Builder.io
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Langfuse
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LastMile AI
Eliminate long delays, missed deadlines, and rigid CMS templates. Visually build and optimize web and mobile experiences on your existing sites and apps to speed up your build-measure-learn cycles and drive growth, faster.
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.
Builder.io LangfuseBuilder.io's answer
Builder is driving the next generation of front-end programming, with the only Visual Development Platform that offers an AI-powered design-to-code tool, a visual editor, and an enterprise CMS.
Builder.io's answer
Our founders, Steve Sewell and Brent Locks, met on the campus of UC Berkeley in 2011, and have been friends and colleagues ever since. In those early days, they bonded over wanting to be entrepreneurs, and having lots of ideas, but not knowing how to code to bring them to life.
Fast forward more than a decade, and Steve is now a visionary technologist (Brent's words) and Brent still doesn't know how to code (beyond some basic HTML and CSS), but they continue to share a passion around building technology that can enable anyone to bring their ideas to life, which is at the core of Builder.io.
Builder.io's answer
Frontend engineering leads who manage a headless site or store. Frontend and full-stack web developers whose team uses Figma to design web and app experiences
Builder.io's answer
Shipping, iterating, and optimizing high-performing digital experiences eats up a ton of time. As a result, teams donโt get nearly as many experiences, pages, content, or experiments to market as they planned. Builder helps teams ship twice as much, without doubling their effort or team size. Builder does this by offering tools that optimize every step of the journey from design through optimization. No other tool comes close because they donโt consider the entire journey and havenโt embedded AI at the same level as Builder. As a result of shipping twice as much with the same team size, companies can significantly increase top-line revenue or decrease costs.
Builder.io's answer
Based on our record, Builder.io should be more popular than Langfuse. It has been mentiond 44 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.
Still want hand-coded pages? You can always mix your no-code app with custom Next.js/React flows, CMSs like Builder.IO, or plug JSON data into your own front-end. - Source: dev.to / 9 months ago
At Builder.io, we rely on this very inline-script hydration trick to select the winning variant in our SDKs. You can see the production implementation here: inlined-fns.ts. - Source: dev.to / about 1 year ago
Figma plugin search interface showing Builder.io AI-Powered Figma to Code plugin in the Plugins & widgets section. - Source: dev.to / over 1 year ago
If youโre not logged in to Builder.io, youโll be prompted to connect your account first. Otherwise, the plugin will jump straight into its AI-powered code generation. It might take a minute or two to process your design. Thatโs normalโitโs building out the code, setting up file structures, and making sure everything is wired correctly. - Source: dev.to / over 1 year ago
Fun fact: the actual Builder.io codebase for our Figma to code product does literally the same thing. Yes, we have advanced machine learning, malicious content detection, yada yada. - Source: dev.to / over 1 year 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 / 17 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 / about 1 month 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 2 months 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 2 months 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
Webflow - Build dynamic, responsive websites in your browser. Launch with a click. Or export your squeaky-clean code to host wherever you'd like. Discover the professional website builder made for designers.
Helicone AI - Open-source LLM Observability for Developers
Locofy.ai - Locofy.ai helps builders launch 4-5x faster by converting designs to production ready code.
LangSmith - Build and deploy LLM applications with confidence
Anima App - Design, get feedback, convert to code, publish, iterate.
LangChain - Framework for building applications with LLMs through composability