Sharing Zen: runs multiple Code CLI instances for peaceful parallel task execution.
Zen allows you to:
+ Run multiple headless Claude Code CLI instances simultaneously.
+ Calm unified results (status, time, token usage)
+ Relax "5-hour limit reached" lockout fears with easy token budget limits
+ Get more value out of your Claude MAX subscription with scheduling features. (--run-at "2am")
+ Learn more about how Claude Code uses tools and other inner workings
Control usage and budget for groups of work or per command
Hey folks! We are really excited to release this. It's been years in the making and it's very much the full application. We make $ from selling the enterprise supported version and are working on new paid enterprise hub product. I personally really see this Open Core release as version 1 of many versions to come over the years. If you have any questions AMA :)
I'm curious why less features is a goal here?
I wish VueJS had more features, or at the very least more tooling. Vue has progressed a lot but there is so much more room https://github.com/vuejs/vue-devtools
Hey HN! We are Anthony and Pablo and we’re building software for human supervision of AI data.
Diffgram started a little over 2 years ago. Recently, as a tiny self funded team, we have won a few customers away from larger venture backed firms that have raised over 40M. While there are gaps, as an underdog we have been able to keep pace with and even exceed these 10x+ larger teams.
Our focus is the basics, with a core assumption that the AI models are one of the best forms of speedup. We have also done our best to move iteratively. One example https://bit.ly/38OxZ7T A visual of internal commit history: https://bit.ly/34RDCRN
The big idea is ongoing human supervision is crucial, important, and cost effective for a large class of practical AI applications. Diffgram is scoped to cover Training Data between Raw Data and Modeling. Diffgram covers Data Prep, Tasks, Image & Video Interface, Data Management, etc.
Teams often get a baseline case covered, for example spatial location (drawing a box, or pixel by pixel etc). The problem is to add depth and breadth. Long story short, representing truly useful, editable, training data is hard.
In Diffgram you can solve this by representing as much of this manual, unspoken work, in reusable abstractions. Many of this happens by default - there’s no “new language” to learn - it’s automatic organization of the stuff you already know.
Benefits 1) Supervision productivity improvements 2) Data engineering productivity: Less up front (integrations), less maintenance work (wheel and spoke included), less risk (central schema definition). 3) Project: Higher chance of overall product success: See https://bit.ly/3n2gfLB
Hey HN! We are Anthony and Pablo and we’re building software for human supervision of AI data.
Diffgram started a little over 2 years ago. Recently, as a tiny self funded team, we have won a few customers away from larger venture backed firms that have raised over 40M. While there are gaps, as an underdog we have been able to keep pace with and even exceed these 10x+ larger teams.
Our focus is the basics, with a core assumption that the AI models are one of the best forms of speedup. We have also done our best to move iteratively. One example https://bit.ly/38OxZ7T A visual of internal commit history: https://bit.ly/34RDCRN
The big idea is ongoing human supervision is crucial, important, and cost effective for a large class of practical AI applications. Diffgram is scoped to cover Training Data between Raw Data and Modeling. Diffgram covers Data Prep, Tasks, Image & Video Interface, Data Management, etc.
Teams often get a baseline case covered, for example spatial location (drawing a box, or pixel by pixel etc). The problem is to add depth and breadth. Long story short, representing truly useful, editable, training data is hard.
In Diffgram you can solve this by representing as much of this manual, unspoken work, in reusable abstractions. Many of this happens by default - there’s no “new language” to learn - it’s automatic organization of the stuff you already know.
Benefits
1) Supervision productivity improvements 2) Data engineering productivity: Less up front (integrations), less maintenance work (wheel and spoke included), less risk (central schema definition). 3) Project: Higher chance of overall product success: See https://bit.ly/3n2gfLB
Hi!
This exists as a standalone library which means you don't have to go through the trouble of cloning the tensorflow/models repository and using the specific functions you need.
Moreover, I'd argue this code is easier to follow (ymmv) than Tensorflow's and it would be easier to debug if the user needs to make modifications themselves.
Additionally, this library also gives you some extra visualizations you can use on your bounding boxes.
Ah yes, this can be a bit confusing. We show you a preview of the screen you are recording, which if it’s the same screen you’re on will cause the looping effect. You can just switch to a different tab and do your video, come back and stop the recording. The 3 second countdown is there to prepare you for the recording starting. We will try to make this less confusing though!
Zen allows you to:
+ Run multiple headless Claude Code CLI instances simultaneously. + Calm unified results (status, time, token usage) + Relax "5-hour limit reached" lockout fears with easy token budget limits + Get more value out of your Claude MAX subscription with scheduling features. (--run-at "2am") + Learn more about how Claude Code uses tools and other inner workings Control usage and budget for groups of work or per command
https://github.com/netra-systems/zen