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🚨 2026 INFRASTRUCTURE SUPERCYCLE IS LIVE – 18 NAMES CATCHING REAL ROTATION $FET — 5–22x $AR — 6–27x $FIL — 4–19x $RNDR — 7–30x $TAO — 5–24x $IO — 8–33x $AKT — 6–25x $GLM — 5–21x $NMR — 7–28x $OCEAN — 4–18x $HBAR — 5–20x $ALGO — 4–17x $ICP...

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🔥 Late June 2026: The Great Selective Rotation is Accelerating $KAS $0.45–4.5 $HBAR $1–8 $FIL $35–150 $ICP $45–250 $AR $80–400 $STX $8–40 $QNT $400–2000 $RIVER $6–60+ (abstraction + stablecoin plays gaining steam) $AKT $18–80 $HNT $30–150 $PYTH $1.5–12 $WIF $7–35 $BONK $0.00007–0.0007 $FLOKI $0.0015–0.015 $BRETT $0.8–6 $PENDLE $9–45 $LDO $12–45 $GMX $80–300 $CRV $2–10 $DYDX $20–80 $MANTRA $6–30 $CFG $1.5–10 $GRT $1.3–8 $MINA $2.5–14 $KSM $100–400 $GLMR $1.4–7 $CFX $1.2–6 $AXS $16–70 $SAND $2–12 $VET $0.14–0.7 $ALGO $0.7–4 $TRX $0.65–3 $DOGE $1.3–7 $PIXEL $2–15 $ILV $400–2000 $BEAM $0.12–1.2 Not the chaotic 2021 flood — this is precision capital flowing into projects delivering real utility, on-chain activity, and infrastructure moats. BTC dominance holding firm \~58-59% but showing fatigue. Altseason Index in that measured "build quietly" zone. Smart money isn’t chasing noise; it’s stacking narratives with actual traction: DePIN compute demand, decentralized intelligence, high-performance trading infra, and BTC-aligned ecosystems. My Core Conviction Basket for this cycle (long-term targets, NFA, DYOR — position size responsibly): High-Momentum Plays Heating Up Right Now: $HYPE $60–200+ — Perp DEX king with insane volume, staking mechanics, and ecosystem flywheel $TAO $250–2500 — Decentralized AI subnets exploding with real compute demand and agent growth $RENDER $12–90 — GPU workloads going parabolic as AI infra demand surges This rotation rewards conviction in builders who ship, not hype cycles. Quality narratives + patient capital = asymmetric upside. What’s your strongest conviction bag heading into Q3? Top 3 plays or hidden gems? Drop them below — let’s crowdsource the alpha 👇 Steady rotation. Data over delusion. Builders win. #Altseason3 #DePIN #DeAI #RIVER River River4FUN 🐝

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MLP in PyTorch by hand ✍️ ~ 7 steps walkthrough below Goal: fill in every blank in the PyTorch code to build a multi-layer perceptron. 1. Given Let us start with a code template on the left and the network it is supposed to build on the right. Every blank in the code can be worked out from the picture. 2. Linear layer We count: 3 features in, 4 features out. So the weight matrix is 4 by 3. There is an extra column for the biases, which means bias = T. 3. ReLU Let us apply the activation. ReLU crosses out the negatives, so -1 becomes 0. 4. Linear layer The input size is 4, because that is what the previous layer put out. The output size is 2. A 2 by 4 weight matrix, and this time no extra column, so bias = F. 5. ReLU We cross out the negatives again. 6. Linear layer Two features in, five out. A 5 by 2 weight matrix, with a bias column, so bias = T. 7. Sigmoid Let us finish. Sigmoid squashes the raw scores (3, 0, -2, 5, -5) into probabilities between 0 and 1. You have just implemented a three-layer deep neural network by hand. ✍️ == Story == Three years ago I gave this exercise to my students, to connect the code to the math. They found it odd. Every other AI course they were taking lived inside a Jupyter notebook, and here I was handing out paper. Three years later, my colleagues are the ones rushing to move their materials to paper. The exercise has not changed. Paper still asks the one thing a notebook lets you skip: do you actually understand what the code is doing? If you can tell me why the weight matrix is 4 by 3, and why bias is F on the second layer, you understand nn.Linear better than someone who has been copy-pasting it for a year. 💾 Save this post! #AIbyHand #PyTorch #DeepLearning

Tom Yeh

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