Some weeks ago I shared a post about using Fortran into JupyterLite. Here an blogpost about it.
You can now try it easily here: https://notebook.link/@anutosh491/fortran
I'm sharing the work of a colleague, Anutosh Bhat.
>What if the Clang & LLVM compiler toolchain could run entirely inside your browser ? Meet WasmBolt 😃
Inspired by 🖥️ Matt Godbolt's Compiler Explorer, WasmBolt runs Clang, mlir-opt, opt, llc, Graphviz and wasm-ld locally through WebAssembly, without a remote compilation server.
You can inspect Clang ASTs, progressively lower MLIR, run real LLVM optimization passes, render control-flow graphs, explore instruction selection and MIR, and generate assembly or object files for WebAssembly, AArch64 and x86-64. For WebAssembly, you can go one step further: link, load and execute the generated program directly in the browser.
Here the github project: https://github.com/Anutosh21/WasmBolt
Anutosh Bath and Ondřej Čertík brought LFortran to Jupyterlite
There is a demo here: https://notebook.link/@anutosh491/fortran
source: https://lnkd.in/p/eR49Jve6
Hey,
For those who don't already know it, JupyterGIS is a Jupyter opensource project which allows you to create GIS projects in Python or R.
## Highlights
- **Real-time collaboration** - like Google Docs for GIS, with spatial annotations and comments
- **QGIS interoperability** - import and export QGIS project files
- **Python API** - scriptable workflows in collaborative notebooks
- **Many data formats** - GeoJSON, GeoTIFF/COG, GeoParquet, GeoPackage, Shapefile, PMTiles, WMS/WMTS, and more
- **Grammar-driven symbology** - flexible rule-based styling, similar to Vega-Lite
- **Storymaps** - guided narratives through your maps
- **Hillshade & heatmaps** - terrain and density visualization
- **GDAL processing** - rasterize, warp, and translate in the browser or on the server
- **STAC & OpenEO** - cloud-native data catalog and processing support
- **Pangeo integration** - dynamic raster tile serving via [TiTiler](https://developmentseed.org/titiler/) backed by the Pangeo stack
- **Layer gallery** - pre-built layer catalog for quick setup
- **Embeddable** - use as a standalone map, an IDE, or from within a notebook
- **AI skills** - map operations in natural language via Jupyter AI
There is a big update about story maps that you can check here: https://notebook.link/@martinRenou/jupytergis-announcement and a medium article for technical stuff
Do you already know this tool ? Do you use it ?
Following this post, it's now possible to use Numba directly in your browser in wasm: https://notebook.link/blog/numba-in-the-browser/
You can try it here: https://notebook.link/@anutosh491/numba-ecosystem
Hi,
I took the official OpenCV tutorials and created a series of notebooks.
You can run them entirely in your browser, you don't have to clone them: https://notebook.link/@Alexis_Placet/opencv_tutorials
Don't hesitate to give me feedback or create issue/pullrequest on this repo: https://github.com/Alex-PLACET/opencv_tutorials
Hey,
I made a series of notebooks about Apache Arrow and Parquet with pyarrow that you can run in your browser: https://notebook.link/@Alexis_Placet/apache-arrow-courses
It covers basics to advanced topics
Hi,
Anutosh Bhat, a colleague of mine, just posted this on Linkedin:
>What if real compiler infrastructures could run entirely inside the browser? > >I’ve successfully patched llvmlite for JupyterLite, making it possible to build >LLVM IR, run optimization passes, visualize control-flow graphs, compile to >WebAssembly and execute the result — without a native LLVM setup or server-side >kernel. > >I’ve shared a step-by-step notebook showing how a loop evolves from stack-based IR >to SSA, then to an LICM-style form, into a fully optimized closed form, eventually >to benchmark all of these steps. > >The bigger idea is a zero-install compiler workbench for reproducible optimization >experiments, lightweight compiler prototyping, IR debugging, custom code->generation pipelines, shareable research demos, and eventually interactive >tutorials and compiler courses. > >Try out the interactive notebook using notebook.link: https://notebook.link/@anutosh491/llvmlite > >I’m also in the process of bringing MLIR Python bindings into the same JupyterLite >and WebAssembly environment, starting with a 2D CNN-style Conv2D + bias + ReLU >kernel. I’ll share the full pipeline in a separate technical blog post soon. > >I’ve also started patching Numba for Wasm and have simple scalar u / jit functions >running inside JupyterLite. Array and tensor workloads are the next challenge. > >I’m keen to take this further. If you’re working on LLVM, MLIR, Numba, WebAssembly >or browser-native developer tools — or see a real use case for this — I’d be >interested in connecting with teams looking to collaborate, support the work or >explore what we could build together.
Next step is to patch Numba to get scalar jit !: https://www.linkedin.com/feed/update/urn:li:ugcPost:7485014172934541312/?dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287485014751178211329%2Curn%3Ali%3AugcPost%3A7485014172934541312%29
emscripten‑forge is essentially a conda‑forge‑style channel for the emscripten-wasm32 platform. It lets you build and install conda‑compatible packages that target WebAssembly via Emscripten, using the same recipes and tooling you already use on native platforms.
What it does:
emscripten-forge/recipes) and builds them for emscripten-wasm32..wasm + JS glue) that can be consumed in the browser or other Wasm runtimes.conda/mamba/micromamba just like regular conda‑forge packages: mamba install -c emscripten-forge some-packageOne real‑world example is JupyterLite Xeus, which runs entirely in‑browser via WebAssembly and uses emscripten‑forge built packages for the runtime. This stack is live on https://notebook.link, where you can run numpy, onnx-runtime, Apache Arrow and other C/C++ based libraries in the browser without any local installation.