We made a Tesseract Python wrapper using pybind11 (not the CLI)
We made a Tesseract Python wrapper using pybind11 (not the CLI) launched on November 26, 2019 with 7 votes, #971 of 3,203 launches that month and more than 50% of that year's launches. The site was still online in September 2026, 6 years on.
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tesserocr – A Python wrapper for the tesseract-ocr API2016 · github.com · ▲78A Python wrapper for the tesseract-ocr API. Contribute to sirfz/tesserocr development by creating an account on GitHub.
Tesseract Core – Autodiff-native, self-documenting software components2025 · github.com · ▲15Tesseract Core is a command line app + Python SDK that lets scientists and engineers wrap complex scientific and machine learning code into containerized, self-documenting, differentiable functions—making them easier to use, compose, serve, share, and deploy. Its main use case is to build end-to-end differentiable pipelines consisting of wildly different components like physical simulators, geometric operators like differentiable meshers / renderers, generic data transforms, and neural networks. Just installing these heterogeneous components can be a challenge for scientists who simply…
A Spatial Environment for Python2022 · python.natto.dev · ▲169Hi all! A little background: I've been working on natto.dev, a spatial environment for JavaScript. I'm really excited about new interfaces for code (leveraging metaphors we're good at, spatial reasoning, making state visible, design tools, etc). With all the buzz around PyScript, I discovered Pyodide and got it working inside natto. This Python version is a stripped down version of https://natto.dev (eg interactive outputs, multiplayer) so please check that out if this interests you. I'm excited to share this spatial environment for Python. Imagine Jupyter cells arranged on a 2D…
Run TRELLIS.2 Image-to-3D generation natively on Apple SiliconApr 2026 · github.com · ▲202I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels: none of which work on Mac. I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files. Generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro (24GB). Not as fast as…
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