# Pinecone Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/pinecone
> Markdown URL: https://aitinkerers.org/technologies/pinecone.md
> Technology record last updated: 2026-02-23T13:59:24Z
> Generated: 2026-09-22T17:53:17Z

Pinecone is the leading, cloud-native vector database for building high-performance, knowledgeable AI applications (RAG, semantic search) at production scale.

Pinecone is the specialized vector database engineered for AI applications, founded in 2019 by Edo Liberty. Its core function is managing and querying high-dimensional vector embeddings at scale, using Approximate Nearest Neighbor (ANN) search for rapid similarity matching. The platform offers a fully managed, cloud-native architecture, including a Serverless option that scales automatically and charges only for data stored and operations performed. Key features include hybrid search (combining sparse and dense vectors), real-time indexing, and enterprise-grade security: it is SOC 2, GDPR, and HIPAA certified. Companies like Gong and Vanguard leverage Pinecone to power their intelligent systems, achieving faster, more accurate retrieval for applications like customer support and smart tracking.

- Official technology site: https://www.pinecone.io/
- Public AI Tinkerers demos and talks: 25
- Result page: 1 of 2

## Recent Public Talks and Demos

### [Failing Fast with AI: Rapid Prototyping Using Google AI Studio](https://pereira.aitinkerers.org/talks/rsvp_BLcMVGVLlTs)

This demo explores a paradigm shift in product development: failing fast to learn faster. I’ll demonstrate how Google AI Studio can be used to rapidly prototype AI-powered applications, validate ideas, and surface potential product value—without investing significant time, money, or engineering resources upfront. The focus will be on hands-on experimentation, iteration cycles, and technical workflows rather than slides or theory.

- Event context: Cambio de paradigma AITINKERERS PEREIRA 2026 — 2026-02-26 — Pereira
- Public talk page: https://pereira.aitinkerers.org/talks/rsvp_BLcMVGVLlTs

### [Building Knobase: Personalized AI Tutoring with mem0 Memory, ZeroEntropy RAG, and Real-Time Content Safety](https://hong-kong.aitinkerers.org/talks/rsvp_cSLy04LtLvE)

Context: Knobase powers personalized AI tutoring for 1,000+ students across various schools in Asia. The core insight we learned: teachers need AI agents they can configure and trust without writing code, and students need personalization that remembers their learning style across sessions — not just generic ChatGPT wrappers that hallucinate or give direct answers when Socratic questioning is more effective. This talk is a technical deep-dive into how we orchestrate multiple AI systems to deliver that experience at scale, walking through five implementation layers: 1. Personalization with mem0 — A hybrid memory architecture combining mem0’s semantic vector search with a local confirmed-memories table in Supabase. As students chat, we extract preferences, learning styles, goals, and challenges via regex pattern matching and prompt scoring (confidence threshold ≥ 0.85). Students confirm these memories (“Yes, I’m preparing for IB exams”), and they’re injected into every subsequent chat. A 10th-grader studying physics gets reminders about their preference for step-by-step explanations; a university student preparing for finals gets context about their exam timeline. I’ll walk through  getContextualMemoriesForPrompt()  and how we merge local + mem0 results to build a per-student profile that persists across days and subjects. 2. RAG with ZeroEntropy — Teachers upload textbooks, lecture slides, problem sets, and institutional syllabi. Document ingestion pipeline: file upload → Supabase Storage → base64 encoding → ZeroEntropy with semantic chunking (chunk_size: 1800, overlap: 200). Collections are scoped per school ( school_{id} ) so students only retrieve content their teachers authorized. Retrieval uses topSnippets queries with metadata filtering by document/knowledge IDs, plus a parallel RAG agent that expands queries and aggregates deduplicated results. This means when a Harrow student asks “What’s Newton’s second law?”, the AI cites their specific uploaded physics textbook, not generic web content. I’ll show the filter-building logic and how we resolve documents through bot → knowledge → collective → document chains. 3. Real-Time Context API — Teachers can connect external data sources (Google Sheets of upcoming assignments, Notion databases of class resources, live sports scores for a journalism class analyzing data) via webhook-based context providers. On every message, we call registered providers, AI-process the response with token optimization (60-90% reduction), and inject it alongside RAG results. Example: A history teacher at ISF Academy configured a timeline of World War II events that updates the AI’s context window in real time, so students always get era-appropriate answers. I’ll trace the full flow from  chat.context_config  → provider webhook → context processing → system prompt assembly. 4. No-Code AI Chat Builder for Teachers — Educators configure role, tone, age group (elementary/middle/high school/university), complexity, subject, language, Socratic mode toggle (forces the AI to ask guiding questions instead of giving direct answers), citation preferences, and custom instructions — all stored as  custom_details  JSON. Organization-level master prompts override per-bot settings for school-wide safety policies. I’ll show how the system prompt is assembled in 10 steps: master prompt → bot intro → RAG context → memory context → custom instructions. A teacher creating a “Socratic Math Tutor” for 8th graders clicks 6 dropdowns and writes 2 sentences of instruction; the system generates a 2,000-token prompt behind the scenes that enforces age-appropriate language, refuses to solve homework directly, and cites only the uploaded textbook. 5. Education Safety Stack — Real-time prompt scoring (Clarity/Specificity/Task Definition/Context/Structure on a 0-100 scale) via a Supabase Edge Function runs on every student input. Content flagging across 7 categories (sexual content, bullying, profanity, racial bias, political sensitivity, harmful advice, PII detection) plus custom organization-defined flags (e.g., Harrow added “exam cheating detection”). A daily digest cron emails flagged messages to designated safety managers. Student interest keywords and learning purpose analysis are extracted as a side effect of scoring and fed back into mem0, creating a feedback loop where the AI becomes more personalized the more the student uses it. We’ve processed 16,000+ messages since December 2025 with this stack in production. ** Code walkthrough will focus on the chat route orchestration (~3,700 lines) that ties all five layers together in a single request lifecycle. No slides — just live code, architecture diagrams on a whiteboard, and real examples from our production deployment.

- Event context: AI Tinkerers Hong Kong & GBA: Using AI as a Superconductor for Learning — 2026-02-26 — Hong Kong
- Public talk page: https://hong-kong.aitinkerers.org/talks/rsvp_cSLy04LtLvE

### [How we made our RAG truly multimodal](https://tokyo.aitinkerers.org/talks/rsvp_xQClQ7BcKJ4)

While text-based RAG is a solved problem, building a truly multimodal retrieval pipeline remains the Wild West. At Archie, we needed our AI to understand not just code repositories, but the visual context of architecture diagrams and screenshots. In this talk, I will share the engineering journey of building a multimodal RAG system from scratch when no tutorials existed. I will cover: Data Ingestion: How to process and chunk distinct modalities (images vs. text) effectively. Embedding Strategies: Comparing CLIP vs. newer multimodal embedding models—and what actually worked for technical diagrams. Retrieval Architecture: How we structured our vector search to perform hybrid retrieval (text + image) to ground the MLLM in the correct context. The "Gotchas": Specific failures we encountered when trying to scale vision-based retrieval.

- Event context: AI Tinkerers Ginza Meetup – January 15, 2026 — 2026-01-15 — Tokyo
- Public talk page: https://tokyo.aitinkerers.org/talks/rsvp_xQClQ7BcKJ4

### [Universal Context Layer](https://chicago.aitinkerers.org/talks/rsvp_m5aSvRoAkqc)

A unified context layer that makes long AI threads usable across tools. It stores key facts and conversation history in Pinecone, auto-compresses before token limits, and lets Claude retrieve the exact slice you need—decisions, constraints, summaries—without re-explaining threads.

- Event context: AI Tinkerers Chicago December Meetup ft Turing — 2025-12-09 — Chicago
- Public talk page: https://chicago.aitinkerers.org/talks/rsvp_m5aSvRoAkqc

### [MindServe AI: Real-Time GPU-Powered Computer Vision + RAG Coaching Pipeline](https://nyc.aitinkerers.org/talks/rsvp_GzawB-Spyz0)

MindServe AI is a fully-deployed real-time inference system that analyzes tennis match video on GPU and streams structured coaching insight back to the user. The backend runs YOLOv8 + MediaPipe pose models in parallel, detects rallies using a state machine, and pushes frame overlays via WebSockets as processing happens. At the same time, structured match data is fed into a retrieval-augmented coaching engine using Pinecone + LLM reasoning to provide mental-performance feedback. This talk will walk through the full infrastructure stack — from GPU scheduling to async model serving to vector-database-driven reasoning — all demonstrated live with running code.

- Event context: The Future of AI Infrastructure (Ft. Oracle & NVIDIA) — 2025-12-09 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_GzawB-Spyz0

### [XCT v2: a multi-agent lab that turns science and tech ideas into Product Briefs](https://la.aitinkerers.org/talks/rsvp_Y9QW1nDSKA0)

From Idea to Previz in 3 Steps. An agentic pipeline that takes a user simple prompt, mines sources, structures a brief including product design pre-visualization. Turn bold ideas into science, design, and visuals — with depth and rigor. The latest release, XCT v2 – NextMatter Labs, helps game studios, showrunners, futurists, product designers, and journalists transform scientific and technological concepts into production-ready designs and narratives. Built on a chain-of-thought agent architecture, Supervisor and Workers workflow, OODA framework, integrating research, design synthesis, and visualization workflows, it converts complex ideas into high-quality and high-fidelity outputs.

- Event context: AI Agentsgiving Los Angeles — November 20th, 2025 — 2025-11-21 — Los Angeles
- Public talk page: https://la.aitinkerers.org/talks/rsvp_Y9QW1nDSKA0

### [Mojek Personal Finance Chatbot](https://toronto.aitinkerers.org/talks/rsvp_oFPbTmomTUI)

Mojek is a personal finance application that lets users link their banks, credit card and investment accounts into a single interface. Users can get a holistic view of their finances. For the past few months (almost year) we have been working on a chat interface to sit on top of this data and allow users to interact with their data in natural language. The idea is that our ETL pipeline and database can provide the relevant user context to answer financial questions accurately and democratize personalized financial advice for everyone. We're a bootstrapped team of 5 people.

- Event context: AI Tinkerers Toronto - October 2025 Meetup at CIBC | Simplii — 2025-10-30 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_oFPbTmomTUI

### [MCP vs Vector Database](https://valencia.aitinkerers.org/talks/rsvp_Fo1y5UD6Px0)

In this talk, I’ll explore the difference between Model Context Protocol (MCP) and Vector Databases, and explain how they work together to power the next generation of intelligent AI systems. We’ll look at how Vector Databases enable semantic search and long-term memory for large language models, while MCP allows models to securely access live, dynamic data and perform real-time actions through APIs or databases. Through practical examples, like using AI to search and report on enterprise SharePoint data. I’ll show when to use each approach, their strengths and trade-offs, and why combining them creates a more powerful, context-aware AI architecture. By the end of this session, you’ll understand how memory (Vector DB) and action (MCP) complement each other to move beyond static RAG pipelines toward adaptive, real-time AI systems.

- Event context: Opening AI Tinkerers Valencia x VDS - 22 October — 2025-10-22 — Valencia
- Public talk page: https://valencia.aitinkerers.org/talks/rsvp_Fo1y5UD6Px0

### [Teaching AI to read ancient Maya Glyphs](https://montreal.aitinkerers.org/talks/rsvp_Z7tI1x8ww7I)

The ancient Maya developed one of the most beautiful, complex, and intriguing writing systems in human history. For centuries, these glyphs recorded stories, rituals, dynasties, and the deep relationship between people, time, and the cosmos. Yet this knowledge was silenced — burned, buried, and forgotten for nearly a thousand years. Even today, only a few thousand people in the world can read Maya writing. I will the development of an AI agent designed to help bridge that gap. Using computer vision and deep learning, the system can detect glyph blocks, identify individual signs, and suggest possible readings by comparing them to a vast epigraphic corpus. It is not perfect — but it is a powerful first step toward using artificial intelligence to preserve and reactivate knowledge that was nearly lost forever. In doing so, we demonstrate how AI can be more than just code — it can be a force for memory, preservation, and cultural resilience. I will be presenting a live demo.

- Event context: AI Tinkerers Montreal – Spooky October 2025 Meetup — 2025-10-21 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_Z7tI1x8ww7I

### [Shipping AI for Good with LLM-as-Judge Evals](https://nyc.aitinkerers.org/talks/rsvp_Iv0GfB9IJjk)

Immigrants facing detainment need urgent, clear answers but face dense legal docs. This critical gap inspired me to build a tool to provide immediate support in a high-stakes situation. I built a bilingual (EN/ES) RAG chatbot that provides instant, conversational answers to immigration questions, drawing from trusted non-profit legal FAQs.

- Event context: NYC October Demo Day — 2025-10-02 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_Iv0GfB9IJjk

### [A Google-Like Search Experience for Your Photo Library with Opensource Tools](https://raleigh.aitinkerers.org/talks/rsvp_EEHGIBjFDGQ)

This demo showcases a photo album curation workflow using FiftyOne’s Brain similarity indexing and multimodal search. Images are embedded and indexed once via compute_similarity(), enabling rapid retrieval of visually similar photos and natural language search (e.g., “sunset at the beach”) using CLIP, which aligns text and images in the same embedding space. Users can sort albums by similarity to a reference image or a text prompt, find duplicates, and surface on-theme groups for curation. Low‑dimensional embedding visualizations further reveal clusters and outliers that guide pruning and organization. The experience is fully interactive in the FiftyOne App, and scalable via vector database integrations when working with large libraries.

- Event context: AI Tinkerers - Raleigh Inaugural Meetup (September 2025) — 2025-09-30 — Raleigh
- Public talk page: https://raleigh.aitinkerers.org/talks/rsvp_EEHGIBjFDGQ

### [12 Factor Agents - You dont need a complex framework to build agents.](https://nairobi.aitinkerers.org/talks/rsvp_4a6pUocIJ6U)

Break down modular components of how AI agents really work under the hood. Show how to use composable components by using regular code to make powerful production scale AI agents. Give ingredients of building agentic experiences and demistify what goes on behind the scenes when we use a framework to build AI agents.

- Event context: AI Tinkerers Nairobi – Sep 25, 2025 Meetup — 2025-09-25 — Nairobi
- Public talk page: https://nairobi.aitinkerers.org/talks/rsvp_4a6pUocIJ6U

### [Sistema legal multiagente](https://pereira.aitinkerers.org/talks/rsvp_IPc9tm9HLrk)

Sistema inteligente de consultas legales especializado en derecho colombiano, construido con IA multiagente, RAG y servicios de voz

- Event context: AI Tinkerers Pereira: De Usuarios a Makers: Prototipos que Inspiran — 2025-09-24 — Pereira
- Public talk page: https://pereira.aitinkerers.org/talks/rsvp_IPc9tm9HLrk

### [Building an Agentic AI System That Replaces Digital Ad Agencies: Multi-Stage LLM Architecture with Recursive Planning, Multimodal Analysis, and Novel Optimization Strategies](https://orange-county.aitinkerers.org/talks/rsvp_y1ihXS44YRA)

I'll demonstrate how I built AdRes, a sophisticated agentic AI system that eliminates digital ad agencies through advanced multi-stage Lambda architecture. The demo will showcase our recursive planning system that uses Anthropic Claude 3.5 Sonnet with custom fine-tuned prompts, vector embeddings for campaign similarity matching, and a multimodal creative analysis pipeline using OpenCV and computer vision. I'll walk through the code that powers our unified context system, agentic workflow with tool selection and self-correction, and real-time campaign optimization engine that processes complex multi-source data analysis.

- Event context: AI Tinkerers - Orange County Meetup- Thursday July 31st 2025 — 2025-08-01 — Orange County
- Public talk page: https://orange-county.aitinkerers.org/talks/rsvp_y1ihXS44YRA

### [ChatGPT for City Data: Chicago Edition](https://chicago.aitinkerers.org/talks/rsvp_q-R_Hj_iEBU)

Building a conversational AI that answers Chicago residents' questions about city services - inspired by Landon's newsletter idea. Live demo: Ask "How do I report a pothole?" or "What are parking rules on Michigan Ave?" and watch it pull answers from city documents and data. Technical walkthrough: - RAG pipeline: PDFs → chunks → vector embeddings → retrieval - Pinecone for vector storage of city procedures/FAQs - Tool calling to external APIs (future: Chicago Open Data Portal) - Prompt engineering for helpful government tone It will be live by demo day and in early beta!!

- Event context: AI Tinkerers Chicago June 2.0 Meetup ft Comet — 2025-06-24 — Chicago
- Public talk page: https://chicago.aitinkerers.org/talks/rsvp_q-R_Hj_iEBU

### [What Broke When I Tried to Vectorize 5M CIA Docs](https://nyc.aitinkerers.org/talks/rsvp_hazuF5wwMyU)

I’ll demo how I built a custom RAG pipeline on top of Cloudflare to ingest, embed, and serve 5M+ declassified government documents through an AI chat interface. The stack runs through Workers, queues, and Vectorize for semantic search—but building on Vectorize came with surprises. I’ll walk through my ingestion setup, how I chunked and embedded data at scale, and the technical issues I ran into: slowdowns from inserting vectors one-by-one, and serious bottlenecks from using timestamp metadata in milliseconds, which broke range queries. I’ll show the fixes I landed on after debugging with the Cloudflare team and reflect on the tradeoffs between Vectorize and tools like Pinecone for future scaling.

- Event context: Demos and Dim Sum with Deel and Apollo — 2025-06-03 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_hazuF5wwMyU

### [The Death of Agents: Building AI Workflows That Actually Scale](https://toronto.aitinkerers.org/talks/rsvp_zz0Coi-4yZM)

In this live walkthrough, I’ll demo how I built an LLM-powered pipeline system that replaces brittle agent frameworks with modular, composable reasoning stages. The system processes unstructured inputs (like emails and legal docs), extracts structured data, embeds it, retrieves semantically relevant context from a vector DB, and then makes decisions like: “Is this part of an existing deal or a new one?” No agents. No RAG gimmicks. Just pipelines, embeddings, and judgment calls from a model with traceability. I’ll walk through: Canonicalization (turning messy inputs into structured data) Vector-powered context graph retrieval LLM reranking and decision logic Real-time deal creation or attachment via reasoning How I kept it all observable, debuggable, and scalable across domains Everything will be live—from the code to the reasoning decisions. No slides, no fluff, just raw execution.

- Event context: AI Tinkerers Toronto - April 2025 Meetup at Shopify — 2025-04-24 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_zz0Coi-4yZM

### [AI MAXIMALIST](https://nairobi.aitinkerers.org/talks/rsvp_6xOTEkqX7oU)

Elyxa is an AI assistant that enables you to focus on the more important details of you day and leave the obnoxious details to systems optimized to streamline just that. A dedicated personal assistant for yourself.

- Event context: AI Tinkerers - Nairobi Inaugural Meetup (April) — 2025-04-09 — Nairobi
- Public talk page: https://nairobi.aitinkerers.org/talks/rsvp_6xOTEkqX7oU

### [Sales Intelligence Assistant](https://seattle.aitinkerers.org/talks/rsvp_w36HDtNPrT8)

This is a workflow I crafted to help me build a repository of knowledge based on the research I was conducting for validating BearHug’s market opportunities. Because BearHug is operating at the intersection of government and healthcare, it can get pretty complex and confusing to keep my facts and figures straight. I have used Perplexity to help me conduct research, but I don’t want the best knowledge to be lost to the void. So I developed a small script and workflow that allows me to use Perplexity to research a topic, identifying some of my favorite sources that it pulled from, then using Jina Reader I extract a TXT file of the wisdom contained within the webpage. If the content is a PDF, I download it and use Llama Parse to get good markdown out of it. Markdown is best for document retrieval, because it makes it very easy to decide where to chunk the documents, so I am planning to use some BAML to get structured markdown out of the TXT files I get from Jina. Then with all the markdown, I have had a good experience with Pinecone Assistants to help me process, chunk, and read the data from a vector store, making sure that I always have the best wisdom at my fingertips for this complex industry.

- Event context: AI Tinkerers Seattle - March Meetup — 2025-03-28 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_w36HDtNPrT8

### [Not Your Average Billboard: AI That Sees, Thinks, and Engages](https://cincinnati.aitinkerers.org/talks/rsvp_gP2EnDJlMc8)

In this presentation, I will explore the technical architecture behind AI Billboards, a system that leverages AI, computer vision, and contextual analysis to deliver dynamic, personalized advertising. I will break down how AI processes video content, extracts meaningful tags using Google Cloud Vision API, and matches ads to real-world conditions like weather, time of day, and audience mood. Additionally, I will explain how OpenAI CLIP embeddings, text-embeddings, Generative AI and Pinecone vector search enable intelligent ad selection without predefined rules. The presentation will also cover the frontend implementation, real-time ad delivery mechanisms and a code walkthrough

- Event context: 🤖 Join us at Cincinnati AI Tinkerers: Midwest Innovation Hub 🚀 — 2025-03-27 — Cincinnati
- Public talk page: https://cincinnati.aitinkerers.org/talks/rsvp_gP2EnDJlMc8

### [Legal AI](https://manizales.aitinkerers.org/talks/rsvp_R0wvvgLGGUQ)

We are developing an AI assistant designed to help lawyers streamline legal research, document analysis, and case preparation. Generative AI and retrieval-augmented generation (RAG), the assistant quickly extracts relevant information from legal documents, case law, and regulations by integrating with Pinecone for efficient retrieval through indexes. Key functionalities include retrieving case law and statutes, summarizing lengthy legal documents, answering legal questions, assisting in drafting contracts and memos, and recognizing multiple queries. Built on AWS, reducing research time, and improving decision-making for legal professionals. this presentation will be given with mi partner Edgar Rafael Jimenez

- Event context: Segundo Encuentro de AI Tinkerers Manizales — 2025-03-18 — Manizales
- Public talk page: https://manizales.aitinkerers.org/talks/rsvp_R0wvvgLGGUQ

### [Elder Caregiving Companion](https://seattle.aitinkerers.org/talks/rsvp_CnCNDJuVjd8)

An app that connects elder caregivers to knowledge and resources using OpenAI, Perplexity, and Pinecone.

- Event context: AI Tinkerers Seattle - February 2025 Meetup — 2025-02-22 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_CnCNDJuVjd8

### [Autonomous Web Agents with Planning and Self-correction](https://palo-alto.aitinkerers.org/talks/rsvp_iggNFATOFJs)

MultiOn's Agent Q sets a new major milestone for autonomous web agents, combining advanced search techniques, AI self-critique, and reinforcement learning to overcome current limitations, representing a substantial leap forward in autonomous agents capabilities. These capabilities allow building really reliable and robust agents, and we are excited to make these available in upcoming launches to our partners

- Event context: AI Tinkerers - Palo Alto - August 2024 @ Groq — 2024-08-23 — Palo Alto
- Public talk page: https://palo-alto.aitinkerers.org/talks/rsvp_iggNFATOFJs

### [EcoLoop: a platform that uses advanced AI to help you evaluate new ideas easily and effectively](https://toronto.aitinkerers.org/talks/rsvp_WT4ZaQdSf5c)

- Chatbot Interaction: Effortlessly articulate problem-solution pairs with an intuitive chatbot interface. - Visualizations: Gain profound insights through graphical representations, maturity stage, and viability scores. - 3D-VR Integration: Immerse yourself in proposed solutions with realistic virtual reality experiences. - EcoLoop's intelligence is implemented using the Retrieval-Augment-Generator framework LangChain, which connects the Language Model to the extensive knowledge repository encoded in the vector database. -Technologies used: Pinecone (fast similarity search for vectorized data), LORA Model (generates the 3-d VR solution Web Scraper), Semantic Classifier (discern semantic meanings for idea categorization), Semantic Summarizer (summarize content based on its meaning Nextjs and TailWindCSS), and Flask (handles backend and endpoint creations, and runs the ML models)

- Event context: AI Tinkerers - Toronto - June 2024 Meetup — 2024-06-27 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_WT4ZaQdSf5c

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