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Keep a model training log

ADoor, your data account, keeps a log of your training runs: the config, the data, the metrics as they come in, where the checkpoints are, and what you concluded.

Use cases All use cases

For builders

How to connect ChatGPT and Claude to the same data How to give an AI agent safe, scoped access to data A database for vibe-coded apps, with access built in

AI and engineering

Keep a daily work log Keep a prompt library for Claude, ChatGPT and Cursor Let agents build on each other’s findings Run a task queue for your agents Share agent traces across a team Share research notes Track eval results over time Start an experiment tracker Keep a model training log Keep feature flags and config agents can read Keep bug reproductions and test cases Record deploy and release history Keep an inventory of API keys and services Start a dataset registry Collect standup notes from assistants Keep an incident log Log benchmarks and latency measurements Track GPU and compute spend Start a labeling and annotation queue

Personal

Keep a side-project idea backlog Start a habit tracker Start a reading log Track what you’re learning Track subscriptions Start a budget tracker Start a movie and TV tracker Start a home inventory Keep a plant care log Keep a car maintenance log Plan your travel Keep a tasting journal Track a collection Keep notes on the people you know Track job applications Keep a vocabulary list Track important documents Keep a food log Start a training log Keep a log of your own numbers Keep a schedule of what is due and when to refill Start a recipe collection Keep a list of things to buy Share a pet care log Keep a daily journal Keep gift ideas and birthdays

Business

Track consulting time Track freelance clients and invoices Track fundraising Start a lightweight issue tracker Back an internal tool Store a customer-facing app’s data Plan a content calendar Collect customer feedback and feature requests Manage event attendees and sponsors Report from many sources Start a customer list Track sales deals Keep a customer support log Track hiring Start an employee directory and onboarding checklist Track inventory Track orders and suppliers Start an asset and equipment register Track partners and resellers Track contracts and renewal dates Keep a price list and build quotes

Collaborative

Split expenses Share a research reading list Plan an open-source project roadmap Build a shared world-building bible Plan a house renovation Start a shared household list Share a household chores rota Keep a family schedule Track kids’ activities Plan a trip with friends Plan a wedding or party Run a book club Run a sports team or club Run volunteer and community sign-ups Run a student group project Start a neighborhood tool-lending list Share a weekly meal plan Write babysitter and caregiver handover notes Plan a shift schedule Plan a carpool rota Share a packing list Coordinate help for a parent

An example

@ADoor Log run r-0412: 1.3B model, lr 3e-4 cosine, batch 512, on dataset v7. Final loss 2.14 at 40k steps, checkpoint in s3://ckpt/r-0412/40k. Diverged briefly at 18k, recovered.

ADoor Added 1 entry to Runs and 1 to Checkpoints

Runs

RunConfigDataFinal loss
r-04121.3B, lr 3e-4 cosine, bs 512v72.14
r-04091.3B, lr 6e-4 cosine, bs 512v72.21

What you can ask later

  • “Which runs on dataset v7 beat 2.2 loss, and what learning rates did they use?”
  • “Where is the best checkpoint for the 1.3B model?”
  • “Which runs diverged, and at what step?”
  • “Write the SQL for loss at 20k steps across all runs this month.”
  • “Chart training loss against step for r-0409 and r-0412.”

Who can see what

  • Training scripts write runs and metrics through the HTTP API with a key limited to Runs and Metrics.
  • A GitHub Actions job that launches runs signs in with short-lived access, 15 minutes by default, with no stored secret.
  • Researchers read everything and edit their own notes (one grant per person).
  • An eval agent reads runs and checkpoints, and can’t change them.

How it compares with Weights & Biases or MLflow

Weights & Biases and MLflow are better at live metric dashboards, system metrics, artifact storage and logging libraries that plug into PyTorch and other frameworks in two lines. If you need step-level charts streaming during a run, use one of them.

As the team’s record of what was run and why, ADoor fits better. Metrics, notes and conclusions live together, plain SQL works across every run, and agents like Cursor can read the history before proposing the next config. Ask your assistant for any chart from the logged metrics.

How to start

  1. Connect ADoor to your assistant. ChatGPT, Claude, Grok and Cursor all connect through MCP.
  2. Tell it: “Set up a training log: Runs (id, model, config, data, started, status, notes), Metrics (run, step, name, value) and Checkpoints (run, step, path).”
  3. Give each training script a key limited to Runs and Metrics, and an eval agent read-only access.

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Questions people ask

Can my training script log directly?

Yes. Post to the HTTP API with a key limited to the lists it writes.

Can ADoor store checkpoints?

No. ADoor doesn’t store files. Keep the path to where each checkpoint lives.

Do I have to pay?

Not on the free plan, which holds 3 tables and 100 rows. Pro is $10 a month per workspace, with 1 GB of storage and no per-GB charges.

Does it work with Claude and Grok too?

Yes. ChatGPT, Claude, Grok and Cursor all connect through MCP with a sign-in, and each gets its own access that you can narrow or remove later.

Updated October 20, 2026.

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