We're announcing 100% cashback on every model on the Higgsfield API platform.
Seedance 2.5, Kling 3.0, MiniMax H3, Wan 3.0, and more.
Spend on the API and get your cashback instantly, up to $100,000 per business.
$20,000,000 cashback pool. First come, first served.
You helped us reach a $1B run rate. We’re celebrating by putting $20M back into what you build next.
Unused cashback expires on September 30.
MUSE FEATURE ALERT: y'all have been asking for this one. your @muse is getting its own email address. add it to a thread or forward it stuff and it’ll handle it for you.
muse mail!
Try building Higgsfield clone with our API x GPT-6 Astra.
The only US-based Seedance 2.5 with consistent characters.
Up to 50% off discount on top models.
are personal ai agents that live in iMessage structurally slower?
why is Instinct slower than Muse?
@alexandr_wang can you share your thought process behind choosing an app vs messages
Any pursuit of superintelligence has to be grounded in the core principle that if the AI we build is not helping humanity and under human control, it's not worth pursuing.
We also need to accelerate and spread the benefits of AI, such that they are diffused broadly across countries, communities, and companies. This requires a frontier ecosystem in which both closed and open-source models can thrive.
And for firms, it’s imperative that they retain full control over their unique and tacit knowledge. Every organization should be able to build its own continuous learning loop/hill climbing machine, without becoming dependent on any one model provider, and have the ability to embed its own knowledge into models and weights they control.
So, in this context, we welcome the research, focus, and deliberate pacing needed to get alignment right as the design goal. We also welcome ideas like "embedded evaluators" and the broader efforts to develop the mechanisms to make this more than just talk.
The key is that this cannot be controlled by a handful of entities, but must have broad representation across the ecosystem, countries, and fields, including academia.
This is the approach we are taking: broad access and choice at every layer of the AI stack; enterprise control of learning loops and models; and the “Code of Conduct” that underlies our own first party MAI models that we’ll publish tomorrow for public consultation.