FOfoltik·Sep 27, 2024Submission
Perpetual: A self-generalizing gradient boosting machine
2 points · Open discussionperpetual-ml
perpetual
Rust2024–2024
perpetual-ml / perpetual
perpetual
Perpetual is a high-performance gradient boosting machine. It delivers optimal accuracy in a single run without complex tuning through a simple budget parameter. It features out-of-the-box support for causal ML, continual learning, native calibration, and robust drift monitoring, along with Rust core and zero-copy bindings for Python and R
3Distinct people
3Mentions
0Explicit recommenders
718GitHub stars
What to know before exploring
What it is
Perpetual is a high-performance gradient boosting machine. It delivers optimal accuracy in a single run without complex tuning through a simple budget parameter. It features out-of-the-box support for causal ML, continual learning, native calibration, and robust drift monitoring, along with Rust core and zero-copy bindings for Python and R
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
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Also in these discussions
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 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 week3 dated mentions
Observed mentions: 2024-06-09 08:02:47 UTC to 2024-09-27 05:09:54 UTC. Weeks start Monday, UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Jun 3 | 2024-06-03 00:00:00 UTC | 2024-06-10 00:00:00 UTC | 1 | 1 | 0 |
| Jun 10 | 2024-06-10 00:00:00 UTC | 2024-06-17 00:00:00 UTC | 0 | 0 | 0 |
| Jun 17 | 2024-06-17 00:00:00 UTC | 2024-06-24 00:00:00 UTC | 0 | 0 | 0 |
| Jun 24 | 2024-06-24 00:00:00 UTC | 2024-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 1 | 2024-07-01 00:00:00 UTC | 2024-07-08 00:00:00 UTC | 0 | 0 | 0 |
| Jul 8 | 2024-07-08 00:00:00 UTC | 2024-07-15 00:00:00 UTC | 0 | 0 | 0 |
| Jul 15 | 2024-07-15 00:00:00 UTC | 2024-07-22 00:00:00 UTC | 0 | 0 | 0 |
| Jul 22 | 2024-07-22 00:00:00 UTC | 2024-07-29 00:00:00 UTC | 1 | 1 | 0 |
| Jul 29 | 2024-07-29 00:00:00 UTC | 2024-08-05 00:00:00 UTC | 0 | 0 | 0 |
| Aug 5 | 2024-08-05 00:00:00 UTC | 2024-08-12 00:00:00 UTC | 0 | 0 | 0 |
| Aug 12 | 2024-08-12 00:00:00 UTC | 2024-08-19 00:00:00 UTC | 0 | 0 | 0 |
| Aug 19 | 2024-08-19 00:00:00 UTC | 2024-08-26 00:00:00 UTC | 0 | 0 | 0 |
| Aug 26 | 2024-08-26 00:00:00 UTC | 2024-09-02 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2 | 2024-09-02 00:00:00 UTC | 2024-09-09 00:00:00 UTC | 0 | 0 | 0 |
| Sep 9 | 2024-09-09 00:00:00 UTC | 2024-09-16 00:00:00 UTC | 0 | 0 | 0 |
| Sep 16 | 2024-09-16 00:00:00 UTC | 2024-09-23 00:00:00 UTC | 0 | 0 | 0 |
| Sep 23 | 2024-09-23 00:00:00 UTC | 2024-09-30 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
3 sourcesPerpetual: Self-generalizing gradient boosting machine algorithm
2 points · Open discussionShow HN: A self-generalizing, hyperparameter-free gradient boosting machine
PerpetualBooster is a gradient boosting machine (GBM) algorithm which doesn't have hyperparameters to be tuned so that you can use it without needing hyperparameter optimization packages unlike other GBM algorithms. Similar to AutoML libraries, it has a budget parameter which ranges between (0, 1). Increasing the budget parameter increases predictive power of the algorithm and gives better results on unseen data.
5 points · Open discussion