage with spreadsheets and a little bit of scripting.

My employer uses Google Sheets with app scripts [1].

It automatically reads emails, updates a bunch of spreadsheets, makes other API calls, creates entire report documents etc.

The initial setup might take a while though.

[1] https://developers.google.com/apps-script

I have completed https://grasshopper.app entirely with no prior knowledge of coding. It is very hands on easy to follow. There is more doing coding than reading and each question has a walkthrough available. It is by Google and directs you too Google's own coding platform when you finish. It covers the basics of coding, automation, HTML/CSS/JavaScript and Google's AppScript that work with apps like Google Drive and Sheets. Each course has a PDF certificate of completion. (Last I checked the app was bricked and suggested using the webpage until they figured out the bug with the new operating system.)

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.

Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.

TensorFlow: An open-source machine learning framework for high-performance numerical computations, especially well-suited for deep learning.

The tool used to build the model per se was TensorFlow, a very powerful and end-to-end open source platform for machine learning with a rich ecosystem of tools. And in order to to create the needed script using TensorFlow Jupyter Notebook was used, which is a web-based interactive computing platform.

10. TensorFlow - Make Machine Learning Work for You 🤖

AI models, particularly those built on deep learning frameworks like TensorFlow, exhibit a high degree of parallelism. Neural network training involves numerous matrix operations, and GPUs, with their expansive core count, excel in parallelizing these operations. TensorFlow, along with other popular deep learning frameworks, optimizes to leverage GPU power for accelerating model training and inference.

#2 TensorFlow

I am on a new ftc team trying to get vision to work. I used the ftc machine learning tool chain but I have yet to get a good result with at best a 10% accuracy rate. I have changed everything possible in the tool chain with little luck. To fix this, I have tried making my own .tflite model using the google colab from https://www.tensorflow.org/. When ever I try to run the same code with my own .tflite model, it gives me the error "User code threw an uncaught exception: IllegalStateException - Error getting native address of native library: task_vision_jni". It gives me the same error with official tensor flow tflite test models, and when I put them on a raspberry pi, both worked just fine. Does anyone have a fix to this error or even just tips for the machine learning toolchain?