Shion Badge War S2E2 stats: Badges earned: 0 Marshmallows... climbed: 0 Members successfully fended: 0 Times laughed: 987529 “안돼”’s mentioned: 4 Is this your GOAT?show more

0-0 thru 4‼️ 2024 RHP Tyler Walton Tyler Walton... | CTJ Baseball After giving up a leadoff baserunner, Walton left him stranded at 3rd. His fastball has been in the upper 80’s all night and touched 90/1 a handful of times. The Texas Baseball recruit is keeping a dangerous lineup quiet for now. @PBRGowinsshow more

this is how you can promote your app for... $0. 1. 3-4 pics 2. strong hook first 3. title + advices next it’s the EASIEST format to go viral with literally 0 effort. just drop a subtle CTA in the 3rd pic. don’t overthink it.show more

Some absurd Jacob Wilson stats. 1. He has swung... at 234 pitches in the zone this season, making contact with 223 of them. 2. He has more games with 4 hits (7) than 0 hits (4) 3. He has more games with 4+ RBI (3) than 2 strikeouts (2) 4. He is hitting over .400 vs. all pitch typesshow more

This is not Tejasvi Surya , This is the... language of Modi ji ... Not once but many times, The creation of our dream , Our decades of struggle for Telangana State, is ridiculed by BJP.. If only Shri KCR's party members were in LokSabha today, They would have not been silent like the present 8 + 8 =0 We condemn BJP comparing Telangana - AndhraPradesh to India and Pakistanshow more

We all know #AHYEON’s natural voice is very loud,... and her mic is usually at 20-50% volume compared to the others. But yesterday, her mic was at 0% during the members greetings to fans, so her voice wasn't heard 🥲🥲. She's had this issue multiple times. It might not be maltreatment just a mistake so don't panic, even though some people are calling it that 🙂.show more

There is something magical about watching your kids fall... in love with football. They have seen their team come from 4-0 down in the SF to win with virtually the final touch at Wembley. I’ve told them it’s not always like this but… there isn’t much like it #SWFC #PlayoffFinal ❤️show more

Here we go GPT Image 2 and Seedance 2.0... is now live on insMind #insmind #insmindai Generated this GRWM video using the Prompt : Aesthetic “Get Ready With Me – Gym Edition” storyboard layout, minimal neutral-toned design, soft beige and cream color palette, clean editorial grid. Top header text: “GET READY WITH ME” subtitle: “gym edition” in elegant script font subheading: “step-by-step activewear routine” The layout is divided into 4 blocks, each showing a sequence (1–8 steps per row), featuring the same young woman throughout with consistent face, natural makeup, athletic toned body, hair tied in a messy bun or sleek ponytail. BLOCK 1 – BASE (1–8) 1–2: putting on a fitted sports bra 3–4: wearing high-waisted gym leggings 5–6: adjusting waistband / smoothing fit 7–8: mirror check, relaxed confident pose BLOCK 2 – LAYERS (9–16) 1–2: putting on oversized gym t-shirt or cropped top 3–4: adding lightweight zip-up hoodie or jacket 5–6: tying hair tighter / adjusting outfit 7–8: slight movement pose (stretching arms or twisting body) BLOCK 3 – DETAILS (17–24) 1–2: wearing smartwatch / fitness band 3–4: adding minimal jewelry (thin chain, studs) 5–6: putting on gym gloves or lifting straps 7–8: wearing sunglasses or tying hair final look BLOCK 4 – FINISH (25–32) 1–2: putting on training shoes (clean white sneakers) 3–4: grabbing gym bag / water bottle 5–6: holding headphones / protein shaker 7–8: full-body mirror shot, confident final look Side icons representing categories: base, layers, accessories, shoes, final look. Soft natural lighting, indoor minimal room or modern apartment, neutral background, clean shadows, editorial fashion photography style, consistent framing across all panels. Footer text: “You’re ready. Go own your workout.” Video prompt : Use provided storyboard image as reference CONCEPT: Get Ready With Me — Gym Edition TIMELINE: 0 : 00–0:04 Sports bra on High-waisted leggings wear Waistband adjustment Mirror check 0 : 04–0:08 Oversized tee / cropped top Lightweight hoodie or jacket Hair tie (ponytail/bun) Light stretch / body turn 0: 08–0:12 Smartwatch / fitness band Minimal jewelry Gym gloves / lifting straps Sunglasses on 0: 12–0:15 Training shoes Grab gym bag + water bottle Headphones / shaker Walk-out + final confident look STYLE: Minimal, neutral tones, soft beige/grey palette, natural indoor lighting, clean modern interior, athletic aesthetic CAMERA: Close-up + mid shots, soft focus, shallow depth of field, steady framing, subtle handheld realism TRANSITIONS: Match cuts, outfit snap transitions, fabric motion cuts, quick clean jump cuts synced to movement OUTPUT: Loopable, smooth pacing, satisfying flow, social media ready (vertical 9 : 16show more

Another terrible “provably fair” implementation: Betpanda​.io their setup lets... them cherry pick outcomes and cheat players they rotate the server seed EVERY bet client seed stays fixed nonce = always 0 what this means: -after your first bet the server knows your input -it can simulate outcomes before serving one -cherry-pick a seed that makes you lose on larger bets -keep small bets normal so stats look fine your verification still passes because the math is correct for the seed they chose what you can’t prove is how many winning seeds were discarded first this isn’t provably fair the house can pick the outcome before you see it no legit pf system works like this standard is one committed seed + incrementing nonce Have you played at betpanda? what do you think?show more

Such an impressive for the Denver Nuggets in so... many ways. 1) Nikola Jokic proves once again why he’s the best player on the planet 2) I thought this game was over when the Nuggets led big with 3 minutes to play in regulation 3) I thought this game was over when the Wolves led by 7 in OT 4) I thought this game was over about five times 5) The Nuggets are now 3-0 against Minnesota. Full Ownership 6) The Nuggets won this game with three starters tied behind their back 7) Nikola Jokic is the greatest player on earth 8) Jamal Murray is a bad man 9)WTF did I just see? 10) Merry Christmasshow more

IT WILL COST YOU $0 TO RETWEET THIS!😢 This... is our current situation and reality now in some Northern part of the country. Women who are presumed to be cult members kills peoples by just answering them once they come to ur house. They usually seeks for help, either kettle, water etc, once you offer them, you will faint and they left. The woman right here was caught somewhere in Gusau they said and was forced to wake this lady, then later police came and arrested her before people take matters into their own hands. Please share with your loved ones and stay safe. HOLD ONTO YOUR AZKAR!! 📿🙏🏾show more

[Graph Convolutional Network] by hand ✍️ Graph Convolutional Networks... (GCNs), introduced by Thomas Kipf and Max Welling in 2017, have emerged as a powerful tool in the analysis and interpretation of data structured as graphs. This exercise demonstrates how GCN works in a simple application: binary classification. -- Goal -- Predict if a node in a graph is X. -- Architecture -- 🟪 Graph Convolutional Network (GCN) 1. GCN1(4,3) 2. GCN2(3,3) 🟦 Fully Connected Network (FCN) 1. Linear1(3,5) 2. ReLU 3. Linear2(5,1) 4. Sigmoid Simplications: • Adjacent matrices are not normalized. • ReLU is applied to messages directly. -- Walkthrough -- [1] Given ↳ A graph with five nodes A, B, C, D, E [2] 🟩 Adjacency Matrix: Neighbors ↳ Add 1 for each edge to neighbors ↳ Repeat in both directions (e.g., A->C, C->A) ↳ Repeat for both GCN layers [3] 🟩 Adjacency Matrix: Self ↳ Add 1's for each self loop ↳ Equivalent to adding the identity matrix ↳ Repeat for both GCN layers [4] 🟪 GCN1: Messages ↳ Multiply the node embeddings 🟨 with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [5] 🟪 GCN1: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The purpose is the pool messages from each node's neighbors as well as from the node itself. ↳ The result is a new feature per node [6] 🟪 GCN1: Visualize ↳ For node 1, visualize how messages are pooled to obtain a new feature for better understanding ↳ [3,0,1] + [1,0,0] = [4,0,1] [7] 🟪 GCN2: Messages ↳ Multiply the node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [8] 🟪 GCN2: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The result is a new feature per node [9] 🟪 GCN2: Visualize ↳ For node 3, visualize how messages are pooled to obtain a new feature for better understanding ↳ [1,2,4] + [1,3,5] + [0,0,1] = [2,5,10] [10] 🟦 FCN: Linear 1 + ReLU ↳ Multiply node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is a new feature per node ↳ Unlike in GCN layers, no messages from other nodes are included. [11] 🟦 FCN: Linear 2 ↳ Multiply node features with weights and biases [12] 🟦 FCN: Sigmoid ↳ Apply the Sigmoid activation function ↳ The purpose is to obtain a probability value for each node ↳ One way to calculate Sigmoid by hand ✍️ is to use the approximation below: • >= 3 → 1 • 0 → 0.5 • <= -3 → 0 -- Outputs -- A: 0 (Very unlikely) B: 1 (Very likely) C: 1 (Very likely) D: 1 (Very likely) E: 0.5 (Neutral)show more

🔥5 drills to destroy doubles #1 Pad Punch When... you feel pressure from the adjacent blocker, combat his force with your hips. #2 Sled Punch The key to defeating a double team is to split it. Here’s a great drill to help you attack 1 man, get skinny and throw off. #3 Double splits Splitting doubles can be super difficult. The double splits drill helps simulate the force you’ll need to apply into your primary blocker to ultimately split the double team. #4 Punch recover When aligned in an even technique (0, 2, 4) the key is to survive the collision by staying square, absorbing force and transitioning to the crease. The punch recover drill does a great job of simulating this. #5 Hip Swivels Use hip swivels to practice wedging your hips between the primary and adjacent blocker.show more

Microsoft made 100B parameter models run on a single... CPU. bitnet.cpp: The official inference framework for 1-bit LLMs. The math behind 1-bit LLMs is what makes them revolutionary. Traditional LLMs use 16-bit floating point weights. Every parameter is a number like 0.0023847 or -1.4729. When you run inference, you multiply these floats together. Billions of times. That's why you need GPUs, they're optimized for floating point matrix multiplication. BitNet b1.58 uses ternary weights: {-1, 0, 1}. That's not a simplification. That's a fundamental change in the math. When your weights are only -1, 0, or 1: → Multiply by 1 = keep the value → Multiply by -1 = flip the sign → Multiply by 0 = skip entirely Matrix multiplication becomes addition and subtraction. No floating point operations. No GPU required. This is why bitnet.cpp achieves: → 2.37x to 6.17x speedup on x86 CPUs → 1.37x to 5.07x speedup on ARM CPUs → 71.9% to 82.2% energy reduction on x86 → 55.4% to 70.0% energy reduction on ARM The speedups scale with model size. Larger models see bigger gains because there are more operations to simplify. A 100B parameter model running at human reading speed (5-7 tokens/second) on a single CPU. That's not optimization. That's a different paradigm. Why 1.58 bits? Because log₂(3) ≈ 1.58. Three possible values = 1.58 bits of information per weight. The key insight: These models aren't quantized after training. They're trained from scratch with ternary weights. The model learns to work within the constraint. No precision loss. No quality tradeoff.show more

Ok. So, it is very early and I'm only... catching up now. If I got this right: 0. The organizers of the event took government money to facilitate the event, fully knowing that Messi playing was key to the money being awarded as a grant. 1. Messi and Suarez were injured, but Martino never mentioned to the organizers that they wouldn't play. The game was held without warning fans that the main reason they were there was not going to feature. 2. David Beckham was booed during the award ceremony for this friendly. 3. Martino acknowledges the failure in letting folks know Messi was not going to play. 4. They play Japan's Vissel Kobe on Wednesday after embarrassing losses and a win in Hong Kong. Yup, this preseason tour is going great and leaving a wonderful image of MLS to the rest of the world. Well done, Inter Miami. #InterMiamiCF | #mls Inter Miami CF 📷:CNN and Joel Chanshow more

I haven’t missed a workout in over 10+ years... 0 misses in over 3650 days is great discipline if you zoom out however, that doesn’t mean each workout is perfect, not rested, don’t have enough food, not enough time, etc but none of that matters if you showed up and tried your best finishing this, then flying with the team to vegas for the weekend! we’re focusing on the 1st workout of this 4 week period, attempting to push my limits here, going for 110% attempts usually I stay around 80-90% work for most then try to max out once in a while but we hit our max today!!!! ahhhh find what works for you and stay consistent to it id say it’s been a key factor to an understanding that life and business is a marathon it may not be a sprint but we can work on running on pace faster and without pain health is wealthshow more

1 Neural Network + Obsidian + Karpathy’s 1-file method... = the most unhinged second brain build of 2026. It remembers everything you’ve ever done, and it costs $0 on top of what you already pay. The base is Karpathy’s append and review: 1 giant note, new thoughts stack on top, old ones sink, every few days you reread and pull the survivors back up. No folders, no tags, no plugins the rereading IS the system, because review is what turns storage into thinking. The flaw: past 10,000 lines, no human rereads anything. That’s where the neural network takes over. You keep the note in Obsidian 1 vault, everything dumps to the top: ideas, links, meeting fragments, half-thoughts. You never organize, you only dump. It all lives as plain markdown on your own disk, and that detail is the whole trick. Because now you point Claude Code at the vault folder, and it reads every line you’ve ever written. “What did I think about pricing in March.” “Find the 3 ideas I keep circling.” “What did I drop that deserves a second look.” It answers from YOUR notes, with quotes, in 15 seconds. Then once a week, 1 prompt closes the loop: read the last 7 days, surface the 5 entries worth pulling back up, flag anything that contradicts what I wrote a month ago. The model does the sinking and surfacing Karpathy did by hand, and the note stays alive instead of turning into a graveyard. Week 1 feels like nothing. Week 4 you hit the first “I already solved this in January.” Month 3 you consult your past self more than Google. Most second brains die in 11 days under 40 plugins and 200 folders. This one is 1 file and a loop, and it compounds because dumping takes 0 discipline. Notion stores what you thought. This thing argues back.show more

[Discrete Fourier Transform] by Hand ✍️ In signal processing,... the Discrete Fourier Transform (DFT) is no doubt the most important method. But the math involved is extremely complex, literally, involving a summation over a complex number term e^(-iwt). I developed this exercise to demonstrate that underneath such complexity, DFT is just a series of matrix multiplications you can calculate by hand. ✍️ Once you see that, it should not surprise you that a deep neural network, which is also a series of matrix multiplications, with activation functions in-between, can learn to perform DFT to process and analyze signals so effectively. How does DFT work? [1] Given ↳ Signals A, B, and C in the 🟧 frequency domain: ◦ A = cos(w) + 2cos(2w) ◦ B = cos(w) + cos(3w) + cos(4w) ◦ C = -cos(2w) + cos(3w) ◦ Each signal is a weighed sum of four cosine waves at frequencies 1w, 2w, 3w, and 4w. ◦ We will apply Inverse DFT to convert the signals to time domain representations, and then demonstrate DFT can convert back to their original frequency domain representations. ↳ Signal X in the 🟩 time domain. X is sampled at 10 time points 1t, 2t, …, 10t: ◦ X = [-2.5, -1.8, 3, -0.7, -1.0, -0.7, 3, -1.8, -2.5, 5] ◦ Suppose X is also a weighted sum of the same four cosine waves, but we don’t already know their weights. We will apply DFT to discover them. [2] 🟧 Frequency Matrix (F) ↳ Write the coefficients of A, B, C as a matrix F. Each signal is a row. Each frequency is a column. ↳ A → [1, 2, 0, 0] ↳ B → [1, 0, 1, 1] ↳ C → [0, 1-, 1, 0] [3] Cosine → Discrete ↳ Sample from the continuous cosine waves at discrete time points 1t, 2t, 3t, to 10t. [4] Cosine Matrix (W) ↳ Write the samples as a matrix, Each frequency is a row. Each time point is a column. [5] Inverse DFT: 🟧 Frequency → 🟩 Time ↳ Multiply the frequency matrix F and the cosine matrix W. ↳ The meaning of this multiplication is to linearly combine the four cosine waves (rows in W) into time-domain signals (rows in T) using the weights specified in F. ↳ The result is matrix T, which are signals A, B, C converted to the time domain. Each signal is a row. Each time point is a column. [6] Transpose ↳ Transpose T, converting each signal’s time domain representation from a row to a column. [7] DFT: 🟩 Time → 🟧 Frequency ↳ Multiply the cosine matrix W with the transpose of matrix T. ↳ The purpose of this multiplication is to take a dot-product between each time-domain signal (columns in the transpose of T) and each cosine wave (rows in W), which has the effect of projecting the signal onto a cosine wave to determine how much they are correlated. Zero means not correlated at all. ↳ The result is an intermediate version of the “recovered” frequency matrix where each column corresponds to a signal and each row corresponds to a frequency. ↳ Compared to the original frequency matrix F, this intermediate matrix has non-zero weights in the correct places, but scaled up by a factor of 5 (n/2, n=10). For example, signal A, originally [1,2,0,0], is recovered at [5,10,0,0]. [8] Scale ↳ Multiply each value by 2/n = 1/5 to scale down the intermediate matrix to match the magnitude of the original frequency matrix F. [9] Transpose ↳ Transpose the recovered frequency matrix back to the same orientation of the original frequency matrix F. ↳ Like magic 🪄, the result is identical to the original F, which means DFT successfully recovered the frequency components of signals A, B, C. [10] Apply DFT to X: 🟩 Time → 🟧 Frequency ↳ Now that we have some confidence in DFT’s ability to recover frequency components, we apply DFT to X’s time-domain representation by multiplying W with X. ↳ The result is the an intermediate matrix. [11] Scale ↳ Similarly, we scale down by a factor of 5 to obtain the recovered frequency components of X (a column). [12] Transpose ↳ Similarly, we transpose the recovered column to row to match the orientation of the frequency matrix. ↳ Using the coefficients [0,0,3,2], we can write the equation of X as 3cos(3w) + 2cos(4w). Notes: I hope this by hand exercise helps you understand the essence of DFT. But there is more technical details, such as: • Sine: The complete DFT math also includes sine waves that follow a similar calculation process. • Phase: Here, we assume all the cosine waves are aligned at the origin, namely, phase is 0. If a phase p is added, for example, cos(w+p), we will need to calculate the sine component and use their ratio to figure out what p is. • Magnitude: If phase is not zero, the magnitude will need to be calculated by combining both cosine and sine terms.show more

Karpathy method + Claude Code reading your whole Obsidian... vault is the smartest second brain on earth. The method is simple and brutal. If you can’t build a thing from scratch, you don’t know it. Tutorials are fake learning and your brain deletes them in 3 days. Most people ignore this. They build a second brain that just sits there, folders of notes nobody reopens, dead text. Point Claude Code at the vault and it wakes up. 5,000 notes, one mind. It reads all of it and answers in your own words and your own proofs, not a model’s guess. Then the loop closes. Want to understand neural nets? Skip the 3-hour video and ask Claude Code to build a tiny one. 200 lines from scratch. Watch it train, break a layer, watch it fail, fix it. It clicks in 20 minutes instead of 3 weeks. The second it lands the note gets written. One idea per file, linked to 10 others, dropped into the vault while the memory is still hot. Now it compounds. Month 1: is 60 notes. Month 6 is 900. Every new note pulls in old ones, so you ask anything and the answer comes from your brain, not the internet. Before: 40 tabs, 6 half read PDF, 0 retained. After: build it once, own it for life. Setup takes 4 minutes. Plain text, no lock-in. A second brain nobody reads is a graveyard. Yours just started thinking.show more

Elon Musk pays one wallet $115K. He doesn't even... know about it. Every time he opens Twitter and starts typing someone on the other end earns money. Quietly. Methodically. Tweet by tweet. I stumbled upon this wallet by accident. Was scrolling through the Polymarket leaderboard. Looking for interesting strategies. Most tops are politics crypto mix of everything. And then I saw Prexpect → Opened the profile. Looked at positions. Closed it. Opened again. Thought it was a bug. Every position is the same market. Will Elon Musk post X tweets this week? Not ten different bets. No hedge on politics. One market. Over and over. For months. Scrolled through closed trades history. Won. Won. Won. Won. Scrolled further. Won. Won. Won. This is not trading. This is harvesting. Started breaking down how this works. Every Monday Polymarket opens fresh markets on Elon's tweets. Buckets of 20: 400-419 tweets 420-439 440-459 and so on. At the moment of opening chaos. Nobody knows where to set prices. Zero liquidity. Spreads like a canyon. Prexpect is already there. Pours in limit orders on YES at 1-2 cents across several buckets simultaneously. Becomes the order book before the order book exists. Looking at his active positions right now: 10000 shares 400-419 tweets entry 1¢ now 22¢ 10000 shares 420-439 tweets entry 1¢ now 18¢ 10000 shares 380-399 tweets entry 1¢ now 17¢ x17-x22 in a few days. The week isn't even over. But that's only half of it. Elon is a chaotic poster. 50 tweets in the morning then silence. Night raid at 3am then sleep. The market reacts slowly. People at work. People sleeping. People don't count tweets manually every hour. Prexpect counts. Imagine: you're watching football on TV. Score is 0-0. But someone at the stadium already saw the goal. For him it's 1-0. He places a bet while your picture catches up to reality. Prexpect is at the stadium. Always. Elon starts speeding up at 2am? Prexpect sees the pacing. Recalculates probabilities. Adjusts positions. By morning the market wakes up and prices have already moved. Closed positions tell the whole story: $3280 → $14871 March $11450 → $22979 November $27123 → $44597 December $41117 → $60388 January The fattest gain 353%. On tweets. Just on counting how many times a person hit Post. Scrolling further. Looking for at least one other bet. Maybe crypto. Maybe elections. Something for variety. Nothing. Only Elon. Week after week. Month after month. No diversification. No just in case. One edge sharpened to automation. While all of Twitter argues about WHAT Elon wrote one trader quietly collects money for HOW MUCH he wrote. $115314 and growing. Right now while you're reading this post Elon is possibly typing another tweet. And someone already bet on it.show more