ChatDBG is an AI-based debugging assistant for C/C++/Python/Rust code that integrates large language models into a standard debugger (pdb, lldb, gdb, and windbg) to help debug your code. It can provide error diagnoses and suggest fixes.
PaperCoder is a multi-agent LLM system that transforms scientific papers into code repositories through a three-stage pipeline: planning, analysis, and code generation. It aims to create faithful, high-quality implementations.
This tutorial demonstrates how to integrate Google’s Gemini 2.0 with an in-process Model Context Protocol (MCP) server using FastMCP, creating tools for weather information and integrating them into Gemini's function calling workflow.
This tutorial details how to use FastAPI-MCP to convert a FastAPI endpoint (fetching US National Park alerts) into an MCP-compatible server. It covers environment setup, app creation, testing, and MCP server implementation with Cursor IDE.
This document details how to run Qwen models locally using the Text Generation Web UI (oobabooga), covering installation, setup, and launching the web interface.
Model Context Protocol server to run Python code in a sandbox using Pyodide in Deno, isolated from the operating system.
This article details a method for training large language models (LLMs) for code generation using a secure, local WebAssembly-based code interpreter and reinforcement learning with Group Relative Policy Optimization (GRPO). It covers the setup, training process, evaluation, and potential next steps.
A popular and actively maintained open-source web crawling library for LLMs and data extraction, offering advanced features like structured data extraction, browser control, and markdown generation.
This repository organizes public content to train an LLM to answer questions and generate summaries in an author's voice, focusing on the content of 'virtual_adrianco' but designed to be extensible to other authors.
This Splunk Lantern article outlines the steps to monitor Gen AI applications with Splunk Observability Cloud, covering setup with OpenTelemetry, NVIDIA GPU metrics, Python instrumentation, and OpenLIT integration to monitor GenAI applications built with technologies like Python, LLMs (OpenAI's GPT-4o, Anthropic's Claude 3.5 Haiku, Meta’s Llama), NVIDIA GPUs, Langchain, and vector databases (Pinecone, Chroma) using Splunk Observability Cloud. It outlines a six-step process:
- Access Splunk Observability Cloud: Sign up for a free trial if needed.
- Deploy Splunk Distribution of OpenTelemetry Collector: Use a Helm chart to install the collector in Kubernetes.
- Capture NVIDIA GPU Metrics: Utilize the NVIDIA GPU Operator and Prometheus receiver in the OpenTelemetry Collector.
- Instrument Python Applications: Use the Splunk Distribution of OpenTelemetry Python agent for automatic instrumentation and enable Always On Profiling.
- Enhance with OpenLIT: Install and initialize OpenLIT to capture detailed trace data, including LLM calls and interactions with vector databases (with options to disable PII capture).
- Start Using the Data: Leverage the collected metrics and traces, including features like Tag Spotlight, to identify and resolve performance issues (example given: OpenAI rate limits).
The article emphasizes OpenTelemetry's role in GenAI observability and highlights how Splunk Observability Cloud facilitates monitoring these complex applications, providing insights into performance, cost, and potential bottlenecks. It also points to resources for help and further information on specific aspects of the process.
