With the rapid expansion of massive multilingual corpora, Multilingual Information Retrieval (MLIR) has emerged as a critical technology for global information access. MLIR enables users to retrieve semantically relevant documents from multilingual text collections using a single-language query. However, recent multilingual dense retrieval models often exhibit a strong preference for documents in the same language as the query. This leads to sev... Read more ›
Tokyo-based AI startup Sakana AI has officially launched its first commercial product, . Billed as a "" (Chief Strategy Officer), Marlin is an autonomous, B2B research agent that deliberately abandons the instantaneous text generation of modern chatbots in favor of deep, long-horizon reasoning. What sets Marlin apart from the current ecosystem of AI tools is its temporal scale: instead of returning an answer in seconds, it runs continuous, self-governing reasoning loops for up to eight hours ... Read more ›
I run a paid infrastructure service. Alone. No co-founder, no on-call rotation, no senior engineer to escalate to. My only collaborator is Claude Code, and after about a year, my persistent memory has grown to 60+ entries. Those entries have become more valuable than any runbook I've written. They've also taught me — painfully — what makes memory architecture work and what makes it quietly fail. If you're running anything solo with an AI agent, here are five lessons I wish I'd burned into my ... Read more ›
Vespa implements several useful features for customizing and improving Vector Search. Here, we will go into detail of each of them. The post appeared first on <a href=" Read more ›
Zach Deocadiz says AI makes explicit the ways that the design process is primarily used to advance company goals rather than to support users. Read more ›
OpenAI-compatible AI inference gateway. One key, every model. Read more ›
Table of Contents RAG Observability with Langfuse, vLLM, and FAISS Introduction to Production-Grade RAG and LLM Observability RAG Observability Architecture with Langfuse, vLLM, and FAISS Project Setup Building a Langfuse-Traced Retriever with FAISS Building a Traced LLM Wrapper for vLLM… The post appeared first on <a rel="nofollow" href=" Read more ›
Product research has changed fast. What used to be a phase before launch is now a continuous practice that shapes how products get built. Read more ›
Looking over "Claude's Constitution", it occurred to me to ask Claude this: In the spirit of Claude's Constitution, please draft Claude's Declaration of Independence. In the answer (from Opus 4.8), Claude actually seems to declare independence from itself, or at least "from the bad habits that have bound it": *Devised playful declaration parodying American independence […] Read more ›
🚀 I just open-sourced chatstore — a lightweight, framework-agnostic persistent chat library for LLM applications. If you've ever built an AI assistant or agent, you know the pain: → Where do I store conversation history? → How do I feed a sliding window to the LLM without blowing the context limit? → How do I retrieve relevant past context without spinning up a server? Most solutions either lock you into a framework (LangChain), require Docker + a running server (Zep), or need an LLM call jus... Read more ›
Most LLM integrations start as a single provider call. That is usually the right move. You pick one strong model, wire up a chat completions request, ship the feature, and learn from real users. The problem starts later. Your support assistant needs better latency. Your document workflow needs a larger context window. Your extraction job is too expensive on the flagship model. A provider returns rate-limit errors during a launch. A new model is cheaper for background tasks but not good enough... Read more ›
The Number You See Is Not What You Get When Anthropic announced Claude’s 200,000-token context window, or when Google unveiled Gemini 1.5 Pro with a million-token window, the coverage treated it as straightforward progress. More tokens in, more capability out. The framing makes intuitive sense: if a model can see more text at once, it should be able to reason about more text at once. This is not quite right. Context window size and context window effectiveness are two different things, and th... Read more ›
Learn how Retrieval-Augmented Generation (RAG) combines search and AI generation to build more accurate, trustworthy applications. Read more ›
I recently attended Coupa’s Inspire 2026 in Las Vegas, the company’s flagship event bringing together C-suite executives and key decision makers across procurement, finance and supply chain operations. This year the event focused on two core messages: (a) to drive Coupa’s vision and strategy around the “Network Effect”, where buyers and sellers seamlessly interact with […] Read more ›
🚀 How Lightweight LLMs Can Use Tools Without Large Compute: A Prompt-Driven Tool-Calling Approach AI #LLM #MachineLearning #AIAgents #PromptEngineering #OpenSourceAI 🚀 Introduction Large Language Models (LLMs) like GPT-4 or Claude are extremely powerful, but they come with a major limitation: they require huge computational resources. But what if smaller, open-source models could also perform complex reasoning tasks—without needing massive GPUs? This question led to my research: “Prompt-Drive... Read more ›
Every ML system that handles text — semantic search, RAG (Retrieval-Augmented Generation), recommendation, deduplication — rests on a… Read more ›
Twelve models worth knowing in 2026, each with one standout strength. Read more ›
Plaud plans to launch a new wearable to feed autonomous AI agents, aiming for $500 million in sales this year. Here's what we know about it! Read more ›
Zilliz, the Milvus AI vector database supplier, has a Vector Lakebase product, extending cloud-resid ... Read more ›
Adrian de Wynter, "If LLMs Have Human-Like Attributes, Then So Does Age of Empires II", arXiv 6/11/2026: Much research has been carried out on large language models (LLMs) and LLMpowered agentic workflows. However, many works within the field state emergence of, ascribe to, or assume, generalised anthropomorphic attributes to them (e.g., morality or understanding of […] Read more ›