CoCalc by SageMath, Inc.’s cover photo
CoCalc by SageMath, Inc.

CoCalc by SageMath, Inc.

Software Development

Renton, Washington 2,770 followers

Collaborate in real time while using Jupyter, Linux, LaTeX, and more (like a Google Suites for computational science).

About us

CoCalc is a web-based environment that enables real-time collaboration while performing research or teaching computational science. SageMath, Inc. is the company that creates/maintains the hosted platform https://cocalc.com and distributes software licenses for CoCalc OnPrem: https://onprem.cocalc.com/ CoCalc runs a Ubuntu-based Linux environment, giving users access to shared file systems and the flexibility of a terminal. Jupyter Notebooks in CoCalc are real-time collaborative, just like Google Docs, and the platform runs on Google Cloud via Kubernetes. Editors for LaTeX, Markdown, and Quarto are also available. Moreover, you can select from various other IDEs such as VS Code, RStudio (not affiliated with Posit), JupyterLab, Jupyter Classic, and Pluto. Editors for LaTeX, Markdown, and Quarto are available. Moreover, CoCalc provides other IDEs such as VS Code, RStudio (not affiliated with Posit), JupyterLab, Jupyter Classic, and Pluto. Furthermore, X11 Desktop allows the use of various graphical interfaces, such as Vim, Emacs, Spyder, and even a web browser (Firefox). It has a one-click launcher for many applications and a terminal for full control. If you need more powerful resources for Machine Learning, training LLMs, or performing computationally intensive tasks or simulations, consider using compute servers for on-demand access to NVIDIA GPUs (H100s, A100s, L40s, T4s, RTX A6000s, and more) and CPU machines. https://doc.cocalc.com/compute_server.html Our integrations with Google Cloud and Hyperstack allow you to skip the hassle associated with traditional cloud consoles. You can even use local resources or another cloud provider of your choice. Lastly, CoCalc was built with teaching in mind! We make computationally oriented courses run more smoothly. Please check our dedicated instructor guide https://doc.cocalc.com/teaching-instructors.html and reach out to help@cocalc.com anytime if you have any questions.

Website
https://cocalc.com
Industry
Software Development
Company size
2-10 employees
Headquarters
Renton, Washington
Type
Privately Held
Founded
2015
Specialties
jupyter, sagemath, teaching, education, programming, collaboration, research, data science, r, mathematics, analysis, latex, markdown, terminal, and students

Locations

Employees at CoCalc by SageMath, Inc.

Updates

  • CoCalc by SageMath, Inc. reposted this

    Here is why our new soon-to-be-launched CoCalc-ai matters. Writing code using AI agents feels natural to me because it is much closer to how real mathematics research works than how programming is often described. Mathematicians do not mainly work by writing formal proofs. They work by thinking, sketching, arguing in words, trying examples, finding counterexamples, and only then producing a formal artifact. In computational mathematics (e.g., SageMath), that process already continues into code. I often loved to turn theory into algorithms, run them, and use the results to refine understanding or discover mistakes. That is what good AI coding looks like. One stays focused on the problem, the structure, the constraints, and the checks, and the agent helps turn that into executable artifacts. The right environment for AI coding is not just a chatbot that emits code, it is a research environment: chat, code, review, logs, history, and tests all in one place. I think the future of AI coding is not replacing thought with code generation; it is letting people work more at the level where real discovery happens. -WS

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  • Happy New Year from Washington, D.C.! We're attending the Joint Mathematics Meeting! The energy here is always incredible, and we are glad to be back! We were honored to participate in the JMM Exhibitor Committee meeting today (pictured right), helping to shape the future of this gathering for the mathematical community. We are also trying to create a buzz about our soon-to-be major Product Launch. We'll have three new releases: CoCalc+, CoCalc Launchpad, and CoCalc.ai, within the next couple of weeks. Want a sneak peek? We are scheduling 1:1 "coffee syncs" throughout the conference. Send us a message or drop a comment if you'd like to connect! #JMM2026 #CoCalc #SageMath #Mathematics #WashingtonDC

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  • We are absolutely excited about the opportunities ahead in 2026 and proud to be part of this project! #openscience #earthscience #egu #agu #esa

    🎉 Happy to announce a major update to the EarthCODE Portal, with enhanced documentation and tooling. Here's what's new on https://earthcode.esa.int/ : * Access to the EarthCODE Workspace - sign in with your GitHub account for SSO to all the integrated #earthobservation Platforms: ** EOX IT Services GmbH's Euro Data Cube with CoCalc by SageMath, Inc. and Pangeo Community, Brockmann Consult GmbH's DeepESDL, VITO Remote Sensing's openEO federation on Copernicus Data Space Ecosystem, terradue's G-TEP, Polar View's Polar TEP, CGI's Insula * A clear picture of the EarthCODE Ecosystem * A New Resources Page with Tutorials and Examples for how to prepare and publish your FAIR data in EarthCODE * A Dedicated Page for the EarthCODE Communities Looking ahead, in 2026 we'll launch also the Engineering Services, we'll help the Cluster communities build and distribute thematic data collections, and we're preparing a lot of community engagement activities! #openscience #earthscience #earthobservation

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  • Professional molecular dynamics LaTeX template for drug discovery research with quantum chemistry calculations and virtual screening workflows. Includes DFT methodology, AMBER protein simulations, AutoDock molecular docking, HOMO-LUMO analysis, transition state searches, and ADMET predictions. Get automated ψ orbital plots, RMSD trajectories (Å precision), binding energy decomposition (kcal/mol), and reaction pathway ∂E/∂r analysis during compilation. Features PythonTeX integration, chemistry packages (chemfig, mhchem), and proper scientific unit formatting (siunitx) for reproducible computational workflows. Download this molecular dynamics template: https://lnkd.in/gHn_QcAs #MolecularModeling #DrugDiscovery #ComputationalChemistry #DFT #VirtualScreening #QuantumChemistry

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  • Professional machine learning LaTeX template featuring gradient descent ∇L(θ), backpropagation ∂L/∂θ, and neural network optimization frameworks. Download pre-formatted algorithmic methodologies: • Loss functions ℒ(θ) with L₁/L₂ regularization λ||θ|| • Activation functions σ(x), ReLU(x)=max(0,x), softmax • Optimization algorithms: SGD, momentum β∇L, Adam with adaptive α • Hyperparameter search over α×λ×architecture space • Automated gradient convergence and loss landscape visualization Get this neural network template for reproducible ML research with live Python computation (scikit-learn, PyTorch, TensorFlow compatible): https://lnkd.in/g7w9CtxZ #MachineLearning #DeepLearning #GradientDescent #NeuralNetworks #AIResearch

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  • Professional bioinformatics LaTeX template for sequence analysis and comparative genomics with BioPython integration. Core capabilities: Phylogenetic algorithms (UPGMA, neighbor-joining), distance matrix computation with Δ-metrics, conservation scoring, and hierarchical clustering. Custom biological notation commands plus Nature journal citation style. What you get: Calculate evolutionary distances, build phylogenetic trees with O(n²) efficiency, perform gene family analysis, and generate publication-quality figures automatically during document compilation. Perfect for comparative genomics research, phylogenetic studies, and sequence evolution projects needing version-controlled computational workflows. Download this bioinformatics LaTeX template: https://lnkd.in/gc4KrHyW Built for computational biologists who need reproducible analysis where sequence data → algorithmic processing → results happen in one document. #latex #templates #scientificpublishing #pythontex #python

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  • Professional PDE LaTeX Template for Finite Difference Methods Download a differential equations LaTeX template for analytical and numerical solution methods. Key Features: • ODE solutions with integrating factors and phase space analysis • Heat equation PDE: ∂u/∂t = α∇²u using finite difference methods • Stability analysis: r = αΔt/Δx² ≤ 0.5 criterion verification • Damped oscillator regimes: x'' + 2γx' + ω₀²x = 0 • Python-integrated live computation with PythonTeX • Numerical vs analytical error quantification Applications: Heat transfer modeling, oscillatory dynamics, mathematical physics research, academic coursework with reproducible workflows. Download: https://lnkd.in/g6gJQPeM #PDETemplate #FiniteDifference #LaTeXTemplate #NumericalMethods #CoCalc

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  • Professional computational chemistry LaTeX template for DFT calculations, molecular dynamics simulations, and drug discovery workflows. Get automated ψ orbital analysis, HOMO-LUMO gap calculations, Δ energy plots, and RMSD trajectories (Å precision) generated during compilation. Includes PythonTeX integration for live quantum chemistry calculations, AMBER MD workflows, virtual screening analysis, and binding energy decomposition. Perfect for quantum chemistry manuscripts, molecular dynamics papers, drug discovery research, and reaction mechanism studies with reproducible computational workflows. Download this computational chemistry template: https://lnkd.in/gHn_QcAs #ComputationalChemistry #QuantumChemistry #MolecularDynamics #DFT #DrugDiscovery #ScientificComputing

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