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

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 — 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