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Enter the infinite through UON Visuals’ "Fractal": an endless, algorithmic landscape where color runs impossibly deep... 🌀🌈 His piece features 6 variations of the Menger Koch fractal which transform into each other while emitting 20 different spatial 3D lighting patterns on the Exosphere.

22,805 görüntüleme • 10 ay önce •via X (Twitter)

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AI's Secret Pattern: The Surprising Role of Fractals in Neural Networks In the realm of artificial intelligence (AI), a groundbreaking discovery has emerged, challenging our conventional understanding of neural network training and optimization. This revelation centers around the identification of fractal patterns at the boundary between trainable and untrainable neural network hyperparameters, presenting a series of profound implications and avenues for further research. Fractals, known for their intricate, self-similar patterns that recur at every scale, have long fascinated mathematicians and scientists alike. Typically associated with simple, one-dimensional iterative functions, the appearance of fractals within the complex, multivariate domain of neural network training introduces a striking contrast. The organic and asymmetric nature of these fractals, as derived from the training processes, suggests a deeper, unexplored connection between the mathematical properties of fractals and the functional dynamics of neural networks. The study’s focus on two-dimensional slices of hyperparameter space barely scratches the surface of the complexity inherent in neural networks, which are characterized by a vast array of hyperparameters. The existence of fractals in this context hints at an underlying high-dimensional structure, a concept that challenges our current capabilities and understanding. Extending fractal analysis to these higher dimensions represents a significant, yet exciting, challenge that could illuminate new aspects of neural network behavior and learning capabilities. An unexpected finding from the research is the persistence of clean fractal patterns even in the presence of stochastic elements introduced during minibatch training. This resilience suggests a parallel to Lyapunov fractals, where the iterative process involves randomly changing functions. This phenomenon prompts a reevaluation of how stochastic and deterministic processes influence fractal formation within neural networks, potentially offering new insights into the fundamental mechanisms of learning and adaptation. From a practical standpoint, the fractal nature of the boundary between trainable and untrainable hyperparameters has significant implications for the field of metalearning. The chaotic behavior of the meta-loss landscape, attributed to its extreme sensitivity, presents a formidable challenge for algorithms designed to optimize hyperparameters. Understanding the fractal characteristics of this landscape could provide valuable guidance for navigating its complexities, ultimately improving the efficiency and effectiveness of metalearning strategies. Beyond the technical and theoretical implications, the discovery also reveals an unexpected aesthetic dimension to neural network fractals. The visual beauty and meditative qualities of these patterns offer a unique opportunity to engage with the material in a deeply personal and contemplative manner. This aspect suggests potential psychological and physiological benefits from exposure to the intricate designs of neural network fractals, opening up novel intersections between technology, art, and well-being. In conclusion, the identification of fractal patterns within neural network hyperparameter spaces unveils a fascinating new frontier at the intersection of fractal geometry and deep learning. This discovery not only challenges existing paradigms but also opens up myriad possibilities for mathematical characterization, algorithmic development, and even subjective exploration. As researchers continue to delve into this rich vein of inquiry, the promise of uncovering new knowledge and advancing our understanding of neural networks and their training processes remains as compelling as ever.

Carlos E. Perez

133,528 görüntüleme • 2 yıl önce

this chinese developer making $320k/year as a solo contractor his secret: 5 AI agents running in parallel, each one a specialist architect, coder, reviewer, tester, ops they don’t share context, don’t step on each other, just ship he takes on projects meant for teams of 5-8 engineers delivers in half the time keeps the entire budget found this video on bilibili at 3am and watched it four times guy sitting at his desk, two monitors filled with code, and he’s barely touching the keyboard here’s what’s happening on his screen: > agent 1 (architect): designs system structure, breaks down features into tasks, decides what gets built first > agent 2 (coder): writes the actual implementation based on architect’s specs > agent 3 (reviewer): checks every piece of code for bugs, edge cases, security issues > agent 4 (tester): generates test cases, runs them, reports failures back > agent 5 (ops): handles deployment, monitoring, infrastructure five separate claude code instances running simultaneously each one has its own system prompt, its own context, its own specialty they communicate through a shared task queue, not through each other that’s the key insight - no shared context means no conflicts agent 2 doesn’t know what agent 3 is doing agent 4 doesn’t care what agent 1 decided they just pick up tasks, complete them, move on he showed his contract history: > 3D rendering pipeline for a gaming studio: $25k > automated trading dashboard: $33k > enterprise CRM rebuild: $44k all completed solo, all delivered early, all clients thought they were hiring a team the code on his screen is python with blender integration - complex stuff that would normally require 3-4 specialists he’s shipping it in days while the client expects weeks while he’s explaining the system to camera, commits are happening in the background, tests running, deployments going out all while he’s literally not touching the keyboard his API costs run about $2k/month his revenue averages $26k/month that’s a 13x return on his AI investment this is the new solo developer playbook don’t compete with teams become the team

regent0x

183,659 görüntüleme • 2 ay önce

🇳🇱 AMSTERDAM SCIENTISTS CREATE “CHAMELEON SKIN” THAT CHANGES COLOR WITHOUT PIGMENT Scientists at the University of Amsterdam have developed a new nanomaterial that changes color like a chameleon, using no pigment, no paint, and no electricity. It is made from an extremely thin layer of silicon, about one thousandth the width of a human hair. The surface is cut with patterns so small they are invisible to the eye, inspired by the Japanese art of kirigami, which uses cuts and folds to make flexible designs. When the material stretches, those tiny shapes twist and tilt, changing how light bounces off the surface. The color shift comes from light interference, not chemical dyes. As the spacing between the nanostructures changes, different wavelengths of light cancel or reinforce each other, creating visible color shifts. The same natural physics explains why soap bubbles shimmer with rainbow patterns or why butterfly wings flash blue and green. Lead researcher Davide Ruzzene explained, “By nanopatterning the thin silicon membrane, we made it act as both a mechanical metamaterial and an optical metasurface, letting structure, not pigment, control color.” In simpler terms, the material physically moves and optically transforms at the same time. Because it does not rely on power or fading dyes, it could be used in military camouflage that changes color in motion, medical bandages that show strain or swelling, or flexible displays that never need charging. This is not science fiction but real nanoscience, turning light and motion into a living display. Source: Eugene, PhysOrg

Mario Nawfal

100,412 görüntüleme • 7 ay önce

🟧VIVID Gallery is launching ‘Looming Emotion’ by Shaderism | Arttu 🪞 A mesmerizing exploration of the overlooked aspects of our deepest emotions. 📅Save the Date: June 12th on Magic Eden on Bitcoin 🟧 Launchpad About the Artist 🧑‍🎨 Arttu Koskela is an algorithmic full-time artist, who uses web technologies to create real-time artwork, often incorporating aspects of audio-visuality, interactivity, and physics simulations, while exploring the themes of playfulness and self-reflection. Among his most notable works stands ‘Blinds Spots’, an Art Blocks collection curated. Through the looking-glass 🪞 Looming Emotion is an algorithmic art collection that depicts the conscious and unconscious habitual patterns distracting individuals from the unresolved emotions lurking behind them, which contribute to many of life's challenges. 🗺️ The collection style is inspired by wood carving artworks, glass distortion and Tom Patti’s sculptures. The variety of the 400-piece collection is created by the visual expression of colours in the glass and background, the noise patterns, tile configurations and different aspect ratios. Collector Benefits 💎 All Vivid Gallery and Shaderism | Arttu collectors can apply for a discounted private sale at 0.008 BTC. To participate in the private sale, complete the forms announced in our Discord Server (link in bio). In addition, every SATS holder is eligible for a free piece 🧡 Become a ‘Looming Emotion’ collector 🖼️ Around 100 pieces will be left after the private sale for a FCFS WL at 0.01 BTC. You can get a WL by filling out Vivid & Shaderism's collectors forms, engaging with our content, winning a collaboration raffle from our partner communities and participating in our upcoming spaces. Find all the info on our Discord (link in bio). Try to win a WL spot 🎁 If you'd like to take part in the WL, we're giving away 10 spots through the looking-glass. Participate in the raffle below ⬇️

Vivid Gallery

15,332 görüntüleme • 2 yıl önce

[Self-Attention] by Hand ✍️ Self-attention is what enables LLMs to understand context. How does it work? This exercise demonstrates how to calculate a 6-3 attention head by hand. Note that if we have two instances of this, we get 6-6 attention (i.e., multi-head attention, n=2). -- 𝗚𝗼𝗮𝗹 -- Transform [6D Features 🟧] to [3D Attention Weighted Features 🟦] -- 𝗪𝗮𝗹𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵 -- [1] Given ↳ A set of 4 feature vectors (6-D): x1,x2,x3,x4 [2] Query, Key, Value ↳ Multiply features x's with linear transformation matrices WQ, WK, and WV, to obtain query vectors (q1,q2,q3,q4), key vectors (k1,k2,k3,k4), and value vectors (v1,v2,v3,v4). ↳ "Self" refers to the fact that both queries and keys are derived from the same set of features. [3] 🟪 Prepare for MatMul ↳ Copy query vectors ↳ Copy the transpose of key vectors [4] 🟪 MatMul ↳ Multiply K^T and Q ↳ This is equivalent to taking dot product between every pair of query and key vectors. ↳ The purpose is to use dot product as an estimate of the "matching score" between every key-value pair. ↳ This estimate makes sense because dot product is the numerator of Cosine Similarity between two vectors. [5] 🟨 Scale ↳ Scale each element by the square root of dk, which is the dimension of key vectors (dk=3). ↳ The purpose is to normalize the impact of the dk on matching scores, even if we scale dk to 32, 64, or 128. ↳ To simplify hand calculation, we approximate [ □/sqrt(3) ] with [ floor(□/2) ]. [6] 🟩 Softmax: e^x ↳ Raise e to the power of the number in each cell ↳ To simplify hand calculation, we approximate e^□ with 3^□. [7] 🟩 Softmax: ∑ ↳ Sum across each column [8] 🟩 Softmax: 1 / sum ↳ For each column, divide each element by the column sum ↳ The purpose is normalize each column so that the numbers sum to 1. In other words, each column is a probability distribution of attention, and we have four of them. ↳ The result is the Attention Weight Matrix (A) (yellow) [9] 🟦 MatMul ↳ Multiply the value vectors (Vs) with the Attention Weight Matrix (A) ↳ The results are the attention weighted features Zs. ↳ They are fed to the position-wise feed forward network in the next layer.

Tom Yeh

101,010 görüntüleme • 2 yıl önce

Congrats to Ryan Ward for having his contract selected and being placed on the Dodgers' 40 Man Roster. Wardo was awarded as the Most Valuable Player in the PCL this past season after hitting .290, with an OPS of .937. He posted a WRC+ of 132, and his 36 home runs led ALL of the Minor Leagues During this season he also became the ALL-TIME home runs leader (90) and RBIs (318) in the history of the Bricktown Ballpark Era for OKC baseball. On a personal note, I've been going to AAA OKC games since 1975. That dates back to All-Sports stadium, and 6 different affiliation changes, 5 of which have been in my lifetime, and that I remember VERY well. So, to see this with my own eyes was truly historical on a personal level for me as well. #89ERS And, to have it happen in the first year of the new branding was a real "page turner" of OKC baseball, so that's SUPER cool too. Ward also drove in 122 runs, which is the most of ANY player in the PCL since 2010, and racked up the most total bases (315) of any player in the PCL since 2001. Ward also set single-season records for the Bricktown era in hits (164), runs (113), and RBIs (122). As per his defense... His outfield play has gotten better and better as each year has passed, and, while his bat will likely always be the"show stealer", he is a good athlete and has become a very solid outfielder. He can also play 1st base, and dabbled in a touch of 2nd base while at Bryant Baseball Speaking of Bryant, he's the only Bulldog ever to hit over .400 for a single season, and he struck out just 1 time his senior Season at Millbury, so the "hit tool" is NOT a new thing for him. As an aside, he graduated with fewer than 100 kids in his High School, and he is not shy to show pride in his "small town" work ethic. With all the home runs over the fence, his highlight of last year was an at-bat where he kept the ball IN the park. Wardo knocked an INSIDE-the-park WALKOFF home run back at the beginning of May. Congrats, Wardo, I could type ALL DAY about this dude, but that will do it for NOW. #dodgers

Dodgers Daily

21,255 görüntüleme • 8 ay önce

The history of Fibonacci's clock is quite intriguing, as it combines the fascinating world of mathematics with the practical application of timekeeping. The clock is named after Leonardo Fibonacci, also known as Leonardo of Pisa, an Italian mathematician from the Middle Ages. Fibonacci is most famous for introducing the Hindu-Arabic numeral system to the Western world and for his work in the Fibonacci sequence. However, the Fibonacci clock we know today is not a direct creation of Leonardo Fibonacci. The modern Fibonacci clock is a tribute to his mathematical genius and the beauty of the Fibonacci sequence. The clock's design is based on the sequence, with each number represented by a specific color or pattern. The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, starting with 0 and 1. The sequence goes as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, and so on. The sequence appears in many natural phenomena, such as the arrangement of leaves on a stem, the spiral patterns of a pinecone, and the growth of a nautilus shell. The Fibonacci clock is a unique timepiece that displays the time using the Fibonacci sequence. The clock face is divided into sections that correspond to the numbers in the sequence. The time is shown by illuminating specific sections of the clock face, with each section representing a different unit of time. For example, one section might represent hours, another minutes, and another seconds.

Historic Vids

1,397,440 görüntüleme • 2 yıl önce

‘Into the Sun’ made by Taehyung has now surpassed 100million streams on Spotify! Into the Sun is one of the most critically acclaimed tracks in the Arirang album often ranked in the Top 3 by critics and listeners. Into the Sun is also a mega viral hit on SNS platforms as fans recreate the impromptu choreography Taehyung created during the concert. “The final track is the mesmerizing ‘Into the Sun,’ where falsetto harmonies blend with a slow, shimmering tempo, creating the feeling of watching a majestic sunset alongside BTS while witnessing the endless possibilities that lie ahead of them.” - Rolling Stone “A hypnotic song that soothes the mind with a composition in which melancholic guitar and whistling sounds seem to contrast, combined with a chorus that sounds as if borrowed from The Fountains of Wayne." - New York Times “Through 'Into theSun,' BTS is advancing into their own new territory.” - The Hollywood Reporter "This experimental and fun song transforms the members' voices through digital effects, adding a poignant and mysterious atmosphere to their message of singing about eternal love. During the last minute, it transitions into a magnificent stadium rock sound." - The BBC “At this point, it’s clear that this is not just another track, ‘Into the Sun’ is a key piece that completes the album.” - Seoul Economy TV "There is one last surprise left. Filled with vocal effects and streamlined to recreate a live band jam session, 'Into the Sun' marks an intriguing finale." - The Guardian GENIUS COMPOSER TAEHYUNG

Taehyung Naver

37,202 görüntüleme • 2 ay önce

People made fun of Alex Finn for buying three Mac Studios to run AI at home. Then Fable got banned for a week, GLM 5.2 dropped, and those exact Mac Studios started reselling for 4x what he paid. He showed me how he built his home AI lab from scratch. Here's the playbook: 1) The hardware. three 512GB Mac Studios, an NVIDIA DGX Spark, a custom RTX 5090 build, and a few Mac Minis. ~$30k all in. 2) The buying framework... - Mac Studio: huge memory, runs GLM 5.2 (open weights, near Opus 4.8 on benchmarks), but slow. - DGX Spark ($4,800): the sweet spot for most people. - RTX 5090: smaller models at blazing speed (Qwen's 29B now hits Sonnet 4 level). 3) Tailscale networks every machine into one private network with root access to each other. Only one machine is plugged into a monitor. 4) A Nous Research Hermes agent is his IT guy. New model drops? It SSHs into the right box, loads 5 candidates, runs evals overnight, and reports back which task belongs on which machine. Alex has literally never loaded a model himself. 5) The whole point: achieving "ambient intelligence." Always-on jobs that would bankrupt you on per-token billing. A security sweep of his API endpoints every hour. Code optimization every 20 minutes. Database anomaly & churn detection. Hourly scraping of X, Reddit & Hacker News for business opportunities. 6) Running those workloads on frontier models would cost thousands a month. His actual cost: ~$60 more in electricity. 7) Btw he's not anti-frontier. He still maxes out his Claude plan. The way he sees it: frontier is for hard thinking, local is for the foot soldiers that never sleep. 8) "We own everything except for the intelligence. Why can't we own the intelligence?" 9) He thinks frontier-level intelligence runs on consumer hardware within 6 months.

Alex Lieberman

57,044 görüntüleme • 22 gün önce