
TensorFlow.NET
.NET Standard bindings for Google's TensorFlow, enabling C# and F# developers to build, train, and deploy machine learning models.

The notebook for reproducible research and collaborative data science.

Nextjournal is a high-performance, browser-based notebook environment designed specifically to solve the 'reproducibility crisis' in data science and scientific research. Unlike traditional Jupyter notebooks, Nextjournal encapsulates the entire stack—including the operating system, drivers, libraries, code, and data—within versioned Docker containers. This ensures that a notebook created today will execute with identical results in 2026 and beyond. Its technical architecture leverages a reactive execution engine and a content-addressed storage system, allowing for granular versioning of every change. The platform is notably polyglot, permitting users to run cells in Clojure, Python, R, and Julia within a single notebook session, facilitating seamless data hand-offs between languages. For 2026, Nextjournal has positioned itself as the premier environment for high-stakes AI research and enterprise-grade data transparency, offering 'Garden' for simplified hosting and a desktop application for local-first development. It bridges the gap between the flexibility of interactive coding and the rigor of software engineering best practices, making it indispensable for labs and data-heavy organizations requiring audit-trailed computations.
Nextjournal is a high-performance, browser-based notebook environment designed specifically to solve the 'reproducibility crisis' in data science and scientific research.
Explore all tools that specialize in train machine learning models. This domain focus ensures Nextjournal delivers optimized results for this specific requirement.
Explore all tools that specialize in collaborative coding. This domain focus ensures Nextjournal delivers optimized results for this specific requirement.
Captures the exact state of the file system and environment at every execution point using content-addressed storage.
A notebook engine that tracks dependencies between cells and updates them automatically when upstream data changes.
Allows multiple kernels to share data via the filesystem or memory within a single document flow.
Seamlessly offloads heavy computations to high-performance cloud instances while maintaining the frontend in the browser.
Transparently versions code using Git under the hood, allowing for branching and merging of notebooks.
A simplified deployment platform for turning notebooks into interactive web applications or API endpoints.
Users can import any Docker image to serve as the runtime environment, providing total control over system dependencies.
Sign up via GitHub or email at nextjournal.com.
Create a new notebook and select a base environment (e.g., Python 3.11, CUDA-enabled, or R).
Define your runtime environment using the built-in package manager or custom Dockerfile.
Upload datasets to the 'Files' pane for persistent, versioned access.
Write and execute code in polyglot cells (Python, R, Julia, Clojure).
Use the 'Environment' pane to save an immutable snapshot of your current setup.
Enable real-time collaboration by inviting team members via email.
Connect remote compute resources if local browser-based compute is insufficient.
Publish the notebook with a DOI for academic reference or a private link for internal review.
Utilize the version slider to audit changes or revert to previous computation states.
All Set
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Verified feedback from other users.
"Users praise the absolute reproducibility and clean UI, though some find the Clojure-centric ecosystem slightly intimidating at first."
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