3.9K
BAHow to reduce the costs of your Vector Database like Qdrant, Pinecone, Weviate or PostgreSQL/Pgvector?
#ai #rag #llm
@baniascodes
#Pinecone Vector Database
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8.9K
BYHere’s how you can setup your very own vector database using Pinecone, to build a vertical AI tools on top of. #vibecoding #coding
@byjackprice
27.8K
COVectorless RAG is quietly changing how we build retrieval systems.And most engineers haven't noticed yet.
For 3 years we've been told RAG = embeddings + vector database.
Chunk your docs.
Embed everything.
Store in Pinecone.
Similarity search at query time.
It works.
But the baggage is real →
→ Embedding drift across model versions
→ Chunk size tuning that never feels right
→ Semantic search that misses exact keywords
→ Vector store infrastructure costs
→ Re-indexing nightmares when your model changesVectorless RAG skips all of that.BM25 for keyword precision.
Knowledge graphs for relationship reasoning.
SQL retrieval for structured data.
Long context LLMs that skip indexing entirely.
The real unlock in 2026?
Hybrid systems.One query. Multiple retrievers. Route to the right method at runtime. Best result wins.Stop defaulting to vector search because the tutorial said so.
Start asking → what does my data actually need?
Save this and send it to an engineer who's still embedding everything 🔥.
.
.#RAG #AI #MachineLearning #LLM #VectorDatabase #AIEngineering #GenerativeAI #ArtificialIntelligence #TechContent #DataEngineering #NLP #BuildInPublic #AIArchitecture #DeepLearning #MLOps
@codewithbrij
7.6K
TEHow did AI go from Wait! ‘Who Are You?’ to ‘Welcome Back’. We are going to talk exactly about that in today’s episode. It’s called the Vector Database Revolution
Pinecone became a billion-dollar unicorn by solving a significant problem in AI, that every conversation started from scratch. Imagine texting your best friend “I hate mushrooms” and the next day they invite you to a mushroom festival. That’s exactly how dumb AI was before vector databases were invented in 2019.
Vector databases play a significant role in RAG models (about which we discussed in the previous episode on the channel), because they store all the information in the form of vectors. The vector database basically tells RAG “here are the 10 most relevant paragraphs about what they’re asking” - without this memory search, RAG would have no information to cite and would just make stuff up like regular GPT model.
Now, your shopping app remembers you browse expensive stuff but only buy them during sales. Pinecone’s value exploded because they made AI stop being a goldfish. Companies paid millions because finally their AI could build actual relationships with users instead of treating everyone like strangers. The wild part? This whole $2B industry exists because one guy got tired of AI being forgetful.
.
🏷️ Al, Artificial Intelligence, Generative Al, Study, Late night study, Important Concepts
@techwithnt
12.7K
SEYou don’t need a 100+ team. 🧐🔥
You need a powerful AI stack.
Here’s a serious SaaS founder toolkit:
• Exploding Topics Pro – Trend prediction
• SparkToro – Audience research
• Framer AI – Website building
• Cursor AI – AI coding assistant
• Supabase – Backend infrastructure
• Pinecone – AI vector database
• LangChain – LLM workflows
• PhantomBuster – Growth automation
• Instantly.ai – Cold email scaling
• PostHog – Product analytics
• Stripe Billing – Subscription engine
This is how modern SaaS founders win.
Build smarter.
Move faster.
Scale globally.
#SaaSFounder #AITechStack #StartupTools #BuildInPublic
@seoing.hamza
2.7M
ZAMan’s Skull Crushed By 16-Pound Pine Cone 😨
@zackdfilms
66.9K
HAPinecone 🌲
Pinecone is the third and final design in our emboss/deboss study mini-series. It follows our “Acorn” and “Maple” prints to complete our Autumnal Collection. Each piece has brought its own challenges, from finding the right balance so that the paper is neither too wet nor too dry to settling on the right colour, but we’re thrilled to be able to share them all with you now.
We hope you’ve enjoyed following our little collection as much as we have enjoyed creating them. Let us know which one is your favourite!
All three will be available as part of our Autumn Update tomorrow (Thursday, 30th October) at 5pm GMT.
#linocut #linocutprint #printmaker #printmaking #linoprint #reliefprinting #blockprint #linograbado #linogravure #xilografia #gravura #embossing #debossing #handprinted #embossedprint #texturalart #artprocess #handmade #natureprint #botanicalart #pinecone #autumnvibes #fallart #artreels #videoprocess #handcarved
@hallyandchristyprints
1.7K
LEComment “RAG” to get the RAG Agent with Pinecone Vector Store
Stop manually searching through docs. Instead, use this workflow in 4 steps:
1️⃣ Watch – auto-detect new files in Google Drive
2️⃣ Embed – convert docs into embeddings with OpenAI
3️⃣ Store – upload into Pinecone vector database
4️⃣ Chat – ask any question & get instant, context-aware answers
Turn your files into a knowledge-powered AI assistant.
@leadgenman
96.8K
FU“Vector DB – AI की याददाश्त का असली राज़ ”
AI smart है, लेकिन याददाश्त कहाँ से आती है?
Answer: Vector Database!
जैसे लाइब्रेरी का index card सही किताब ढूँढने में मदद करता है, वैसे ही Vector DB हर डॉक्यूमेंट को “numbers वाले fingerprints” में बदल देता है।
फायदे:
Similarity Search = सवाल पूछो → सबसे relevant जवाब
Core of RAG (Retrieval Augmented Generation)
Popular Vector DBs → Pinecone, Weaviate, ChromaDB, Milvus
Quick process:
Data → embeddings बनाओ
Store → Vector DB में डालो
Query → embeddings compare करके सही result
💡 Short mein:
LLM = दिमाग
Vector DB = याददाश्त
#VectorDB #AIExplained #RAG #Pinecone #ChromaDB #Weaviate
#FullstackRajuExplains #BabubhaiyaAurRaju #AIMemory #GenAI
#LLM #AIProjects #TechReels #MachineLearning
@fullstackraju
377
AKWatch Claude Code build a multimodal RAG system from scratch.
I gave it one prompt:
“Build me an AI that searches through videos, PDFs, and images.”
Here’s what happened:
Claude Code autonomously:
→ Wrote 500 lines of Python
→ Set up Google Gemini Embedding Model 2 (multimodal embeddings)
→ Connected Pinecone vector database
→ Built ingestion pipeline with chunking and batch upserts
→ Implemented OpenCV for video frame extraction
→ Configured Claude Sonnet 4.5 streaming via OpenRouter
→ Fixed OpenAI SDK breaking changes mid-build
→ Added deterministic IDs and error handling
Time to build: 1 session
Lines of code I wrote: 0
This is what agentic AI actually means.
Not “AI that answers questions.”
Not “AI assistants.”
Real systems. With pipelines, databases, streaming, error handling, and production-grade architecture.
The difference between builders and consumers:
→ Consumers ask AI questions
→ Builders use AI to ship systems
What are you building?
#claudecode #ai #rag #buildinpublic #agenticai
@akshat_wrld28
1.1M
ASHow to draw a pine cone the best way I could figure out 😅
#pinecone
@aska.hall
19.5K
LIHere are a few fun facts about pine cones you can share with friends on your next hike!
AND you can also see them change shape if you collect a few (check foraging regulations in your area to make sure it’s allowed), expose them to water by rinsing or spraying, and watch as they close and open. It takes 15-30 minutes so I suggest taking a Timelapse video and watching it back— this would be especially fun with kids!! But it’s cool to nerd out on nature at any age 🤓
#naturelovers #naturenerd #letslearn #inspiredbynature #pinecone
@lilylearnsfromnature
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