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HumanSignal
label-studio
TypeScript2020–2026
HumanSignal / label-studio

label-studio

Label Studio is a multi-type data labeling and annotation tool with standardized output format

5Distinct people
13Mentions
0Explicit recommenders
28.7KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Label Studio is a multi-type data labeling and annotation tool with standardized output format

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 7:24:48 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year13 dated mentions

Observed mentions: 2020-12-01 21:19:28 UTC to 2026-04-01 17:13:11 UTC.

02462020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 6 mentions; 2 submissions, 4 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 3 mentions; 2 submissions, 1 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments20232024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC101
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC624
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC321
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC110
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC101
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

3 sources
Heartex | Remote (EU, North America, South America) | https://heartex.com/careers The power of prediction starts with Heartex (https://heartex.com). We build software that helps people do what only they can do – give meaning (https://github.com/heartexlabs/label-studio) We are currently hiring for Backend Engineers, Architects, and Engineering leaders. We are a fully distributed organization with people all over the world. With team members in North America, Europe, and South America across 6 countries. Stack: React, Python, Django, Postgres, Kubernetes As the VP of Engineering feel free to reach out to me directly (joe@hearex.com), contact our Head of Talent (brandi@heartex), or apply via our careers page (https://heartex.com/careers) More about Heartex: Data fills our modern world. It flows prolifically inside organizations, customer i
Read the rest
nteractions, through product usage, environmental research, healthcare imaging, and beyond. What if we could use any of that historical data to predict the future? The fact is, in most cases we can make predictions through Machine Learning and AI, but to do so in a meaningful and impactful way, historical data needs to be accurate, comprehensive, and absent of bias. In order to make the best predictions, we believe internal teams with domain expertise should be responsible for annotating and curating data. It's called data labeling, and it’s a process of real people giving meaning to the information they see on the screen. Heartex was founded to take data labeling to the next level. We believe that data labeling is a team sport. Data scientists, subject matter experts, engineers, operations, and annotators must work collaboratively to ensure quality results and an efficient process. That’s why we created Label Studio, which has quickly become the most popular open source data labeling platform with 100,000 users around the world, alongside a community of thousands of data scientists sharing knowledge and working to advance data-centric AI. We are excited to announce that Heartex has recently raised $25 million in Series A funding, bringing our total funds raised to $30 million from notable investment partners Redpoint Ventures, Unusual Ventures, Bow Capital, Swift Ventures, as well as angel investors.
In Ask HN: Who is hiring? (June 2022)
joealfaro·Jun 2, 2022Submission

Heartex – Remote (EU, North America, South America)

Heartex | Remote (EU, North America, South America) | https://heartex.com/careers The power of prediction starts with Heartex (https://heartex.com). We build software that helps people do what only they can do – give meaning (https://github.com/heartexlabs/label-studio) We are currently hiring for Backend Engineers, Architects, and Engineering leaders. We are a fully distributed organization with people all over the world. With team members in North America, Europe, and South America across 6 countries. Stack: React, Python, Django, Postgres, Kubernetes As the VP of Engineering feel free to reach out to me directly (joe@hearex.com), contact our Head of Talent (brandi@heartex), or apply via our careers page (https://heartex.com/career) More about Heartex: Data fills our modern world. It flows prolifically inside organizations, customer in
Read the rest
teractions, through product usage, environmental research, healthcare imaging, and beyond. What if we could use any of that historical data to predict the future? The fact is, in most cases we can make predictions through Machine Learning and AI, but to do so in a meaningful and impactful way, historical data needs to be accurate, comprehensive, and absent of bias. In order to make the best predictions, we believe internal teams with domain expertise should be responsible for annotating and curating data. It's called data labeling, and it’s a process of real people giving meaning to the information they see on the screen. Heartex was founded to take data labeling to the next level. We believe that data labeling is a team sport. Data scientists, subject matter experts, engineers, operations, and annotators must work collaboratively to ensure quality results and an efficient process. That’s why we created Label Studio, which has quickly become the most popular open source data labeling platform with 100,000 users around the world, alongside a community of thousands of data scientists sharing knowledge and working to advance data-centric AI. We are excited to announce that Heartex has recently raised $25 million in Series A funding, bringing our total funds raised to $30 million from notable investment partners Redpoint Ventures, Unusual Ventures, Bow Capital, Swift Ventures, as well as angel investors.
1 points · Open discussion

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