Langfuse is an open-source LLM engineering platform designed to empower developers by providing insights into user interactions with their LLM applications. We offer tools that help developers understand usage patterns, diagnose issues, and improve application performance based on real user data. By integrating seamlessly into existing workflows, Langfuse streamlines the process of monitoring, debugging, and optimizing LLM applications. Our platform's robust documentation and active community support make it easy for developers to leverage Langfuse for enhancing their LLM projects efficiently. Whether you're troubleshooting interactions or iterating on new features, Langfuse is committed to simplifying your LLM development journey.
Based on our record, Nuxt.js seems to be a lot more popular than Langfuse. While we know about 149 links to Nuxt.js, we've tracked only 11 mentions of Langfuse. 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.
In recent years, projects like Vercel's NextJS and Gatsby have garnered acclaim and higher and higher usage numbers. Not only that, but their core concepts of Server Side Rendering (SSR) and Static Site Generation (SSG) have been seen in other projects and frameworks such as Angular Universal, ScullyIO, and NuxtJS. Why is that? What is SSR and SSG? How can I use these concepts in my applications? - Source: dev.to / over 1 year ago
One reason to opt for server side rendering is improved SEO, so if this is especially import for your project you could have a look at for instance https://remix.run/ or https://nextjs.org/ for react or https://nuxtjs.org/ if you use Vue. Source: about 2 years ago
Well nuxtjs.org work smooth on ios 12, maybe you didn't understand what I'm talking about. Source: about 2 years ago
E.g. Most nuxtjs.org documentation is Nuxt 2 and therefore Vue 2, while nuxt.com documentation is always Nuxt 3 and therefore Vue 3. Source: about 2 years ago
For detailed explanation on how things work, check out the documentation. - Source: dev.to / about 2 years ago
And then there’s evaluation and observability—two things you must consider when your AI app is live. You need to know if the model is doing its job, and why it failed when it didn’t. Tools like LangSmith and LangFuse can help with this, but you’ll need to spend time experimenting with what works best for your stack. - Source: dev.to / about 17 hours ago
Langfuse is another open-source platform for debugging, analyzing, and iterating on language model applications. It offers tracing, evaluation, and prompt management. While Langfuse offers many capabilities, some (like the Prompt Playground and automated evaluation) are only available in the paid tier for self-hosted users. - Source: dev.to / about 2 months ago
It is reportedly used on websites like Langfuse and Million.dev. - Source: dev.to / 3 months ago
LangFuse is a monitoring and debugging platform for LLM-powered applications. It provides insights into token usage and costs. It can also analyze latency, and the performance of AI interactions. The platform allows debug prompts, and analyzes how they behave in production. - Source: dev.to / 4 months ago
You'll notice there's a lot of prompts in these examples. As you develop your prompts, you'll likely want to iterate and refine them over time. I recommend using tools like Langfuse or Langsmith for prompt management and metrics, making it easier to track performance and make improvements. - Source: dev.to / 4 months ago
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