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Releases: Agnuxo1/KlipperLearn

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KlipperLearn — Free Community Preview (2026-09-17)

KlipperLearn — Free Community Preview (2026-09-17) Pre-release
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@Agnuxo1 Agnuxo1 released this 17 Sep 04:18
community-preview-20260917
21404b7

Free community preview — experimental, supervised software

KlipperLearn is an open-source calibration-evidence and reviewed slicer-preset project. This snapshot publishes independent tools for existing 3D-printing workflows, not ten upstream integrations or a public ChatGPT directory approval.

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  • Printer Optimizer: portable skill and offline Python helper; six paired Quality/Standard/Speed Orca presets from acceptable repeatable evidence. Installation in ChatGPT is not automatic and public listing is pending.
  • Cura Evidence: independent post-processing script, no G-code changes.
  • OctoPrint Evidence 0.1.1: install the standalone ZIP, not the full project.
  • Slicer Evidence: file fingerprints for Orca and PrusaSlicer.
  • Ecosystem Toolkit: local-file diagnostics, history, preset and evidence checks.
  • Calibration Card: original STL, not 3DBenchy; slice with a verified machine profile.
  • Source archive and SHA256SUMS: complete reviewed source and checksums.

Start here · Review scope

Validation

508 Python tests and 185 subtests; 12 JavaScript test programs; 25 exporter tests; 47 reference tests. Native OctoPrint 1.11.8 plugin-manager/event-manager verification passed with synthetic events, temporary storage and blocked network access. Static-site desktop/mobile and local-demo browser checks passed.

Native slicer import, physical printer acceptance and maintainer requirements remain explicit gates. A native Orca CLI attempt did not produce sliced output; it is not recorded as success. No printer, live LAN or production host was changed.

Safety, cost and provenance

No publisher fee, advertising or API subscription. ChatGPT platform limits still apply. Preserve originals; review effective settings; supervise trials; never bypass thermal protections. DEMO artifacts are synthetic, not printer presets to adopt. Flashforge Benchy tests were operator-plus-ChatGPT work; Anycubic charts/cubes used a separate KlipperLearn setup.

App source is GPL-3.0-or-later with separately scoped MIT reference tools. AI-assisted development is disclosed. This is a prerelease, not a claim of certified safety or guaranteed speed/quality gains.

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