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jeff-regier
Celeste.jl
Jupyter Notebook2017–2020
jeff-regier / Celeste.jl

Celeste.jl

Scalable inference for a generative model of astronomical images

3Distinct people
3Mentions
0Explicit recommenders
187GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Scalable inference for a generative model of astronomical images

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, 12:45:45 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 year3 dated mentions

Observed mentions: 2017-06-22 13:56:22 UTC to 2020-10-11 18:55:05 UTC.

012017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20172018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20182019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020

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
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC101
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC101
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC101

What Hacker News says

1 sources
This is totally fair. Even the Julia creators regularly mention "stay with existing tools for now, if you are happy." Julia is an increasingly attractive option for greenfield, high-performance development, but for folks who aren't doing a lot of that, a more stable language and mature ecosystem is obviously important. Julia and many libraries can still require some elbow-grease, and the maturity and coverage varies between fields. However, this is rapidly changing as folks do new research in Julia and build up everything else they need along the way. The current situation is very similar to SciPy in the mid-2000s, though with perhaps a somewhat different mix of backgrounds. Here are a few examples: https://github.com/JuliaOpt https://github.com/jeff-regier/Celeste.jl https://github.com/dlfivefifty https://github.com/JuliaInv http://docs.
Read the rest
juliadiffeq.org/latest/ https://github.com/denizyuret/Knet.jl http://nemocas.org/ https://juliaplots.github.io/ So, hopefully you'll have reason to take another look in a few years. The most important thing, to me, is that all of these communities want more open, reproducible, and accessible science. There is plenty of room for growth, because users of closed-source software still probably outnumber py/r/jl by 5:1 or more.
In Julia Computing Raises $4.6M in Seed Funding

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