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ZEISS arivis Cloud: a cloud-based platform for deep learning model training and scalable bioimage analysis

Modern biological imaging generates large, complex datasets that require scalable and reproducible image analysis methods. Deep learning has demonstrated strong performance on bioimage segmentation tasks, but training custom models has remained inaccessible to many researchers due to requirements for GPU infrastructure, programming expertise, and large annotated training datasets. ZEISS arivis…

We haven't written up this one. bioRxiv has the full story — the link below goes straight to it.

Read the original at biorxiv.org →

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Dev Log: 2026-08-12 — a 96s suite that became 42s, a capability that wasn't a scope, and four steps to a passkey

Fifteen commits, three repos, and the bulk of it in one: a control plane that got an MCP surface, a much faster test suite, and a handful of things that turned out to be quietly wrong once I looked…

  • A 96-second suite was optimized to 41 seconds through fifteen commits and three repositories.
  • Redundant seeder calls were removed, and test impact analysis was improved with pcov over Xdebug.
  • Four steps were outlined to enable passkeys in the application.

SNEPPX-Alg: Project Structure, Current Status, and How to Contribute

A transparent look at the 522-commit C++ AI runtime Project Overview SNEPPX-Alg is an open-source (MIT) AI runtime written in C++ with Python bindings.

  • SNEPPX-Alg is an open-source C++ AI runtime with Python bindings.
  • Project includes memory encryption, control-flow obfuscation, and runtime monitoring.
  • Contributions sought for Python bindings and kernel optimization.

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