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@halinkahart: Low Back Pain SUCKS but here are my 2 top tips 👇Read & Share with a friend 👇 Walk to warm up 🔥 Assess don’t guess 📐 Walking is NOT a CURE but it is definitely a missing PUZZLE PIECE in many of our days 🧩 By sitting more and walking less we simply do not ‘warm up’ & ‘open up’ enough to give us access to do more work after 😰 This can be common during busy work days (I have certainly been there)😔 Low back pain has been linked to so many joint function limitations that it is worth assessing instead of guessing so you know what to work on (& how). Some of the limitations include 👇 🌕 Hip Abduction & Adduction 🌖 Hip Flexion 🌗 Hip Extension 🌘 Hip Internal Rotation 🌑 Hip External Rotation 🌒 Thoracic Rotation 🌓 Big Toe Extension 🌔 The list goes on but you get the main point! #lowbackpain #backpain #fyp

Halinka Hart Mobility Coach
Halinka Hart Mobility Coach
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Tuesday 19 March 2024 18:51:41 GMT
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2024-03-19 20:29:14
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RAG vs CAG explained 👇 Most people learning AI engineering know RAG. But CAG, Cache Augmented Generation, is another approach you should understand. With RAG: → Documents are chunked → Chunks are turned into embeddings → Embeddings are stored in a vector database → A user query is embedded → Similar chunks are retrieved → The LLM generates an answer using that retrieved context With CAG: → You preload the documents directly into the model’s context → The model creates a KV cache → User queries are added to that context → The LLM answers using the information already loaded Sounds simpler, but CAG has tradeoffs. You still have to think about: ⚙️ Context window limits ⚙️ Scalability ⚙️ Cost ⚙️ Latency ⚙️ Accuracy ⚙️ Data freshness Knowing how to build a RAG demo is one thing. Knowing when to use RAG, CAG, or another retrieval architecture is what starts moving you toward production-level AI engineering. Comment “CAG” and I’ll DM you the link to my AI engineering community with: ✅ A full AI engineering learning roadmap ✅ Daily calls with working AI/ML engineers ✅ Recruiter and career guidance ✅ Projects, interview prep, and everything you need to work toward landing a $150K+ AI engineering role
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