Raoul Pal's $2.7T → $100T crypto thesis implies $97T of new wealth creation in 10 years. This 24-minute talk is the macro briefing your portfolio needs right now.
No fluff. Pure edge.
AI producing more words than all of human history by 2028. Agentic economies transacting on-chain. The chess analogy alone reframes how you think about surviving the AGI transition.
In 2026, knowing where the exponential curve bends = knowing where the money flows first.
Save this. Watch tonight. Thank me later.
Link to full lecture & transcript in first reply.
$19B in real-world assets just landed on Ethereum while Bitcoin ETFs bled $1B in a single week.
This 31-minute NYSE panel breaks down what that actually means for your positioning — no fluff. Pure edge.
Hyperliquid ETFs debuted on Wall Street, the Senate Clarity Act passed 15-9, and CME/ICE are already lobbying to kill HYPE before it eats their lunch. The macro picture is ugly — PPI +6% YoY with rate hike odds near 50% — but the structural shift is accelerating regardless.
2026 is the year on-chain finance stops being theoretical.
Save this. Watch tonight. Thank me later.
Link to full lecture & transcript in first reply.
#Crypto
X just open-sourced its recommendation algorithm.
The entire For You feed is now on GitHub.
Here's what actually matters for creators:
→ Phoenix (Grok-based transformer) ranks everything
→ Replies and reposts weighted 10x more than likes
→ Early engagement in first 30 min = everything
→ Media posts get direct boost
→ Spam patterns detected by grox module
The algorithm rewards real conversations, not hacks.
https://t.co/EqyZtTQJh1
#AI
April 2025: $625M stolen across 30+ crypto incidents — the worst hacking month in history. And pre-IPO markets are quietly running a parallel fraud machine.
This 34-minute Uneasy Money episode covers both. No fluff. Pure edge.
Anthropics synthetic perp was trading at an implied $1.4T valuation vs an $800B actual raise. SPVs layered on SPVs. Bucket shop mechanics disguised as equity access. Meanwhile AI/LLM-mediated exploits are bypassing 2FA and GitHub supply chains are being quietly compromised.
In 2026, quants and builders who don't understand blast radius containment and synthetic private-equity pricing will get wrecked on both sides.
Save this. Watch tonight. Thank me later.
Link to full lecture & transcript in first reply. #QuantTrading
🔥 /goal is currently the MOST powerful feature in Claude Code right now.
No more babysitting the agent with endless “continue” every 30 seconds.
Just drop /goal + a crystal-clear success condition (“all tests green”, “90% coverage”, “feature fully shipped + documented”) — and Claude runs autonomously until done. Haiku validates it after every turn.
The thread breaks it down perfectly:
• How it works under the hood
• What goals actually work vs what burns tokens
• Pro setup (CLAUDE.md + Auto Mode)
Game changer for big refactors and full features.
Huge thanks to @akshay_pachaar for the breakdown 🔥
Who’s already using /goal in their workflow? Drop your experience below 👇
Just saw @blinktrade reply to @toly with "Kingmade already" 👑
Came out of stealth yesterday and honestly looks pretty promising.
Onchain exchange on Solana focused on real speed: verifiable FIFO, physical colocation, and sub-millisecond execution.
For people building trading agents or trading perps, this might become proper infrastructure in 2026. Still flying under the radar for most.
Curious to see how it develops.
Anyone else checking out @blinktrade?
#QuantTrading
Leading-edge programmers are 20x more productive than one year ago.
Twitter cut 70-80% of staff. platform runs better than before.
33 minutes. free. by Marc Andreessen, General Partner at a16z.
→ AI vampires: exhausted, euphoric, unstoppable
→ "builder" replacing coder + PM + designer
→ AI bloat exposed as corporate layoff cover
→ golden age thesis: AI as universal superpower
This is the closest you'll ever get to seeing inside the mind of Silicon Valley's most powerful venture capitalist.
Bookmark & watch. Then start building.
@maarcoofdezz This is the most transparent “inside a hedge fund” talk I’ve seen. The eval pipeline they mentioned is what 99% of retail “AI trading bots” miss. Already watching it twice.
The two-phase pretraining framework is what caught my attention.
Phase 1: general knowledge acquisition
Phase 2: reasoning-centric integration
The bottleneck was never compute.
It was always data ordering.
Most people still don't know this.
Stanford just dropped this. Free.
From next-token prediction to next-generation intelligence.
The future of LLM pretraining — revealed.
48 minutes. free. Stanford University | April 2026.
→ why next-token prediction hits a ceiling
→ data ordering strategy that unlocks reasoning
→ two-phase training framework
→ what comes after pretraining as we know it
This is the closest you'll ever get to seeing inside
Stanford's AI research engine.
Bookmark & watch. Then start building.
Stanford just dropped this. Free.
From next-token prediction to next-generation intelligence.
The future of LLM pretraining — revealed.
48 minutes. free. Stanford University | April 2026.
→ why next-token prediction hits a ceiling
→ data ordering strategy that unlocks reasoning
→ two-phase training framework
→ what comes after pretraining as we know it
This is the closest you'll ever get to seeing inside
Stanford's AI research engine.
Bookmark & watch. Then start building.