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Most AI backends are held together with duct tape and optimism. 🩹 Here's the architecture blueprint senior AI engineers use — and rarely document. A solid AI backend isn't magic. It's 4 clean layers:
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Choosing the wrong vector database today = painful migration tomorrow. Most developers pick based on hype… not use case. Here’s the reality: Pinecone → easiest, but $$$ at scale Chroma → perfect fo
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Most developers don't realize they can run AI completely offline — no API keys, no bills, no data leaving your machine. Ollama makes it dead simple. One command to install. One command to pull a model
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Most devs call the AI API directly from their business logic. That's the first mistake. Production AI features need retries, timeouts, rate limit handling, cost tracking, and output validation — not j
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OpenAI, Anthropic, Groq — three very different APIs. Most developers just default to OpenAI and never question it. But depending on your use case, you could be leaving serious speed or cost gains on t
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Most developers type this into Claude Code:"Write me a REST API"And wonder why the output is generic, incomplete, and nothing like production code. 😤The problem isn't Claude Code. It's the prompt.Her
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Most developers are spending 3 days building demos they'll throw away. There's a smarter way. Flowise lets you build a fully working RAG chatbot — with PDF loading, embeddings, vector store, and an LL
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I wasted weeks getting mediocre output from Claude Code. Then I figured out why. 🧵 Most developers treat Claude Code like a search engine — type a vague request, hope for the best. That's not how it
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Most AI backends are held together with duct tape 🩹 Here's the architecture blueprint that senior engineers use — and rarely document. 5 layers. Any language. Any cloud. 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂'𝗹𝗹 𝗹𝗲𝗮𝗿
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Stop writing boilerplate backend code. AI can generate a complete REST API using a single prompt. Includes: • Controller • Service • Repository • DTO • Validation Comment "CLAUDE" and I'll send the
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What is LangChain? LangChain is a framework used to build applications powered by Large Language Models. It helps developers connect LLMs with: • APIs • Databases • Documents • Vector databases Th
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LLM vs RAG explained in 10 seconds. LLMs answer from training data. RAG retrieves external documents before generating the answer. That’s why RAG is used for: • Company knowledge bots • PDF chat •
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About @aidevstack

Mitesh Vanaliya • AI Developer 🖥️ is a Instagram growing creator. With 7 followers and 16 posts, aidevstack's account shows a follower-to-following ratio of 0.2:1. View all content anonymously on Pictame - no login required.

aidevstack's engagement rate sits at 47.62%. That's 852% above what you'd typically see for accounts with 1K-10K followers (average 5%) — a sign of a genuinely connected audience.

Out of the last 12 posts, 100% are videos and 0% are photos. New posts appear roughly every 2 days — a steady, reliable rhythm. Captions lean detailed, averaging 799 characters.

47.62% engagement rate — 852% above average for 1K-10K follower accounts.
New content every 2 days — a steady rhythm.

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aidevstack
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@aidevstack

Mitesh Vanaliya • AI Developer 🖥️

Public
Posts
16
Followers
7
Top Hashtags
#BackendDevelopment#AIEngineering#AIForDevelopers#MachineLearning#AITools#llm#rag#machinelearning

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Popular Hashtags

#photographer#naturephotography#landscape#travelphotography#architecture#portrait#wanderlust#adventure#vacation#trip#weekend
Following
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Avg. Likes
3
Avg. Comments
1
Engagement
57.14%

Building with RAG • Agents • Claude Code I teach devs to build AI apps faster 🚀 🎁 Free Claude prompts 👇

devstacker.gumroad.com/l/promp...
#morning
#night
#goodvibes
#positivevibes