T-Rex @doublewedge

@JunkSavvy @IBKR @IBKR_Europe nothing from Saxo bank

So following these principles here is the architecture that falls out and it’s quite different than what is thought to be optimal today. Starting with the memory: LPDDR6 tops out around 14.4 Gb/s per pin. We would want to as many channels as the die’s edge can carry. If we can fit 24 channels, that’s 576 data pins, and you get ~1 TB/s per chip with 192 GB of storage. Same as the b200 in size but about an a100 in bandwidth. But remember that LPDDR access latency is ~100 ns which is about the same as HBM. So streaming weights from LPDDR costs you nothing on latency and we just give up per package bandwidth, and remember our pipelining already collapsed that requirement. Now work backwards to compute. Tokens/sec = ~aggregate BW / bytes touched per token, and at batch 64 in FP4 every delivered byte wants ~256 FLOPs. 1 TB/s × 256 = ~260 TF of FP4. Size ~280 TF for some headroom and stop! That’s all we need, the rest of the alpha is in latency tuning not flops. Now process node, again using first principles thinking. On TSMC N6 that MAC array would be about 30 mm^2. The rest of the die goes to SRAM buffers, 24 LPDDR PHYs, and 56 lanes of plain 32 GT/s NRZ SerDes PCIe 5.0 transceivers, nothing exotic. Because we didn’t provision more flops we can provision what we really need which is ports. If we do 7 ports × x8 we can get a full mesh across 8 chips with ~224 GB/s any to any ops, sized for 8 KB activations instead of 100 MB gradients. Approximate Total Area: ~144 mm^2, ~180W with the DRAM. Fucking Mobile SoC level economics. And we get away without a crazy process node. N3 shrinks your logic, but that’s actually the one thing this die barely has. SRAM stopped scaling and the PHYs and SerDes are analog so they don’t shrink at all. An N3 port shrinks ~20% of the floorplan, and saves maybe 10-15W, but doubles the cost on a small die that already has beautiful *chefs kiss* 👨‍🍳💋 yield. Now do the node math: 8 chips (b/c full mesh w/ 7 ports) = 8 TB/s aggregate BW, 1.5 TB total of memory, and ~1.44 kW. A B200 inside an NVL72 is 8 TB/s, 192 GB, ~1.67 kW all in. So we are basically neck and neck on BW with a b200 with slightly lower power because we have more chips connected with quicker interconnects allowing them to act as a single unit. If we scale to the rack level at 72 nodes, that 576 chips, 576 TB/s which is again the same aggregate bandwidth as a GB200 NVL72, so approximately the same tokens/sec by construction but with ~104 kW of power against ~120-130 and 110 TB of memory against 13.8 TB of the NVL72. So in summary 4.5x less compute silicon, zero HBM, zero CoWoS, zero leading-edge nodes and still token/watt ~20% better. So what’s stopping everyone from doing this? The number I told you hold whether you want to believe it or not and get even more competitive with longer reasoning chains or agentic workloads because as you scale those the all reduces now sits on the critical path of every token and on a 30 ns link with a full mesh that’s orders of magnitudes of token throughput improvements over Nvidia’s stack. Counterintuitively the link latency, not the chip architecture, decides your memory, logic, and everything else! Thanks for reading if you made it this far.

@userofintellect ✅✅✅

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@userofintellect @XspaceZone1 @EzPzTrading 🙌🙌🙌🙌🙌🤩

@OopsGuess 🤣🤣🤣🤣🤣

How to find leaders before they go parabolic: My favorite swing trades are in stocks I call the "grinding leaders that can become parabolic leaders". Grinding leaders = stocks grinding up <10% a month across longer timeframes (months) Parabolic leaders = stocks making a 50%+ parabolic move within a very short timeframe (1-2 weeks) Join strength during the grinding phase, pre-parabolic move...here's how to find them: Use a screener (I use finviz), and filter by: - price above SMA20 - price above SMA50 - price above SMA200 - performance month: up 5% to 15% - performance quarter: up 20%+ - RSI(14): 55-70ish (optional, but avoid already insanely overheated names above 80 RSI) - average volume: over 500k - market cap greater than $300M (or $2B+) Optional catalyst layer...this is the real alpha!! After the scan, I manually look for: - hot theme exposure (ai, robotics, defense) - earnings acceleration - recent news - sector rotation - big partnerships Few names sticking out to me: $SATS $IRDM $FLY $EBAY $IBKR ...stay tuned for deep dives. 💙Luc

@userofintellect @konomi1234567 @EzPzTrading 🔥🔥🔥

@masuzafi

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I promise this will be the best 20 min you spend today! Robotics: Endgame, the sequel to my last year's Sequoia AI Ascent talk, "Physical Turing Test". I laid out the roadmap for solving Physical AGI as a simple parallel to the LLM success story. Be a good scientist, copy homework ;) And stay till the end, more easter eggs and predictions for your polymarket! 00:30 DGX-1 origin story at OpenAI, I was there in 2016 signing with Jensen and Elon. Heading to the Computer History Museum! 01:42 The Great Parallel 03:31 Robotics, the Endgame 03:39 Why VLAs fall short 04:32 Video world models as the 2nd pretraining paradigm 06:09 World Action Models (WAM) 07:46 Strategies for robot data collection and the FSD equivalent to physical data flywheel for robot manipulation 11:06 EgoScale and the Dexterity Scaling Law we discovered recently 14:00 Physical RL: bridging the last mile 15:39 DreamDojo: an end-to-end neural physics engine for scaling RL in silico 17:00 Civilizational Technology Tree and my predictions for the near future. Spoiler: it's closer than you think. Thanks to my friends at Sequoia for inviting me back to AI Ascent this year! I had a blast! Last year's talk is attached in the thread if you missed it.

The 9/21EMA crossback continues to be one of my favorite areas for momentum returning to a leader. You just needed to recognize the character change and catch a piece of the trend. +50% on the underlying in 12 trading sessions, right off the 9/21EMA crossback. Chart: $MU.

iain @ohiain

4 months ago

Tried $MU yesterday off 443 +9EMA, took nice trims this morning, then got stopped at b/e midday before price reversed at my average, which is part of the game. What stands out to me is that $SPY is closing red while $MU is closing green and printing a very tight inside day,

8 2 120 70K 20

@userofintellect @EzPzTrading epic move again 🙌🤝

⛽️ The West hasn't felt the full force of the oil shock yet, but the ripple effects are already visible 📉 This chart reveals a critical insight: The oil shock isn't initially felt in prices. It's first felt in the barrels that never reach their destination. 🚢 The Blue Line: Global Oil on Water This represents oil currently being transported by tankers between producers and consumers. When this line suddenly plunges, it indicates a serious disruption to the physical oil supply chain. Simply put, the oil that should be on the water is gone. This is always where the trouble begins for the physical oil market. 🛢 The Red Line: Commercial Crude Oil Inventories in Europe and the Americas Interestingly, this line hasn’t dropped significantly yet. This shows that Western nations are currently relying on existing reserves, alternative routes, and emergency management to delay the main impact. Think of it like a dam where the inlet water has been cut off, but the city doesn't go thirsty immediately because the reservoir is still full. 🧠 The Hidden Danger Many believe a full-blown crisis only occurs when Western gas stations go empty. But the oil market doesn’t work that way. The barrels that aren't on the water today won't be in the storage tanks tomorrow. This delay is what makes the crisis so dangerous. The market may appear normal for a time, but underlying pressure is building. ⚡️ The Domino Effect If the red line (inventories) also begins to fall dramatically, the situation will escalate from a geopolitical concern to a real, tangible shortage. This won’t just impact oil prices. It will cascade through: ⛽️ Gasoline and Diesel Prices 🚛 Transportation Costs 🌾 Food Inflation 🏭 Production Costs 🏦 Central Bank Decisions on Interest Rates The shock is on its way. The data shows it. It's only a matter of time...

@userofintellect weekly 🤩

@SecScottBessent pathetic words. You do not deserve your chair.

@userofintellect oil&gas supply shock> less energy&derivates > less production capacity > smaller markets > lower GDP 🩸

@stamatoudism @stamatoudlsm_ @chartfanatics @Wordsofrizdom natural educator , full clarity.

linktw.in/MariosCF I gave a mini masterclass on the Parabolic Shorts setup in an episode that just dropped on @chartfanatics & @Wordsofrizdom . If you wanna have an A-Z way of how to trade things like $SMCI, $MSTR, $SLV, etc, I recommend watching the full episode, it's full of nuggets This strategy is a setup that I've used since my early trading days, and it's great both for quick injections of capital into the equity as well as, even more importantly timing your partials if you're already on the long side! Hope you enjoy!

"Only 7 stocks are carrying the market. That's unhealthy. We're due for a correction." Half of FinTwit has posted some version of this in the last two weeks. 22 years. 5,696 trading days. $SPY vs $RSP (equal-weight S&P 500). We measured the 20-day rolling spread between cap-weighted and equal-weighted returns, the concentration signal. When SPY outperforms RSP, mega-caps are leading. When RSP outperforms SPY, the broad market is leading. When mega-caps lead (top 10% concentration), $SPY 20-day forward returns are +1.53% with a 69% win rate. When the broad market leads (bottom 10%), it's +0.53% with a 64% win rate. The difference is +1.00% with a p-value of 0.002. Bootstrap confidence interval excludes zero. Narrow leadership isn't bearish. It's nearly triple the forward return of "healthy breadth." $QQQ loves concentration even more. Top 5% concentration, QQQ 20-day forward: +3.22%, 79% win rate. When mega-caps lead, they keep leading. The drawdown data flips the narrative entirely. Probability of a >5% drawdown in the next 20 days when mega-caps lead (top 10%): 27%. When the broad market leads (bottom 10%): 33%. The "healthy breadth" regime has HIGHER drawdown risk. When equal-weight starts beating cap-weight, it often means mega-caps are rolling over and they drag the index down next. Sustained concentration doesn't weaken the signal. After 21-40 consecutive days of mega-cap leadership, 20-day forward returns are +1.23% with a 74% win rate. Duration makes it stronger, not weaker. Is concentration a crash predictor? Only 3 of 7 major drawdowns (>10%) since 2003 started from a concentrated reading. The other 4 started from normal or broad-led regimes. Concentration alone doesn't predict crashes. What predicts trouble is when concentration BREAKS meaning when mega-caps stop leading and the spread rolls over. Current reading: the SPY vs RSP 20-day spread is +3.39%, the 97.6th percentile. This is extreme concentration. FinTwit is calling it a warning sign. The data says it's the strongest forward return regime in the dataset. The actionable framework: When SPY is outperforming RSP (concentration rising): stay long, favor $QQQ and mega-cap exposure. The momentum is self-reinforcing. 69% win rate at 20 days. When the spread is above the 90th percentile: lean into it. The top 10% regime has the best forward returns and the lowest short-term drawdown risk in the dataset. The warning sign to watch for: when the spread REVERSES meaning when RSP starts outperforming SPY after a period of extreme concentration. That's when mega-cap leadership breaks, and historically that transition carries the highest drawdown risk. We'll flag it when it happens. The danger signal isn't narrow leadership. It's when narrow leadership stops. $SPY $QQQ $RSP $NVDA $MSFT $AAPL $GOOGL $META $AMZN

时不时有人会问我油价的事情。原油不是我的专长，但是我可以拿我的本行化肥举个例子。做多Supply Shock其实是三个不同阶段。 今年从一月末就有要对伊朗动手的信号了。这个时候如果去做原油/化肥，属于提前埋伏，做预期。真的打了就涨，打不了就不涨，属于个人倾向和分析能力的事情，就不多说了。这个是做事件，左侧交易。 但是除了本质的做多事件以外，还分“做多风险溢价”、“做多价格中枢”和“做多边际影响”三种。 做多风险溢价比较好举例子。比如说现在，伊朗封锁海峡，美国也封锁海峡，船就是出不来，没有油就是没有油，海峡是死的。但是每次有利好利空消息出来依旧有涨跌，尽管就算海峡真的开发了，供给恢复也需要几个月；或者说海峡继续关闭对于原油运输量不存在新的减少，但是还是会跌。这种时候的价格变动属于纯粹的风险溢价变化，不属于基本面。所以这种时候要想明白，买卖的是波动。麻烦之处在于风险溢价一旦定价完成之后，如果没有更坏的消息，恐慌是要回吐的。快进快出。 做多价格中枢，是指价格中枢必然上移。要有耐心。有人这次看化肥涨了，就想着去做多食品企业或者农产品企业，不是这么回事。食品价格上涨容易压缩食品企业利润，反而跌。另一头，农民也不是傻的。春耕时间段的化肥是不是提前准备好了、化肥价格上涨是否应对增量需求，农民是不是少种一点玉米多种一点大豆。市场会自行进行调节，在短期内缩减一部分需求，转移一部分渠道，寻找一些替代品，比如从别的国家买原油、使用别的能源或者运输路径，或者减少生产支出。就算是以中期来看，价格上涨依旧不是线性的。如果有信念，那就时间换空间，等待新的价格均衡。 最后一条是做多边际，去吃那个被作为新供应商的产业或者国家。但这个东西的前提是要经过了供需再平衡之后，价格开始钝化了，才去有做的意义。不然就一直等。 这四个阶段策略基本是按照时间分的。事件发生前，事件刚发生，事件发酵，事件结束。预期，恐慌，重构。搞清楚自己做的是哪一段。