Visualizes the world's infrastructure using OpenStreetMap data.
>"Announcing V2 of my stellar atlas inspired by the in-ship map on Project Hail Mary! "
A real-time visualization tool for Claude Code and Codex agent orchestration that makes complex agent behaviors visible through interactive node graphs. It allows developers to monitor how agents think, branch, and coordinate during execution, facilitating easier debugging of tool call chains and reasoning processes.
- Live agent visualization via an interactive node graph with real-time streaming
- Concurrent support for Claude Code and Codex runtimes
- Integrated VS Code extension for direct workspace monitoring
- Interactive canvas with pan and zoom capabilities to inspect details
- Timeline, transcript panels, and JSONL log file replay functionality
- Live agent visualization via an interactive node graph with real-time streaming
- Concurrent support for Claude Code and Codex runtimes
- Integrated VS Code extension for direct workspace monitoring
- Interactive canvas with pan and zoom capabilities to inspect details
- Timeline, transcript panels, and JSONL log file replay functionality
This system transforms unstructured text documents into interactive knowledge graphs by using an LLM to extract knowledge in the form of Subject-Predicate-Object triplets. It features automated text chunking, entity standardization to ensure consistent naming across segments, and relationship inference to discover connections between disconnected parts of the data. The final output is an interactive HTML visualization that includes color-coded communities, node sizing based on importance metrics, and both original and inferred relationship types.
- Support for any OpenAI-compatible API endpoint including Ollama and vLLM
- Multi-pass processing for triple extraction, entity alignment, and relationship inference
- Interactive visualization with zoom, pan, and physics controls
- Community detection using the Louvain method
- Support for any OpenAI-compatible API endpoint including Ollama and vLLM
- Multi-pass processing for triple extraction, entity alignment, and relationship inference
- Interactive visualization with zoom, pan, and physics controls
- Community detection using the Louvain method
This discussion focuses on the technical efforts to emulate and potentially hardware-reimplement Marvin Minsky’s 2500 computer. Users share resources including a GitHub emulator (`simh/tt2500`) capable of running SITS (Small Incompatible Timesharing System), as well as schematics and SUDS drawings that have been cleared for public viewing. The conversation explores the complexities of recreating the hardware, such as the difficult vector display components, and notes how modern simulation software like "Digital" can be used to model the original design using its existing component libraries.
**Facts Uncovered:**
* **Emulator Availability:** A `simh/tt2500` emulator is available on GitHub for running SITS (Small Incompatible Timesharing System).
* **Documentation:** Schematics and SUDS drawings have been made available to the public following permission requests.
* **Hardware Challenges:** Replicating the vector display is identified as one of the most complicated aspects of a hardware implementation.
* **Obscure Components:** Original parts mentioned include 2507 flip-flops (with shared register enable), an AOI gate (7464), and six-bit 3-state buffer chips.
* **Simulation Tools:** "Digital," a Java-based simulator, is being used to assist in reimplementing the design's components.
**Facts Uncovered:**
* **Emulator Availability:** A `simh/tt2500` emulator is available on GitHub for running SITS (Small Incompatible Timesharing System).
* **Documentation:** Schematics and SUDS drawings have been made available to the public following permission requests.
* **Hardware Challenges:** Replicating the vector display is identified as one of the most complicated aspects of a hardware implementation.
* **Obscure Components:** Original parts mentioned include 2507 flip-flops (with shared register enable), an AOI gate (7464), and six-bit 3-state buffer chips.
* **Simulation Tools:** "Digital," a Java-based simulator, is being used to assist in reimplementing the design's components.
Reviving a 47-year old variant of the Logo programming language with Claude’s help
An interactive tool designed to visualize the relationships and flow of code reviews within a development team or project. It helps developers and managers understand how changes move through the review process, identifying bottlenecks and key contributors in the codebase evolution.
- Visual mapping of pull requests and code reviews
- Analysis of reviewer engagement and response times
- Identification of workflow patterns and potential delays
- Visual mapping of pull requests and code reviews
- Analysis of reviewer engagement and response times
- Identification of workflow patterns and potential delays
Milad Alizadeh introduces 'kitcat', a new matplotlib backend designed for plotting directly within compatible terminals. Frustrated with Jupyter notebooks as a development environment, the author sought a leaner, script-based workflow for matplotlib plotting. Kitcat leverages the graphic protocol popularized by Kitty, allowing image data to be sent via escape codes to terminals supporting this functionality, such as Yazi.
The backend encodes PNG pixel data into base64 and transmits it, supporting chunked data for larger images. While acknowledging the existence of 'matplotlib-backend-kitty', kitcat aims for broader compatibility across terminals supporting the protocol. Future development may include support for iTerm2 animations and the Sixel graphics standard.
The backend encodes PNG pixel data into base64 and transmits it, supporting chunked data for larger images. While acknowledging the existence of 'matplotlib-backend-kitty', kitcat aims for broader compatibility across terminals supporting the protocol. Future development may include support for iTerm2 animations and the Sixel graphics standard.
This article details a five-step process for memorizing and understanding complex concepts, combining mnemonic techniques like the Memory Palace with active learning strategies such as spaced repetition, active recall, and note-taking. It emphasizes memorizing the names of concepts first, then understanding them, and connecting them across multiple fields.
This article details seven terminal-based tools – Gonzo, Lazyjournal, Toolong, Humanlog, GoAccess, Logrotate, and Logwatch – that can significantly improve the experience of working with logs for debugging, analysis, and management. It highlights how these tools offer interactive visualization, efficient navigation, and automated management to make log analysis more manageable and even enjoyable.
