Each directory in this repo is a separate research project carried out by an LLM tool - usually Claude Code. Every single line of text and code was written by an LLM.
See Code research projects with async coding agents like Claude Code and Codex for more details on how this works.
I try to include prompts and links to transcripts in the PRs that added each report, or in the commits.
litestream-restarts (2025-12-16)
Experiments in this project evaluate Litestream’s robustness when SQLite writes occur while Litestream is stopped and later restarted, with focus on replication to S3. Both the simple restart and the scenario where the WAL is checkpointed (truncated) while Litestream is offline confirm no data loss: Litestream either streams pending WAL changes upon restart or detects a database change and uploads a new full snapshot (“generation”). This ensures that S3 replication remains consistent even if Litestream’s process is interrupted, making the tool highly reliable in dynamic environments. Detailed mechanisms and generations can be inspected using Litestream’s CLI and the generation listing feature.
Key findings:
- No data loss occurs in either restart or checkpoint scenarios—Litestream recovers via WAL streaming or generation snapshots.
- Creating new generations after checkpointing uploads the entire database, potentially incurring significant storage/bandwidth overhead for large files.
- Frequent restarts with heavy write loads can increase S3 storage due to more generations.
- Litestream’s fallback mechanisms support safe replication during container restarts, server maintenance, or deployment updates.
bs4-justhtml-investigation (2025-12-16)
BeautifulSoup 4 can be integrated with JustHTML, a pure Python HTML5 parser, enabling full compliance with the HTML5 parsing algorithm according to the WHATWG specification. By implementing a custom JustHTMLTreeBuilder, BeautifulSoup’s parser plugin system can leverage JustHTML for parsing, allowing seamless use of BeautifulSoup’s familiar API and features—like find_all() and CSS selectors—while inheriting robust, standards-adherent HTML handling. The integration correctly supports HTML5 implicit element insertion, malformed HTML recovery, and other advanced features. Comprehensive tests confirm that all major parsing and API elements function as expected, making this pairing a practical choice for strict HTML5 parsing within Python.
Key Findings:
- All major BeautifulSoup features work (including CSS selectors and handling of malformed HTML)
- Implicit HTML5 element insertion behaves per spec (e.g., auto-created
<html>,<head>,<body>) - Developed a reusable integration module:
bs4_justhtml.py - Full details and code at JustHTML
streaming-file-upload-prototype (2025-12-14)
Demonstrating efficient large file uploads, this prototype integrates the streaming-form-data library with a Starlette-based ASGI server to enable true streaming of multipart file data directly to disk, bypassing memory bottlenecks. It incrementally parses incoming form data and supports checksum calculation on-the-fly, handling multiple simultaneous file uploads via async workflows. The included test suite validates robust performance across scenarios including large files, chunked uploads, and multiple files. This architecture makes file handling scalable for production environments, with extensibility for further enhancements such as file size limits and external storage targets.
Key Findings:
- Streaming upload prevents memory exhaustion and improves performance for large files.
- Checksum is computed during upload—eliminating the need for a post-upload scan.
- Multiple files can be uploaded in one request; all endpoints are fully ASGI compatible.
- See streaming-form-data documentation for further integration options.
js-api-tagger (2025-12-13)
Efficiently categorizing the 155 HTML tools in simonw/tools by their JavaScript API usage, this project developed an automated pipeline combining Cheerio for HTML parsing and Acorn for JavaScript AST analysis. The solution robustly filters out false positives from comments, strings, and non-code regions, accurately tagging over 60 Web APIs and handling modern ES modules and edge script types. Beyond API detection, the system analyzes external libraries, HTML structure, accessibility, interaction patterns, and data handling, providing multidimensional insight into each tool’s capabilities and design. Results show frequent use of APIs like Fetch, Clipboard, and localStorage, common libraries such as Pyodide and Marked, and a dominant pattern of utilities and file processors among the tools.
Key findings:
- Fetch API, Clipboard API, and localStorage are among the most widely used JS APIs.
- CDN sources like jsdelivr and cloudflare are common for library loading.
- Pyodide for Python, Marked for Markdown, and React for UI are notable detected libraries.
- Most tools fall into categories such as utility, clipboard handler, or file processor.
vite-wasm-browser-compiler (2025-12-14)
Investigating the feasibility of Vite as a browser-based bundler, this project demonstrates that while Vite itself cannot operate directly in the browser due to its Node.js dependencies, client-side file bundling is achievable using alternative strategies. Three approaches were prototyped: a pure JavaScript "simple" bundler for inlining assets, an esbuild-wasm browser integration for ES module support, and full Vite bundling via StackBlitz WebContainers using vite-plugin-singlefile. Each solution offers a different tradeoff between capability, speed, and complexity, with WebContainers standing out for its completeness but requiring Cross-Origin Isolation headers. The project includes live demos, automated Playwright tests, and step-by-step integration of core technologies such as esbuild-wasm and Vite Single File Plugin.
Key Findings:
- Vite cannot run purely in-browser; heavy Node.js APIs and native integrations block direct use.
- esbuild-wasm and @rollup/browser enable ES module bundling client-side but are less performant and require HTTP-based module plugins.
- StackBlitz WebContainers offer full Node.js/Vite support in-browser, achieving identical output to local builds.
- The simple bundler is fastest and most portable but lacks ES module support; WASM-based approaches are slower but more capable.
ast-grep-import-rewriter (2025-12-11)
Leveraging ast-grep and custom YAML rules, the AST-Grep Import Rewriter offers a structured approach to automatically extract, analyze, and rewrite obfuscated JavaScript import statements across ES6, CommonJS, dynamic imports, and webpack bundles. By parsing source files, it generates mapping templates and applies user-defined mappings, converting unreadable module paths into meaningful names with either regex- or AST-based transformations. Featuring a command-line interface, the tool integrates with Python and ast-grep CLI, ensuring accurate code rewriting and comprehensive import discovery. Limitations include restricted support for runtime-evaluated imports and complex obfuscations, but the workflow simplifies code cleanup and migration in modern JS projects.
Key features:
- Supports multiple import styles (ES6, CommonJS, dynamic, webpack, re-exports).
- Generates and applies JSON mapping files for path deobfuscation.
- Provides both regex and AST-guided transformation options.
- Includes rules and config for direct use with ast-grep scan.
- Handles webpack-specific patterns automatically.
offline-notes-sync (2025-12-11)
Building on offline-first principles, this notes sync system enables robust note creation and editing without active internet connectivity, using IndexedDB and service workers on the client side. It employs operation-based sync and vector clocks for fine-grained conflict detection and resolution, and features a three-way character-level merge algorithm inspired by Apple Notes. Server-side logic is powered by Python Starlette and SQLite, with advanced CRDT constructs ensuring that concurrent edits from multiple clients merge seamlessly and converge correctly. A Datasette plugin extends API access and automates database table management, facilitating both testing and integration.
Explore the CRDT module and Datasette plugin for key architectural components.
Key Findings:
- Achieves automatic, character-level merging for non-overlapping edits; overlaps prompt user intervention.
- CRDT implementation ensures commutativity, associativity, idempotency, and convergence of all note states.
- Test suite confirms sync, merge, offline persistence, and conflict-handling behavior across edge cases.
epsilon-python-wrapper (2025-12-09)
Epsilon Python Wrapper provides seamless Python bindings to Epsilon, Google's pure Go WebAssembly 2.0 runtime, enabling efficient and dependency-free WASM execution within Python projects. The wrapper exposes a simple API for module instantiation, function calls (with type safety), memory operations, and export inspection, supporting advanced features like SIMD and resource limiting. While it allows for configurable memory restrictions and function timeouts, true execution interruption (context cancellation or instruction counting) is not supported; thus, alternative CPU limiting strategies are suggested. Epsilon prioritizes clean architecture, zero external dependencies, and ease of embedding, making it a practical choice for Python users needing Go-native WASM capabilities but does not offer WASI or multi-threading.
Key points:
- Enables direct loading, calling, and memory manipulation of WASM modules from Python.
- Supports WebAssembly 2.0—including SIMD, multiple memories (experimental), and host functions.
- Resource limits: configurable memory per module; fixed call stack (1000 frames); no preemptive timeout or instruction counting.
- For CPU/time limiting, subprocesses or system-level approaches are recommended.
- Inspired by projects like wazero-python, but focused on Epsilon's Go-native strengths.
datasette-lite-js-init (2025-12-07)
Datasette-lite faces a core limitation: HTML content injected via innerHTML does not execute embedded JavaScript, breaking interactive features and plugin functionality. The proposed solution introduces a standardized initialization event (datasette_init) triggered after each content update, allowing dependent scripts and plugins to reinitialize reliably. This approach uses a public API (window.__DATASETTE_INIT__) that can target specific DOM containers and signal reinitialization, ensuring clean-up between navigations and preserving backwards compatibility. By aligning with Datasette's event-driven JavaScript architecture, the solution enables smooth operation both in classic and single-page environments like Datasette-lite, with minimal code changes for plugin authors. Prototype files, example integration code, and migration guidelines are provided (datasette-lite, Datasette core).
Key Findings:
- Reinitialization event pattern solves SPA script execution.
- Plugins must scope DOM queries to injected content and handle clean-up.
- Solution does not require risky manual script eval or iframes.
- Maintains full compatibility with existing Datasette usage.
- Offers a clear migration strategy for plugin developers.
datasette-lite-npm-package (2025-12-07)
Converting Datasette Lite into a self-hostable NPM package enables seamless client-side data exploration using SQLite, CSV, JSON, and Parquet files directly in the browser, powered by Pyodide. The project removes analytics, adds a CLI server for local testing, and exposes all necessary static assets for easy deployment to platforms like GitHub Pages, Netlify, or Vercel. Users can install the package, start a local server, and deploy the static build, making advanced Python-powered data analysis accessible without backend infrastructure. The package also supports various URL parameters to customize data sources and package installation.
Key findings:
- Analytics were stripped for privacy and universality.
- Node.js CLI server allows simple local testing with proper CORS.
- The package is lightweight (~13 KB) and quick to deploy, though initial loads depend on Pyodide CDN availability.
- Extensive URL parameters offer flexible data loading and customization.
sqlite-ripgrep-function (2025-12-07)
SQLite Ripgrep Function enables fast code and text search inside SQLite queries by integrating the powerful ripgrep search tool as a custom SQL function. It offers both a pure Python implementation and a performant C extension, allowing users to search files within a configurable directory, restrict output with glob patterns (e.g., *.py), and enforce time limits to avoid runaway queries. While the Python version returns JSON for lightweight use, the C extension provides true table-valued virtual tables for flexible SQL integration, supporting constraints and column selection directly in queries. This project draws inspiration from datasette-ripgrep and is installable in both Python and SQLite environments.
Key features:
- Direct code/text search from SQL using ripgrep
- Configurable base directory and file filtering via glob patterns
- Time limit enforcement to prevent slow queries
- Both JSON (Python) and table-valued (C extension) results suitable for further SQL querying
- Easy integration with both Python and SQLite CLI environments
apptron-analysis (2025-12-05)
Apptron is a browser-based cloud IDE that hosts a full x86 Linux environment using emulation and WebAssembly, delivering a seamless developer experience directly in the browser. By tightly integrating VS Code, a Linux terminal, and persistent cloud storage via Cloudflare R2, users are able to work on customizable environments without any local setup. Notably, the Linux guest can execute WASM binaries as first-class executables, and all cloud resources—including storage—are managed with POSIX-like filesystem semantics. The stack is built atop Wanix, an open-source Plan 9-inspired OS layer for WebAssembly, ensuring files and processes are accessible and controllable through uniform filesystem protocols. Learn more at tractordev/apptron and Wanix.
Key findings:
- Bidirectional cloud-browser communication enables seamless VS Code and Linux integration.
- WASM binaries run transparently inside the Linux VM, leveraging novel binfmt_misc and 9P-based IPC.
- Cloudflare R2 serves as a full-featured, metadata-rich filesystem rather than simple object storage.
- Highly customizable environments are supported, with persistent and synced storage.
- Plan 9-inspired filesystem protocols unify resource access and control for all environment layers.
github-cli-api-proxy (2025-11-29)
Proxying GitHub CLI (gh) API traffic can be achieved through standard HTTP/HTTPS proxies or via a Unix domain socket, each suited to different use cases and levels of flexibility. The CLI, implemented in Go, natively supports proxy environment variables (HTTPS_PROXY, HTTP_PROXY, NO_PROXY), making integration with existing HTTP proxies seamless and requiring no changes to the CLI configuration. For advanced needs like local debugging or custom proxy logic, routing traffic through a Unix domain socket is supported via a configuration option and allows for fine-grained control over requests. Changing the target host (using GH_HOST) is not a proxy method but useful for connecting to GitHub Enterprise Server.
Key tools and references:
Key Findings:
- Standard proxy integration is easiest (environment variable configuration).
- Unix socket proxying offers advanced flexibility for development, debugging, and custom logic.
- Changing
GH_HOSTallows targeting GitHub Enterprise Server, but does not act as a proxy. - Example proxy servers (HTTP/HTTPS proxy and Unix socket proxy) are provided for inspection and debugging scenarios.
self-host-datasette-lite (2025-11-28)
Datasette Lite, a browser-based SQLite explorer powered by Pyodide and WebAssembly, can be fully self-hosted and used offline by bundling all core files, required Python wheels, and optional sample databases locally instead of relying on external CDNs and PyPI hosts. Achieving this involves downloading Pyodide's core runtime, all necessary wheels for Datasette and its dependencies, modifying key paths in webworker.js and index.html, and ensuring correct server MIME settings for .wasm files. The minimal offline bundle is around 20–25 MB, while a full Pyodide distribution increases this to about 350 MB and enhances extensibility. Careful dependency resolution and version pinning are needed to avoid runtime conflicts, and users should provide their own databases or include local samples.
Key findings:
- Minimal offline bundles (~25 MB) are practical; full Pyodide versions enable more flexibility at a storage cost.
- Dependency wheels must be manually downloaded and installed in correct order; version mismatches (e.g. in httpx) can cause issues.
- Pyodide supports custom hosting, but index paths and MIME types require adjustment (Pyodide GitHub Issue #4600).
- Sample server scripts and static analysis tools help automate the bundling and local hosting process.
datasette-sql-permissions-review (2025-11-27)
A comprehensive architecture review of Datasette's new SQL-based permissions system (introduced in v1.0a20) finds that transitioning from a callback-driven model to SQL query resolution greatly improves scalability for large deployments. The redesigned system efficiently checks access by evaluating compiled permission rules through internal catalog tables, substantially reducing processing overhead compared to the multiplicative N x M callback pattern. Despite this advancement, the review highlights that much of the core logic, especially in default_permissions.py, has grown complex and difficult to maintain—making it prone to subtle bugs, particularly around interactions between config-based permissions and actor restrictions. Recommendations include refactoring for clarity, improving documentation and debugging tools (see the new debug endpoints), and adding early validation for config errors. The SQL query construction approach is effective but would benefit from more declarative abstractions and rigorous parameter handling.
Key Findings:
- Major performance improvements, but implementation complexity is high—especially for config/actor restriction interplay.
- Risk of configuration confusion and subtle permission bugs; documentation and validation are critical.
- Debugging and auditability can be enhanced with new endpoints and clearer error messages (permission system tools).
- Consistent use of parameterized queries is recommended to prevent SQL injection.
- Refactoring and codifying type hints/utilities would improve long-term maintainability.
sqlite-utils-iterator-support (2025-11-22)
Enhancements to the sqlite-utils library now allow its insert_all and upsert_all methods to efficiently process Python iterators yielding lists, in addition to the original dict-based input. Detection of the iterator type is automatic and maintains full backward compatibility, streamlining bulk inserts from row-based data sources like CSV streams and reducing memory usage by avoiding dict construction. Performance benchmarks show list mode delivers up to 21.6% speed improvement for datasets with few columns, though gains diminish or reverse with wider tables. All 1001 existing tests pass, alongside 10 new tests for list mode, confirming robust and production-ready implementation.
Key findings:
- List mode is up to 21.6% faster for typical (5-10 column) datasets; dict mode regains advantage for 15+ columns.
- Memory usage drops for list mode due to lack of dict overhead.
- No breaking changes or new dependencies introduced; backwards compatibility is ensured.
- sqlite-utils-list-mode.diff provides the implementation patch.
svg-to-png-renderer (2025-11-17)
A lightweight SVG to PNG renderer has been developed using Python, leveraging the xml.etree.ElementTree and Pillow libraries to parse SVG XML data and convert it to raster PNG images. This minimal library supports a range of SVG elements, including paths, basic shapes, and containers, as well as attributes such as colors, styling, and transforms. The renderer can be used as a command-line tool or imported as a library, and has been tested with complex SVG files, including the "Ghostscript Tiger" SVG. For more information on the project, see the Pillow documentation or the SVG specification.
- Key features:
- Support for SVG paths, basic shapes, and containers
- Support for colors, styling, and transforms
- Can be used as a command-line tool or imported as a library
- Tested with complex SVG files, including the "Ghostscript Tiger" SVG
svg-to-png-comparison (2025-11-17)
Multiple Python-based approaches for converting SVG files to PNG were benchmarked using the tiger.svg image, evaluating file size, output quality, and ease of installation. Pure Python solutions like CairoSVG and svglib+reportlab offered simple pip-based installs with predictable PNGs, though svglib lacks alpha channel support. Wand (ImageMagick bindings) and ImageMagick CLI yielded the highest quality output (16-bit RGBA) at the cost of larger files and system-level dependencies. In contrast, rsvg-convert CLI stood out for speed and batch suitability, while Pillow+CairoSVG enabled further in-Python image manipulation. Ultimately, selection depends on priorities—portability (CairoSVG, svglib), maximal quality (Wand, ImageMagick), minimal footprint (svglib), or performance (rsvg-convert).
Key findings:
- svglib+reportlab produced the smallest files, but without alpha (RGB only).
- Wand and ImageMagick CLI generated 16-bit PNGs, twice the size of other methods.
- CairoSVG remains the simplest, most widely compatible pure Python solution.
- rsvg-convert CLI is fastest for batch or server-side use but needs system install.
absurd-in-sqlite (2025-11-12)
Durable execution workflows can be implemented using SQLite, as demonstrated by the Absurd-in-SQLite project, which is inspired by Armin Ronacher's Absurd. This project provides a proof-of-concept implementation of durable execution using SQLite, allowing for reliable and long-running workflows that can survive crashes and network failures. The project utilizes a pull-based model, where workers pull tasks from a queue, and features a replay model that replays the entire function from the beginning when a task resumes. For more information, visit the Absurd and Absurd Workflows resources.
- Key findings:
- Durable execution can be achieved using a database like SQLite
- The replay model allows for reliable execution of tasks
- A pull-based model can simplify workflow management
- SQLite can handle moderate workloads, but Postgres may be more suitable for high-concurrency scenarios
yt-dlp-install-report (2025-11-12)
A detailed analysis of installing yt-dlp[default] via pip on Linux with Python 3.11 reveals that the process brings in six new packages totaling about 39 MB and over 3,000 files, including 44 binary libraries (mainly for cryptography and compression) consuming 8.55 MB. The main package, yt-dlp, is a feature-rich video downloader whose full capabilities rely on its optional dependencies, enabled by the [default] extra: Brotli (compression), pycryptodomex (cryptography), websockets (live streaming), mutagen (metadata), and yt-dlp-ejs (JavaScript extractors). The installation is dominated by Python source and bytecode files, with binaries used for performance-critical tasks; all binaries are standard Linux ELF shared objects with typical system dependencies. For downloading encrypted content, handling compression, live streams, or audio metadata, installing with [default] is recommended.
Key tools: yt-dlp, pycryptodomex
Key findings:
- 6 packages installed, major size contributor is yt-dlp itself (~22 MB).
- 44 compiled .so binaries (mostly for crypto/compression, not full executables).
[default]extra adds significant functionality for encrypted, compressed, live, and tagged media.- Binaries built for standard x86-64 Linux, depending only on common system libraries.
uv-run-flow-analysis (2025-11-10)
Running uv run myscript.py in a directory with a pyproject.toml launches a multi-phase workflow that automates Python script execution within an isolated, dependency-managed environment. uv scans for project metadata, resolves and validates interpreter and package requirements, manages virtual environments, locks dependencies with a TOML-based uv.lock file using the PubGrub algorithm, efficiently syncs the environment with parallel downloads and caching, and finally executes the desired command with robust error handling. This process is orchestrated via performant Rust crates, resulting in fast, reliable, and reproducible Python executions superior to traditional tools like pip or poetry. For more details on the tool, see uv documentation or the PubGrub resolution algorithm.
Key findings:
- uv automatically discovers and parses project dependencies from
pyproject.toml, supporting PEP standards and custom configurations. - Dependency resolution is lock-file-driven (universal, reproducible) and faster than pip/poetry due to Rust implementation, PubGrub, and aggressive caching.
- Python interpreter management is integrated, with automatic downloads and validation against project constraints.
- Installation of packages occurs in a virtual environment, with fine-grained error handling and atomic operations.
- The architecture enables cross-platform consistency, incremental sync, and seamless user experience with zero configuration required.
env86-analysis (2025-11-09)
env86 is a Go-based management tool that enables users to run x86 Linux virtual machines within browser contexts via the v86 WebAssembly emulator. By combining a native desktop application (embedding a browser), a robust CLI, and an integrated virtual networking stack, env86 provides an easily distributable and reproducible Linux environment that can boot instantly from snapshots, support host-guest communication, and mount host filesystems. Images are efficiently distributed through GitHub releases, and the system can be used interactively or in headless/automation contexts, making it especially suitable for development, education, sandboxing, legacy software execution, and rapid demonstration scenarios. While performance is limited by browser-based emulation, env86 uniquely excels in cross-platform portability and accessibility, allowing VMs to run anywhere a browser or desktop is available.
Key findings/features:
- Instant VM boot from state snapshots, dramatically reducing startup time.
- Secure host-guest communication using CBOR RPC over serial ports.
- User-space networking via go-netstack and transparent port forwarding.
- 9P protocol-based filesystem mounting for host directory access.
- Image distribution and updates handled via GitHub releases, supporting reproducible environments.
- Desktop integration via tractor.dev/toolkit-go enables GUI and CLI/CI workflows.
See the env86 repo for details: https://github.com/progrium/env86
Learn more about the v86 emulator: https://github.com/copy/v86
llm-pyodide-openai-plugin (2025-11-09)
Leveraging the LLM Python package and pyodide, this project successfully adapts LLM’s OpenAI model interface for direct use in browser environments by bypassing the standard openai library (which fails in browsers due to its httpx dependency) and instead using the browser-native fetch API for CORS-compliant API calls. The plugin implements the LLM KeyModel interface and registers new models with OpenAI support through custom hooks, allowing prompt execution and chat completions entirely within pyodide’s async event loop, without server-side Python. No changes to LLM’s core were required; all adaptations reside in the plugin, which integrates cleanly with the browser’s JS-Python bridge and achieves dynamic model registration, API calls, and response parsing directly in the browser. For reference, the core plugin implementation is contained in llm_pyodide_openai.py while pyodide provides the Python-in-browser runtime.
Key findings:
- The openai package installs in pyodide but is incompatible with browser HTTP requests, necessitating direct fetch API integration.
- All plugin logic, including API calls and response parsing, operates in Python via pyodide, using JavaScript interop for networking.
- No modifications to the LLM core package are required; plugin flexibility is sufficient for adapting to browser constraints.
- Streaming (partial responses) is not yet implemented, but could be achieved with ReadableStream and async generators.
- CORS restrictions are resolved naturally by using fetch, making production-grade browser-based AI agents feasible.
codex-sandbox-investigation (2025-11-09)
OpenAI Codex CLI's sandbox employs strong, platform-specific isolation to securely constrain the behavior of AI-driven code agents. On macOS, it uses Apple's Seatbelt sandbox with finely tuned dynamic policies, while on Linux, it combines Landlock for strict filesystem controls and seccomp for syscall-based network blocking—ensuring that agents can only write to user-approved directories and have no outgoing network by default. Both platforms feature special protection for .git repositories, path canonicalization to thwart symlink attacks, and enforce least-privilege principles, all integrated with user-configurable approval policies for flexibility. Key tools include the OpenAI Codex CLI and related sandbox documentation.
Key findings:
- Codex's sandbox distinguishes between DangerFullAccess (no sandbox), ReadOnly, and WorkspaceWrite modes, tightly controlling file/network access.
- Version control metadata (e.g.,
.git) is always read-only, preventing AI from corrupting repositories. - Linux sandboxing requires kernel 5.13+ for Landlock; macOS relies on built-in Seatbelt.
- All attempted network operations are blocked at the syscall level, except for local IPC.
- Windows support is experimental; robust isolation is limited outside macOS/Linux.
sqlite-query-linter (2025-11-04)
The SQLite Query Linter is a lightweight Python library that wraps the standard sqlite3 module to provide configurable linting and rule-based analysis of SQL queries before execution. Acting as a drop-in replacement, it helps catch common syntax errors and platform incompatibilities—such as invalid types in CAST, use of unsupported functions, SELECT *, missing WHERE clauses, and string quoting mistakes—helping developers avoid runtime errors and improve code quality. Users can choose built-in rules, set severity levels, and easily define custom rules via an extensible API. Designed for flexibility, it can block execution on critical issues or run in permissive/audit-only modes, with zero dependencies other than Python's standard library. Explore code and integration options at GitHub or view usage in the included demo.py script.
Key Features & Findings:
- Detects SQL mistakes commonly encountered when migrating between databases or writing raw SQLite queries
- Flexible configuration: Enable/disable rules, set strictness, and use audit-only monitoring
- Easy to extend for custom organizational or project rules
- Applicable to development, automated testing, database migrations, and production monitoring
h3-library-benchmark (2025-11-04)
A systematic performance benchmark was conducted on two prominent Python libraries implementing Uber's H3 geospatial indexing system: h3-py (official, C-based) and h3o-python (Rust-based). Results show h3o-python consistently outperforms h3-py on core operations, achieving over 2x speedup for coordinate conversions and up to 13x faster neighbor queries, while area calculations remain comparable. The performance advantage holds steady across varied dataset sizes and H3 resolutions, suggesting h3o-python's Rust backend is highly optimized for geospatial workloads. Differences in API coverage and cell representation (string vs. integer) should inform choice based on project requirements.
Key Findings:
- h3o-python is 2.2x faster for coordinate-to-cell and 1.8–2x for cell-to-coordinate conversions.
- Neighbor queries with grid_disk are 10–13x faster in h3o-python.
- Both libraries perform similarly for cell area calculations.
- h3-py offers more features and broader API support; h3o-python excels in raw speed for core operations.
h3o-python (2025-11-03)
h3o-python delivers efficient Python bindings for the h3o Rust library, enabling fast and convenient access to H3 geospatial indexing from Python. Utilizing PyO3 and packaged with maturin, it allows encoding geographic coordinates into 64-bit H3 cell indexes, decoding indexes, performing neighborhood queries, calculating great-circle distances, and retrieving surface area metrics—all without requiring a separate H3 installation. The module bundles its Rust extension in the distributable wheel for seamless deployment, and the API mirrors the upstream Rust crate for high performance and compatibility.
Key capabilities:
- Simple conversion between latitude/longitude and H3 cell indexes
- Neighborhood and adjacency checks, and disk queries
- Accurate area and distance calculations using H3 algorithms
- Lossless string/integer conversions of H3 indexes
wazero-python-claude (2025-11-02)
Wazero Python Bindings enable seamless integration of the wazero WebAssembly runtime—written in Go—with Python applications, delivering a zero-dependency solution for running WASM modules natively from Python. The project exposes a clean, Pythonic API for instantiating modules, calling exported WASM functions, and managing resources efficiently with context managers. Performance benchmarks demonstrate rapid execution and minimal overhead between Python and WASM. While the library excels at speed and ease of use, current limitations include support only for integer argument and return types, restricted WASI features, and lack of direct memory access.
Key findings:
- Near-native performance for compute-intensive WebAssembly code via wazero.
- Simple Python interface with automatic resource management and no external dependencies.
- Presently limited to i32/i64 arguments/results and basic WASM module features; WASI filesystem and direct memory access are not available yet.
datasette-plugin-skill (2025-10-24)
Covering every aspect of Datasette plugin development, this project creates a comprehensive skill set for authors—from bootstrapping with cookiecutter to deploying on GitHub and PyPI. It provides precise guides and working code samples for essential plugin hooks like custom SQL functions, authentication, custom views, and output formats. The resource includes an extensive API reference, best practices for configuration, static assets, and templates, plus testing and publishing workflows to ensure reliable plugins. Developers can use this to rapidly build a variety of plugins—custom SQL, visualizations, authentication handlers, data exporters, and more.
Key tools/projects:
Key findings:
- Covers both sync and async hook design for performance.
- Explains complete request/response and database APIs.
- Provides tested patterns for authentication, authorization, routing, and output customization.
blog-tags-scikit-learn (2025-10-24)
Automatically assigning meaningful tags to historic, untagged blog posts, this project leverages the Simon Willison blog database and scikit-learn to train and compare multi-label text classification models. Four approaches—TF-IDF + Logistic Regression, Multinomial Naive Bayes, Random Forest, and LinearSVC—were tested on posts’ title and body text using the 158 most frequently used tags. LinearSVC, with probability calibration, yielded the best overall performance, striking a balance between precision (85%) and recall (56%) with an F1 score of 68%, proving especially effective for assigning multiple tags to each entry. This open-source toolkit not only automates metadata enrichment but facilitates rapid quality assessment and scalable tag prediction for content libraries.
Key findings:
- LinearSVC outperformed other models, delivering the highest F1 score (0.6791) and recall.
- Logistic Regression and Random Forest prioritized precision but were more conservative—missing more actual tags.
- Naive Bayes offered a fast, simple solution with a solid balance of metrics.
- TF-IDF features and OneVsRest multi-label strategies proved robust for text classification in high-dimensional spaces.
cmarkgfm-in-pyodide (2025-10-22)
By rewriting cmarkgfm's bindings from CFFI to the Python C API, the project successfully ported GitHub's cmark-gfm Markdown parser to Pyodide. The resulting wheel is fully functional, requires no further building, and supports all GitHub Flavored Markdown features with high performance, thanks to direct C code execution via WebAssembly. Users can integrate the package into Pyodide (see Pyodide documentation) and render robust Markdown—including tables, strikethrough, and task lists—directly in the browser. This port demonstrates a practical technique for bringing other CFFI-based packages to WebAssembly/Pyodide environments.
Key Findings:
- All GFM features (tables, strikethrough, smart typography, etc.) work accurately.
- Integration and pytest test suites pass 100%.
- The port uses only Python C API bindings, improving compatibility and speed.
- Project source & wheel available.
python-markdown-comparison (2025-10-22)
Comparing seven prominent Python markdown libraries, cmarkgfm—bindings to GitHub’s C-based CommonMark/GFM parser—proved dramatically faster (10-50x) than pure Python options such as mistune, Python-Markdown, and marko. The benchmark, spanning small to large markdown documents, consistently found cmarkgfm excels in both speed and stability, making it ideal for high-volume or performance-critical applications. However, cmarkgfm trades extensibility and custom output formats for speed, so libraries like mistune (for fast pure Python and custom rendering) or Python-Markdown (for extension-rich configurability) may be preferable for projects prioritizing flexibility or ease of customization. See cmarkgfm's repository and mistune for details.
Key findings:
- cmarkgfm is 10-50x faster than pure Python markdown libraries, especially for large documents.
- Pure Python options offer greater extensibility, custom output formats, and API access, but at the cost of speed.
- Best library choice depends on project needs: cmarkgfm for raw speed/GFM compatibility, mistune for pure Python speed/customization, Python-Markdown for plugins/extensions.
datasette-plugin-alpha-versions (2025-10-20)
Datasette Plugins Analysis presents a systematic evaluation of 44 key plugins from the Datasette ecosystem, focusing on dependencies, permissions hooks, and release patterns as of October 2025. The study finds that 89% of these plugins rely on ALPHA versions of Datasette, with only 8 plugins having stable releases and just 5 supporting stable Datasette while using advanced hooks like register_permissions(). The open datasets, such as datasette_plugins_analysis.json and analysis scripts, support deeper inspection and maintenance planning as Datasette nears its 1.0 milestone. This enables maintainers to prioritize updates for plugins with alpha dependencies and track release maturity across the ecosystem.
Key Findings:
- 39 plugins depend on Datasette ALPHA versions; 34 of these have no stable releases.
- Only 5 plugins use
register_permissions()without requiring ALPHA Datasette. - 8 of the analyzed plugins currently offer at least one stable release.
- Main analysis and scripts are available here for further plugin and dependency tracking.
deepseek-ocr-nvidia-spark (2025-10-20)
Successfully deployed DeepSeek-OCR on an NVIDIA GB10 (ARM64, sm_121) by upgrading to PyTorch 2.9.0+cu130 so CUDA 13.0 wheels could be used instead of building from source. The repo includes automated scripts (setup.sh, run_ocr.py) that load the 6.3GB safetensors model (~34s) and run GPU inference (~58s for a 3503×1668 image), producing annotated images, markdown/text outputs and bounding boxes with validated multi-column accuracy. Flash-attn failed to compile on ARM64 and the pipeline falls back to eager attention, but overall accuracy and production readiness were confirmed. Reproducible instructions, logs and scripts are provided in the DeepSeek-OCR repo and the PyTorch cu130 wheel index linked below.
- Key findings: PyTorch 2.9.0+cu130 provides forward compatibility for sm_121 (no source build needed).
- Performance: model load ≈34s, inference ≈58s; detected 2257 text tokens / 921 vision tokens.
- Artifacts & links: DeepSeek-OCR code/model (https://github.com/deepseek-ai/DeepSeek-OCR) and PyTorch cu130 wheel index (https://download.pytorch.org/whl/cu130).
sqlite-permissions-poc (2025-10-20)
A proof-of-concept implements a fully SQLite-based hierarchical permission system that computes allowed database/table pairs by cascading rules across child (table), parent (database), and global levels with DENY-over-ALLOW semantics; it uses only plain SQL (CTEs + SQLite JSON functions) and is built on SQLite (https://sqlite.org). Actor and token inputs are JSON-parsed inside the query so a single CTE-based SQL statement resolves per-resource decisions (child → parent → global) and then intersects results with optional token scope, ensuring tokens can only restrict, not grant, access; behavior is validated with a pytest test suite (https://pytest.org). The demo includes a minimal schema, multiple simulated “hook” rule sources, example data, and 11 test scenarios that show child-level ALLOW overriding parent DENY, child-level DENY blocking parent ALLOW, default-deny behavior, and token intersection semantics.
Key findings:
- Pure-SQL implementation (no UDFs/extensions) using CTEs and sqlite JSON helpers.
- Cascading precedence: child > parent > global; at the same level DENY beats ALLOW.
- Token scoping applied via INTERSECT; tokens cannot elevate permissions.
- Single-query engine returns final db/table pairs; schema and tests are compact and extensible.
- 11 pytest scenarios confirm intended conflict-resolution rules and edge cases.
minijinja-vs-jinja2 (2025-10-19)
Benchmarking the Python bindings for minijinja (https://github.com/mitsuhiko/minijinja) against Jinja2 (https://palletsprojects.com/p/jinja/) on Python 3.14 and 3.14t measured template render performance using a realistic e-commerce template with inheritance, loops, and ~65KB HTML output. The suite runs 200 iterations per scenario, captures mean/median/std/min/max, and provides reproducible scripts (run_benchmark.sh, benchmark.py) plus matplotlib charts to visualize results. Jinja2 is faster on stock Python 3.14, while minijinja gains more from the free-threaded 3.14t build, indicating minijinja may be better positioned for free-threaded Python even though it’s currently slower in absolute terms. Everything needed to reproduce the 15–20 minute benchmark and view detailed analysis is included in the repository.
- Jinja2 (3.14): 0.990 ms mean vs Minijinja: 1.528 ms mean — Jinja2 ≈ 1.54× faster on 3.14
- Jinja2 slows ~14% on 3.14t (1.127 ms); Minijinja speeds up ~13% on 3.14t (1.336 ms)
- Artifacts: JSON results, comparison/distribution/speedup/timeline charts, and BENCHMARK_RESULTS.md with full analysis
node-pyodide (2025-10-19)
A compact demo shows how to run Python scripts inside a WebAssembly sandbox from Node.js using Pyodide: after npm install, launching node server-simple.js executes example-simple.py and writes generated files to the output/ directory. The project demonstrates a minimal server-side integration pattern for Pyodide (https://pyodide.org/) under Node.js (https://nodejs.org/) and is aimed at quick experimentation with sandboxed Python execution. It requires Node.js v16 or later and provides a simple starting point for extending Python-in-WASM workflows in Node applications.
- Executes Python in WebAssembly via Pyodide and writes outputs to output/
- Minimal commands: npm install; node server-simple.js
- Recommended Node.js v16+ for best compatibility
This README uses cogapp to automatically generate project descriptions.
A GitHub Action automatically runs cog -r -P README.md on every push to main and commits any changes to the README or new _summary.md files.
To update locally:
# Run cogapp to regenerate the project list
cog -r -P README.mdThe script automatically:
- Discovers all subdirectories in this folder
- Gets the first commit date for each folder and sorts by most recent first
- For each folder, checks if a
_summary.mdfile exists - If the summary exists, it uses the cached version
- If not, it generates a new summary using `llm -m github/gpt-4.1
- Creates markdown links to each project folder on GitHub
- New summaries are saved to
_summary.mdto avoid regenerating them on every run
To regenerate a specific project's description, delete its _summary.md file and run cog -r -P README.md again.