Pivot from Hype and OverwhelmRead about our pivot to end the perpetuation of hype and realigning to create original content to help people responsibly deliver value with ML.
If you're not familiar with the old MWML platform, it used to be the ProductHunt of ML where the community could share and upvote projects. We had indexed nearly 3K tutorials, surveys, blog posts, libraries, etc. to help people build. But, in fact, this was not what was happening.
hype: mostly only hype projects were trending (transformer architectures, image generation with GANs, etc.) and very little on applied ML or MLOps.
quality: quality of projects decreased as the platform grew in popularity.
misuse: became a social platform where people gamified project upvotes.
mistakes: many tutorials and blog posts written by the general public had errors (caution: this is happening in a lot of places).
stale: many libraries quickly became stale after the initial hype phase.
We had nearly 8K daily active users and now after nine months, we're redefining our focus to end the hype and deliver (actual) value. Which means no more overwhelming (and outdated):
❌ massive list of resources
❌ posting daily trends
❌ unintentionally perpetuating hype
Unfortunately, we can't stop others from perpetuating this kind of hype (most do it for followers / social validation and they themselves would've gone through very little). But you need to approach information with an explore vs. exploit lens. Always tinker and work on projects, this way you know when something is worth reading or looking into. At the same time, explore new things but be aware of your larger goals. The worst thing to do is save large lists of resources only to feel overwhelmed and never get to a single one.
Luckily, we don't depend on ads or traffic so we decided to make the changes necessary to realign with our goal of helping people build. So instead, we will have more original content that drives intuition and application:
- applied MLOps
- sound software engineering
- delivering (actual) value with ML
Made With ML very quickly gained a lot of traction and we are guilty of creating overwhelm. We apologize and now we're focused on helping people learn how to responsibly deliver value with ML.