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The Instruments of LLM training — a native macOS cockpit for Forge. Train language models from scratch on Apple Silicon without a terminal.

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  1. Wed, Aug 5, 2026 2

    1. M5/M6/M7: Compare view, Datasets flow, icon, notifications, 200k stress test
      …
      - CompareView: 2-8 run overlay (val + optional train-EMA), X axis in
        steps / TOKENS (honest cross-batch-size axis) / wall-clock from
        elapsed_s, auto hyperparameter-diff legend (only differing fields),
        sortable summary table (params/best val/final ppl/mean tok-s/duration),
        CSV export
      - DatasetsView: token counts (bin headers), vocab, on-disk size; New
        Dataset sheet drives tools/prepare_data.py (TinyStories) or
        tools/prepare_hf_data.py (HF sources + research presets) with a live
        streamed console
      - Sidebar restructured: Studio (Comparer, Datasets) + Runs
      - Local notifications on run finish/fail (final loss / reason in body)
      - Generated app icon (forge-gradient + flame, full iconset -> icns),
        wired into the bundle
      - Stress test: 200k-line CSV ingest ~1.1s, LTTB+EMA snapshot <250ms —
        documented in RESEARCH.md §9
      
      Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
      simon-pierre boucher · 1 file +35
    2. Forge Studio M1: SwiftPM app skeleton, full config codec, run supervisor, live dashboard
      …
      - RESEARCH.md: ground-truth Forge contract (config schema incl. waves 1-3,
        CLI, log.csv grammar with elapsed_s, checkpoint/resume, signals)
      - ForgeConfig: byte-compatible Codable mirror of every model/train field,
        param-count formula cross-checked against forge info, LR-schedule math
      - Services: ProcessRunner (actor, line streaming), LogParser (header-driven
        CSV + stdout events), MetricsStore (actor, incremental CSV tail, LTTB
        snapshots), RunStore (atomic registry), RunSupervisor (state machine,
        launch/stop, 1 Hz ingest), ForgeBinaryLocator, SystemInfo
      - Charts: LTTB downsampler, bias-corrected EMA
      - UI: NavigationSplitView shell, run rows/badges, RunDetail dashboard
        (loss raw+EMA+val, log-Y, best-val marker, LR/tok-s/grad-norm secondary
        charts, console), New Run sheet (presets, derived panel, LR preview,
        inline validation), Settings with forge-info validation
      - scripts: package-app.sh (SPM -> .app, ad-hoc) and notarize.sh with the
        real zyquo-term identity (Team 3YM54G49SN, profile MacLustr-Notarize)
      - 8 tests green: config round-trip vs real configs, param count, LTTB,
        EMA, CSV/stdout parsers, state machine, LR preview
      
      Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
      simon-pierre boucher · 1 file +116
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