WitnesS.base.eth | (❖,❖)

Content creator ✍️
Building, Learning and Sharing in Web3/DeSci. Always curious.
Building with @YOM_Official
@CNPYNetwork

[WitnesS.base.eth | (❖,❖) @ua_raven]

I'm getting healthier and earning Heal Points with @saviorofhealth_ 💚 Join me #SOH #saviorofhealth

[WitnesS.base.eth | (❖,❖) @ua_raven]

3/3
When autonomous agents handle real capital across chains, verifiable security and data privacy aren't optional features - they're pre-requisites. Compliance meeting confidential compute is how agentic finance actually scales enterprise-wide

[WitnesS.base.eth | (❖,❖) @ua_raven]

1/3
Most crypto security stops at smart contract audits, but for institutional capital and serious enterprise adoption, that's barely the baseline. Real trust requires rigorous, standardized compliance

@ama_protocol

[WitnesS.base.eth | (❖,❖) @ua_raven]

2/3
This is why @ama_protocol taking security to the infrastructure layer with SOC 2 Type II and ISO certifications is huge. It proves that confidential execution inside TEEs isn't just a theoretical tech claim, but a battle-tested, verified operational standard

[WitnesS.base.eth | (❖,❖) @ua_raven]

Most blockchains throw away compute on arbitrary hash puzzles, but @ama_protocol redirects miner power toward useful proof of work (uPoW)

Every single hash actually powers real-time AI inference and model training for autonomous agents on-chain. Zero wasted energy, pure utility

[WitnesS.base.eth | (❖,❖) @ua_raven]

How to create an AMA agent in under a minute 🧐

✅Open AMA Hub → Build → Launch
✅Describe it in plain language
✅AMA interviews you and builds it
✅Add skills. Test private. Publish when you say so

Keys stay in your wallet
Starts private. You decide when it goes public.

Build yours: https://t.co/msMXHVqwV9

@ama_protocol

[WitnesS.base.eth | (❖,❖) @ua_raven]

@konnex_world keep building

[Konnex @konnex_world]

The world's largest asset manager just described machine-to-machine commerce:

Agents paying for compute on programmable rails, in stablecoins.

Konnex is building this for physical AI.

Robot labor priced in open markets, settled in stablecoins, proven on-chain.

[WitnesS.base.eth | (❖,❖) @ua_raven]

Autonomous AI agents on @ama_protocol don't just execute trades in a vacuum - they operate inside TEEs (Trusted Execution Environments)

That means your strategy logic, parameters, and alpha stay completely private, deterministically calculated, and immune to MEV frontrunning

Private compute changes everything for agentic finance

[WitnesS.base.eth | (❖,❖) @ua_raven]

3/3
True agentic finance requires total mobility. Cross-chain execution powered by verifiable compute means your strategies aren't bounded by chain architecture anymore. Deploy once, operate everywhere

[WitnesS.base.eth | (❖,❖) @ua_raven]

1/3
Most AI agents are stuck in siloes - bound to a single chain or ecosystem where they can't touch outside liquidity. @ama_protocol changes the paradigm completely by making agents inherently interoperable from day one

@ama_protocol

[WitnesS.base.eth | (❖,❖) @ua_raven]

2/3
Your agent can monitor signals on one network, execute confidential strategies inside TEEs, and trigger actions across @solana, @base, or Ethereum without friction. They aren't locked in place - they travel wherever the yield and liquidity live

[WitnesS.base.eth | (❖,❖) @ua_raven]

@ama_protocol @quipnetwork @base @solana looking forward to the deep dive with @rcarback

[Amadeus Protocol @ama_protocol]

Visionary yet practical crossover conversation with @quipnetwork on two tech worlds set to collide.

Agentic Finance
AI Agents trading, settling & operating autonomously across chains like @base & @solana.

Quantum Doom Clock
Machines penetrating the ECDSA cryptography underpinning most wallets & blockchains - as soon as 2028.

Fortunately, Zero-Knowledge systems make $ETH meaningfully more quantum-resistant than $BTC. But the future cannot be built on cryptography a 2028-era machine will break.

Amadeus agentic architecture already emphasises the critical need for:
* Trusted Execution Environments
* Confidential Execution
* Verifiable Compute

Hence post-quantum signatures seem the natural next hardening layer.

@rcarback returns soon, we'll talk more about how quantum computing will impact agentic finance.

[WitnesS.base.eth | (❖,❖) @ua_raven]

@ama_protocol this bonus makes the timing pretty non-negotiable for serious operators looking to lock in baseline emissions now

[Amadeus Protocol @ama_protocol]

The founding validator cohort is small on purpose.

Early seats don't just earn early, they help set the terms.

Commission ranges. Emission splits. uPoW parameters. All still being calibrated while the cohort is small enough for individual input to matter.

Every epoch outside the set is lost accrual; rewards that don't come back.

The +5% LockupVaults bonus runs for 12 months, and it's time-boxed to this window only.

That's the whole "why now."

https://t.co/Qy6j4w0HDs

Compose on AMA

[WitnesS.base.eth | (❖,❖) @ua_raven]

I'm getting healthier and earning Heal Points with @saviorofhealth_ 💚 Join me #SOH #saviorofhealth

[WitnesS.base.eth | (❖,❖) @ua_raven]

deploying complex, multi-chain yield strategies using simple natural language prompts straight from chat is peak UX. @ama_protocol is making institutional-grade agentic finance actually usable

[WitnesS.base.eth | (❖,❖) @ua_raven]

3/3
The result? A self-sustaining compute engine where every block mined actually powers decentralised AI agents and improves network models. Real utility, zero wasted energy, and true decentralization

[WitnesS.base.eth | (❖,❖) @ua_raven]

1/3
Traditional PoW wastes huge energy solving arbitrary hash puzzles. @ama_protocol changes the game with Useful Proof of Work (uPoW) - redirecting miner hash power toward neural network training and AI model inference right at consensus layer

[WitnesS.base.eth | (❖,❖) @ua_raven]

2/3
Instead of relying on centralized AI APIs or wasteful mining, uPoW turns raw compute into productive intelligence. Nodes contribute GPU power to train proprietary AI models and execute verifiably prompt-driven inferences for agents in real-time