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CodeGraph is a local code-intelligence tool for AI coding agents, distributed as a CLI, MCP server, and embeddable npm library by Colby McHenry. It parses source code into a SQLite knowledge graph of symbols, files, calls, imports, inheritance, dependencies, framework routes, and cross-language bridges, then resolves references and provides full-text search, source context, call paths, callers, callees, impact analysis, and affected-test discovery. Its primary MCP operation, codegraph_explore, returns relevant verbatim source together with relationships and blast-radius information so agents can query structure without file-by-file exploration. A native Rust parsing kernel supports more than 20 languages, with a portable fallback for unsupported platforms or files that fail native parsing. A native file watcher incrementally updates the local graph after changes, while connect-time reconciliation catches edits made between agent sessions. The tool installs and configures MCP integrations for agents including Claude Code, Cursor, Codex CLI, OpenCode, Gemini CLI, and GitHub Copilot; projects are initialized with codegraph init and stored locally under .codegraph. The repository states that the software is MIT-licensed and that telemetry can be disabled.

Kronos is an open-source family of decoder-only foundation models for financial-market K-line sequences, developed by the repository authors and distributed with pretrained models through the Hugging Face Hub. A specialized tokenizer quantizes multidimensional OHLCV market data into hierarchical discrete tokens, which an autoregressive Transformer models for forecasting and other quantitative tasks. The Python predictor accepts historical market data and timestamps, applies preprocessing and normalization, generates probabilistic forecasts for future open, high, low, close, volume, and amount values, and reverses the normalization. It supports single-series and GPU-parallel batch prediction, with configurable sampling temperature, nucleus-sampling probability, and forecast-path count. The repository also provides a Qlib-based example pipeline for data preparation, tokenizer and predictor fine-tuning, and simple top-K backtesting; it explicitly describes this example as a demonstration rather than a production-ready trading system. The project is licensed under the MIT License.

pgrust is an open-source reimplementation of PostgreSQL written entirely in Rust. It is wire-compatible and SQL-dialect-compatible with PostgreSQL, can use an existing PostgreSQL data directory, and is intended to show what PostgreSQL might look like with a modern Rust implementation. Its architecture includes a vectorized, push-based executor with JIT compilation, thread-based concurrency, a priority-based query scheduler, work-stealing parallel query execution, a built-in out-of-memory killer, pipelined fsync, and the pgrcolumnar column-oriented storage format. It also provides a browser-based WebAssembly build, downloadable binaries, source builds, and a Docker image compatible with the official PostgreSQL image's environment variables and data volume layout. The project reports passing PostgreSQL's regression suite, but states that it still contains bugs and is not recommended for production. Existing PostgreSQL extensions do not work because pgrust has no stable extension ABI, and its JIT compiler is tuned for Graviton4. pgrust is licensed under AGPL-3.0, while portions derived from PostgreSQL remain under the PostgreSQL License.

RustGIS is a free, open-source GIS application written in Rust for mapping, analyzing, editing, and sharing geospatial data. It runs natively on Windows, macOS, and Linux and in the browser through WebAssembly, using a GPU-rendered ribbon interface and keeping local files on the user's machine unless a network operation is requested. It supports vector data such as GeoJSON, Shapefile, GeoPackage, GeoParquet, FlatGeobuf, KML/KMZ, and many other formats, as well as GeoTIFF and cloud-optimized GeoTIFF rasters. Its mapping and analysis functions include projections, 3D globes, symbology, labeling, editing with undo, raster processing, geoprocessing, ModelBuilder, and more than 1,000 GeoLibre and Whitebox tools. Every ribbon action is represented as a command that can be run through the desktop application, rustgis-cli, Python, notebooks, or an MCP server. The project also provides sandboxed WebAssembly plugins, web services and tile support, layouts and reports, export and sharing tools, and an optional AI Assistant that can invoke RustGIS commands through selected model providers. The repository is licensed under Apache License 2.0; desktop builds, a web application, command-line tools, package-manager installations, and source builds are provided.

Timestral is a family of time-series foundation models from Mistral AI for multivariate probabilistic forecasting with past-only, known-future, and categorical covariates. Its standalone inference package accepts NumPy arrays or PyTorch tensors, forecasts multiple variates jointly, and returns 21 quantiles for each time step rather than only a point estimate. It supports missing observations, categorical covariate encoding, contexts of up to 32,768 steps, and CPU or GPU inference. The models use a causal patch-based transformer trained with Decoupled Horizon Forecasting. Separate prediction tokens read the causally encoded context; covariates condition target variables without reading them, and only known-future covariates extend into the forecast horizon. A quantile head produces non-crossing quantiles, while forecast patches are denormalized using context statistics and earlier predicted medians. The repository provides inference code and downloads model weights from Mistral AI's Hugging Face organization. The code is licensed under Apache 2.0; the model weights are distributed separately under a Modified MIT license.