Osaurus 0.25.32 (no downloads, use 0.25.33)

Warning

No downloads for this release. Install 0.25.33 instead. It has the same app code.

0.25.32 was published without its DMGs because of a release-script bug: the release was published before its files were uploaded, and immutable releases reject uploads after publishing. The build and notarization succeeded, but the files could never be attached, so the same code was republished as 0.25.33. The fix is in #3356.

In-app updates were not affected and never offered 0.25.32. The original notes are kept below for history.

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Highlights

• Add Ripple-65B Preview support for JANGH2 and JANG_4D bundles, including bundled DFlash2 speculative decoding.
• Improve SSD prefix reuse across tool calls, model reloads and growing conversations, with more compact Ripple sliding-window cache storage.
• Correct model download and running-memory estimates for SSD-backed n-gram tables and mixed JANG formats.
• Strengthen generation-limit, sampler, image-processing and model-specific reasoning correctness.

Performance and known limitations

On the qualified M5 Max app runs, Ripple code generation with DFlash2 was about 2.8× faster than AR on JANGH2 and 2.4× on JANG_4D. Results depend on the model and task: JANGH2 prose was close to AR, and a degraded long-lived process showed a small prose slowdown. An intermittent whole-process slowdown remains under investigation; these measurements are not a guarantee for every workload or Mac.

Adaptive decoding preserves sampling choices. Requests with ineligible sampling options can use AR even while Adaptive is selected; the selector explains this fallback. When switching models, reset unsupported saved reasoning choices to the model default or Inherit. See the runtime qualification and remaining limitations.

What's Changed

• Preserve agent Max Tokens with the merged runtime output-limit fix by @jjang-ai in #3292
• Honor native generation settings and preserve agent control applicability by @jjang-ai in #3294
• fix(claude-code): hide ungranted tools, explain refusals, add effort to picker by @tpae in #3293
• Pin merged K2 grouped-normalization fusion by @jjang-ai in #3297
• Pin merged Spark projection input-cast fusion by @jjang-ai in #3299
• Align workspace mutation line counts with file reads by @jjang-ai in #3301
• Measure SSD table storage and RAM estimates from bundle data by @jjang-ai in #3303
• Keep installed model header totals consistent when filtering by @jjang-ai in #3304
• Count host reclaimable RAM without oversized internal aggregates by @jjang-ai in #3306
• keep every agent on the relay after re-pairing the phone and keep a phone run's stopped reply on the mac by @RaajeevChandran in #3305
• Preserve inherited output-limit provenance in Insights by @jjang-ai in #3308
• Consume merged Gemma unified vision LayerNorm correction by @jjang-ai in #3311
• Consume merged unified Gemma stored RGB correction by @jjang-ai in #3312
• Consume ordinary Gemma stored-RGB and white-alpha correction by @jjang-ai in #3313
• Lean agent-run tool surface, batch file reads, and opt-in specialist tools by @tpae in #3307
• Consume loaded tied-head precision and strengthen cache qualification by @jjang-ai in #3315
• Honor image-edit engine strength contracts by @jjang-ai in #3318
• Pin request-owned DFlash sampling and stabilize cache migration fixtures by @jjang-ai in #3323
• Consume SSD context and model reload runtime fixes by @jjang-ai in #3324
• Consume JANG semantic-width runtime correctness fix by @jjang-ai in #3325
• Consume speculative sampler vocabulary-axis fix by @jjang-ai in #3328
• Pin native MTP accepted stop-tail SSD cache fix by @jjang-ai in #3329
• Consume DFlash media fallback and hybrid cache boundary capture by @jjang-ai in #3331
• Pin vmlx-swift: Ripple-65B-Preview (JANGH2, JANG_4D) with DFlash2 by @jjang-ai in #3332

Full Changelog: 0.25.31...0.25.32