A well-crafted, self-contained application for analyzing and visualizing Claude Code usage logs. The system reads local JSONL transcript files (written by Claude Code regardless of subscription plan), parses them into a local SQLite database, and surfaces token counts, cost estimates, session histories, and subagent attribution through both a terminal CLI (`python cli.py today` / `stats` / `week`) and a rich single-page web dashboard (`python cli.py dashboard`). The dashboard is served by a stdlib-only Python HTTP server and bundles all HTML/JS/CSS inline, using Chart.js from CDN for visualizations. A companion VS Code extension embeds the same dashboard as a sidebar webview, auto-discovering the Python runtime via a flexible install-mode resolution chain (bundled `.py` sources, Homebrew shim, workspace clone, or explicit path). Architecturally, the code is split into three top-level Python modules—`scanner.py` (JSONL parsing, SQLite schema management, incremental scanning with deduplication by message ID), `cli.py` (CLI dispatch with per-turn cost calculation using a tiered pricing lookup), and `dashboard.py` (HTTP server with an embedded SPA). There are no third-party Python runtime dependencies; the VS Code extension uses TypeScript with minimal Node.js dependencies (only `@vscode/vsce` for packaging). The test suite is comprehensive, covering unit, integration, and HTTP-level scenarios across all three Python modules plus the entire TypeScript extension. A strict version parity test (`test_version.py`) enforces lockstep between the Python constant, the CHANGELOG heading, and the extension's `package.json`. Notable craftsmanship includes: the incremental scan engine that tracks file mtimes and line counts, processes only new/changed lines, and deduplicates streaming events via a conditional unique index on `message_id`; the one-time topic backfill mechanism for schema upgrades that re-reads only title records without touching token totals; and the VS Code extension's server-manager with configurable readiness probes that distinguishes the dashboard's JSON response shape from arbitrary HTTP 200s. The project is actively maintained with frequent releases, a weekly triage automation, and a detailed AGENTS.md that documents non-obvious invariants (streaming dedup, session model priority, cost calculation per-turn).
Radix is a native macOS disk space analysis application built with SwiftUI, Swift concurrency, and deep integration with macOS filesystem APIs (APFS clones, FSEvents, getattrlistbulk). The core scanning engine uses actor-based parallelism with atomic directory summarization, incremental rescanning via FSEvent history, and cancellation-aware sort and layout operations. The app features interactive sunburst and treemap visualizations with viewport pan/zoom, a searchable file browser with sorted tables, discard pile for staged trash operations, cross-snapshot comparison showing file changes over time, and export/import of scan snapshots in a versioned compressed archive format (.radixscan). Architecture follows a clean separation of Models, Services, ViewModels, and Features, with heavy use of dependency injection closures for testability. The codebase includes extensive unit, integration, and benchmark tests (Swift Testing) covering correctness, performance, and concurrency safety. It demonstrates high craftsmanship in file tree storage (compact arena with logical scopes), cancellation patterns, APFS storage accounting, and interactive chart geometry.