
Helicone AI
Langfuse
LangSmith
Portkey
liteLLM
OpenRouter
Eden AI
LangChain
massCode
GitHub Gist
Lepton
SnippetsLab
Quiver
Codespace
Pastebin.com
Cacher
Helicone AI
massCodeNo features have been listed yet.
massCode might be a bit more popular than Helicone AI. We know about 6 links to it since March 2021 and only 5 links to Helicone AI. 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 / 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 1 month 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 / 3 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 / 4 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
To be honest, it didn't take off as I hoped. I struggled to attract enough users to make it sustainable. Eventually, I lost motivation and abandoned it to focus on my other open-source project, massCode. - Source: dev.to / 5 months ago
`cask "lepton"` [link][oss] + `cask "masscode"` [link][oss] for storing snippets as github gists or locally. Source: about 3 years ago
There are a plethora of snippet manager apps for developers, with syntax highlighting, etc, available for macOS, eg: - SnipperApp - Snip - massCode - SnippetsLab - Quiver. Source: over 3 years ago
I found out what it was; I went through the 'Download for Mac' button on masscode.io and it looked to default to the arm64 installer. I grabbed the Intel version from the repo and working now. Source: about 4 years ago
I use MassCode. Syntax is supported for several languages, and is selfhosted. Source: over 4 years ago
Langfuse - Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.
GitHub Gist - Gist is a simple way to share snippets and pastes with others.
LangSmith - Build and deploy LLM applications with confidence
Lepton - Lepton image compression: saving 22% losslessly from images at 15MB/s
Portkey - Build production-grade & reliable AI apps with Portkey
SnippetsLab - SnippetsLab is an easy-to-use snippets manager.