
Helicone AI
Langfuse
LangSmith
Portkey
liteLLM
Eden AI
OpenRouter
LangChain
Practice.dev
Scrimba
Codelita
Programming Hero
Tynker
PyTogether
CodeDammit
Glyphide
Helicone AI
Practice.devNo features have been listed yet.
Based on our record, Helicone AI should be more popular than Practice.dev. It has been mentiond 5 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.
Helicone takes the simplest possible approach to LLM monitoring: it's a proxy. Change your OpenAI base URL from api.openai.com to oai.helicone.ai, add your Helicone API key as a header, and every LLM request is logged โ latency, tokens, cost, prompts, and completions. No SDK integration, no code changes beyond a URL swap. - Source: dev.to / 2 months 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 / 2 months ago
For many teams, especially those starting out or with simpler needs, commercial solutions like Portkey, Helicone, OpenPipe, or LiteLLM Proxy offer off-the-shelf capabilities that cover many common proxy use cases (caching, logging, cost tracking). NeuroLink itself can be seen as an SDK that complements these, allowing you to integrate with them or build similar features on top. - Source: dev.to / 4 months ago
TL;DR: Go with Langfuse if you want open-source and self-hosted. Pick Helicone if you want the fastest setup (2 minutes, no SDK). Stick with LangSmith if your stack already runs on LangChain. And if your org already pays for Datadog, their LLM module slots right in. - Source: dev.to / 5 months ago
Hey HN, we're Justin and Cole, the founders of Helicone (https://helicone.ai) or self-deploy with our new fully open-source helm chart (https://helicone.ai/selfhost). Yet even with detailed traces, probabilistic systems are notoriously hard to debug at scale. So, we released evaluators (either via LLM-as-judge or custom Python evaluators leveraging the CodeSandbox SDK - https://codesandbox.io/docs/sdk/sandboxes).... - Source: Hacker News / over 1 year ago
If you want to benchmark yourself when you learn React. Iโve completed most of the medium/hard react problems at https://practice.dev to get my job. Source: over 4 years ago
It took me a few months to build practice.dev. Here I extracted the IDE and added live collaboration and npm resolver. It took me 1 week to release live-ide.dev. Source: almost 5 years ago
The idea of practice.dev is to create basics tutorials (currently it's in progress) similar to FreeCodeCamp, and create hundreds of challenges with greater difficulty. Think of it like leetcode/codewars for frontend. Source: almost 5 years ago
Langfuse - Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.
Scrimba - Interactive coding screencasts created in an instant
LangSmith - Build and deploy LLM applications with confidence
Codelita - Anyone Can Code
Portkey - Build production-grade & reliable AI apps with Portkey
Programming Hero - Personalized, fun, and interactive way to learn programming