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1/ 🔥 New paper: Differentiable Vector Quantization (DiVeQ) 🔥 Vector quantization (VQ) is a key building block in modern AI. It links continuous data like images and audio to discrete representations (tokens) used by transformers.

Arno Solin

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78,580 次观看 • 3 个月前 •via X (Twitter)

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Vector Database by Hand ✍️ Vector databases are revolutionizing how we search and analyze complex data. They have become the backbone of Retrieval Augmented Generation (#RAG). How do vector databases work? [1] Given ↳ A dataset of three sentences, each has 3 words (or tokens) ↳ In practice, a dataset may contain millions or billions of sentences. The max number of tokens may be tens of thousands (e.g., 32,768 mistral-7b). Process "how are you" [2] 🟨 Word Embeddings ↳ For each word, look up corresponding word embedding vector from a table of 22 vectors, where 22 is the vocabulary size. ↳ In practice, the vocabulary size can be tens of thousands. The word embedding dimensions are in the thousands (e.g., 1024, 4096) [3] 🟩 Encoding ↳ Feed the sequence of word embeddings to an encoder to obtain a sequence of feature vectors, one per word. ↳ Here, the encoder is a simple one layer perceptron (linear layer + ReLU) ↳ In practice, the encoder is a transformer or one of its many variants. [4] 🟩 Mean Pooling ↳ Merge the sequence of feature vectors into a single vector using "mean pooling" which is to average across the columns. ↳ The result is a single vector. We often call it "text embeddings" or "sentence embeddings." ↳ Other pooling techniques are possible, such as CLS. But mean pooling is the most common. [5] 🟦 Indexing ↳ Reduce the dimensions of the text embedding vector by a projection matrix. The reduction rate is 50% (4->2). ↳ In practice, the values in this projection matrix is much more random. ↳ The purpose is similar to that of hashing, which is to obtain a short representation to allow faster comparison and retrieval. ↳ The resulting dimension-reduced index vector is saved in the vector storage. [6] Process "who are you" ↳ Repeat [2]-[5] [7] Process "who am I" ↳ Repeat [2]-[5] Now we have indexed our dataset in the vector database. [8] 🟥 Query: "am I you" ↳ Repeat [2]-[5] ↳ The result is a 2-d query vector. [9] 🟥 Dot Products ↳ Take dot product between the query vector and database vectors. They are all 2-d. ↳ The purpose is to use dot product to estimate similarity. ↳ By transposing the query vector, this step becomes a matrix multiplication. [10] 🟥 Nearest Neighbor ↳ Find the largest dot product by linear scan. ↳ The sentence with the highest dot product is "who am I" ↳ In practice, because scanning billions of vectors is slow, we use an Approximate Nearest Neighbor (ANN) algorithm like the Hierarchical Navigable Small Worlds (HNSW).

Vector Database by Hand ✍️ Vector databases are revolutionizing how we search and analyze complex data. They have become the backbone of Retrieval Augmented Generation (#RAG). How do vector databases work? [1] Given ↳ A dataset of three sentences, each has 3 words (or tokens) ↳ In practice, a dataset may contain millions or billions of sentences. The max number of tokens may be tens of thousands (e.g., 32,768 mistral-7b). Process "how are you" [2] 🟨 Word Embeddings ↳ For each word, look up corresponding word embedding vector from a table of 22 vectors, where 22 is the vocabulary size. ↳ In practice, the vocabulary size can be tens of thousands. The word embedding dimensions are in the thousands (e.g., 1024, 4096) [3] 🟩 Encoding ↳ Feed the sequence of word embeddings to an encoder to obtain a sequence of feature vectors, one per word. ↳ Here, the encoder is a simple one layer perceptron (linear layer + ReLU) ↳ In practice, the encoder is a transformer or one of its many variants. [4] 🟩 Mean Pooling ↳ Merge the sequence of feature vectors into a single vector using "mean pooling" which is to average across the columns. ↳ The result is a single vector. We often call it "text embeddings" or "sentence embeddings." ↳ Other pooling techniques are possible, such as CLS. But mean pooling is the most common. [5] 🟦 Indexing ↳ Reduce the dimensions of the text embedding vector by a projection matrix. The reduction rate is 50% (4->2). ↳ In practice, the values in this projection matrix is much more random. ↳ The purpose is similar to that of hashing, which is to obtain a short representation to allow faster comparison and retrieval. ↳ The resulting dimension-reduced index vector is saved in the vector storage. [6] Process "who are you" ↳ Repeat [2]-[5] [7] Process "who am I" ↳ Repeat [2]-[5] Now we have indexed our dataset in the vector database. [8] 🟥 Query: "am I you" ↳ Repeat [2]-[5] ↳ The result is a 2-d query vector. [9] 🟥 Dot Products ↳ Take dot product between the query vector and database vectors. They are all 2-d. ↳ The purpose is to use dot product to estimate similarity. ↳ By transposing the query vector, this step becomes a matrix multiplication. [10] 🟥 Nearest Neighbor ↳ Find the largest dot product by linear scan. ↳ The sentence with the highest dot product is "who am I" ↳ In practice, because scanning billions of vectors is slow, we use an Approximate Nearest Neighbor (ANN) algorithm like the Hierarchical Navigable Small Worlds (HNSW).

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K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!

K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!

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K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!

K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!

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I vibe coded a visual PDF search app with ColQwen2. This is how it works: - Store PDF files as images in a Weaviate AI Database vector database - Embed images and text with a multimodal late-interaction model (ColQwen2) - Generate token-wise (and summed) similarity maps to highlight image patches with high similarity Now I need to refactor the messy vibe-coded project. In the meantime, you can check out the Notebook this demo is based on to try it out yourself:

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Researchers made KMeans 200x faster. And the new technique also beats approaches like cuML and FAISS. Flash-KMeans is an IO-aware implementation of exact KMeans that redesigns the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 33x speedup over cuML - 200x speedup over FAISS This speedup comes from how it moves through GPU memory. Standard KMeans runs in two steps, and both are bottlenecked by reads and writes to GPU memory: 1) The first step matches every point to its nearest centroid. Standard KMeans computes the full point-to-centroid distance matrix, writes it out to GPU memory, then reads it back to find each nearest centroid. That write-then-read round trip is the bottleneck. Flash-KMeans combines the distance calculation with the nearest-centroid step, so the result is computed on-chip and the full matrix is never written out. 2) The second step recomputes each centroid by averaging the points assigned to it. Standard KMeans has thousands of threads writing into the same centroid slots at once, so they stall waiting for their turn. Flash-KMeans sorts points by cluster first, turning scattered writes into sequential reductions that read and write memory in one efficient pass. Using these two optimizations at the million-scale, Flash-KMeans completes a standard KMeans iteration in a few milliseconds. The video below depicts this in action. Several reasons why this is important: KMeans has always been an offline primitive. Something you run once to preprocess data and move on. These speedups make the approach viable in several runtime-critical systems. ↳ Vector indices like FAISS use KMeans to build search indices. Faster KMeans means you can re-index dynamically as data changes. ↳ LLM quantization methods need KMeans to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Flash-KMeans makes it viable to run this inside the inference loop, not just in preprocessing. I have shared the paper in the replies. That said, memory is the real constraint Flash-KMeans solves, and the problem is not just limited to clustering. The vectors a RAG system stores after indexing create similar bottlenecks. I wrote a detailed walkthrough recently on cutting this vector memory by 32x with binary quantization, querying 36M+ vectors in a few milliseconds. Read it below.

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ATLAS NAVI | AI Navigation APP with 1M Downloads

35,574 次观看 • 2 年前

I created this desk calendar as a source of inspiration for anyone learning AI in 2026. It includes 24 AI algorithms and architectures, all drawn and calculated by hand. ✍️ 𝗝𝗮𝗻𝘂𝗮𝗿𝘆: [1] Matrix Multiplication; [2] Discrete Fourier Transform (DFT) 𝗙𝗲𝗯𝗿𝘂𝗮𝗿𝘆: [3] Support Vector Machine (SVM); [4] Vector Database 𝗠𝗮𝗿𝗰𝗵: [5] Multi-Layer Perceptron (MLP); [6] Backpropagation 𝗔𝗽𝗿𝗶𝗹: [7] Batchnorm; [8] Dropout 𝗠𝗮𝘆: [9] Recurrent Neural Network (RNN); [10] Long-Short Term Memory (LSTM) 𝗝𝘂𝗻𝗲: [11] Residual Network (ResNet); [12] Graph Convolutional Network (GCN) 𝗝𝘂𝗹𝘆: [13] Autoencoder; [14] Variational Autoencoder (VAE) 𝗔𝘂𝗴𝘂𝘀𝘁: [15] Generative Adversarial Network (GAN); [16] U-Net 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿: [17] Transformer; [18] Self Attention 𝗢𝗰𝘁𝗼𝗯𝗲𝗿: [19] Reinforcement Learning with Human Feedback (RLHF); [20] Contrastive Language-Image Pre-training (CLIP) 𝗡𝗼𝘃𝗲𝗺𝗯𝗲𝗿: [21] Diffusion Transformer; [22] Switch Transformer 𝗗𝗲𝗰𝗲𝗺𝗯𝗲𝗿: [23] Sparse Autoencoder; [24] BitNet

I created this desk calendar as a source of inspiration for anyone learning AI in 2026. It includes 24 AI algorithms and architectures, all drawn and calculated by hand. ✍️ 𝗝𝗮𝗻𝘂𝗮𝗿𝘆: [1] Matrix Multiplication; [2] Discrete Fourier Transform (DFT) 𝗙𝗲𝗯𝗿𝘂𝗮𝗿𝘆: [3] Support Vector Machine (SVM); [4] Vector Database 𝗠𝗮𝗿𝗰𝗵: [5] Multi-Layer Perceptron (MLP); [6] Backpropagation 𝗔𝗽𝗿𝗶𝗹: [7] Batchnorm; [8] Dropout 𝗠𝗮𝘆: [9] Recurrent Neural Network (RNN); [10] Long-Short Term Memory (LSTM) 𝗝𝘂𝗻𝗲: [11] Residual Network (ResNet); [12] Graph Convolutional Network (GCN) 𝗝𝘂𝗹𝘆: [13] Autoencoder; [14] Variational Autoencoder (VAE) 𝗔𝘂𝗴𝘂𝘀𝘁: [15] Generative Adversarial Network (GAN); [16] U-Net 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿: [17] Transformer; [18] Self Attention 𝗢𝗰𝘁𝗼𝗯𝗲𝗿: [19] Reinforcement Learning with Human Feedback (RLHF); [20] Contrastive Language-Image Pre-training (CLIP) 𝗡𝗼𝘃𝗲𝗺𝗯𝗲𝗿: [21] Diffusion Transformer; [22] Switch Transformer 𝗗𝗲𝗰𝗲𝗺𝗯𝗲𝗿: [23] Sparse Autoencoder; [24] BitNet

Tom Yeh

14,246 次观看 • 8 个月前

New Version of HyperStore is now live! 🔥 We’re excited to announce that HyperStore has officially been upgraded to a new system version. This is not a simple UI update, it’s a full platform evolution. ⚡ What’s new? HyperStore now delivers a significantly faster and more intelligent experience powered by its rebuilt infrastructure. - 5000+ AI apps, fully structured into a living ecosystem - A new prompt-based discovery system - Faster navigation, cleaner interface, smarter results Now users don’t search for tools, they instantly reach solutions. 🧠 HyperClaw Integration HyperClaw is now fully active within HyperStore. It acts as a continuous intelligence layer that: - Keeps the platform updated in real time - Curates and optimizes AI apps dynamically - Ensures the ecosystem is always evolving 🔥 What this means? HyperStore is no longer just an AI marketplace. It is now an AI execution layer. Designed for builders, creators, and operators who move fast. 🌐 Try it now: ⚡ Find any AI solution. Instantly.

New Version of HyperStore is now live! 🔥 We’re excited to announce that HyperStore has officially been upgraded to a new system version. This is not a simple UI update, it’s a full platform evolution. ⚡ What’s new? HyperStore now delivers a significantly faster and more intelligent experience powered by its rebuilt infrastructure. - 5000+ AI apps, fully structured into a living ecosystem - A new prompt-based discovery system - Faster navigation, cleaner interface, smarter results Now users don’t search for tools, they instantly reach solutions. 🧠 HyperClaw Integration HyperClaw is now fully active within HyperStore. It acts as a continuous intelligence layer that: - Keeps the platform updated in real time - Curates and optimizes AI apps dynamically - Ensures the ecosystem is always evolving 🔥 What this means? HyperStore is no longer just an AI marketplace. It is now an AI execution layer. Designed for builders, creators, and operators who move fast. 🌐 Try it now: ⚡ Find any AI solution. Instantly.

HyperGPT

99,166 次观看 • 3 个月前