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#Pgvector Reel by @sayed.developer (verified account) - Comment "db" and I will share with you my favourite tutorial on Postgres + PGvector🚀🫡 #softwareengineering #computerscience
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@sayed.developer
Comment “db” and I will share with you my favourite tutorial on Postgres + PGvector🚀🫡 #softwareengineering #computerscience
#Pgvector Reel by @thenewstack - In a groundbreaking move, the open source community has brought vector similarity search right into the heart of Postgres, thanks to the innovative PG
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@thenewstack
In a groundbreaking move, the open source community has brought vector similarity search right into the heart of Postgres, thanks to the innovative PG Vector extension 💡 Join us as Sirish Chandrasekaran, general manager of Amazon RDS, sheds light on this exciting development in a recent episode of The New Stack Makers, recorded at the Open Source Summit North America 🎙️ 👆 Link in bio for the full episode 👆 https://thenewstack.io/postgres-is-now-a-vector-database-too/ #PGVector #Postgres #VectorDatabase #OpenSource #Amazon #AmazonRDS #OpenSourceSummit #NorthAmerica #VectorData #AWS #Database #DataEngineering
#Pgvector Reel by @priyal.py - pgvector & qdrant #learningtogether #womeninstem #progresseveryday #tech #consistency
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@priyal.py
pgvector & qdrant #learningtogether #womeninstem #progresseveryday #tech #consistency
#Pgvector Reel by @jganesh.ai (verified account) - The Simple Mental Model pgvector = Postgres with embeddings Vector DB = search engine for embeddings Both work. Trade-offs are scale, latency, and o
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@jganesh.ai
The Simple Mental Model pgvector = Postgres with embeddings Vector DB = search engine for embeddings Both work. Trade-offs are scale, latency, and operations. ⸻ What pgvector Gives You • One datastore (SQL + metadata + embeddings together) • ACID transactions + mature Postgres ecosystem • ANN search (HNSW / IVF) built in • Cheapest and simplest if you already run Postgres Great when search is part of your system, not the whole system. ⸻ pgvector Limits • Mostly vertical scaling • High QPS search can compete with OLTP workload • Needs tuning at scale • Harder beyond tens of millions of vectors It’s “good enough” — not infinite scale. ⸻ What Dedicated Vector DBs Give You (Pinecone, Qdrant, Milvus, Weaviate, etc…) • Purpose-built ANN engines • Horizontal scaling • Stable latency at high QPS • Hybrid search + filtering features • Managed infra options Best when search is your product. ⸻ When pgvector Is the Right Choice ✔ Few million vectors ✔ Moderate traffic ✔ Strong relational queries ✔ MVP or internal tools ✔ Already using Postgres ✔ Cost-sensitive setup Simple. Cheap. Sufficient. ⸻ When a Vector DB Is the Right Choice ✔ Tens–hundreds of millions of vectors ✔ High QPS + strict latency ✔ Multi-tenant large workloads ✔ Search is core product value ✔ Need horizontal scaling Performance and scale matter more than simplicity. ⸻ The Rule of Thumb Small–medium scale → pgvector Large-scale search system → vector DB Start simple. Scale when needed. ⸻ Bottom line: You don’t choose tools by hype. You choose them by scale and workload. Follow the series for more 🚀 ⸻ Tags: [“ai”, “llm”, “rag”, “pgvector”, “postgres”, “vectordatabase”, “pinecone”, “qdrant”, “milvus”, “systemdesign”, “mlengineering”, “mlops”, “scalableai”, “search”, “datainfrastructure”]
#Pgvector Reel by @arjay_the_dev (verified account) - Create a vector database in just a few minutes with Postgres and pgvector. This is a really cool way to get some experience with vector DBs, which are
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@arjay_the_dev
Create a vector database in just a few minutes with Postgres and pgvector. This is a really cool way to get some experience with vector DBs, which are critical for LLMs and AI. Follow and comment “vector” and I’ll send you the link to the GitHub which has the source code + good instructions for setup and usage. #coding #programming #ai #databases #vectordatabase
#Pgvector Reel by @jam.with.ai (verified account) - These are the free tools you need to deploy your first AI system to production. All of the below are either free or have free tier. Backend: FastAPI
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@jam.with.ai
These are the free tools you need to deploy your first AI system to production. All of the below are either free or have free tier. Backend: FastAPI, LangGraph, Groq, Jina AI, PostgreSQL + pgvector Frontend: Next.js on Vercel Infra: AWS free tier ($200 credits) DevOps: OpenTofu, CircleCI / GitHub Actions, GitHub, Docker Quality: Sentry, Opik, CloudWatch, Ruff, MyPy Save this. Go build. [AI engineering, free tools, production AI, LLM deployment, AI stack, MLOps, AI agents, FastAPI, LangGraph, Groq, PostgreSQL, pgvector, Next.js, Vercel, AWS free tier, OpenTofu, Docker, Sentry, Ruff, MyPy, CloudWatch, GitHub Actions, CircleCI, Opik, Jina AI, AI engineer, machine learning, deploy AI, free AI tools, build with AI]
#Pgvector Reel by @fazttech - ¿Quieres ser Backend Developer en 2026? El ecosistema de Python ha cambiado y aquí te cuento exactamente qué tecnologías están dominando ahora mismo.
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@fazttech
¿Quieres ser Backend Developer en 2026? El ecosistema de Python ha cambiado y aquí te cuento exactamente qué tecnologías están dominando ahora mismo. En este video te muestro las herramientas y stacks más potentes que están usando los desarrolladores profesionales en 2026: FastAPI y Django: Los frameworks favoritos para crear APIs rápidas, aplicaciones en tiempo real con WebSockets y proyectos complejos. UV y Ruff: Las nuevas herramientas escritas en Rust que están reemplazando a pip, venv y otros clásicos. Mucho más rápidas y ligeras. PostgreSQL + PGVector: Cómo convertir tu base de datos en una base vectorial para crear sistemas RAG (Retrieval Augmented Generation) y chats inteligentes con tus propios datos. Tendencias actuales: entornos en la nube, CLI tools, y todo lo que necesitas saber para no quedarte atrás. Si estás aprendiendo o trabajando con Python backend, este video te va a ahorrar semanas de investigación. • El stack backend más usado en 2026 • Cómo crear APIs inteligentes con IA • Herramientas modernas que mejoran tu productividad • Por qué PGVector está explotando en proyectos reales Si te gustó el video dale LIKE, suscríbete y activa la campanita 🔔 para más contenido actualizado de programación. ¿Quieres que te haga un roadmap completo de Backend 2026? Déjamelo en los comentarios 👇 #Python #Backend #FastAPI #Django #PGVector #RAG #DesarrolloWeb #Programacion2026
#Pgvector Reel by @mentor_true - Building AI apps in 2026 isn't that complicated. You don't need 50 tools. You just need the right production-ready stack. Here's the kind of stack r
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@mentor_true
Building AI apps in 2026 isn’t that complicated. You don’t need 50 tools. You just need the right production-ready stack. Here’s the kind of stack real AI builders are using: ⚡ Cursor / Claude Code – coding ⚡ Next.js + Tailwind – frontend ⚡ Supabase / Postgres – backend ⚡ OpenAI / Claude – AI models ⚡ Pinecone / pgvector – RAG ⚡ Stripe – payments ⚡ Vercel – deployment ⚡ Cloudflare – infra Simple. Powerful. Production-ready. I’ve put together a complete AI Builder Stack list covering LLMs, databases, agents, vector DBs, infrastructure, monitoring, testing, and more. Comment “STACK” and I’ll send you the full list + resources. If you’re building AI products, this will save you weeks of research. #ai #genai #aiengineering #aitools #buildinpublic
#Pgvector Reel by @davidson.nocode - Qual banco vetorial escolher para RAG? Neste short eu comparo as opções mais usadas (pgvector/Postgres, Weaviate e Pinecone) e explico qual faz mais s
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@davidson.nocode
Qual banco vetorial escolher para RAG? Neste short eu comparo as opções mais usadas (pgvector/Postgres, Weaviate e Pinecone) e explico qual faz mais sentido dependendo do seu cenário: projeto simples dentro do Postgres, alta performance com filtros, busca híbrida (vetor + keyword) e escala em produção. #RAG #VectorDatabase #IA #LLM #pgvector #Qdrant #Weaviate
#Pgvector Reel by @opensiteai - Just open-sourced our AI coding agent skills library + multi-agent sync tooling. 20+ production-grade skills across Rust/async, Rails, React 19, pgve
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@opensiteai
Just open-sourced our AI coding agent skills library + multi-agent sync tooling. 20+ production-grade skills across Rust/async, Rails, React 19, pgvector, RAG pipelines, zero-downtime migrations, security review, and more + Playwright-powered automation that keeps Claude Code, Codex, Cursor, Copilot, Claude Desktop, and Perplexity Computer all in sync from a single repo. No more copy-paste drift across agents. http://github.com/opensite-ai/opensite-skills
#Pgvector Reel by @techwithprateek (verified account) - Everyone thinks legal RAG leaks happen in the model. Actually: they start in retrieval. Three guardrails prevent cross-client data leaks in legal ass
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@techwithprateek
Everyone thinks legal RAG leaks happen in the model. Actually: they start in retrieval. Three guardrails prevent cross-client data leaks in legal assistants. ⚡ The Database Guardrail Application-level filters are fragile. One missing WHERE clause leaks data. Use PostgreSQL Row Level Security with pgvector so enforcement lives inside the database. Actions → Enable RLS on the embeddings table storing document chunks → Define policy: client_id = current_setting(‘app.client_id’) → Inject client context after authentication using a session variable Even if a developer writes a bad query, Postgres blocks the read Payoff: 0 cross-client retrieval, enforced by the database engine — ⚡ The Metadata Lock Embeddings alone cannot enforce confidentiality. Similar cases across clients will collide Every chunk must carry immutable identity metadata Actions → Tag chunks during ingestion with client_id, case_id, document_id → Enforce NOT NULL + immutable columns in the schema → Run vector search with metadata filtering inside SQL This metadata becomes the security spine of retrieval Payoff: Every retrieved chunk maps to exactly one client case — ⚡ The Context Firewall Even correct retrieval can leak information during generation. Shared memory or cached answers can expose another client’s case. Actions → Maintain client-scoped session memory for conversations → Disable global caching across tenants → Log retrieved document_ids for audit and traceability Security must extend beyond retrieval into generation Payoff: No cross-client leakage during response generation — Secure Retrieval Flow Auth → set app.client_id → RLS-enforced vector search → Top-k chunks → LLM response — Optimize database isolation, not just embeddings 🔖 Save this for your next secure RAG architecture review 💬 Comment “RLS” to get a solution architecture ➕ Follow for more production-grade AI system design
#Pgvector Reel by @thesecondbrain_ - Migration to pgvector enabled so many things, and reusing context + easily adding/removing them is just one of the use cases. Besides being way WAY f
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@thesecondbrain_
Migration to pgvector enabled so many things, and reusing context + easily adding/removing them is just one of the use cases. Besides being way WAY faster than Pinecone, it also opens up doors for: - Further optimizations - Way more flexibility about context There are way less limitations, so more cool new features can be added. That’s why I always prefer building things from scratch. If I have 0 knowledge about something, then I start with an external tool/framework. But when I learn it over time, I often realize I can build a fully custom solution from scratch that will work much better, because it’s made perfectly for my needs. #buildinginpublic #aistartup #thesecondbrain

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