Omimi
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huge shoutout to the @MemeMax_Fi team, they just threw us a bone!
They extended the pre-release window, meaning you have extra time to open those MaxPacks.
But the real alpha? The chance for high-value M tokens just went UP.
Seriously, if you've been "meaning to" get a good position, this is the final, boosted chance. You literally asked for better opportunities, and this is it. Go secure your bag!
Open. Track. Accumulate. Let's go!
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Privacy is the single biggest missing piece for real on-chain finance.
No safety, no adoption.
Shoutout to @0xMiden for tackling this as the #1 requirement. This is how we unlock the next wave of users and real world use cases.
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I've had this nagging question: How do we get real control in open AI and stop the monopoly creep?
I think I just found the answer: @inference_labs, their modular stack is genius. They are addressing the missing piece in infrastructure by creating a system that is:
> Fully transparent: You can see all the data pathways.
> Plug-and-Play: Swap components like compute and models.
> Auditable: Full control over what's happening.
This move toward an anti-monopoly framework gives us faster iteration and real protection.
For anyone serious about the future of open AI, this is the sharpest developmen
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Look, I spent time studying the new zk stacks, and this one stands out.
I think @0xMiden is sitting at the center of the next major shift for private computation. The concept is simple, but powerful: Make privacy a baseline rule, not an optional feature.
They achieve this by making users execute the logic locally and only publishing a proof to the chain.
> User controls data visibility.
> Apps run without leaking data.
If you're building in web3 or just curious about where the tech goes next, you owe it to yourself to check them out.
You won't regret paying attention early.
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If you're building with AI today, you are probably sacrificing trust for performance.
It's the painful trade-off: Full verification (ZK) is too slow. No verification is a black box risk.
That trade-off is over.
My take on the @inference_labs stack (DSperse / JSTprove) is simple: They realized you don't need to verify the whole model.
You only need to verify the high-impact slices, the policy blocks and proprietary logic.
This is how you upgrade reliability without tanking throughput. It's the only pragmatic path to trustworthy AI at scale.
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If you want a technical edge that actually lasts in AI, the answer is transparency.
And that's exactly the mission of @inference_labs. They're building the infrastructure that lets us inspect the entire workflow.
Why? Because you need to verify where the data came from (provenance) and track every model update. Without that, you're flying blind.
Stop relying on trust; start demanding verification.
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I am a big believer that the quality of a CLI is a strong signal for a project’s internal discipline. @0xMiden's toolkit keeps getting cleaner.
It’s the easiest way to interface with that robust basic mutable account: check the Asset Vault, update the 255 state slots, and manage the Nonce-driven transitions.
The Rust template pushes you into a smooth dev loop.
This is an environment that prioritizes the builders.
It doesn't distract you; it gives you low-level, high-control blocks.
If you aren't deploying something small on the Testnet with this setup, you are missing the practical alpha.
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