YC-alum-ai-tools — Agent Tool by sidhq

by sidhq · Agent Tool · ★ 196

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About YC-alum-ai-tools

Y Combinator Alum – AI Dev Tool Disclaimer: This repository is maintained by the founders of SID Tech Inc. and other volunteers of the Y Combinator Community. This repository and SID Tech Inc. are not affiliated with, sponsored or endorsed by Y Combinator. This is a curated collection of AI developer tools built by YC companies. We're aiming to serve as a reliable starting point for LLM/ML developers. Overview 🔭 Analytics & Monitoring 💾 Vector DB & Embeddings 🎯 Data Integrations & Retrieval 🚧 Infrastructure 🔎 LLM Serving & Fine-Tuning 📝 Dataset Generation & Handling 🔒 Security 💬 Prompt Management & Testing 🥁 Orchestration 🔈 Audio 🤖 Making Development Easier Analytics & Monitoring Humanloop: Humanloop is like datadog for LLMs. They give you the tools you need to evaluate LLM apps and then take action to improve them. Helicone: The easiest way to capture data from your LLMs (Open Source). Langfuse: Open-source analytics for LLM applications. (Demo / Docs) [UpTra

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Quick Facts

Stars196
Forks13
CategoryAgent Tool
LicenseMIT
Quality Score57.5671199836881/100
Open Issues3
Last Updated2023-08-17
Created2023-07-21
Est. Tokens~63k

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Frequently Asked Questions

What is YC-alum-ai-tools?

YC-alum-ai-tools is Curated collection of AI dev tools from YC companies, aiming to serve as a reliable starting point for LLM/ML developers. It is categorized as a Agent Tool with 196 GitHub stars.

How do I install or use YC-alum-ai-tools?

You can find installation instructions and usage details in the YC-alum-ai-tools GitHub repository at github.com/sidhq/YC-alum-ai-tools. The project has 196 stars and 13 forks, indicating an active community.

What license does YC-alum-ai-tools use?

YC-alum-ai-tools is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to YC-alum-ai-tools?

The top alternatives to YC-alum-ai-tools on Agent Skills Hub include cai, superml, Awesome-LLM-Red-Teaming. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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