Roadmap

Drag & drop, file picker, and full client-side .zip extraction. Cloud connectors (Drive, S3, Hugging Face…) are roadmap.

Fast searchable/filterable grid with an inspector panel. Embedding-space navigation is roadmap.

Real SHA-256 duplicate detection, corrupt-image decoding checks, empty-file detection, filename normalization.

Linear commit history with diffs and rollback, in-session. Branching, merge, and on-disk storage are roadmap.

Live stat cards, format breakdown, and size-distribution charts computed off your real dataset.

Real Canvas 2D pipeline: rotate, flip, brightness, contrast, blur, noise. Weather/perspective effects are roadmap.

Chain the real steps above into an ordered pipeline and run it for real. Grows as more modules land.

Rule-based answers ship for free and instantly. Connect a BYOK provider in AI Settings and open-ended questions go to a real model, grounded in your real dataset stats.

Manual classification labeling ships free. Connect a vision-capable BYOK provider and "AI-suggest unlabeled" runs the real label-20/AI-labels-the-rest loop, capped per batch, always shown as "needs review" until you confirm.

A rule-based assistant ships free. With a BYOK key connected, unmatched questions go to your chosen model (OpenAI, Anthropic, Google, Groq, OpenRouter, Mistral, DeepSeek, or xAI).

Real, working fetches from Hugging Face's dataset hub and Wikipedia's REST API (both CORS-open, work on the web build), plus paste-in text/HTML — every candidate passes a free heuristic screen and, with a BYOK provider connected, an AI review/curation pass before you can commit it to the dataset. Fetching arbitrary blog/wiki URLs works from the Tauri desktop build (native fetch_url, no CORS); the web build tries direct CORS first and otherwise tells you honestly to use the desktop app or paste instead.

Generative augmentation of the dataset itself. Needs a model backend — not attempted in this milestone.

One-click PyTorch/HF/YOLO training. Needs a real compute backend — out of scope for a browser tab.

Automatic weak-class and bias detection after training. Depends on Module 10 existing first.

Community publishing and discovery. Needs real backend infrastructure and moderation.