Helps my company solve many issues. We collect information from clients and employees. We conduct surveys and collect data. Very useful software. Helps form a common opinion, conduct analysis and analytics.
One of our customers said: Our small mining operation needed to go from paper based process to digital forms. At first, Google forms allowed us to use this Web-based platform that lets individuals and businesses of all sizes build customizable forms to conduct surveys and generate real-time response charts.
We saw that a small sample of our field workers quickly adopted the new way of working.
Step 1: accomplished.
Now unto step 2.
How do we deploy this unto our whole team? We needed email notifications, offline response collection when without wifi on the field. Our CIO and his director of operations needed deep data and trends analysis as well. Our inspectors, when doing their audits, needed to capture approx. 25 high definition pictures, some audio notes and a video which wasn't really possible with google forms.
So, we can 100% credit the use of google forms to our transition towards a paperless process, but as we navigated saashub.com a little more, we were able to discover a world of alternatives. We strongly suggest to start using google forms before undergoing a big implementation plan towards such enterprise level inspection tools like nspek or even cheaper solutions like prontoforms.
I am not sure if we would start with google's solution first if we would to do this digital transformation all over, but it did allow us to discover it's limits pretty quickly.
At some point, we needed custom fields and functions, and none of us was able to code, so the nSpek training that comes with the application definitely sets it's self apart, giving us full autonomy.
Based on our record, Jupyter seems to be more popular. It has been mentiond 216 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / about 2 months ago
LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 3 months ago
Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 4 months ago
Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 8 months ago
One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 11 months ago
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