awenix
Joined 7 years ago
Karma
40
Submissions
16
Member since
Jul 7
Creator of https://pippy.dev
View on Hacker NewsRecent submissions
Comment · 10 months ago
I understand that orchestration,log storage, keeping software updated can cost money, which they seem to recover from charging for software. Hopefully there is support now included with self hosted runners being charged.
Comment · 1 year ago
groupcache[https://github.com/golang/groupcache] has been around for some time now.
Comment · 2 years ago
Another solid code search tool https://github.com/hound-search/hound.
Based on regexp
Comment · 2 years ago
Thanks for trying this out. The command line version is open source and completely free, its local only and interacts with Github only.
you can try it here https://github.com/nixmade/pippy
Comment · 2 years ago
I updated the site to include differentiators. Agree that most of it has already been offered by Github.
Comment · 2 years ago
Sorry about the site not being clear. Agree most of this is already offered by Github, I updated the site to have key differentiators
- Automatic rollback on monitoring failures
- Datadog monitoring, queries configured datadog monitors after the workflow execution upto 15mins (right now hard coded). Optionally rollback if monitors are in failed state. This is also achievable with having a Github Action workflow query datadog monitors and stop further stages.
Comment · 2 years ago
I have been made aware of deployments in Github https://docs.github.com/en/actions/deployment/targeting-diff... which offers similar functionality, without external monitoring or Rollback. The advantage of Azure devops is integration with Azure identity, making setup of approvals, reviewers easier.
pippy makes it easy to setup pipeline from simple workflows using UI (which Azure devops is good at), while in Github you may need to create a parent workflow which comprises of these individual simple workflows.
Comment · 2 years ago
Github does offer deployments https://docs.github.com/en/actions/deployment/targeting-diff... which is similar in comparison, but i feel there are lot of steps to go through to get this working and creating pipeline for simple workflows makes it hard.
Azure pipelines offered this by setting up reviewers, approvals, but reading more about Github deployments, they might be similar.
Comment · 2 years ago
Thank you for the feedback. Agree that most of the features are offered as https://docs.github.com/en/actions/deployment/targeting-diff..., unfortunately i haven't played with it. Will improve the landing page to focus on the pieces different from what is offered by Github.
>but I always want to click on those feature boxes for more info
Sorry about that, will work on not using cards :( I do not have much design/UI experience, and the lack of it shows here.
Comment · 2 years ago
If you are referring to https://docs.github.com/en/actions/deployment/targeting-diff..., unfortunately i haven't played with it, and agree that it does offer most of the features offered by the product. As you mentioned the differentiator is the automatic rollback and support for external monitoring (datadog as of now). Would love to learn more about how you are using Github actions so i can make the product better, if you can drop an email to hello @ nixmade.com
For Cloud API, my post have been a little confusing, was suggesting/hoping 3rd party services to integrate with open source orchestrator https://github.com/nixmade/orchestrator so they can have their own version of pippy (not Github Actions).
Comment · 2 years ago
Nice to see an open source implementation, i have been seeing many startups get into this space like https://www.retellai.com/, https://fixie.ai/ etc. They always end up needing speech-to-speech models (current approach seems speech-text-text-speech with multiple agents handling 1 listening + 1 speaking), excited to see how this plays with recently announced gpt-4o
Comment · 2 years ago
Looks very similar to Service Fabric https://learn.microsoft.com/en-us/azure/service-fabric/servi.... I have heard it scale upto 2000 nodes and extensively used internally at microsoft.