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
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
Try Diffgram today: https://diffgram.com/user/new
Thank you very much for reading!!!