Author here—sharing my thoughts on why products shouldn't assume hypothetical user needs. Key points:
- Start from real user behavior, not imagined scenarios.
- Validate by observing actual user activities.
- Prioritize solving existing, painful problems rather than speculative features.
Hey HN,
I built RAG Logger, a lightweight open-source logging tool specifically designed for Retrieval-Augmented Generation (RAG) applications. LangSmith is excellent, but my usage is quite minimal, and I would prefer a locally hosted version that is easy to customize.
Key features:
Detailed step-by-step pipeline tracking
Performance monitoring (embedding, retrieval, LLM generation)
Structured JSON logs with timing and metadata
Zero external dependencies
Easy integration with existing RAG systems
The tool helps debug RAG applications by tracking query understanding, embedding generation, document retrieval, and LLM responses. Each step is timed and logged with relevant metadata.
Leveraging advanced multimodal AI models, this app recognizes and interprets even irregular or handwritten menus with ease. It generates translations, provides contextual dish descriptions, and suggests personalized recommendations, all while simplifying the ordering process with pre-formatted phrases.
This is an interesting work. Which model do you use? Have you used the chain of thought method to allow the agent to solve some problems? It seems that the AI agent can recognize problems and find solutions.
> Assistant: I apologize for the confusion. It seems that the 192.168.1.0/24 subnet is not the correct one for your network. Let's try to determine your network configuration. We can do this by checking your IP address and subnet mask:
<bash>
ifconfig | grep "inet " | grep -v 127.0.0.1
</bash>
inet 192.168.1.152 netmask 0xffffff00 broadcast 192.168.1.255
Assistant: Thank you for that information. It appears that your computer is indeed on the 192.168.1.0/24 network, but we're having trouble reaching other devices. Let's try to ping your router, which is typically at 192.168.1.1:
I believe that on-device models will become a significant trend. I previously released a local file manager app called Riffo, and we have been using the OpenAI API since its launch. However, our users have informed us that they need on-device models. This indicates to me that it's an important issue.
We have been developing an on-device model recently. For macOS, we are using Ollama to host the on-device model on Mac. We have not yet found a perfect solution for the Windows version. Thank you for sharing Llamafile; we might test it for the Windows on-device model.
Are you tired of the tedious process of managing calendar events and extracting key details from natural language descriptions? Meet my latest open-source project that seamlessly integrates the Nylas API with GPT-4, bringing intelligent automation to your calendar workflow.
This project enables developers to easily fetch, create, and manage calendar events using AI-driven insights. It leverages GPT-4's natural language understanding to parse event details, extract participant information, and even convert fuzzy time descriptions into precise UNIX timestamps. Built with a clean OOP architecture, it's modular, easy to extend, and perfect for integrating into your existing systems.
Whether you're working on a productivity tool, building a custom CRM, or just looking to automate your personal scheduling, this project provides a robust foundation with clear, reusable components. Dive in, contribute, or customize it to fit your needs—let's make calendar management smarter together!
- Start from real user behavior, not imagined scenarios. - Validate by observing actual user activities. - Prioritize solving existing, painful problems rather than speculative features.
Happy to discuss more!