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Top 7 Go Ruby Projects
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awesomo
Cool open source projects. Choose your project and get involved in Open Source development now.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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parca-agent
eBPF based always-on profiler auto-discovering targets in Kubernetes and systemd, zero code changes or restarts needed!
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SaaSHub
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In 2017, I wrote a toy language called Goby[1] to learn how Ruby works. A few folks contributed quite a bit to it and one of them later referred me to my previous job (as a backend developer).
Fast-forward to 2021, I got interested in debugging tools so I started contributing to the then newly created Ruby debugger[2]. In less than a year I opened more than a hundred PRs and became the 2nd biggest contributor of it. And that eventually landed me a job to work on Ruby's development tools, like LSP servers, REPLs, and of course, the debugger :-)
[1] https://github.com/goby-lang/goby
[2] https://github.com/ruby/debug
If that's true, you should probably update the docs. Everything I could find implied dotnet, jvm, python were still unsupported. For example, the roadmap section of the readme mentions most of these but nothing mentions dotnet. However I did find your tickets and a demo being merged in which makes it seem maybe supported?
Ticket: https://github.com/parca-dev/parca-agent/issues/161
Demo: https://github.com/parca-dev/parca-demo/pull/18
Project mention: Generating Code Without Generating Technical Debt? | news.ycombinator.com | 2023-11-10I’ve built conviction that code generation only gets useful in the long term when it is entirely deterministic, or filtered through humans. Otherwise it is almost always technical debt. Hence LLM code generation products are a cool toy, but no sensible teams will use them without an amazing “Day 2” workflow.
As an example, in my day job (https://speakeasyapi.dev), we sell code generation products using the OpenAPI specification to generate downstream artefacts (language SDKs, terraform providers, markdown documentation). The determinism makes it useful — API updates propagate continuously from server code, to specifications, then to the SDKs / providers / docs site. There are no breaking changes because the pipeline is deterministic and humans are in control of the API at the start. The code generation itself is just a means to an end : removing boilerplate effort and language differences by driving it from a source of truth (server api routes/types). Continuously generated, it is not debt.
We’ve put a lot of effort into trying to make an LLM agent useful in this context. However giving them control of generated code directly means it’s hard to keep the “no breaking changes”, and “consistency” restrictions that’s needed to make code generation useful.
The trick we’ve landed on to get utility out of an LLM in a code generation task, is to restrict it to manipulating a strictly typed interface document, such that it can only do non-breaking things to code (e.g. adjust comments / descriptions / examples) by making changes through this interface.
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Deploying flask app to Kubernetes using Minikube
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Deploy a Full Stack Next.js App to a DigitalOcean VPS With Docker!
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Grafana Pyroscope v1.0.0 Release
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Enabling local project collaboration with Gitea
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JSON Feed: An Atom/RSS feed alternative
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Ultimate EKS Baseline Cluster: Part 1 - Provision EKS
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A note from our sponsor - SaaSHub
www.saashub.com | 4 May 2024
Index
What are some of the best open-source Ruby projects in Go? This list will help you:
Project | Stars | |
---|---|---|
1 | awesomo | 9,233 |
2 | gaia | 5,159 |
3 | goby | 3,466 |
4 | nodebook | 1,616 |
5 | parca-agent | 480 |
6 | speakeasy | 140 |
7 | eclectica | 28 |
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