惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

aimingoo的专栏
aimingoo的专栏
宝玉的分享
宝玉的分享
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
WordPress大学
WordPress大学
V
V2EX
Apple Machine Learning Research
Apple Machine Learning Research
J
Java Code Geeks
腾讯CDC
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Engineering at Meta
Engineering at Meta
L
LangChain Blog
Jina AI
Jina AI
博客园 - 叶小钗
B
Blog RSS Feed
Recent Announcements
Recent Announcements
H
Help Net Security
小众软件
小众软件
大猫的无限游戏
大猫的无限游戏
B
Blog
云风的 BLOG
云风的 BLOG
Blog — PlanetScale
Blog — PlanetScale
D
DataBreaches.Net
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
罗磊的独立博客

Hacker News: Ask HN

The New Window Delete ChatGPT Atlas Spyware Tell HN: Qwen Free Tier Is Discontinued Ask HN: SeedLegals Partnerships in London, worth it? Ask HN: How to highlight talent from untraditional backgrounds? Ask HN: We dont need a programming language now? Durable Object alarm loop: $34k in 8 days, zero users, no platform warning What if Time at the subatomic level has multiple arrows? How to add MidnightBSD Key to UEFI Secure Boot DBX? (Revoked and Forbidden Keys) Ask HN: What's your experience working at xAI as an AI tutor? Any engineers here with experience of clinical data standards? Ask HN: Who is using OpenClaw? Agent Skills for Software Test Automation Ask HN: Who needs contributors? Claude Code is thinking too much Ask HN: What Is the Big-O Order of a Jigsaw Puzzle? Ask HN: Stepping into a new role as a Senior, mentoring dos and dont's? Founder from Zurich heading to SF and Austin for the first time Hacker News No Manual Screenshots: I Built a Scalable Screenshot API Using Cloud Playwright Ask HN: Thought experiment: AGI giving us answers we don't like? Ask HN: I quit my job over weaponized robots to start my own venture 1% Vacancy, 81% Preleased: Where Midmarket Compute Deploys in 2026 Ask HN: Preferred pricing model for sound effects libraries? Copy of the email I sent to my undergraduate professors on Nov 30, 2025 Model API Performance | Hacker News Ask HN: Are open-weight LLMs the new offline encyclopedias? Valgrind 3.27 RC1 is out Claude Code OAuth down for >12 hours Ask HN: What's Better?–Tauri or Electron?
Atlassian "Data Contribution" – Privacy and Welfare
yells_jovial · 2026-06-15 · via Hacker News: Ask HN

> For the uninitiated:

Atlassian contains the data of over 300k organizations. Companies of all sizes use their products, including free users, small teams, large organizations, and enterprises.

Starting Aug 17th, if your company has not opted out of “Data Contribution”, Atlassian will use your company’s data to train their AI products (“Rovo”).

The intrinsic value of the data residing in Atlassian’s products is uniquely high. Additionally, how Atlassian is rolling out Data Contribution is hard to view favorably.

> On intrinsic value:

Atlassian has several product offerings but their main two are Jira and Confluence. Confluence is a documentation platform containing the knowledge base of many companies. Jira, a ticketing/product system, contains a temporally organized record of a company's operational processes and their execution steps for delivering their products. Many Jira instances contain long term execution intentions towards an overarching company strategy.

The synergy of both of those, the knowledge base and tasks/intentions, is impressively valuable. For many organizations, the completeness of this data in both of these tools is high. Additionally, the recency and freshness of the data is near real time. The pairing of both Jira and Confluence data adds incredible contextual relevance to understanding the company.

Continuing, the very position and nature of these tools, be it their ease of integrations, the fluidity of adding attachments, the social aspect of the platforms, the requisite requirement of using the tools in many development processes, etc. has allowed these platforms to accumulate a large amount of intellectual property from companies.

> On the rollout:

There are two types of data to be collected and trained on, 1) “Metadata” and 2) “Data”. The only way a company can opt-out of both is if they are on an Enterprise subscription, otherwise Data opt-out is a manual slider and Metadata is always contributed. The problem with Atlassian Enterprise is its inaccessibility. Some SaaS services (GitHub, for ex) - allow smaller organizations to easily self-sign for Enterprise. It is more costly per seat but organizations can get access to the same features as enterprises. Atlassian does not have this level of accessibility, a company has to contact sales to discuss an Enterprise account. Even then, the cutoffs for user counts are significantly higher (800+ users is my understanding, but there are probably more accurate numbers).

Atlassian has made an effort to separate the types of data into Metadata and Data - but their definition of Metadata is not metadata in the classical definition. Their “Metadata” includes 1) numeric fields like story points, dates (which they call numbers in their docs), SLAs, etc. 2) computed features on your data (similarity scores, readability scores, etc.), and more. Those are stored as “Metadata” for use.

> Extrapolating:

A weird corporate welfare forms. We essentially have partially-opt-outable organizations “contributing” their organizational processes + IP in some anonymized form to Atlassian for Rovo development, while the largest and most successful enterprises are not having to share their same value back. Many small organizations make a market for themselves by being first to market, filling a niche, and building responsive products faster than larger firms.

While Atlassian will anonymize and remove PII and specifics, where on the sliding scale of reproducible business strategy process will we land – New York Times + ChatGPT regurgitation? Which organizations will be able to capitalize the most on that trained information coming from thousands of smaller organizations?

> TLDR - Atlassian training on your data via “Data Contribution” creates a privacy and IP concern, and their policy rollout results in small organizations contributing their knowledge and process to large organizations without commensurate contribution in return.