IS-LM Curves & Aggregate Demand | CFA Level 1

Choosing Fixed-Effects, Random-Effects or Pooled OLS Models in Panel Data Analysis using Stata

Manthan Patel | Lead Gen Man on Instagram: "LLMs are AI models, but not all AI models are LLMs 👀 Here are 8 specialized architectures pushing AI beyond text: 1️⃣ LCMs – concept-level (Meta SONAR) 2️⃣ VLMs – vision + language 3️⃣ SLMs – small, fast edge models 4️⃣ MoE – efficient mixture of experts 5️⃣ MLMs – the OG masked models 6️⃣ LAMs – action-taking models (do tasks) 7️⃣ SAMs – pixel-level segmentation 8️⃣ LLMs – text + reasoning Each is built for a purpose: speed, size, or multimodality."

Aishwarya Srinivasan | LinkedIn AI Top Voice | AI Advisor on Instagram: "WTH is MLOps vs. LLMOps? 🤔 If you’re building with traditional ML models (like XGBoost or CNNs), you’re in the MLOps world — where pipelines, data versioning, and model deployment are key. But if you’re working with foundation models, prompt tuning, RAG, or agent stacks — you’re in LLMOps land. Here, you’re managing prompts, fine-tuning checkpoints, vector stores, context windows, and model evaluations with language