debugging

Kindly: Open-Source Web Search MCP for Coders

Kindly, a newly open-sourced Web Search MCP server, addresses the limitations of existing search tools by providing comprehensive context for debugging complex issues. Unlike standard search MCPs that offer minimal snippets or cluttered HTML, Kindly intelligently retrieves and formats content using APIs for platforms like StackOverflow, GitHub, and arXiv. This allows AI coding assistants to access full, structured content without additional tool calls, effectively mimicking the research process of a human engineer. By enhancing the retrieval process, Kindly supports tools such as Claude Code, Codex, and Cursor, making it a valuable asset for developers seeking efficient problem-solving resources. This matters because it significantly improves the efficiency and accuracy of AI coding assistants, making them more effective in real-world debugging scenarios.

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Posted on

Jan 7, 2026

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AIGeekery

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How-Tos, Tools

Topics: open source, developer tools, GitHub

Introducing mcp-doctor: Streamline MCP Config Debugging

Debugging MCP configurations can be a time-consuming and frustrating process due to issues like trailing commas, incorrect paths, and missing environment variables. To address these challenges, a new open-source CLI tool called mcp-doctor has been developed. This tool helps users by scanning their configurations and pinpointing errors such as the exact location of trailing commas, verifying path existence, warning about missing environment variables, and testing server responsiveness. It is compatible with various platforms including Claude Desktop, Cursor, VS Code, Claude Code, and Windsurf, and can be easily installed via npm. This matters because it streamlines the debugging process, saving time and reducing frustration for developers working with MCP configurations.

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Posted on

Jan 5, 2026

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Commentary, How-Tos

Topics: open source, developer tools, debugging

MCP Chat Studio v2: New Features for MCP Servers

MCP Chat Studio v2 has been launched as a comprehensive tool for managing MCP servers, akin to Postman. The new version introduces a Workspace mode with an infinite canvas and features like draggable panels and a command palette, enhancing user interaction and organization. It also includes an Inspector for running tools and viewing protocol timelines, a visual Workflow builder with AI integration, and a Contracts feature for schema validation. Additionally, users can generate and connect mock servers, export workflows to Python and Node scripts, and utilize analytics for performance monitoring. This matters because it streamlines the development and testing of MCP servers, improving efficiency and collaboration for developers.

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Topics: AI Integration, debugging, MCP servers

Infer: A CLI Tool for Piping into LLMs

Infer is a newly developed command-line interface tool that allows users to pipe command outputs directly into a large language model (LLM) for analysis, similar to how grep is used for text searching. By integrating with OpenAI-compatible APIs, users can ask questions about their command outputs, such as identifying processes consuming RAM or checking for hardware errors, without manually copying and pasting logs. The tool is lightweight, consisting of less than 200 lines of C code, and outputs plain text, making it a practical solution for debugging and command recall. This innovation simplifies the interaction with LLMs, enhancing productivity and efficiency in managing command-line tasks.

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Topics: debugging, CLI tool, OpenAI API

CNN in x86 Assembly: Cat vs Dog Classifier

An ambitious project involved implementing a Convolutional Neural Network (CNN) from scratch in x86-64 assembly to classify images of cats and dogs, using a dataset of 25,000 RGB images. The project aimed to deeply understand CNNs by focusing on low-level operations such as memory layout, data movement, and SIMD arithmetic, without relying on any machine learning frameworks or libraries. Key components like Conv2D, MaxPool, Dense layers, activations, forward and backward propagation, and the data loader were developed in pure assembly, achieving a performance approximately 10 times faster than a NumPy version. Despite the challenges of debugging at this scale, the implementation successfully runs inside a lightweight Debian Slim Docker container, showcasing a unique blend of low-level programming and machine learning. This matters because it demonstrates the potential for significant performance improvements in neural networks through low-level optimizations.

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Topics: machine learning, performance optimization, debugging

Introducing Syrin: Debugging and Testing MCP Servers

Building MCP servers often presents challenges such as lack of visibility into LLM decisions, tool call issues, and the absence of deterministic testing methods. Syrin, a local-first CLI debugger and test runner, addresses these challenges by offering full MCP protocol support, multi-LLM compatibility, and safe execution features. It includes CLI commands for initialization, testing, and development, and supports YAML configuration with HTTP and stdio transport. Future developments aim to enhance deterministic unit tests, workflow testing, and runtime event assertions. This matters because it provides developers with essential tools to efficiently debug and test MCP servers, improving reliability and performance.

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Topics: OpenAI, debugging, Ollama

TraceML’s New Layer Timing Dashboard: Real-Time Insights

TraceML has introduced a new layer timing dashboard that provides a detailed breakdown of training times for each layer on both GPU and CPU, allowing users to identify bottlenecks in real-time. This live dashboard offers insights into where training time is allocated, differentiating between forward and backward passes and per-layer performance, with minimal overhead on training throughput. The tool is particularly useful for debugging slow training runs, identifying unexpected bottlenecks, optimizing mixed-precision setups, and understanding CPU/GPU synchronization issues. This advancement is crucial for those looking to optimize machine learning training processes and reduce unnecessary time expenditure.

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Topics: machine learning, PyTorch, debugging

Gemma Scope 2: Enhancing AI Model Interpretability

Large Language Models (LLMs) possess remarkable reasoning abilities, yet their decision-making processes are often opaque, making it challenging to understand why they behave in unexpected ways. To address this, Gemma Scope 2 has been released as a comprehensive suite of interpretability tools for the Gemma 3 model family, ranging from 270 million to 27 billion parameters. This release is the largest open-source interpretability toolkit by an AI lab, designed to help researchers trace potential risks and better understand the internal workings of AI models. With the capability to store 110 petabytes of data and manage over a trillion parameters, Gemma Scope 2 aims to assist the AI research community in auditing and debugging AI agents, ultimately enhancing safety interventions against issues like jailbreaks and hallucinations. Interpretability research is essential for creating AI that is both safe and reliable as AI systems become more advanced and complex. Gemma Scope 2 acts like a microscope for the Gemma language models, using sparse autoencoders (SAEs) and transcoders to allow researchers to explore model internals and understand how their "thoughts" are formed and connected to behavior. This deeper insight into AI behavior is crucial for studying phenomena such as jailbreaks, where a model's internal reasoning does not align with its communicated reasoning. The new version builds on its predecessor by offering more refined tools and significant upgrades, including full coverage for the entire Gemma 3 family and advanced training techniques like the Matryoshka technique, which enhances the detection of useful concepts within models. Gemma Scope 2 also introduces tools specifically designed for analyzing chatbot behaviors, such as jailbreaks and chain-of-thought faithfulness. These tools are vital for deciphering complex, multi-step behaviors and ensuring models act as intended in conversational applications. By providing a full suite of interpretability tools, Gemma Scope 2 supports ambitious research into emergent behaviors that only appear at larger scales, such as those observed in models like the 27 billion parameter C2S Scale model. As AI technology continues to progress, tools like Gemma Scope 2 are crucial for ensuring that AI systems are not only powerful but also transparent and safe, ultimately benefiting the development of more robust AI safety measures. This matters because understanding and improving AI interpretability is crucial for developing safe and reliable AI systems, which are increasingly integrated into various aspects of society.