SQL School
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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.
SQL School
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Langfuse might be a bit more popular than SQL School. We know about 28 links to it since March 2021 and only 19 links to SQL School. 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.
Tutorials: Many websites offer free SQL tutorials and exercises, such as SQLZoo and Mode Analytics. Source: over 3 years ago
Follow this tutorial. Sign up for a free account and follow along in the Mode report editor. Solve all the practice problems along the way. Source: over 3 years ago
If you are looking to practice your SQL skills, I like Mode to give you a good understanding of the basics as well as the advanced concepts. In this situation, I would simply learn to the test. Source: over 3 years ago
If youre learning SQL for the first time -> mode analytics is my favorite Especially for data analytics, great place to start and I recommend doing beginner and moderate levels. Source: over 3 years ago
I recommend this tutorial to all SQL beginners. My partner, who also had no background in programming, found this very helpful. Source: over 3 years ago
In this project we will build a Python banking assistant agent using Strands Agents and make it observable and continuously evaluated using Langfuse โ step by step. - Source: dev.to / 11 days ago
Langfuse is the open-source standard for LLM observability. It traces every LLM interaction โ prompts, completions, latency, token usage, cost โ and provides the tooling to debug, evaluate, and optimize LLM applications in production. Think of it as "Datadog for LLM calls" with a focus on prompt engineering workflows. - Source: dev.to / 30 days ago
You're monitoring production traffic. You need Langfuse / Phoenix / Helicone / Braintrust for that. Online eval is a different problem class: implicit feedback, drift detection, hallucination rates on your data, not on HellaSwag. - Source: dev.to / about 1 month ago
Gateway or proxy attribution. A reverse proxy in front of the model-provider API records the request, computes the cost, and exposes per-customer breakdowns. Open-source options include Helicone, LiteLLM, Langfuse, and OpenLLMetry. Hosted equivalents serve as the AI cost observability layer for teams that want centralized visibility: LangSmith, Datadog LLM Observability, Arize Phoenix. Adds a network hop.... - Source: dev.to / about 1 month ago
Same approach works with Langfuse, Phoenix, Braintrust, or your existing OTel pipeline โ the metadata.userId pattern is the universal part. - Source: dev.to / about 2 months ago
SQLBolt - SQLBolt provides a set of interactive lessons and exercises to help you learn SQL
Helicone AI - Open-source LLM Observability for Developers
PopSQL - Modern SQL editor for teams
LangSmith - Build and deploy LLM applications with confidence
Numeracy - A SQL pad that gives you x-ray vision for your data
LangChain - Framework for building applications with LLMs through composability