CT | Content Creator 🖊️ | Trader 📊 | Educational 📖
Joined February 2022
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Dear @Barbera_1870, this is how I start my day:
🌅 5:00 AM. My alarm breaks the silence.
While the house is still quiet.I get dressed, head to the kitchen, and wash my face.
The day hasn't started for everyone else yet. But for me, it has.
#Coffee #GoodMorning
I tested @edgecraftapp SOL Golden Cross (1D) template.
The result? +156%… but just 3 closed trades. 👀
Simulated results, not a guarantee of future performance.
Costs: $319 commission, $0 funding, 0 liquidations.
Maximum drawdown: -$31,321.
Simulated results, not a guarantee of future performance.
My takeaway: look beyond returns. Check trade count, costs and drawdown.x`
Test your ideas: edgecraft.app/
HyperEVM generates roughly $1M a YEAR in network value.
HyperCore’s perps engine can generate that much in a single day, sometimes several times over.
That gap is why @Kinetiq_xyz just announced Elysium, a purpose-built L2 for @HyperliquidX.
Here’s why it actually matters. 🧵
Honest take:
The mechanism is strong, but it still needs proof.
No concrete throughput data.
No audited technical spec.
No confirmed launch date.
“Value-accretive” gets proven by live burn data, not a manifesto.
As a Social Miner at @TheDAOLabs, this is one of the more interesting tokenomics designs I’ve studied this cycle.
Elysium’s thesis is simple:
Elysium activity → Hyperliquid growth → KNTQ buybacks → KNTQ burns.
The sharper piece: L1Read.
Elysium customizes the precompile to give PropAMMs top-of-block pricing directly from Hyperliquid.
Solana PropAMMs already process ~30× HyperCore’s spot volume.
Elysium is built to close that gap.
AI agents are moving beyond chat.
They can research, manage files, use websites and execute real tasks.
But greater autonomy raises a question:
"How do we let AI act without giving it uncontrolled access?"
@NEARProtocol IronClaw 1.0 has an interesting answer.
🧵
As a writer for @TheDAOLabs, the infrastructure side interests me too.
@near_ai is building confidential, verifiable infrastructure for user-owned AI.
Staking secures NEAR, while Staking for NEAR AI can provide compute credits for confidential inference and IronClaw.
For production AI agents, what matters more: raw model capability or the architecture controlling it?
I explored IronClaw 1.0, its benchmarks, NEAR AI and staking here:
👉linkedin.com/pulse/ironclaw-…
