#opg $OPG @OpenGradient
I tried using just one AI app for everything for a week, from asking sensitive questions, generating images, to switching models at will, to see what’s left about me in the system by the weekend. That app is OpenGradient Chat. The results made me rethink my perspective on privacy in AI.
On Day 1, I tested the platform's foundation. Every other AI assistant asks you to trust a lengthy privacy policy that no one reads. OpenGradient backs its promise with mathematical proof, encrypting messages right on the device, stripping identity before hitting the model.
On Day 3, I tried Image Studio, generating images through Gemini, ByteDance, and xAI simultaneously. The same layer of privacy applies to all three, no model gets preferential data collection.
On Day 5, I switched from Claude Fable 5, the tightest model, to Nous Hermes, a model with no content guardrails. Two opposing philosophies, both in the same list, with the same layer of encryption.
By the weekend, I looked back at the usage log. Every credit I bought and used on OpenGradient Chat counts towards eligibility for the S2 OPG airdrop, no extra steps required, just using the app as usual.
What surprised me the most is that four things I thought were separate—privacy proof, multi-model image generation, dual censorship chat, and airdrop eligibility—are all just manifestations of the same infrastructure layer. Switching models doesn’t change privacy. Changing features doesn’t alter the protection mechanisms.
The question I’m keeping an eye on is whether, as OpenGradient adds more models and features, that common foundational layer can maintain this consistency.
I tried using just one AI app for everything for a week, from asking sensitive questions, generating images, to switching models at will, to see what’s left about me in the system by the weekend. That app is OpenGradient Chat. The results made me rethink my perspective on privacy in AI.
On Day 1, I tested the platform's foundation. Every other AI assistant asks you to trust a lengthy privacy policy that no one reads. OpenGradient backs its promise with mathematical proof, encrypting messages right on the device, stripping identity before hitting the model.
On Day 3, I tried Image Studio, generating images through Gemini, ByteDance, and xAI simultaneously. The same layer of privacy applies to all three, no model gets preferential data collection.
On Day 5, I switched from Claude Fable 5, the tightest model, to Nous Hermes, a model with no content guardrails. Two opposing philosophies, both in the same list, with the same layer of encryption.
By the weekend, I looked back at the usage log. Every credit I bought and used on OpenGradient Chat counts towards eligibility for the S2 OPG airdrop, no extra steps required, just using the app as usual.
What surprised me the most is that four things I thought were separate—privacy proof, multi-model image generation, dual censorship chat, and airdrop eligibility—are all just manifestations of the same infrastructure layer. Switching models doesn’t change privacy. Changing features doesn’t alter the protection mechanisms.
The question I’m keeping an eye on is whether, as OpenGradient adds more models and features, that common foundational layer can maintain this consistency.