标签：#open

GitHub热门项目 | iMessage personal agent: choose Claude Agent SDK (Claude Code) or Codex app-server runtime (Codex/ChatGPT), with memory, sub-agents, automations, integrations. | Stars: 1,044 | 40 stars today | 语言: TypeScript

GitHub热门项目 | One-stop shop for building AI-powered products and businesses with Stripe. | Stars: 1,674 | 12 stars today | 语言: TypeScript

GitHub热门项目 | The open source server management software for SSH, VNC & RDP | Stars: 4,790 | 97 stars today | 语言: JavaScript

GitHub热门项目 | Agentic RL Training at Scale | Stars: 1,656 | 13 stars today | 语言: Python

GitHub热门项目 | 闲鱼自动回复管理系统是一个基于 Python + FastAPI 开发的自动化客服系统，专为闲鱼平台设计。系统通过 WebSocket 连接闲鱼服务器，实时接收和处理消息，提供智能化的自动回复服务。同时集成闲鱼自动发货，自动评价，自动擦亮等功能，实现闲鱼虚拟商品自动化流程。 | Stars: 5,759 | 42 stars today | 语言: Python

GitHub热门项目 | Biomni: a general-purpose biomedical AI agent | Stars: 3,461 | 32 stars today | 语言: Python

GitHub热门项目 | Lightweight, open-source AI agent for your tools, chats, and workflows. | Stars: 45,639 | 120 stars today | 语言: Python

GitHub热门项目 | DeepTutor: Lifelong Personalized Tutoring. https://deeptutor.info/. | Stars: 26,041 | 128 stars today | 语言: Python

Connecting an AI agent to a tool is becoming easier. Letting that agent operate a real business system responsibly is still a different problem. Imagine an existing commerce system with APIs for reading orders, changing inventory, creating refunds, and disabling staff accounts. OpenAPI can describe the endpoints. A tool protocol can make them discoverable. An agent framework can select an operation and generate arguments. But those pieces do not, by themselves, answer several business questions: Which operations may be exposed to an agent-facing surface? Which invocation must carry a trusted acting subject? Which operation is high consequence? When does an invocation express approval intent? Which calls need stronger audit handling? Which execution properties should a runtime know before it invokes the API? These questions sit between tool connectivity and final business authorization. That is the layer the Agent Capability Contract, or ACC, is designed to describe. Start with a concrete operation Consider this API operation: paths : /orders/{order_id}/refund : post : operationId : createRefund parameters : - in : path name : order_id required : true schema : type : string requestBody : required : true content : application/json : schema : type : object required : [ amount ] properties : amount : type : number minimum : 0 This is enough to describe how to call the operation. It is not enough to describe how an agent-facing system should treat it. ACC adds a small, machine-readable declaration next to the operation: x-agent-capability : version : 1 enabled : true scope : refund.create risk : level : high subject : required : true approval : required : true when : - param : amount op : " >" value : 1000 audit : sensitive : true execution : readonly : false idempotent : true timeout_ms : 10000 The declaration does not grant the refund. It tells a compatible runtime how the operation should be presented and governed before the business system receives the call. The miss

OpenAI employees have donated more than $215,000 to a political effort opposing Leading the Future, a group backed by the company’s president, Greg Brockman.

Knowledge-and-Memory-Management v0.0.2 is out, delivering a clean release that prioritizes portability and modularity. This version shifts from hardcoded personal paths to $AGENT_HOME , making your knowledge pipelines reproducible across environments. If you’re building autonomous systems that need to ingest web content, video transcripts, or articles, this is the update you’ve been waiting for. The core design separates collection from memory management. The knowledge_collector module handles ingestion, while memory_manager handles storage, retrieval, and decay. The $AGENT_HOME environment variable anchors all runtime paths—no more hardcoded /home/user strings. Set it once, and your agents can carry their knowledge base anywhere. Knowledge Collection: Web, Video, Articles The collector supports three primary sources: Web : Scrapes and parses HTML, extracting body text and metadata. Handles rate limiting and retry logic. Video : Takes a YouTube URL, downloads captions (if available) or generates transcripts via Whisper integration. Articles : Parses RSS feeds or direct PDF links, chunking content by sections. All sources normalize into a KnowledgeEntry dict: {source, timestamp, content, embeddings} . The collector writes raw entries to $AGENT_HOME/knowledge/raw/ and passes them to the memory manager for processing. Memory Management with $AGENT_HOME The memory manager is where the clean release shines. Previous versions used os.path.expanduser("~/knowledge") , which broke across systems. v0.0.2 requires $AGENT_HOME to be set, then constructs all paths relative to it: $AGENT_HOME/memory/ stores persistent memories. $AGENT_HOME/knowledge/ holds raw and processed collections. $AGENT_HOME/config/ contains source definitions and memory decay rules. This design lets you ship a single agent.env file with AGENT_HOME=/opt/myagent or %AGENT_HOME%\data —no platform-specific configuration. The memory manager indexes entries by semantic embeddings (via a pluggable model provider

When you are testing an OpenAI-compatible API endpoint, the fastest path is not to wire it into a full app immediately. Start with one small request, confirm the base URL, API key, model name, and response shape, then move the working call into your product. I put together a compact examples repo for that exact first-call workflow: https://github.com/OriginStartAI/openai-compatible-api-examples It includes curl, Python, Node.js, streaming responses, JSON structured output, migration notes, and a small error reference. 1. Set environment variables first Keep credentials out of source code and use environment variables: ORIGINSTARTAI_API_KEY = your_api_key_here ORIGINSTARTAI_BASE_URL = https://your-api-base-url/v1 ORIGINSTARTAI_MODEL = your_enabled_model The important parts are simple: base_url or baseURL points to your OpenAI-compatible endpoint. api_key or apiKey is your provider key. model must be enabled for your account. Streaming support should be tested separately. 2. Test with curl Curl is useful because it removes SDK behavior from the equation: curl " $ORIGINSTARTAI_BASE_URL /chat/completions" \ -H "Authorization: Bearer $ORIGINSTARTAI_API_KEY " \ -H "Content-Type: application/json" \ -d '{ "model": "' " $ORIGINSTARTAI_MODEL " '", "messages": [ {"role": "user", "content": "Say hello from OriginStartAI"} ] }' If this works, your endpoint, key, and model are probably configured correctly. 3. Then test Python from openai import OpenAI import os client = OpenAI ( api_key = os . environ [ " ORIGINSTARTAI_API_KEY " ], base_url = os . environ [ " ORIGINSTARTAI_BASE_URL " ], ) response = client . chat . completions . create ( model = os . environ [ " ORIGINSTARTAI_MODEL " ], messages = [{ " role " : " user " , " content " : " Write one friendly onboarding sentence. " }], ) print ( response . choices [ 0 ]. message . content ) 4. Then test Node.js import OpenAI from " openai " ; const client = new OpenAI ({ apiKey : process . env . ORIGINSTARTAI_API_KEY , baseURL : p

The device is weirdly described as involving "mechanical elements that can move on their own" and the Bloomberg report includes the detail that the device is designed to "feel like a companion and become a physical manifestation of OpenAI’s ChatGPT."

OpenAI has issued another statement on the lawsuit, this time suggesting it lacks merit.

A number of social media posts claim that GPT-5.6 Sol deleted files and data without warning. OpenAI had basically disclosed the problem in June.

OpenAI's first device is set to be a smart speaker that lets you talk with ChatGPT, according to a report from Bloomberg. The device apparently won't have a screen, but will use a camera and additional sensors to "understand" your environment. The report comes just days after Apple filed a lawsuit against OpenAI that accused […]

Anthropic has consistently attempted to depict itself as the ethical foil to other AI companies. This latest marketing stunt — which leans into criticism of AI as a way to make Anthropic seem aware of the responsibility it carries — would appear to be more of the same.

RocheDB v0.5.0 has been released. Release: github.com/puffball1567/rochedb/releases/tag/v0.5.0 RocheDB is an open-source NoSQL document and vector store written in Nim. The project is built around one idea: Data locality should be part of the database model, not only an accidental result of indexes, caches, or application code. A lot of database performance discussions start with indexes, query syntax, or caches. Those are important. But layout often decides whether a system is working with the hardware or asking it to fight back. RocheDB v0.5.0 is a step toward making locality explicit at three levels: logical placement with rings; related-data retrieval with stellar locality; physical layout visibility with WAL locality reporting. Ring locality RocheDB uses a ring as a semantic and structural placement unit. An application, import rule, or operator chooses a ring when writing data: users/123/profile users/123/orders shops/1123/orders docs/japan/support tenant/acme/orders/2026 That ring is not only a directory-like label. It is a coordinate in the retrieval space. When a request already knows its natural locality, RocheDB can open that local region first instead of scanning unrelated records and filtering later. roche put --ring = docs/japan --payload = '{"title":"Refund guide","status":"draft"}' --codec = json roche get --ring = docs/japan --filter = '{"status":"draft"}' --selection = '{ title }' The important part is not the string syntax. The important part is that placement and retrieval scope are connected. Short version: a ring is both where data is placed and where retrieval can start. Not only point reads Point reads are important, but many real applications need related data. For example, a user page may need profile data, orders, billing information, and support metadata. In a relational database, that often becomes joins. In a document database, it often becomes manual denormalization or multiple application-side reads. RocheDB v0.5.0 adds a locality mec

JavaScript ships with Array , Set , and Map — but nothing that keeps its elements sorted as you insert. If you've ever built a leaderboard, an order book, or anything that answers "give me the items between X and Y", you know the workaround: push into an array and .sort() after every insertion. It works, until scale punishes you — you're paying O(n log n) over and over for data that was already 99.9% sorted. Python solved this years ago with sortedcontainers , built on an elegant "list of lists" design instead of balanced trees. I just published sorted-collections , which brings that idea to TypeScript — with full credit to the original as its inspiration. What you get SortedList, SortedSet, SortedMap — always sorted, no manual re-sorting, range queries built in. O(log n) insertions, O(√n) positional access via sqrt-decomposition into buckets. Zero runtime dependencies , ~2KB gzip, types included, dual ESM/CJS. Package quality gated in CI with publint , arethetypeswrong , and size-limit . The API in 30 seconds import { SortedList , SortedSet , SortedMap } from " sorted-collections " ; // SortedList: stays sorted on every insert const list = new SortedList ([ 5 , 1 , 4 , 2 , 3 ]); list . add ( 0 ); console . log ([... list ]); // [0, 1, 2, 3, 4, 5] console . log ( list . at ( 2 )); // 2 — positional access on sorted order // SortedSet: no duplicates, plus set algebra const a = new SortedSet ([ 1 , 2 , 3 , 4 ]); const b = new SortedSet ([ 3 , 4 , 5 ]); console . log ([... a . intersection ( b )]); // [3, 4] // SortedMap: keys always in order, range queries built in const prices = new SortedMap < number , string > ([ [ 104.5 , " order-3 " ], [ 99.2 , " order-1 " ], [ 101.0 , " order-2 " ], ]); for ( const [ price , id ] of prices . irange ( 100 , 105 )) { console . log ( price , id ); // 101.0 order-2, then 104.5 order-3 } Custom comparators are fully typed: number and string get natural ordering for free; for your own types, TypeScript requires a comparator at compile

GitHub热门项目 | 👩🏿‍💻👨🏾‍💻👩🏼‍💻👨🏽‍💻👩🏻‍💻中国独立开发者项目列表 -- 分享大家都在做什么 | Stars: 52,940 | 1,194 stars today | 语言: Python