pandas

R is an open-source programming language and software environment for statistical computing and graphics, originally created by Ross Ihaka and Robert Gentleman and now maintained by the R Core Team and the R Foundation. It reads and writes common data formats (CSV, Excel via packages, SPSS/SAS/Stata via packages), provides facilities for data manipulation, statistical modeling, hypothesis testing, time series and high-quality plotting (base graphics, grid, lattice, ggplot2), and is extensible through CRAN and Bioconductor packages; it runs on Windows, macOS and Linux and integrates with tools such as RStudio, Jupyter (IRkernel), Rcpp, reticulate and sparklyr. R is distributed under the GNU General Public License.

Matplotlib is a Python library for creating static, animated, and interactive data visualizations. It produces publication-quality figures in multiple hardcopy formats and interactive environments, and can run in Python scripts, Python/IPython shells, web application servers, and graphical user-interface toolkits across platforms.

Apache Spark is a unified analytics engine for large-scale data processing, developed as an Apache project. It provides high-level APIs in Scala, Java, Python, and R, and executes general computation graphs for data analysis. Its higher-level components include Spark SQL and DataFrames, the pandas API on Spark, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. Spark can run locally or submit workloads to clusters through Spark, YARN, or Kubernetes integrations, and is used in large-scale data pipelines such as those underlying Poolside's model factory.

Deasy Labs is an AI data-curation platform with APIs and a Python SDK for preparing unstructured organizational data for RAG systems, search, and AI agents. It connects to sources such as SharePoint and S3, OCRs, parses, chunks, and normalizes files, then builds taxonomies, applies metadata, detects sensitive data, and evaluates file quality and relevance against a use case. Users can slice the enriched corpus by relevance, topic, time, quality, or sensitivity, write metadata back to source systems, or deliver datasets to downstream retrieval pipelines. Data slices are continuously monitored and refreshed as source content changes. The SDK can also generate context files and folder indexes for coding agents, as demonstrated in the video.

Lightpanda Browser is a headless browser built from scratch in Zig for AI agents and web automation. It is not based on Chromium, Blink, or WebKit; its implementation includes an HTML parser, V8 JavaScript support, a DOM tree and APIs, network loading, cookies, form input, proxy support, network interception, and optional robots.txt compliance. It can dump pages as HTML, Markdown, PNG, or PDF, or expose a Chrome DevTools Protocol and WebDriver BiDi server for clients such as Puppeteer and Playwright. Its native agent mode uses an LLM to navigate pages, click through flows, fill forms, and extract structured data. Sessions can be exported as deterministic, token-free PandaScript JavaScript and replayed without a model at runtime. The browser also provides an MCP server over stdio or HTTP, with isolated or shared browsing sessions for multiple agents. The project distributes nightly binaries for Linux and macOS, Docker images for Linux amd64 and arm64, and packages for Homebrew and the Arch User Repository. It can also be built from source with Zig and its V8, Libcurl, and html5ever dependencies.