- $40B in user assets
- $120B in FDV
- 3× TVL valuation multiple
- Funding rates: –0.17%
- Spot range: $180–$195
Solana’s valuation now trades three times above its on-chain collateral base: an FDV/TVL multiple that keeps expanding even as sentiment stays weak.
Normally, that kind of divergence signals overheating.
This time, it signals absorption.
Institutional inflows, led by the Bitwise BSOL ETF with $417M in debut-week volume, are offsetting the unwind in derivatives. Perp funding remains negative, traders are short, yet spot books refuse to break down. The bid has changed hands; from dege

Helium is actively buying back and burning the $HNT tokens daily.
Apparently, 100% of the revenue from helium subscribers is allocated to HNT burn for data credits.
In October, helium started an automated daily token buyback on the public market via Jupiter.
The founder also hinted that a digital asset treasury is coming soon.
Helium is clearly shifting towards real value capture, could this take us above the 2021 ATH?

Helium is up 21% in a week.
This is just the start of the $HNT moment, especially with Depin gaining traction.
Feels good to be positioned!

Wallets used to monetize attention, now they monetize execution.
Once perps, bridges, and swaps are native to the wallet, every transaction becomes orderflow.
The winner isn’t the DEX with deepest liquidity, it’s the wallet that originates the intent.
@rainbowdotme is positioning precisely here:
the router handles execution, the token (RNBW) captures routing spread, and users earn part of the margin back through fee rebates or loyalty tiers.
That flips the revenue logic.
Instead of spending incentives to attract users, the wallet shares execution yield from the flows they already generate.
Thi

.@bluefinapp is quietly running one of DeFi’s most balanced yield engines.
$93.2M in vault deposits. $381K in earnings.
No hype. Just flow.
Top performers:
• Gamma $USDC (14.1%) — base layer stability.
• Third Eye (21%) — active stablecoin arb.
• Basis (16.5%) — structured yield compression.
• Ember BTC (8%) — $BTC yield without CEX risk.
• Rivershore LP (30%) — high-frequency liquidity strategy.
Sui remains the core anchor ($30.5M), while $BTC and stable strategies scale across Arbitrum, Base, and native Sui rails.
The edge isn’t in the 21% or 30% headline APY.
It’s in the execution layer. @b

Every Bitcoin L2 I’ve studied runs into the same wall.
The rails are new, but settlement still clears in dollars.
That mismatch keeps Bitcoin from becoming its own economy.
The Lombard and Resolv partnerships fixes that.
$LBTC represents $1.43B in Bitcoin staked through Babylon, verified by Chainlink CCIP, earning about 4% APR.
Resolv’s $BTC Cluster issues $USR and BTCUSD stables against that collateral; already $312M in circulation and targeting 1,300 $BTC for deployment.
Together, they form a self-reinforcing credit layer that could become the default settlement unit for BTC-native perps, le

Sub-2 s latency and concurrency aren’t vanity stats.
They define what builders can actually ship this quarter...not what they claim on paper.
In AI, speed isn’t polish.
It’s the canvas that determines UX, logic, and monetization.
That’s the principle driving @SentientAGI’s infrastructure philosophy.
α/ From Infra Metrics → Product Reality
Consider @FireworksAI_HQ × @SentientAGI:
+ 25 – 50 % more throughput per GPU under real-world load.
+ Sub-2 s latency across 15-agent workflows.
+ Zero infra sprawl, consistent behavior even under burst.
Those numbers don’t just describe performance, they res

Most projects sell narratives.
@SentientAGI is shipping execution.
From the Fireworks integration:
• +50% throughput per GPU
• <2s latency across 15-agent workflows
• Lower $/query → multi-agent recursion becomes viable at scale
Why this is huge?
• Builders can chain 10–20 agents without hitting latency walls.
• Users get answers instantly, not in 5–10 seconds.
Everyone sees the edge. You can fork UX, but you can’t fake performance economics.
That is the separation.
Ritual builds pipes. Olas builds ownership.
Sentient is proving scale in practice.
Performance isn’t just a feature.
It’s the fly

Yunno, most agent frameworks work linearly:
1 agent → 1 task → 1 output.
Useful, but limited.
You can’t solve complex, multi-step problems when every agent works in isolation.
•••
-- 📌 What ROMA Adds
@SentientAGI's ROMA (Recursive Open Meta-Agent) solves this with recursion:
• Parent agent receives a query.
• ROMA breaks it down and routes sub-tasks to specialized sub-agents.
• Sub-agents iterate in a feedback loop, refining results.
• ROMA merges everything back into a coherent final output.
Think of it like project management for AI: a manager (parent agent) coordinating multiple specialist