Annotation · datasets · fine-tuning · models · developer tools
The ML odd jobs, done in the tab.
Label a folder of images, split a dataset, check what your fine-tune will need in VRAM, read an ONNX file, count tokens, fix a JSON file. 36 tools, each one a page you can bookmark, all running in your browser with nothing uploaded.
Every tool, one workbench
Pick the job.
Each tool opens with its controls on the left and the work on the right, saves your progress in the browser, and exports the file the next step expects.
Annotation
6 tools · Bounding boxes, polygons, OCR text, image classes, text classes and entity spans, exported in the formats training code expects.
Datasets
8 tools · Split, convert, inspect, validate, de-duplicate and augment datasets, and assemble chat datasets for LLM fine-tuning.
Fine-tuning
7 tools · VRAM, time and cost before you start, a runnable script for YOLO, LoRA or a classifier, and the metrics afterwards.
Models
3 tools · Read an ONNX file, count tokens, and turn a conversation into the exact prompt format a model was trained on.
Developer
12 tools · JSON, YAML, TOML, CSV, schemas, regex, diffs, hashes, cron, timestamps, .env and Markdown, done in the tab, not on a server.
Why it works this way
Annotation, hashing, parsing and conversion run in the page. There is no upload endpoint on this site, which is also why it is free and has no account.
Exports are the exact layouts training code reads: YOLO folders with data.yaml, COCO JSON, PaddleOCR Label.txt, TRL chat JSONL, ImageFolder zips. No reformatting script in between.
Training runs on your machine or your cloud. The tools here plan it (VRAM, time, cost, schedule), prepare it (data, script, config) and evaluate it (confusion matrix, mAP).