Top Tweets for #ragstack
🌟 Meet Ashok Vishwakarma 🌟
GDE in Web & AI | Founder & CTO @ Impulsive Web
At #DevFestGoa2025, he’ll show how to make AI think smarter by adding a reasoning layer to your RAG stack with Neo4j 🤖⚡
📅 October 11 ,2025
📍 Panjim Convention Center
#Neo4j #AI #RAGStack #GDGGoa
7/🚀 Time to move beyond toy demos — build production-grade RAG.
Let’s connect if you’re scaling your custom AI stack!
#RAGstack #AIInfrastructure #MLOps #BentoML #LangChain #MachineLearning #Logimonk
RAG, LLMs, embeddings, retrieval pipelines…
Sounds like a tech talk.
But it’s actually the architecture behind how women find answers faster in our system.
Tech = care, when done right.
#AIforHealth #GenAI #RAGstack #responsibleai #empoweringpatients #femispace #marinavieva
⭐️ GitHub →https://t.co/xlyiTkXRck
#LLMDev #OpenSourceAI #FineTuning #RAGstack #MultiAgentSystems #LangChainAlternative #PythonDev #MLOps #BuildInPublic
The AI stack in 2025 isn’t a playground.
It’s the backend.
→ Retrieval as your brain
→ Agents as your teammates
→ Observability as your safety net
Ship fast, monitor deeply, and scale with eyes open.
More here: @zeroxaitales
#LLMOps #AItools #ragstack #agentframeworks
7. LlamaIndex
https://t.co/fSQGnQVZy2
Go-to RAG toolkit.
File loaders, memory layers, chunkers, vector integration.
Used in most doc-based chatbots today.
#llamaindex #RAGstack
8. LlamaIndex
The RAG-native framework.
— Load data from PDFs, Notion, APIs
— Build indexes
— Plug into LangChain or standalone
Go-to for any LLM connected to real data.
#LlamaIndex #RAGstack
Caching
LLMs forget. Your infra shouldn’t.
What to cache:
– Embeddings
– RAG responses
– Model outputs
– Common prompts
Reuse saves cost and improves UX.
#ModelCaching #SemanticSearch #RAGstack
Example:
An AI writing tool uses GPT-4 to win early users.
Hits 10K DAUs → moves to Mixtral + Qdrant stack.
Cost drops 80%.
Output tuned to domain.
#ragstack #AIscaling #AIeconomics
Use cases:
→ RAG pipelines (retrieval-augmented generation)
→ Domain copilots
→ Document search
→ AI agents with long-term memory
Your data becomes recallable intelligence.
#ragstack #agentarchitecture #LLMops
5️⃣ Memory + Retrieval Infra
Want your AI to “remember”?
Use vector DBs like:
→ Pinecone
→ Weaviate
→ Chroma
→ LanceDB
They power RAG pipelines—retrieval-augmented generation.
Context is the new compute.
#RAGstack #VectorSearch #MemoryInfra
What makes this all work:
✅ Defined agent personas
✅ Shared memory (blackboard or vector DBs)
✅ Coordination protocols, not chaos
✅ Tunable conflict resolution
#AgentDesign #AIcooperation #autonomoussystems #RAGstack
In the age of autonomous agents, memory is leverage.
Your agents won’t scale because they answer fast.
They’ll scale because they remember wisely.
Start there.
Follow for more:@zeroxaitales
#aiagents #solysian #agentstack #memorylayer #aiproducts #aiUX #ragstack #founderAI
2025 memory stack recap:
• Embeddings: OpenAI, Cohere, HuggingFace
• Vector DB: FAISS, Weaviate, Pinecone
• RAG Logic: LangChain, LlamaIndex
• State Track: LangGraph, Redis
• Orchestration: CrewAI, AutoGen
#memorystack #llmsystems #ragstack
RAG done right includes:
• Reranking results
• Semantic chunking
• Multi-hop queries
• Real-time freshness
RAG is the glue between static data and dynamic decisions.
It’s context injection—at scale.
#retrievalAI #aiengineering #ragstack
There are 3 memory layers every serious agent needs:
Long-term (embeddings, files, docs
Short-term (active thread context)
Stateful (task vars, history, logic)
Each unlocks a different tier of capability.
#ragstack #vectorstores #aidevelopment
AI patents are exploding:
Over 10,000 filed in 2024 (WIPO).
Most cover:
– Compression
– RAG
– Safety wrappers
The IP race is about how models run—not what they say.
#AIpatents #techIP #modeloptimization #safetyAI #AIresearch #compressedAI #RAGstack
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