mixpanel.comSoftware by Mixpanel
I recommend Mixpanel to anyone who needs to do tracking of user behavior in web applications in order to measure against goals.
Based on our record, Jupyter should be more popular than Mixpanel. 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.
Mixpanel/Amplitude: Event-based analytics platforms configurable for API usage tracking. - Source: dev.to / about 1 month ago
To collect these metrics effectively, you'll need specialized tools like Google Analytics, Mixpanel, Segment, or Amplitude. - Source: dev.to / about 2 months ago
Mixpanel is a product analytics tool that provides product managers with real-time insights into user behavior, helping them make more informed decisions with instant access to their data. It enables detailed event tracking, such as button clicks and page views, across both web and mobile applications. It offers a centralized platform that consolidates product analytics metrics such as data views, event tracking,... - Source: dev.to / about 2 months ago
Even with thorough preparation, the holiday traffic can bring unexpected challenges. To stay ahead, set up dashboards and alert systems using tools like Grafana, Datadog, or New Relic to monitor platform performance, track errors, and detect anomalies. Additionally, leverage analytics tools such as Google Analytics or Mixpanel to identify drop-off points and optimize the customer journey. - Source: dev.to / 5 months ago
I assume you already have a NextJS project setup. Also, create a new Mixpanel account from here (If you haven’t already). - Source: dev.to / 6 months 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 / 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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