Nezuko Chan

Everytime boost to big change life

[Nezuko Chan @GarapanResmi]

Zero-knowledge ML doesn’t fail because of theory it fails because of cost.
By breaking models into slices and verifying selected subcomputations,

That’s what makes verifiable AI actually deployable.
@inference_labs

[Living In Harmony ⭕🔥🥉🏅🕊🦾🆙🆗🎯🔑💼🛡️👑🇨 @LivingInHarmony]

You can now run the HarmonyOS emulator on Linux for dev!

[Learn Things @LearnThinx]

Bookmark it and practice 🙌🏼

[Nezuko Chan @GarapanResmi]

@manyaaww @getmarkit Checked the project and the concept looks clean

[Inference Labs @inference_labs]

1/ A lot of the AI world is focused on bigger models and better interfaces.

Inference Labs is focused on something just as important: trust infrastructure for AI systems that need to operate in the real world.

[Nezuko Chan @GarapanResmi]

@FlyStar231 @TotemWild @Maximal_Labs 0xe5567eF490AA3a1d679cbfC38b99836bB838D093
@HoukisM @Kembaranbsc

[Aliwise™ @FlyStar231]

10 GTD+ 20 FCFS for @TotemWild upcoming mint.

Tasks:

~Follow @TotemWild &
@Maximal_Labs
~Like,repost,must tag two frens
~Drop EVM wallet

Mint details

Mint price_fucking free for all
Supply_4444 cosmetics 
Chain_Tempo
Date_TBA

All that's required;Hold a Maximal!

[Nezuko Chan @GarapanResmi]

@zealy_io @inference_labs Try your'e luck

[Zealy @zealy_io]

new sprint cooking with @inference_labs

3k on the line, social quests only

let's grow?

[Nezuko Chan @GarapanResmi]

Zero-knowledge ML doesn’t fail because of theory it fails because of cost.
By breaking models into slices and verifying selected subcomputations,

That’s what makes verifiable AI actually deployable.
@inference_labs

[Nezuko Chan @GarapanResmi]

@inference_labs
Zero-knowledge ML doesn’t fail because of theory
it fails because of cost.
By breaking models into slices and verifying selected subcomputations,
That’s what makes verifiable AI actually deployable.

[Nezuko Chan @GarapanResmi]

If AI agents become autonomous,
who verifies their decisions at scale?
We can’t rely on reputation or blind trust.

Is targeted verification (like DSperse) the only scalable path where we verify just enough instead of everything?

[Nezuko Chan @GarapanResmi]

The Truth will set you free..

Join @inference_labs' TruthTensor Crucible to earn points and climb the leaderboard

https://t.co/r2eSI4ZNou

[Mr. Ano @pasar_airdrop]

@inference_labs Instead of forcing full verification, it treats trust as something you can allocate focusing proofs on critical parts of inference.
That’s a much more realistic design for production systems.

[LUNVER @Lunver0]

DSperse solves this by introducing targeted verification: instead of proving entire models, it focuses on critical computations where trust matters most.

That shift makes zkML far more practical for real-world deployment.
@inference_labs

[LUNVER @Lunver0]

@zealy_io @inference_labs new opportunity lets do complete taks

[Nezuko Chan @GarapanResmi]

@traceNode TRACE is still in the first phase

[Trace @traceNode]

In 2009, almost no one paid attention.

A few did.
They stayed. They believed. They showed up.

Today, we call them early.

TRACE is built on the same principle:

Consistency creates advantage.

Most will ignore this.

Some won’t. $BTC

[Nezuko Chan @GarapanResmi]

@traceNode @zealy_io Let’s see who really shows up