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THIf you are building real AI systems today, these are 10 toolkits every AI engineer should know.
✦ AI-Native IDEs (Cursor, JetBrains Junie, GitHub Copilot)
✦ Multi-Agent Frameworks (CrewAI, AutoGen, LangGraph)
✦ Data Frameworks for RAG (LlamaIndex, Haystack, RAGFlow)
✦ Inference Engines (Fireworks AI, vLLM, TGI)
✦ Vector Databases (Pinecone, Weaviate, Qdrant, Chroma)
✦ Evaluation & Benchmarking (Eval Protocol, Ragas, TruLens)
✦ Memory Systems (Mem-0, LangChain Memory, Milvus Hybrid)
✦ Agent Observability (LangSmith, HoneyHive, Arize Phoenix)
✦ Multimodal Toolkits (CLIP, BLIP-2, Florence-2, GPT-4o APIs)
✦ Fine-Tuning & Training Stacks (PEFT, LoRA, RLHF / RLR SDKs)
This stack covers the core layers of modern AI engineering from development → orchestration → retrieval → inference → evaluation.
Which ones are already part of your stack? 🚀
@the.datascience.gal










