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GoogleContainerTools
jib
Java2018–2025
GoogleContainerTools / jib

jib

🏗 Build container images for your Java applications.

20Distinct people
21Mentions
0Explicit recommenders
14.5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

🏗 Build container images for your Java applications.

GitHub

Where it is used

No documented product use was verified in the last source check.

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

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 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 year21 dated mentions

Observed mentions: 2018-07-10 14:45:34 UTC to 2025-11-24 18:38:07 UTC.

0123452018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 4 mentions; 1 submissions, 3 comments20182019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 5 mentions; 1 submissions, 4 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025

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
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC413
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC220
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC514
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC404
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC202
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC303
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC101

What Hacker News says

21 sources
Having the author do this for a service written in Go is a mistake. Your first address for containerizing Go services should be ko: https://ko.build/ , and similar solutions like Jib in the Java ecosystem: https://github.com/GoogleContainerTools/jib . No need to require everyone to install something heavy like Nix, no need for privileged containers in CI to connect to a Docker daemon so that actual commands can be executed to determine filesystem contents, just the absolute bare minimum of a manifest defining a base layer + the compiled artifacts copied into the tarball at the correct positions. More languages should support this kind of model - when you see that pnpm's recipe (https://pnpm.io/docker), ultimately, is to pick a pre-existing node base image, then copy artifacts in and set some manifest settings, there's really no technical
Read the rest
reason why something like "pnpm build-container-image", without a dependency on a Docker daemon, hasn't been implemented yet. Using nix, or Dockerfile, or similar systems are, today, fundamentally additional complications to support building containerized systems that are not pure Go or pure Java etc. So we should stop recommending them as the default.
In Nix is a better Docker image builder than Docker's image builder
This is what I think is the most practical approach. Pinning down ALL your dependencies to the exact version is much harder than it sounds. What you’re describing sound like the way Jib [1] does it. The pictures in this [2] blog post help visualize it. The reason I like the approach you describe is because it keeps things simpler at the start of a project and consistent across most projects. I also think it makes sense to have those support containers build on a schedule. For example, you build your build/CI container weekly and that’s the CI container for the week. On demand project builds use that CI container which has all dependencies, etc. baked in. It would be nice if CI systems would let me explicitly tag builds as (non)reproducible. 1. https://github.com/GoogleContainerTools/jib 2. https://phauer.com/2019/no-fat-jar-in-docker
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In VSCode, Dev Containers and Docker
The system you describe seems close to Jib [1] or Nixery [2], which are dependency-aware tools built on top of Docker. I think neither they nor the prototype you've described could be as distro/language-agnostic as Docker and ostree/flatpack are. Debian could offer something like Nixery, any distro could, but then it wouldn't be a cross-distro solution. I personally like appimage, but I don't mind using flatpack because it is so similar to Docker and is also supported on most major distros. Distro repositories aren't going anywhere, and I think flatpack strikes the right balance for users and developers. [1] https://github.com/GoogleContainerTools/jib [2] https://github.com/google/nixery
In Flatpak: A security nightmare – two years later
We haven't used Spring MVC since 2015, all our services are running Spring Boot because it is the most mature solution on the market to create the webservice layer. Our Spring apps are then containerized and optimized with jib[1]. Our frontend is built on React Hooks + TypeScript, fully SPA. All the work nowadays in our company is focused on data engineering, by that I mean: databases, parsers, managing the data pipelines, orchestration, Python for creating the models and Java for inference in production, our teams consist of data scientists that are using Python and software engineers that are using Java and TypeScript. I don't understand why people consider Spring to be overengineered. We find it very productive, time to market is huge and we can focus on other parts of development instead of reinventing the wheel. We are also consideri
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ng Quarkus for some of our services that aren't supposed to run for a long time. [1] https://github.com/GoogleContainerTools/jib
In A Picture of Java in 2020

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