Better Uptime is a radically better infrastructure monitoring platform that calls the right person on your team if anything goes wrong. Schedule on-call duties, receive helpful alerts, and collaborate on solving incidents faster than ever. Get a beautiful branded status page on your domain and keep your users informed. Made to fit into your workflow with over 100+ integrations.
User friendly uptime monitoring tool with loads of easy to set up integrations. Definitely recommend!
I like Better Uptime because it's very reliable and quickly responds to any downtime on my site.
Based on our record, Jupyter seems to be a lot more popular than Better Uptime. While we know about 216 links to Jupyter, we've tracked only 5 mentions of Better Uptime. 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.
I use https://betteruptime.com/ for all of my websites with various checks. You can do keyword checks, status error codes and get push notifications + phone calls if it is down for x number of time. Source: over 2 years ago
For what you are needing, I would try BetterUptime. Source: over 3 years ago
I am using https://betteruptime.com/ if it matters. Source: over 3 years ago
// external functions are called from your Dasha conversation in the body of main.dsl file // external functions can be used for calculations, data storage, in this case, to // call external services with HTTPS requests. You can call an external function from DSL // in your node.js file and have it do literally anything you can do with Node.js. // External function. Acknowledge an incident in... - Source: dev.to / over 3 years ago
Better Uptime, one of the newer alternatives, combines incident management and monitoring in one tool. - Source: dev.to / almost 4 years ago
Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / 3 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 / 4 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 / 5 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 / 9 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 / 12 months ago
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