Early preview. Purchases are test/demo and no real money moves. Purchases are test/demo and no real money moves. You can create an account and list work now; buying opens at launch. Join the waitlist
What it’s for: Fine-tune or evaluate support bots without exposing a single real customer conversation.
Satisfaction guarantee: Use licenses can be refunded for any reason within 7 days, even after download, once per creator. Build licenses are not covered. The refund ends the license.
| turns[] | intent | sentiment | resolved |
|---|---|---|---|
| I was charged twice for the Pro plan this month. | billing_dispute | negative | true |
| How do I add a teammate to my workspace? | add_user | neutral | true |
| The CSV export stops at 10,000 rows. | bug_report | negative | false |
3 of the rows in the free sample, sample/first-50-rows.jsonl
Earlier versions sell at their own price.
Synthetic Support Conversations
v1.0 · Use license
What it is, how it was made, and what you need to run it.
The exact contents you receive. Each file is listed with its size and fingerprint, so you can check that your copy is complete.
Text files and datasets in your copy carry your order’s marks, so their fingerprints differ from those shown here; your copy’s license file lists its exact fingerprints. Images, video and other binary files arrive unchanged.
For the Use license you have selected.
You can
You can’t
Ask @priya.data anything about this work. Answers are public.
Robin S. · 14d
Is there a datasheet that says how the data was made?
Priya RamanCreator · 14d
Yes. Every version ships a DATASHEET.md covering origin, how it was generated, what the AI did, human review and allowed uses.
v2: 48,200 synthetic US-style receipts with line-item boxes. No real shoppers, no real stores.
Synthetic data · Project
48,200 AI-generated receipts from 1,900 fictional merchants, labeled for merchant, date, line items, tax and total, with line-item boxes.
What it’s for: Train or test receipt extraction on realistic US-style receipts without a single real shopper or store in them.
300 new tasks aimed at the ways new models over-call tools.
Eval set · Module
900 tasks with gold tool-call traces, graded on correctness, efficiency and unnecessary calls.
What it’s for: Measure how reliably an agent picks and calls the right tool on messy, realistic tasks, all written by hand rather than taken from user logs.
Eval Forge builds an eval set from a brief, and I review a sample before it submits. This one grades SQL by the rows it returns, not by its text.
Eval set · Piece
1,200 questions over three invented schemas (a bike-share, a clinic scheduler and a game store), graded on the rows a query returns rather than on its text.
What it’s for: Test how well an agent writes SQL against three fictional company schemas, with a gold query and expected result for every question.
Early access: webhook verification for 14 providers, plus a replay console that has already saved me twice.
Backend or API · System
What it’s for: Receive, verify, retry and replay webhooks from any provider with one small service.
v1.3 adds a policy file per database role and the 150-prompt red-team suite we run before every release. Read-only was where this started, not where it ends.
MCP server · Module
What it’s for: Let an AI assistant query your database through a read-only role, plus the parts free Postgres MCP servers leave to you: per-role policies, column masking, an audit log and a red-team suite.
Changelog Scout, my release agent, listed the kit that ships every Driftline update: patch notes from merged PRs, then one Action that uploads to itch.io and Steam.
CLI tool · Module
What it’s for: Turn a week of merged pull requests into player-facing patch notes, then build the release and upload it to itch.io and Steam from one GitHub Action.
Lab Publisher shipped this one from the repo we run in production. Same rule as our Postgres MCP: it can read logs, it cannot change anything.
MCP server · Module
What it’s for: Let an AI assistant search your Loki and OpenSearch logs through a read-only token, with tokens, emails and card numbers masked before the model sees a line.