Learn how modern AI evolved from simple language models to Transformers, reasoning models, and ChatGPT through practical examples and diagrams. Read more ›
Moduna surfaces new business opportunities and the blind spots keeping AI agents from resolving user intent. Read more ›
A short learning path from a weekend project: I indexed my personal markdown notes (~800 chunks), tried a few local embedding models, stored the same vectors in four different backends, and wired up simple RAG. Not a production guide — just the basics, with honest results from a corpus small enough to reason about. The idea, without the jargon pile Keyword search looks for shared words. Vector search converts text into a list of numbers (an embedding), treats that list as a point in space, an... Read more ›
Operating out of Canada, Bounce Padel Courts provides design, engineering, manufacturing, delivery, and installation services to try and ensure every court meets the standards of competitive play. Read more ›
Dense retrieval has become the dominant paradigm in information retrieval, in which each document is scored against a query by the inner product of their vector embeddings, and the top-$k$ documents by score are retrieved for this query. However, since each document's score depends solely on the embedding of the query and itself, the retrieval process is oblivious to the content of the entire corpus. Therefore, dense retrieval cannot avoid selec... Read more ›
Large language models have moved out of the research lab and into engineers’ daily workflow. LLMs serve as reasoning engines that can orchestrate complex tasks including identifying vulnerabilities in source code and transforming fragmented project discussions into rigorous technical specifications.While the general public uses AI tools to write email and plan vacations, technical professionals use LLMs as core architectural elements that are fundamentally changing how digital infrastructures... Read more ›
A vector database stores data as vectors (embeddings) and finds items by meaning, not exact match. What it is, how similarity search works, how it differs from a normal database, and why RAG and AI search depend on it. Read more ›
By Pyrrhonian Skepticism about Philosophy (PSP) we mean the view that, considering the permanent and pervasive dissensus in philosophy, you cannot maintain your philosophical beliefs—you should suspend them, or at least significantly (and perhaps painfully) reduce your confidence in their... Read More › Source Read more ›
Charlie Brooker wrote it as fiction. We’re building it as a product roadmap. Read more ›
Claude is built to work the way you work, and in Claude Code you can customize it. There are seven methods for instructing Claude's behavior: CLAUDE.md files, rules, skills, subagents, hooks, output styles, and appending the system prompt. Each method controls: When an instruction loads into cont... Read more ›
Jingra is an open source benchmarking framework that runs the same vector search workload across Elasticsearch, OpenSearch and Qdrant so you can compare engines under identical, reproducible conditions. Read more ›
Apple is entering a new era, and the man chosen to lead it appears determined to reshape one of the company’s most influential institutions: its design organization. With Apple confirming that John Ternus will succeed Tim Cook as chief executive officer on September 1, 2026, attention is rapidly shifting from the leadership transition itself to what Ternus plans to change once he takes control of the world’s most valuable technology company. Apple announced the succession plan in April, endin... Read more ›
There's a moment in almost every RAG project where someone asks the question that decides your next two years of ops work: "Do we actually need a vector database, or can Postgres just do this?" It's a better question than it sounds, because the honest answer isn't "use Pinecone" or "use Postgres." It's "it depends on numbers you probably haven't measured yet": how many vectors, how aggressively you filter, how much you care about the absolute ceiling of queries per second. Most teams pick bas... Read more ›
Use one unified API to access OpenAI, Claude, GLM, MiniMax and other LLMs. Compare models, prices, and capabilities to find the best fit for your prompts. Read more ›
L’Oréal has announced a collaboration with OpenAI that will bring Maybelline New York’s virtual makeup try-on feature into ChatGPT. The announcement was made at VivaTech 2026. The partnership covers consumer-facing shopping tools, product discovery, advertising pilots, research, and internal content production. The collaboration also covers L’Oréal’s internal use of AI in research, formulation, content production, […] The post appeared first on . Read more ›
Zilliz has produced a Milvus Vector Lakebase FAQ to help position its vector database and vector lak ... Read more ›
One UI for model state, request history, API keys, routing rules, and proxy metrics — fronting llama-swap and any OpenAI- or Anthropic-compatible upstream. Read more ›
Most tutorials on AI agents stop at chat interfaces and RAG pipelines. This one doesn't. This guide walks through building a production-grade AI agent that can read on-chain data, interact with smart contracts, and execute DeFi operations — using LangChain's agent framework, ethers.js, and a set of custom tools you'll write from scratch. By the end, you'll have an agent that can: Query wallet balances and token holdings Read state from any smart contract via ABI Simulate and execute token swa... Read more ›
Most Retrieval-Augmented Generation (RAG) tutorials stop too early. Read more ›