Phoenix77

PT-apxUSD by @apyx_fi shows exactly where on-chain RWA dividend yield is heading.
@apyx_fi is pioneering a brand-new narrative: real dividend-backed synthetic dollar.
A single market on @pendle_fi seamlessly connects real dividend yield from preferred shares of Digital Asset Treasuries (DATs) publicly listed companies with yield tokenization and points-driven capital efficiency in one unified system.
Apyx Pips incentives (32x on YT) are deepening liquidity and turbocharging farming at scale.
apxUSD by @apyx_fi is backed by preferred shares of DATs, transforming off-chain dividend income into f

x402 is a new payment primitive.
@HeyElsaAI made it the foundation of an entire trading stack
@HeyElsaAI opened its entire backend stack as a public API infrastructure through x402, the HTTP 402 payment standard co-developed with Coinbase for machine-to-machine micropayments.
The flow:
→ Agent calls an Elsa endpoint
→ Elsa returns 402 Payment Required + price
→ Agent pays in USDC on Base
→ Elsa executes and returns the result
→ Settlement: instant, on-chain, verifiable
Payment, authentication, and access all collapsed into one
HTTP round-trip.
Pay-per-request, at internet speed.
The numbers go

❯ @HeyElsaAI traction makes the story more compelling.
The agent-layer thesis carries more weight when real usage starts to show up in the numbers.
$540M+ in transaction volume
30M+ total interactions
18.9M+ prompts processed
These numbers matter because they show activity moving through the system at scale.
Volume points to real capital flowing through @HeyElsaAI
Interactions point to repeated engagement with the product and its agent layer.
That is where the story starts to shift.
Crypto already has liquidity, execution, bridging, lending, and staking.
What becomes more valuable now is coor

DeFi is shifting from emissions to real yield, and fixed income is leading that move
On @pendle_fi, AVLT from @alturax Vault is leading the RWA fixed APY board at 18.54%
AVLT is a yield-bearing vault on HyperEVM with a multi-strategy design including delta-neutral market making, funding rates, restaking, and gold-backed RWA to generate real yield
Pendle & Altura are forming a strong flywheel
> AVLT brings real yield from delta-neutral strategies and gold RWA
> Pendle enables yield splitting (PT fixed / YT leveraged), improving capital efficiency
> Altura deploys incentives to boost LP particip

Institutional yield strategies have always been gated by high minimums, accredited investor requirements, and lengthy paperwork.
Retail never had a seat at the table.
@alturax changes that.
Deposit USDT into the vault and receive your AVLT receipt token.
Capital flows through a multisig and deploys into the same institutional strategies that major funds use: delta-neutral market making, basis arbitrage & RWA.
Oracle updates NAV in real-time, so AVLT pricing stays transparent and compounding runs automatically.
This is real DeFi:
+ Remove the gatekeepers
+ Bring real yield from institutional in

Software outsourcing became a $200B industry by arbitraging engineering cost across borders.
@StrikeRobot_ai is applying the same model to robot intelligence.
The product is SR Agentic, an intelligence framework that plugs into any humanoid and handles perception, reasoning, and movement.
Hardware companies can skip years of R&D and get a working brain out of the box.
Four verticals are in the pipeline:
+ Security patrol and autonomous monitoring (live)
+ MMA/Fighting robots for combat benchmarking
+ Dojo simulations for training humanoid movement
+ Sim-to-real pipelines for deployable RL rese

AI infrastructure today exists across multiple layers.
Each layer operates independently across data, storage, and compute.
@inference_labs brings these layers together into one system.
@eigencloud provides AVS and coordination.
@OpenledgerHQ provides decentralized data.
@irys_xyz provides permanent storage.
@cysic_xyz provides decentralized compute.
Inference packages these primitives into a unified stack.
Inference adds a verifiable layer on top of this system.
This structure transforms raw infrastructure into a full-stack product.
Service clients such as Score, VentureVerse, Immunefi, and S

Most people focus on AI models.
This view misses the deeper stack behind AI systems.
@inference_labs builds its architecture on three core technologies:
→ zkML
→ Proof of Inference
→ Distributed + Reputation System
zkML forms the foundation of the system.
This layer ensures that AI computation can be proven with cryptography.
Proof of Inference transforms every AI output into a verifiable result.
This mechanism allows anyone to check the correctness of inference.
Distributed systems run inference across multiple nodes.
Reputation systems assign trust and quality to each output.
These layers cr

There’s a new layer emerging in AI.
AI is moving toward autonomous systems that act across DeFi, robotics, healthcare, and real-world environments.
That shift introduces a core requirement: Trust
Web3 already showed this pattern.
Smart contracts needed data → oracle layer emerged.
Now AI follows a similar path.
Autonomous systems require provable outputs.
A new layer forms: AI verification layer
The stack
AI infra is taking shape:
Compute → where models run
Oracle → where data enters
Verification → where outputs become provable
This is where @inference_labs sits.
They focus on verifiable infer

Most users only look at APR
But when volume scales, liquidity depth is what actually matters
Institutional-scale swaps on @pendle_fi are measured across 3 liquidity size:
ultra-deep (>$20M)
deep ($5–20M)
moderate (<$5M)
based on these swap sizes, institutions and whales can build better strategies to optimize their positions

I think @HyperliquidX is quietly becoming the everything exchange
And the spot side is the part worth paying attention to
+ 60 curated coins (one ticker = one coin, forever)
+ oil & gold perps doing 67% of HIP-3 volume in Q1
+ 250+ tokenized US stocks via @felixprotocol
→ $4.5B TVL · $3T annual volume · 0.05% fees · zero gas and all onchain, all 24/7.
The spot market is what fascinates me most, every token had to win a 31-hour dutch auction just to exist some tickers went for six figures. That single mechanism creates a quality floor that every other chain simply lacks
Hyperliquid solved the t

PRL tokenomics reflects how Perle structures a data network from day one.
> Community: 37.5%
> Ecosystem: 17.84%
~55% of total supply allocated to network growth
> Team: 17% | Investors: 27.66%, 12-month cliff + 36-month vesting
> Community unlock: 7.5% at TGE, remaining vested over 36 months
TGE circulating ~1.75B / 10B
The structure creates a clear flow:
+ Early contributor incentives
+ Gradual supply distribution
+ Value tied directly to participation
As the network expands, contribution scales and value flows with activity.
The token becomes a reflection of system growth.
#PerleAI #ToPerle

Everyone talks about scaling AI.
Few people anchor on what actually determines whether that scale produces intelligence or noise.
That anchor sits in one place:
the data layer.
Perle is building around four core theses, and each one reveals a different part of how AI systems evolve beneath the surface.
Thesis 1: AI quality follows data quality, but compounds with verifiability
Think of AI as a simple pipeline where inputs define outputs over time, and once data carries traceability, structure, and reliability, the system starts producing results that reflect that consistency.
Perle focuses on

One stack. Three properties. Miden.
Most systems optimize for a single dimension.
@0xMiden is built at the intersection of all three.
> Scalable like modern L2s
> Private at the base layer, where execution stays hidden
> Quantum secure, designed for long-term resilience
This combination unlocks a new design space.
Around it, familiar directions are taking shape:
Ethereum driving defi,
Starkware advancing ZK,
Zama pushing encrypted computation.
Miden brings these paths together into a unified execution model.
One stack with three properties.
A foundation for private payments, hidden strategies,

AI is entering a new phase: verifiable intelligence.
Today, AI makes decisions across trading, finance, and agents.
But one gap remains: no way to prove outputs are correct
@inference_labs is building the stack to fix this:
JSTprove → cryptography layer
> turns AI inference into provable computation <
DSperse → execution framework
> splits models to scale verification <
Inference Network → compute layer
> decentralized AI inference + proof <
TruthTensor → evaluation layer
> scores and validates outputs <
Put together:
AI inference → proof → verification → evaluation
Blockchain solved trust for