Lizzy Li writes about the new custom visualizations framework for Dashboard Studio, introduced in Splunk Cloud Platform 10.4.2604 and Splunk Enterprise 10.4, which replaces the legacy framework with a modern sandboxed iframe architecture, simplified JSON-based configuration, and a full CLI/SDK development pipeline supporting React, TypeScript, and watch mode.
- Legacy framework required touching four separate files to add a single configurable option; the new framework consolidates this into a single config.json
- A custom-visualization-builder skill (available in Splunk Agent Skills on GitHub) can scaffold, implement, build, and package a visualization from a data shape description
- Splunk recommends rebuilding important Classic custom visualizations with the new framework rather than relying on backward-compatibility rendering
- Legacy framework required touching four separate files to add a single configurable option; the new framework consolidates this into a single config.json
- A custom-visualization-builder skill (available in Splunk Agent Skills on GitHub) can scaffold, implement, build, and package a visualization from a data shape description
- Splunk recommends rebuilding important Classic custom visualizations with the new framework rather than relying on backward-compatibility rendering
Rich is a Python library that adds colors, tables, progress bars, markdown rendering, and syntax highlighting to terminal output, making CLI tools and REPL sessions significantly more readable and visually polished across Linux, macOS, and Windows.
- Its `print` is a drop-in replacement for the built-in, so you can swap it in with only an import change and embed markup like ` bold magenta » ` directly in strings
- Can be installed into the Python REPL to automatically pretty-print any data structure you inspect
- Supports true color and emoji on modern Windows Terminal, falling back to 16 colors on classic terminals
- Requires Python 3.8+
- Its `print` is a drop-in replacement for the built-in, so you can swap it in with only an import change and embed markup like ` bold magenta » ` directly in strings
- Can be installed into the Python REPL to automatically pretty-print any data structure you inspect
- Supports true color and emoji on modern Windows Terminal, falling back to 16 colors on classic terminals
- Requires Python 3.8+
Hee-Suk Kim writes that Sib is a standard Unix LLM client which stores LLM conversations as a plain Git repository where each user and assistant turn is a commit, providing full context control and free backup and sharing via normal Git remotes. The `sib ask` command reads a prompt from stdin, appends it with the reachable chain from HEAD, records both turns as commits, and prints the reply, while flags like -p, -r, -c enable repeat, edit, fork and head moves without API calls.
- Dependencies are bash >=3.2, git, jq, curl, awk and coreutils
- Conversations can be fetched/pushed via `sib git` to share with sib-project/hub
- Dependencies are bash >=3.2, git, jq, curl, awk and coreutils
- Conversations can be fetched/pushed via `sib git` to share with sib-project/hub
Termaid is a pure Python library and CLI that renders Mermaid diagrams as Unicode or ASCII art directly in the terminal or within Python apps, supporting 18 diagram types with zero dependencies, terminal-aware auto-fitting, optional Rich colored output and Textual widget integration.
- Inspired by mermaid-ascii and beautiful-mermaid
- Offers 11 built-in themes including gruvbox, monokai, dracula, nord and solarized
- Pipe-friendly CLI examples include `cat diagram.mmd | termaid` and `uvx termaid diagram.mmd`
- Inspired by mermaid-ascii and beautiful-mermaid
- Offers 11 built-in themes including gruvbox, monokai, dracula, nord and solarized
- Pipe-friendly CLI examples include `cat diagram.mmd | termaid` and `uvx termaid diagram.mmd`
This article explores how to evaluate and select the most effective interfaces for AI coding agents. It examines various interaction patterns, such as terminal commands versus integrated IDE extensions, and assesses their impact on developer productivity.
* Transparency in agent reasoning processes
* Ease of manual intervention during autonomous tasks
* Efficiency of feedback loops between humans and machines
* Transparency in agent reasoning processes
* Ease of manual intervention during autonomous tasks
* Efficiency of feedback loops between humans and machines
An open-source command-line tool designed to identify the optimal local Large Language Model specifically suited for a user's existing or planned hardware. It automatically detects GPU, CPU, and RAM capacity to rank HuggingFace models using real performance benchmarks instead of relying on parameter size alone.
* Hardware auto-detection for NVIDIA, AMD, Apple Silicon, and CPUs
* Intelligent ranking based on benchmark evidence and recency awareness
* Capability to simulate different GPUs for hardware upgrade planning
* Support for GGUF, AWQ, and GPTQ model formats
* Streamlined workflows including one-command chat sessions and Python code snippet generation
* Hardware auto-detection for NVIDIA, AMD, Apple Silicon, and CPUs
* Intelligent ranking based on benchmark evidence and recency awareness
* Capability to simulate different GPUs for hardware upgrade planning
* Support for GGUF, AWQ, and GPTQ model formats
* Streamlined workflows including one-command chat sessions and Python code snippet generation
A reliable Python tool designed to organize messy Google Photos Takeout exports. It addresses common issues found in Takeout downloads, such as inconsistent JSON sidecar naming, truncated filenames due to Windows path limits, duplicate files sharing single metadata files, and incorrect timestamps.
Key features include:
- Organization by date structure (YYYY/MM)
- Preservation of original album structures
- Robust matching for various JSON sidecar naming variants
- MD5 hash-based duplicate detection
- Support for EXIF fallback and filename pattern extraction
- Dry-run mode to preview changes without writing files
Key features include:
- Organization by date structure (YYYY/MM)
- Preservation of original album structures
- Robust matching for various JSON sidecar naming variants
- MD5 hash-based duplicate detection
- Support for EXIF fallback and filename pattern extraction
- Dry-run mode to preview changes without writing files
Google is transitioning from the Gemini CLI to the new Antigravity CLI, a core component of the Google Antigravity agent-first development platform. This shift addresses the growing need for multi-agent orchestration and unified backends in developer workflows. The new tool provides faster execution using Go and supports asynchronous background tasks for complex operations like large-scale refactoring or research.
Key points:
* Transitioning from Gemini CLI to Antigravity CLI
* Introduction of the Google Antigravity agent-first platform
* Faster, Go-based performance and asynchronous workflow support
* Sunset dates for consumer services starting June 18, 2026
* Continued support for enterprise customers through existing licenses
Key points:
* Transitioning from Gemini CLI to Antigravity CLI
* Introduction of the Google Antigravity agent-first platform
* Faster, Go-based performance and asynchronous workflow support
* Sunset dates for consumer services starting June 18, 2026
* Continued support for enterprise customers through existing licenses
Appify transforms terminal user interface (TUI) applications into native macOS application bundles. This utility allows command-line tools to function as standalone apps with their own Dock icons, Cmd+Tab entries, and Spotlight visibility. By embedding a Ghostty engine, it provides GPU-accelerated Metal rendering without requiring any existing terminal installations or external dependencies.
Key features:
Single binary distribution with zero dependencies
Native macOS experience using Swift and GhosttyKit
GPU accelerated rendering via the Metal renderer
Customizable application names, bundle IDs, and icons
Key features:
Single binary distribution with zero dependencies
Native macOS experience using Swift and GhosttyKit
GPU accelerated rendering via the Metal renderer
Customizable application names, bundle IDs, and icons
Explores how to use the llm CLI tool within a script's shebang line by utilizing the env -S pattern. This approach allows plain text files to become executable via large language models, treating file contents as prompts. The article covers using flags like -f for fragments and -x for code extraction, integrating tools such as llm_time, and leveraging YAML templates with parameters or embedded Python tool functions to create highly functional scripts.
Main topics:
- Using env -S with the llm CLI
- Making text files executable via LLM
- Integrating tools within shebang lines
- Utilizing YAML templates for prompts
- Embedding Python functions in templates
Main topics:
- Using env -S with the llm CLI
- Making text files executable via LLM
- Integrating tools within shebang lines
- Utilizing YAML templates for prompts
- Embedding Python functions in templates
