Product and engineering chat of a developer tools startup

Summary

Seven years of public Slack channels at one developer tools company, from its early days, with the matching issue tracker and design docs. Product debates, design reviews, release coordination and customer feedback triage happen in the open here, with threads intact. Staff identities, customer names, links to private systems and pasted secrets are replaced with consistent placeholders. Only public channels are included.

Collected to order. We collect it from US companies for your order. Scope, volume and timing are confirmed in writing before you sign.

Pricing on request

Request scope and pricing

At a glance

Planned scope. Volume, sources and timing are confirmed in writing before you sign.

Records: 3.3Mchat messages, issues and documents
Size: 1.6 GB
Tokens, estimated: 140M
Years: 2019 to 2026
Firms: 1

Owner profile: US developer tools company selling to engineering teams, 50 to 150 staff
Industry: Software
Data type: Company operations archive
Headcount: 50 to 150 staff
Region: United States
Languages: English
Formats: JSONL
Updated: 2026-09-30

Sources

System	Kind	Records
Slack public channels	Team chat	3.28M messages in 410 channels
Linear	Projects and wiki	48,900 issues
Notion	Projects and wiki	6,700 docs and specs

Good for

training workplace agents to answer questions from a team's own channel history
summarizing long product threads into decisions and owners
linking customer feedback in chat to the issues and specs it produced
evaluating long-context reasoning over years of team conversation

Fields

Field	Type	Description
message_id	string	De-identified message id
channel	string	Public channel name, replaced if it names a customer
thread_ts	string	Thread root id, null for top-level messages
posted_at	timestamp	Post time
author	string	De-identified author
text	string	Message text with mentions replaced
linked_issue	string	Issue key mentioned in the message, if any
reply_count	int	Replies in the thread
reaction_count	int	Total reactions

Example record

{
  "message_id": "C0412-1711469283.004119",
  "channel": "#product",
  "thread_ts": "1711468102.001877",
  "posted_at": "2024-03-26T16:08:03Z",
  "author": "[PERSON_8]",
  "text": "Pulled the numbers. 31% of trial teams never get past importing their spec because our importer chokes on OpenAPI 3.1 webhooks. I'd rather fix that than ship the new dashboard this sprint. Opened API-2207 with three failing specs from [COMPANY_42] attached.",
  "linked_issue": "API-2207",
  "reply_count": 14,
  "reaction_count": 6
}

One record, de-identified. Placeholders such as [PERSON_12] and [COMPANY_4] stay the same across the whole dataset, so threads still read correctly.

Licenses available

Exclusive
One buyer. Nobody else can license it during the exclusive term, usually 2 years.
Non-exclusive
Licensed to more than one buyer.
First look
One buyer gets 2 to 4 weeks to make an offer before anyone else sees the dataset.

What ships with it

Datasheet: what it is, what it is good for, verified size, sources, years, format and limits.
Provenance file: the owner shown by code, staff notice and consent status, exclusions, mapped to the EU AI Act Article 53 training-data summary and California AB 2013.
Manifest with a hash for every file.
QC report and de-identification report.
Your own copy, with unique canary records.

How it is de-identified

Default exclusions removed, files flattened to text, identifiers found in three layers and replaced with consistent placeholders, and the result measured against a 99% bar for each identifier type in each source.

The full method