惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

N
News | PayPal Newsroom
IT之家
IT之家
Jina AI
Jina AI
博客园 - 司徒正美
GbyAI
GbyAI
WordPress大学
WordPress大学
B
Blog
大猫的无限游戏
大猫的无限游戏
Y
Y Combinator Blog
阮一峰的网络日志
阮一峰的网络日志
Blog — PlanetScale
Blog — PlanetScale
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
Recorded Future
Recorded Future
T
Threat Research - Cisco Blogs
AWS News Blog
AWS News Blog
Latest news
Latest news
宝玉的分享
宝玉的分享
小众软件
小众软件
NISL@THU
NISL@THU
C
CERT Recently Published Vulnerability Notes
The GitHub Blog
The GitHub Blog
P
Privacy & Cybersecurity Law Blog
P
Palo Alto Networks Blog
Spread Privacy
Spread Privacy
Last Week in AI
Last Week in AI
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
P
Proofpoint News Feed
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
量子位
博客园_首页
T
The Exploit Database - CXSecurity.com
The Cloudflare Blog
M
MIT News - Artificial intelligence
H
Help Net Security
Security Archives - TechRepublic
Security Archives - TechRepublic
V2EX - 技术
V2EX - 技术
I
InfoQ
D
Darknet – Hacking Tools, Hacker News & Cyber Security
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
O
OpenAI News
MongoDB | Blog
MongoDB | Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
P
Privacy International News Feed
Microsoft Security Blog
Microsoft Security Blog
C
Cybersecurity and Infrastructure Security Agency CISA
Google DeepMind News
Google DeepMind News
H
Hacker News: Front Page
W
WeLiveSecurity
N
News and Events Feed by Topic

Pinecone

Pinecone Assistant: A Managed Knowledge Layer for Production AI Applications Multi-domain RAG in n8n: why one knowledge base is not enough Allspice Transforms the Culinary Experience with Semantic Search Powered by Pinecone | Pinecone Building RAG workflows in n8n: choosing the right Pinecone node Knowledge needs a meta-knowledge layer Garbage Day: How Pinecone Safely Deletes Billions of Objects at Scale When "Performance" Means Two Different Things Pinecone BYOC: Pinecone in your AWS, GCP, or Azure account, no vendor access True, Relevant, and Wrong: The Applicability Problem in RAG Use the Pinecone Plugin for Claude Code to develop AI Applications Faster Millions at Stake: How Melange's High-Recall Retrieval Prevents Litigation Collapse Powering High-stakes Patent Search at Scale: How Melange Built a Reliable AI System on Pinecone | Pinecone Pinecone Assistant Node in n8n: Turn Any Data Source Into Knowledge RAG with Access Control Pinecone Dedicated Read Nodes are now in Public Preview Inside Pinecone: Slab Architecture New Bulk Data Operations: Update, Delete, and Fetch by Metadata The Hidden Cost of Building: Lessons from Aquant Simplifying Vector Embeddings with Pinecone Integrated Inference Capabilities Pinecone joins Microsoft Marketplace as a Launch Partner GTM Engineering: Clay + Pinecone for AI-powered Sales Outbound Build an AI knowledge assistant with Google Docs and Pinecone Moving Pinecone forward with Ash Ashutosh as CEO and Edo spearheading our growing AI ambitions as Chief Scientist Pinecone Founder Edo Liberty to Spearhead Pinecone’s Growing AI Ambitions; Appoints Ash Ashutosh as CEO to Expand Vector Database Market Leadership Fast, Accurate Retrieval for Creators at Scale: Delphi’s Path Toward a Million Conversational Agents with Pinecone | Pinecone Announcing Pinecone Pioneers: A Program for Builders, Organizers, and Community Leaders What is Context Engineering? Chunking Strategies for LLM Applications Beyond the hype: Why RAG remains essential for modern AI Obviant Makes 30% More Accurate Defense Acquisition Recommendations Combining Sparse and Dense Retrieval with Pinecone | Pinecone Build more knowledgeable AI applications with new LLMs and greater control in Pinecone Assistant #NYTECHWEEK 2025 Retrieval-Augmented Generation (RAG) Accurate and Efficient Metadata Filtering in Pinecone’s Serverless Vector Database | Pinecone Terminal X AI Agents, Powered by Pinecone, Turn Complex Financial Data Into Production-grade Insights at Scale | Pinecone Aquant Delivers Scalable, Expert-level Service Intelligence with Pinecone | Pinecone Cascading retrieval with multi-vector representations: balancing efficiency and effectiveness Vector databases aren't just for large-scale enterprise AI Unveiling DIME: Reproducibility, Scalability, and Formal Analysis of Dimension Importance Estimation for Dense Retrieval | Pinecone Fast and Effective Early Termination for Simple Ranking Functions | Pinecone Domain-specific AI Agents at Scale: CustomGPT.ai Serves 10,000+ Customers with Pinecone | Pinecone Using Pinecone asynchronously with FastAPI A Flexible Resource for Top-Weighted Comparisons Between Sets and Rankings | Pinecone Build secure, scalable agentic AI workflows with Rubrik Annapurna and Pinecone Tool up: Pinecone’s first MCP servers are here Add context to your agent with Pinecone Assistant MCP remote server E2Rank: Efficient and Effective Layer-wise Reranking | Pinecone ColBERT-serve: Efficient Multi-Stage Memory-Mapped Scoring | Pinecone Efficient Constant-Space Multi-Vector Retrieval | Pinecone How Vanguard Worked with Pinecone to Boost Customer Support with Faster Calls and 12% More Accurate Responses | Pinecone Pinecone Named to Fast Company's Annual List of the World's Most Innovative Companies of 2025 Launch Week: Pinecone for agents, search, recommendations, and more Optimizing Pinecone for agents (and more) Retrieval Inference for scale and performance How 1up Turns Sales Reps Into Product Experts with Pinecone | Pinecone Don’t be dense: Launching sparse indexes in Pinecone Unlock High-Precision Keyword Search with pinecone-sparse-english-v0 Evolving Pinecone's architecture to meet the demands of Knowledgeable AI Pinpoint references faster with citation highlights in Pinecone Assistant Bringing the leading vector database to your cloud Getting started with llama-text-embed-v2 Natural Language Counterfactual Explanations for Graphs Using Large Language Models | Pinecone Easily build knowledgeable chat and agent-based applications in minutes with Pinecone Assistant, now generally available How to build an agentic, chat or RAG knowledge system using Pinecone Assistant Real-time RAG with Pinecone and Estuary Flow BigQuery to Pinecone in Real-Time with Estuary Flow Stravito Turns Market and Consumer Data Into Actionable Insights with Pinecone Inference | Pinecone Accelerate prototyping and development with Pinecone Local First-of-its-kind Pinecone Knowledge Platform to Power Best-in-class Retrieval for Customers Introducing integrated inference: Embed, rerank, and retrieve your data with a single API Strengthening security and increasing control with CMEK and API key roles Introducing Pinecone Rerank V0 Introducing cascading retrieval: Unifying dense and sparse with reranking From Idea to Action: How Pinecone Assistant Meaningfully Accelerates AI Business Building AI apps on Azure with Pinecone just got a lot easier Building a reliable, curated, and accurate RAG system with Cleanlab and Pinecone Four features of the Assistant API you aren't using - but should Deploying Pinecone with Infrastructure as Code (IaC) Streamlining CI/CD with Pinecone Local September 2024 Product Update Results of the Big ANN: NeurIPS'23 competition | Pinecone Introducing import from object storage for more efficient data transfer to Pinecone serverless Simplify, enhance, and evaluate RAG development with Pinecone Assistant, now in public preview Vectors and Graphs: Better Together August 2024 Product Update Pinecone Helps Deep Talk Deliver World-Class AI Assistants with Lower Engineering Overhead | Pinecone Assembled Delivers Better, Faster AI- Driven Support with Pinecone | Pinecone Llama 3.1 Agent using LangGraph and Ollama Build knowledgeable AI with Pinecone serverless, now generally available on Microsoft Azure Pinecone serverless is now generally available on Google Cloud, adding knowledge to AI assistants and other applications Accelerating Legal Discovery and Analysis with Pinecone and Voyage AI Bridging Dense and Sparse Maximum Inner Product Search | Pinecone Refine Retrieval Quality with Pinecone Rerank Introducing reranking to Pinecone Inference to simplify building accurate AI July 2024 Product Update Connect to Pinecone within your platform to enable a seamless AI development experience Introducing Pinecone API Versioning RAG Brag with Inkeep Co-Founder Nick Gomez LangGraph and Research Agents Introducing Pinecone Inference to streamline your AI workflow
Pinecone Announces $28M Series A Financing to Bring Search into AI Age
2022-03-30 · via Pinecone

SAN MATEO, Calif., March 29, 2022 /PRNewswire/ -- Pinecone Systems Inc., a search infrastructure company, today announced the completion of a $28M Series A funding round to enable companies of all sizes to power search applications with AI. The funding round was led by Menlo Ventures, with participation from new investor Tiger Global and previous investors including Wing Venture Capital, who led the company's seed-stage financing. Since coming out of stealth last year Pinecone has emerged as the leader of a new generation of search technology with thousands of developers signing up for their vector-database product.

"Search technology has revolved around keywords for hundreds of years; books contained search indexes before the invention of the printing press. Amazingly, today's predominant search infrastructure still works the same way," said Edo Liberty, Founder and CEO of Pinecone. "Today's users expect more. They want search results that anticipate and understand their needs, and not just match keywords."

The content stored in consumer and enterprise applications is only valuable if users can find it. Traditional keyword-based search systems struggle with complex data such as unstructured text, user profiles, or images. Yet complex data is only growing, as are customers' expectations. That puts pressure on engineering teams to modernize their search systems.

Recent advances in AI have led to an entirely new way of searching through data. Vector Search is focused on storing and searching through AI-generated representations of content. These AI-generated representations encapsulate the meaning of the original content in a machine-readable format, and enable developers to build better search applications. Legacy systems were not designed to work like this, so vector search requires a new kind of infrastructure called a vector database. With its vector database product, Pinecone provides the search infrastructure for engineering teams to easily implement AI-powered search into their applications without the need to build their own or modify legacy infrastructure.

"Search is in desperate need of modernization," continued Liberty. "Machine learning has changed the way we interact with our data, and those who don't react quickly enough will be left behind. The importance of vectors in search applications moving forward cannot be overstated, and Pinecone has removed the huge infrastructural barrier that has prevented many companies from benefiting until now."

As part of the announcement, Tim Tully, Partner at Menlo Ventures and former CTO of Splunk, will join the board of directors. The addition of Tim's experience builds on the incredible team Pinecone has assembled over the last year, including Peter Wagner (Founding Partner at Wing Venture Capital), Bob Wiederhold (former CEO of CouchBase), and Ram Sriharsha, former Splunk VP of Engineering, who joined the Pinecone team as VP of Engineering.

Pinecone's vector database already has thousands of users, with customers across a wide spectrum of industries including enterprise software, social media platforms, search engines, ecommerce, and AI/ML products. It is being used by companies ranging from small startups to enterprises, handling data ranging anywhere from hundreds of thousands to billions of items. The company has offices in New York and Tel Aviv, as well as some working remotely. Pinecone will utilize the funding to grow out its product, customer success, and R&D teams, and will invest in core research on machine learning (ML), information retrieval (IR), and natural language processing (NLP).

"Many of the largest companies in the world have already embraced the use of vector search, which has given them a distinct advantage over their competitors," said Tim Tully, Partner at Menlo Ventures. "Pinecone is ensuring that regardless of size and budget, companies can integrate next-generation search capabilities into their applications and get to production without the need to develop and maintain an entirely new architecture."

About Pinecone

Pinecone has built the first vector database to enable the next generation of artificial intelligence (AI) applications in the cloud. Its engineers built ML platforms at AWS, DataBricks, Yahoo, Google, and Splunk, and its scientists published more than 100 academic papers and patents on machine learning, data science, systems, and algorithms. Pinecone operates in Silicon Valley, New York and Tel Aviv. For more information, see https://www.pinecone.io.

Media Contact:
Mike Sefanov
mike.s@pinecone.io
Sr. Director, Communications