AskNews(애스크뉴스)는 완전히 무료인가요?

AskNews(애스크뉴스)는 인간 편집과 AI 기술을 활용한 뉴스 플랫폼으로, 편향을 최소화하고 투명한 시각을 제공합니다. AskNews는 AI 기술을 이용한 다양한 뉴스 보도, 실시간 이벤트 추적, AI 애시스턴트와의 채팅, 앱에 뉴스 콘텍스트를 통합하기 위한 개발자 도구를 제공합니다.

AskNews을 어떻게 사용하나요?

AskNews(애스크뉴스)를 사용하려면 선별된 이야기를 살펴보거나 채팅 기능을 이용하거나 API를 사용하여 앱에 풍부한 뉴스 콘텍스트를 통합할 수 있습니다.

AskNews의 핵심 기능

AI 기술을 활용한 인간 편집

전 세계에서 다양한 뉴스 보도

실시간 이벤트 추적

AI 애시스턴트와 채팅

뉴스 통합을 위한 개발자 도구

AskNews의 사용 사례

전 세계 다양한 뉴스를 최신 상태로 유지

앱에 풍부한 뉴스 콘텍스트를 통합

뉴스 관련 작업을 위해 AI 애시스턴트와 채팅

AskNews의 FAQ

AskNews(애스크뉴스)는 완전히 무료인가요?
AskNews(애스크뉴스)가 색인화하는 뉴스 출처는 무엇인가요?

AskNews 불일치

다음은 AskNews Discord입니다: https://discord.gg/5QkTSm94ha. 더 많은 Discord 메시지를 보려면 여기(/ko/discord/5qktsm94ha)를 클릭하세요.
AskNews 회사

AskNews 회사 이름: Emergent Methods, LLC .
AskNews 가격

AskNews 가격 링크: https://my.asknews.app/plans

AskNews 리뷰(1)

5점 중 5점

Sort By:

John Brown

7월 11 2024

... More

AskNews 가격

무료 티어

무료

호출당 $0.005에 뉴스 API 액세스

유료 요금제

호출당 $0.005부터

고수요량 사용에 대해 요금을 지불하십시오

최신 가격을 확인하려면 다음 링크를 방문하세요: https://my.asknews.app/plans

AskNews 분석

AskNews 웹사이트 트래픽 분석

지리적 트래픽

상위 1지역

India

100.00%

Apr 2024 - Feb 2025 데스크톱 장치만 해당

웹사이트 트래픽 소스

검색

68.04%

직접

20.54%

AskNews Discord 사용자 분석

Latest user counts

342

(11)

AskNews Discord

소셜 리스닝

All

YouTube

Tiktok

검색 기록

4:34

What is RAG? Building Better LLM Systems with Qdrant

Large Language Models (LLMs) often struggle to keep up with new information. So, how can we fix this? In this video, discover how Retrieval-Augmented Generation (RAG) integrates real-time data from vector databases like Qdrant to ensure accurate and relevant answers instantly. Join us as we explore fundamental RAG concepts, including indexing, chunking, embedding, retrieving, and generating data. AskNews is able to create smart predictions with Qdrant as its vector database. Try it out yourself: https://asknews.app/ More questions about how this works? Join our GenAI and Vector Search community on Discord: https://discord.gg/qdrant

Qdrant - Vector Database & Search Engine

7월 23 2024

1.7K

https://www.youtube.com/watch?v=rtIyQPJUd_U

33:43

Production-scale RAG for Real-Time News Distillation - Robert Caulk | Vector Space Talks #015

🎤 Meet our guest speaker: - Robert Caulk, Founder at Emergent Methods Title: "Production-scale Retrieval Augmented Generation for Real-Time News Distillation" Abstract: This talk aims to map out the cluster architecture at Emergent Methods for our AskNews.app deployment. We aim to explain how we process over 1 million news articles per day for fast real-time RAG consumption. In particular, we will discuss the roles and interactions of four open-source software packages, including a cluster orchestration software (Flowdapt), a vector database (Qdrant), an LLM server (vLLM), and an embedding server (TEI). This combination of services allows us to infuse a mixture of LLMs with the latest news, and to deploy an enriched chat to thousands of users simultaneously in a public-facing application. We will highlight the role of Qdrant for some of its indispensable features, including efficient batch upserting, fast search on millions of vectors, easy filtering, and multi-node scaling. Finally, we will discuss the key roles that Flowdapt plays for orchestrating production-scale RAG across a variety of services in real-time, including the importance of configurability, scheduling, and resource management. We will conclude with our thoughts on why we believe the incorporation of these tools at the foundation level of a start-up is a huge advantage in comparison to the big tech companies from the 2010s. Talk Takeaways: How we process over 1 million news articles by combining Flowdapt, Qdrant, vLLM, and TEI. Why Qdrant is indispensable for production-scale RAG. How Flowdapt improves deployability, configurability, scaling, scheduling, and resource management. Why new start-ups have a huge tech-stack advantage over 2010 era tech companies. Robert's Bio: Robert is a scientist by trade, dedicating his career to a variety of open-source projects that range from large-scale artificial intelligence to discrete element modeling. He is currently working with a team at Emergent Methods to adaptively model over 1 million news articles per day, with a goal of reducing media bias and improving news-awareness. -------------------------------------------------------------------------------- Connect with Robert and Emergent Methods: https://www.linkedin.com/in/rcaulk/ https://emergentmethods.ai https://asknews.app Connect with Demetrios: https://www.linkedin.com/in/dpbrinkm/ Follow Qdrant: https://www.linkedin.com/company/qdrant/ https://twitter.com/qdrant_engine Join the Qdrant Discord server: https://discord.gg/W4PejyMMKu

Qdrant - Vector Database & Search Engine

2월 23 2024

981

https://www.youtube.com/watch?v=0ORi9QJlud0

46:41

Catching Bad Guys using Graph Entity Resolution with Paco Nathan

Our guest is Paco Nathan, a Principal Developer Relations Engineer at Senzing.com (and listed as an “evil mad scientist” on LinkedIn). Paco works a lot within developer communities and tech events, and tries to provide many pointers to learning materials, industry analysis, and connect with many people. He presents talks, workshops, and hands-on tutorials about entity-resolved knowledge graph practices, and the related AI use cases which are downstream. Episode Notes Speaker Resources: * Neo4j+Senzing Tutorial: https://bit.ly/3AvKneU * When GraphRAG Goes Bad: A Study in Why you Cannot Afford to Ignore Entity Resolution (Dr. Clair Sullivan): https://www.linkedin.com/pulse/when-graphrag-goesbad-study-why-you-cannot-afford-ignore-sullivan-7ymnc/ * Paco’s NODES 2024 session: https://bit.ly/48sCSlD * Graph Power Hour: https://www.youtube.com/playlist?list=PL9-tchmsp1WMnZKYti-tMnt_wyk4nwcbH T* omaz Bratanic on GraphReader: https://towardsdatascience.com/implementing-graphreader-with-neo4j-and-langgraph-e4c73826a8b7 Tools of the Month: * Neo4j GraphRAG Python package: https://pypi.org/project/neo4j-graphrag/ * Spring Data Neo4j: https://spring.io/projects/spring-data-neo4j * Entity Linking based on Entity Resolution tutorial: https://github.com/louisguitton/spacy-lancedb-linker https://github.com/DerwenAI/strwythura * AskNews (build news datasets) https://asknews.app/ * The Sentry https://atlas.thesentry.org/azerbaijan-aliyev-empire/ Announcements / News: Articles: * Neo4j Developer Blog https://bit.ly/3LcYx6q Videos: * NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIc Events * All Neo4j events: https://bit.ly/3Lb1Kn3 * (Nov 5) Conference (virtual): XtremeJ https://xtremej.dev/2024/schedule/ * (Nov 7) Conference (virtual): NODES 2024 https://bit.ly/47VqQkp * (Nov 8) Conference (Austin, TX, USA): MLOps World https://mlopsworld.com/ * (Nov 12) Conference (Baltimore, MD, USA): ISWC https://iswc2024.semanticweb.org/event/3715c6fc-e2d7-47eb-8c01-5fe4ac589a52/summary * (Nov 13) Meetup (Seattle, WA, USA): Puget Sound Programming Python (PuPPY) - Talk night Rover https://www.meetup.com/psppython/events/303896335/?eventOrigin=group_events_list * (Nov 14) Meetup (Seattle, WA, USA): AI Workflow Essentials (with Pinecone, Neo4J, Boundary, Union) https://lu.ma/75nv6dd3 * (Nov 14) Conference (Reston, VA, USA): Senzing User Conference https://senzing.com/senzing-event-calendar/ * (Nov 18) Meetup (Cleveland, OH, USA): Cleveland Big Data mega-meetup https://www.meetup.com/Cleveland-Hadoop/ * (Nov 19) Chicago Java User Group (Chicago, IL, USA): https://cjug.org/cjug-meeting-intro/#/ * (Dec 3) Conference (London, UK): Linkurious Days https://resources.linkurious.com/linkurious-days-london * (Dec 10) Meetup (London, UK): ESR meetup in London by Neural Alpha * (Dec 11-13) Conference (London, UK): Connected Data London https://2024.connected-data.london/

Neo4j

11월 01 2024

556

https://www.youtube.com/watch?v=IaGfOhZX4-k