AI

An AI Gateway is not merely a reverse proxy placed in front of a language model. Once an application reaches production, every model call carries several kinds of uncertainty at once: long-lived streaming connections, token-based billing, provider quotas, sensitive inputs, changing model behavior, and outputs that cannot be trusted by default. A conventional API gateway can handle part of this traffic. It may authenticate clients, terminate TLS, enforce request limits, and route HTTP requests. It does not automatically answer the questions that matter most to an AI product: ...

A long report can look like certainty while merely arranging uncertainty more elegantly. The useful question is not how many pages an agent writes, but how a claim entered the report and whether a reader can walk back to its source. The short verdict GPT Researcher is a good fit when a team needs a programmable research pipeline, source traceability, and deployment control. It is not a truth machine. It automates planning, retrieval, context assembly, and report writing; it does not make weak pages authoritative or make every citation support the sentence beside it. ...

Jina used to be easiest to explain as a cloud-native neural-search framework. That description is still true, but it is no longer sufficient. In 2026, the name covers two related products: Jina Search Foundation: hosted APIs and model families for embeddings, reranking, web reading, search, and research. Jina Serve: the open-source framework for turning Python AI components into services and composing them into distributed flows. The distinction matters. Search Foundation gives an application retrieval intelligence; Jina Serve gives a team control over how its own services run. One is a set of capabilities, the other an orchestration layer. Treating them as interchangeable usually leads either to unnecessary infrastructure or to a hosted dependency that was never consciously chosen. ...

A framework is most dangerous not when it lacks features, but when its old tutorials still look plausible. Familiar code can cross a version boundary long before it produces an obvious error. This guide follows the current LangChain Python 1.x path: model calls, agents, tools, structured output, middleware, memory, RAG, SQL, Ollama, and LangSmith. It is deliberately different from a framework overview. The aim is to leave you with a compact application architecture, runnable examples, and a way to recognize code that belongs to the 0.x era. ...

Introduction: Do Not Learn the Model from the Chat Window The first encounter with a large language model creates a powerful illusion. Something on the other side of the screen appears to have read widely, reasoned carefully, and chosen to explain itself. It can write code, summarize papers, preserve a tone across a conversation, and offer a polished rationale for an answer that is completely wrong. The chat window shows behavior, not mechanism. Fluency hides missing evidence. Completeness hides uncertainty. A human voice invites us to overestimate how firmly the model is connected to the world. ...

In March 2024, this page was a long collection of Sora prompts. It belonged to the brief season when a research preview could become a small open-source movement before most people had touched the product. We copied examples, named camera movements, and tried to infer a grammar from a handful of remarkable clips. That guide can no longer honestly call itself a guide to “mastering Sora.” The Sora web and app experiences closed on April 26, 2026. OpenAI says its Sora API will be discontinued on September 24, 2026. A page that still teaches Sora as a growing platform would turn search traffic into misinformation. ...

Status update, July 2026: The Sora website and app shut down on April 26, 2026. The Sora API is scheduled to shut down on September 24, 2026. This is no longer a guide to getting started; it is a record of what the technology, the product, and their ending can teach us. When I first wrote about Sora in February 2024, the irresistible detail was the one-minute video. Two years later, the more useful story is about boundaries: a research result is not a product specification, a better model does not guarantee a permanent service, safeguards do not erase risk, and generated media does not arrive with a simple answer to copyright. ...

Vector database learning I learned rust today and found that rust is a very suitable language for writing databases. I found a treasure project tikv. Its github project address is https://github.com/tikv/tikv . It is a very active project, but today I The topic is vector database in the field of AI. Without further ado, let’s get started right away. prerequisites 0.1 Introduction to basic knowledge: Understand the basic definition of vector database and its difference from traditional database. 0.2 Data Structure Basics: Learn vectors and other basic data structures, and how to represent and manipulate these structures in databases. 0.3 Introduction to Linear Algebra: Introduction to vector operations, including vector addition, subtraction and dot multiplication. 0.4 Similarity measures: Learn how to calculate similarity between vectors, such as cosine similarity. 0.5 Basics of Database Index: Introduces the basic concepts of database index, especially its application in vector databases. 0.6 Preliminary Search Algorithms: Learn basic search algorithms and understand how to perform effective searches in large data sets. 0.7 Application case studies: Study the application of vector databases in different fields (such as recommendation systems, image recognition). ...

This article began as notes from a large-language-model meetup in January 2024. Back then, the question was what a model could do. Two years later, the harder question is why anyone should trust the system around it. I have not polished the old notes into a story in which every early judgment was right. Mistakes are useful sediment. They remind us that a technical opinion is not a prophecy; it is a bet with an expiry date. Each section therefore keeps the 2024 observation and adds a 2026 reassessment: still true, changed, or wrong at the time. ...

Status note, verified July 31, 2026: this article originally explained how to install the 2023 stand-alone Auto-GPT agent. Those commands are obsolete. The official project now says that AutoGPT Classic is unsupported, its dependencies will not be updated, and it has known security issues. Treat Classic as a historical laboratory, not a production tool. For new work, use the maintained AutoGPT Platform or another actively maintained workflow system. In the spring of 2023, Auto-GPT made a compelling promise: describe an objective, give a model some tools, and watch it plan its own way forward. I installed it because the idea felt less like a chatbot feature and more like a change in the shape of software. ...