Knowledge catalogue
Your AI chatbot is only as good as the data behind it. This n8n template from our friends at @apify shows you how to wire up a RAG pipeline using Apify + Pinecone + Gemini so your chatbot can answer q
Pinecone presents early access customer results and benchmarks for Nexus, their vector database or search product, demonstrating real-world performance metrics and use cases from initial deployments.
This article from Apify's blog covers the process of training AI chatbots, likely including techniques for data preparation, model selection, and fine-tuning approaches. The content is shared by Pinec
Four weeks, three enterprise customers, same pattern across all three. Most inference spend goes to retrieval loops. A generic index carries no knowledge of domain, query types, or task structure, so
95% token reduction. 30x faster execution. 90%+ task completion. Today at #MSBuild, we announced a major shift to move reasoning upstream: Pinecone Nexus now integrates directly with @Microsoft OneLak
This repository demonstrates a search application built using Pinecone's vector database, likely showcasing how to implement semantic search or similarity-based retrieval using bird-related data or im
Pinecone announced an upcoming product launch scheduled for the week of May 4th, with the teaser 'Something's coming.' The post was shared on X (formerly Twitter) but specific details about what produ
Pinecone announced a launch week event featuring new product updates and features for their vector database platform. The event likely showcased innovations in AI infrastructure, vector search capabil
Your search is failing because it's looking at words, not meaning. 🛑 Will Templeton, CTO and co-founder at Allspice, explains the 'Synonym Trap': ❌ User says 'Boston Butt' ❌ DB says 'Pork Shoulder' ❌
Text match filters are a feature in Pinecone that allow users to filter vector search results based on exact text matching criteria, enabling more precise control over which documents or records are r
Sign up if you're in LA! The last AI Agents Happy Hour was so fun, @jvedi and I are going to do it again. This time, co-hosted by @pinecone! We want to see what you're building and learn from your exp
Full text search systems enable efficient querying of large document collections by indexing and searching text content across multiple fields and documents. This article from Pinecone likely covers t
This post from Pinecone references a full demonstration video from Google DeepMind, likely showcasing a new AI model, research breakthrough, or technical capability developed by DeepMind. The video li
Builder Plan is Pinecone’s $20/month flat-rate pricing tier built for builders who’ve outgrown Starter but aren’t ready for usage-based pricing. It adds capacity for dev/staging/production workflows,
Stop token maxxing 🛑 Our CEO @ashashutosh sat down with @a16z General Partner Peter Levine to discuss why we’re moving beyond the vector database: '85% of an agent’s work isn't the model; it's the und
Builder tier. $20/month, flat. Pricing should match the stage of building, not just the scale of it. For the stretch when free feels tight but you're not yet at usage-based scale. 10 indexes, 1,000 na
Pinecone hosted a Launch Week livestream event to showcase new product features, updates, or announcements related to their vector database platform. The livestream was broadcasted on X (formerly Twit
This article likely explores the integration of Model Context Protocol (MCP) with Pinecone's skills, CLI tools, and plugin ecosystem for building AI applications. It probably covers how developers can
in 1996 mitzenmacher showed that sampling two backends and picking the better one drops max load exponentially vs. random selection one extra comparison. that's the whole trick we built pinecone assis
Million of recipes. Fuzzy ingredient data. One afternoon to solve it. 🍳⚡ Will Templeton, CTO and co-founder at Allspice, explains why Pinecone is their fundamental building block: ❌ No weeks of cluste
Pinecone has announced the general availability (GA) of Dedicated Read Nodes, a feature designed to improve query performance and scalability for vector database workloads. Dedicated Read Nodes allow
proud of this launch from a strong team. dedicated hardware for high throughput, low cost search building for the new era of recommenders and agents Dedicated Read Nodes are now generally available. P
390M+ embeddings. 100K+ namespaces. Sustained P50 latency of ~60ms at ~40 QPS. 📈 @ZoomInfo used our Dedicated Read Nodes, now in GA, to bypass the 'infrastructure wall' and ship real-time AI recommend
Dedicated Read Nodes are now generally available. Predictable performance at scale. Up to 97% lower costs on real production workloads. Fixed hourly pricing. No read rate limits. Always-hot data on de
Weaviate's Shared Cloud offering is now generally available on Amazon Web Services (AWS), providing users with a managed, multi-tenant vector database deployment option on AWS infrastructure. This lau
You can’t build an autonomous agent on a static RAG pipeline. Because goals mutate dynamically, agents require an advanced knowledge infrastructure, rather than forcing the model to hunt through a raw
Jeff Zhu (VP of Product) and Jasmeet Singh Gujral (Principal PM) join @jennapederson and Arjun Patel on the livestream today to talk about what we've shipped and what's coming. Then we get into the us
Data agents don't fail at writing SQL. They fail at knowing your business. Schemas show you the columns, but they don't tell you which view is canonical for ARR, how often each metric updates, or whic
AskData implemented optimization techniques to dramatically reduce token consumption in their AI system by over 90%, likely through strategies such as prompt engineering, caching, retrieval optimizati
Connecting agents directly to Snowflake, Slack, or BigQuery via MCP/CLIs is a multi-token disaster. They default to brute-force exploration, firing 20-30 tool calls just to rediscover context on every
Our internal AI agent was a brute-force nightmare. 🛑 It burned 40,000 tokens, took 2 minutes, and only hit 68% accuracy just to answer a single question. Why? Traditional databases are built for human
Agents are now the primary consumers of knowledge infrastructure. Tomorrow @jennapederson, Arjun Patel, and @RoieSchwabco are going live here to talk about what that means for your stack, share this w
This article likely argues that improving the underlying language models is insufficient for building effective AI agents, and that other critical components—such as knowledge retrieval, context manag
The RAG era is ending for agentic AI — a new compilation-stage knowledge layer is what comes next http://venturebeat.com/ai/the-rag-era-is-ending-for-agentic-ai-a-new-compilation-stage-knowledge-layer
What happens when the primary users of AI aren't humans, but agents? This week: A new language, new engine, new marketplace, new economics, and new reach. The agentic era needs its own infrastructure
Don't burn tokens on search. 📉🔥 Will Templeton, CTO and co-founder at Allspice, explains why they don't let LLMs search their 10,000+ ingredient database: ❌ Searching 10k items in a prompt = waste of
Confidently wrong is worse than just wrong. When an AI searches one giant knowledge base for everything, it finds something—and serves it up like it's the answer. That's not a model problem. That's a
The 'bridge of authority' in RAG: ❌ LLMs can reason about 'blue skies' or 'purple skies' with the same authority. ✅ Vector DBs let you peer into the metadata to find the source/author. 'Do you trust a
Knowledge is rather abstract and philosophical. So what does it mean from an agentic context? How does providing the right knowledge at the right time to an agent increase its accuracy, task completio
Next Tuesday, August 11, we're hosting the August LA Agentic AI Meetup, 5 to 7pm at Gulp in Playa Vista. May drew 70 people. July brought 80. Let's break 100 in August. Come hang out with builders, en
Decoupling embedding from ingestion means whatever hardware is on hand can do the work. One script, runtime device check: MPS on Apple Silicon, CUDA on NVIDIA, CPU fallback otherwise. 10K-20K records
We're hosting the August LA Agentic AI Meetup on Tuesday, August 11, 5 to 7pm at Gulp in Playa Vista. May drew 70 people. July brought 80. Let's break 100 in August. Come hang out with builders, engin
Sparse index posting data: BM25 exact matches, SPLADE transformer-expanded matches. Storage layout just changed from document-range blocks to term blocks. A query now loads only the blocks for the ter
We're hosting the LA Agentic AI Meetup this Thursday, July 9, 5 to 7pm at Gulp in Playa Vista. Come talk to engineers, founders, and builders working across RAG, agentic workflows, and the broader AI
Our Founder and Chief Scientist @EdoLiberty just kicked off the Search & Retrieval track @aiDotEngineer. 'Agents don't need to be smarter. They need a Knowledge Layer.' We've got a big update on this
Bulk import is now free up to 1 TB. Reads directly from S3, GCS, or Azure Blob into the index builder. Standard and Enterprise get a 250 credit applied automatically. After 1 TB: 0.25/GB, down 75%. ht
We're hosting an agentic AI meetup in LA on May 28th — 5–7pm at Gulp in Playa Vista. Builders, founders, engineers. Drinks, no fluff. RAG systems, agentic workflows, or just starting out — all welcome
ICYMI Dedicated Read Nodes are now generally available. Predictable performance at scale. Up to 97% lower costs on real production workloads. Fixed hourly pricing. No read rate limits. Always-hot data
Reasoning is becoming commoditized. The uniqueness of your knowledge is your real advantage. Check out our Head of Developer Relations @RoieSchwabco on @TheEntAIShow with @bgracely: 📺 https://www.yout
A million-token context window is not a strategy. 🛑 Our Head of DevRel, @RoieSchwabco , explains why dumping data is killing your RAG performance: 📍 One needle in a haystack? Easy. 📍 Multiple needles?
Your AI isn't failing because of 'bad chunking.' 🛑 It’s failing because of a vibe mentality. Our Head of DevRel @RoieSchwabco explains why anecdotal testing is the #1 roadblock for AI devs: ✅ Determin
Stop trusting LLMs just because they're useful. 🛑 Our Head of DevRel @RoieSchwabco breaks down the two essential parts of a reliable AI stack: ⚙️ Reasoning: What the LLM does (logic/synthesis/formatti
The global buildout of AI infrastructure reached a new milestone today — Firebird, an emerging AI cloud, launched the CIS region’s largest AI factory in Armenia, establishing a new AI computing hub po
Enterprise AI agents lose effectiveness when they must access data outside of protected, governed systems. The article argues that truly reliable agents need to be *data‑native*—run directly inside th
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Sierra isn't the first to build this - Ramp, Stripe, CoinBase also have If you want an open source version - check out OpenSWE: https://github.com/langchain-ai/open-swe We use it internally (mostly fo
Auto-generated index of all companies mentioned across the wiki.
Auto-generated index of all tools mentioned across the wiki.
Google's Gemini is getting a feature called 'notebooks' to help you organize things about certain topics in a single place while using the AI chatbot, the company announced on Wednesday. You can pull
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