AIOZ Storage Vector Database is LIVE. → Meaning-Based Retrieval:... Find relevant results by semantic similarity → Built-In Text Embedding: Send text and generate embeddings on AIOZ infrastructure → Familiar Integration: AWS SigV4 + standard AWS SDKs Store. Embed. Retrieve.show more

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:show more

Chainlink Goes Live On AWS Marketplace Chainlink's (Chainlink) data... standard is now available on Amazon’s AWS Marketplace. The integration gives millions of AWS developers direct access to blockchain data infrastructure for institutional grade applications. Chainlink said the move could accelerate enterprise adoption of tokenized assets and onchain finance tools.show more

Unleashing faster, smarter #AI apps with semantic caching. 🚀... A new approach supercharging data retrieval while ensuring responses are contextually relevant by understanding the meaning behind user queries based on intent, not just literal matches. 🔗show more

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).show more

We're excited to today release the AIOZ W3AI Vision,... detailing how various aspects of our web3 AI infrastructure operates. Read it here: We're proud to be pushing the envelope for what is possible with #DePIN AI. Powered by AIOZ Network. Powered by you.show more

We are excited to unveil the latest version of... the AIOZ Node: The Version 4.0 update! This update includes a new user interface and brings substantial functional improvements, enhancing your overall experience for increased productivity and efficiency. More information below: The standout feature of AIOZ Node v4.0 is the introduction of the Transcoding functionality, which is currently available in beta. This functionality enables your node to participate in video transcoding, which converts video files into different formats for various digital devices and media platforms. By enabling transcoding, your node can contribute more significantly to the AIOZ Network, expanding the network's capabilities and potential $AIOZ token rewards. While the transcoding functionality is currently in beta, the upcoming AIOZ W3Stream integration, a DePIN Video Infrastructure due for release in Q3 2024, will unlock the full potential of your node and enable seamless video transcoding tasks. To get started with AIOZ Node v4.0, you simply need to visit our official website to download the latest version of the AIOZ Node: This download process is very straightforward, and with a one-click installation process, you can set up AIOZ Node v4.0 to start running on your device within a few minutes. If you are already running an AIOZ Node on your device, the version 4.0 update will be applied automatically, ensuring you have the latest features and improvements without hassle! With the Node v4.0 update running on your device, you can proceed to familiarize yourself with the new layout, check out the performance improvements, and start transcoding to see how it enhances your contributions to the network! Learn More: $AIOZshow more

Taking a short break from LLM research to go... back to my roots. Introducing... Concept Lenses! A training-free way to do similarity search along whatever concept you care about. Typically, embedding-based similarity search is constrained by whatever notion of similarity the model learned in training. This means that if the model is primed for semantic similarity, but you actually care more about visual appearance, you'd need to switch models. Under a Concept Lens projection, though, distance between embeddings means distance only along the concept you care about. Blog post: Demo:show more

AI-powered Search by Medusa. Yesterday, we launched Medusa Search:... text, semantic, and hybrid search built directly into Medusa Cloud. • Free 10k queries per month • $20 per 100k text queries (↓ 60% vs. Algolia) • $40 per 100k semantic queries (↓ 77% vs. Algolia)show more

CHAINLINK LANDS ON AWS MARKETPLACE Chainlink is now listed... on AWS Marketplace, giving enterprise buyers a direct procurement path to its oracle stack. The listing covers Chainlink Data Feeds, Data Streams, Proof of Reserve, and the Chainlink Runtime Environment. Pricing runs through AWS's standard private offer channel. The move builds on an existing partnership disclosed in November 2025, when CRE went live with institutional support from Swift, J.P. Morgan's Kinexys, Mastercard, and UBS. $LINK trades near $9.35 as the listing goes live. Oracle services now sit in the same AWS procurement catalog most large firms already use for cloud infrastructure.show more

Claude Platform on AWS is now generally available through... your AWS account. Claude Platform on AWS gives you access to Anthropic's native platform experience through your existing AWS account. Claude Platform on AWS complements Claude models on Amazon Bedrock, so you can access Claude through the approach that fits your needs. With Claude Platform on AWS, customers can: ◽ Access the full set of Claude API and console features with same day availability for all new releases and betas ◽ Use AWS Identity and Access Management (IAM) access control and CloudTrail audit logging ◽ Consolidate billing within their existing AWS accountshow more

The LimeWire Network is now LIVE in Public Beta... 🚀 A decentralized object storage built for enterprise use - fully S3 compatible and easy to migrate from traditional providers like AWS or Google Cloud. Powered as a trustless system built on BNB Chain, LimeWire Network is already handling ~20M uploads every month. Web3 companies & enterprises can now tap into low-latency, cost-efficient storage at scale. Welcome to the next generation of storage 🔗show more

AWS is about to have a Web3 rival. It’s... called Blocknode - and it’s launching from day one as one of the largest decentralized storage networks, built on BNB Chain. Here’s why you should care 🧵👇show more

colour constellations according to CLIP text embeddings—

145 Label inspired vector graphics with an editable text... illustrator file! Comment with the ``link`` and I'll send it.show more

Perplexity Computer orchestrates 20 different AI models - and... now you can embed them directly inside apps you create. To prove it, we built CEO Chat. Text Elon, Jensen, Zuck, and other tech CEOs. They text back.show more

AWS WAF + World ID User hits rate limit:... > Challenged to verify with World ID > Server verifies ZK proof > User issued a temporary session cookie Repo in thread, built by David MacDonald from AWS!show more

iMessage search is still broken in 2025. So I... fixed it myself. It doesn't even work most of the time. When it does, it's slow, misses messages constantly, and only matches exact words. Search "dinner" and you won't find "let's grab food." iMessage Intelligence fixes all of this while also being insanely fast. Here's how it works: → Full text search index on your messages for keyword matching → On-device ML (MiniLM via CoreML) generates semantic embeddings. Your Mac generates these embeddings in under a minute, then syncs to your phone → sqlite-vec stores vectors locally for similarity search → Hybrid results: keyword hits appear in <10ms, semantic matches merge in <60ms It also has advanced filters iMessage doesn't have: sort options, date ranges, conversation filters, sent vs received, groups vs 1-on-1. Search runs 100% on-device. No cloud processing. Sign up for the waitlist:show more

🚨 JUST IN: 🇮🇳 India’s Zoho is opening its... homegrown cloud infrastructure to the next generation of developers. 👇 Zoho co founder Sridhar Vembu said the company built its own cloud infrastructure instead of relying on AWS or Google Cloud. Zoho is now making parts of this infrastructure available to developers through Catalyst, its serverless platform, and plans to offer it free to students. The infrastructure includes databases, storage, computing, identity, messaging and search. The initiative aims to help students build and deploy applications without worrying about infrastructure costs.show more

Employees are vibe-coding company data into external databases IT... can't see. Today we shipped the Superblocks App Database on AWS RDS. Make RDS your default app database and keep every vibe-coded app's data inside your own AWS account. IT governs it for every app; dev and prod stay isolated, and rollbacks restore code, schema, and data together. Your apps. Your data. Your VPC.show more

AWS Builder Center Global 10,000 AIdeas Competition has launched!... $250K in prizes for AI apps built with generative AI tools. Enter your submission by December 30, 2025, for a chance to win $1,000 in AWS credits (for 10 random submissions). Terms Apply. Kiroshow more