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You “turned on caching”… and your cost graph didn’t drop. Sometimes it even spikes. That’s not you being bad at math — it’s because caching has a break‑even point (a minimum reuse count before it pay
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Bhai, ek honest baat, teri SWE background AI Engineering mein waste nahi hoti, seedha kaam aati hai! 🚀Sirf 3 cheezein seekh lo — LLMs, RAG, aur Agents — aur tera career completely transform ho sakta
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You “turned on caching” and latency dropped… but your invoice barely moved.That’s not bad luck — it’s how LLM costs work. Here’s the mistake: most beginners optimize the part that’s cheap (repeated i
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Embeddings ka naam sunke log dar jaate hain, but concept super simple hai. Embeddings basically “meaning ka address” hai—jisse AI similarity samajhta hai. RAG, semantic search, recommendations… sab
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You compress the conversation to “save tokens”… and your AI gets worse.That’s not bad luck — that’s a real failure mode companies look for in interviews. Here’s the uncomfortable truth: “summarize th
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Agent ka tool calling wild ho jaata hai aur hum logs dekh ke bas guess karte rehte hain. Observability ka matlab hai: agent ka decision trail visible ho, sirf output nahi. Tracing se loops, retries, l
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This one question filters senior from mid-level engineers fast. Everyone hears “prompt injection” and thinks: user typed something nasty.In MCP systems, the scarier reality is: your agent can be clea
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Manual testing tumhe false confidence deta hai, especially agent aur RAG apps mein. Eval ka matlab heavy math nahi hai, bas repeatable testing hai jo har change pe chalti hai. Bas input + expected out
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MCP demos look easy: “one tool, one success case.” But real users trigger multi-step plans, retries, and partial failures—and your server can turn into a latency machine. Here are 4 MCP implementatio
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Because most candidates say: “We’ll add more unit tests + an LLM judge.”And that’s exactly how you ship agents that look “green” on dashboards… while silently failing users. Here’s the hire-level ans
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Wo "meri current life pe ek image bana" wala trend magic nahi hai. Developer POV se dekho toh system 3 layers chalata hai: short-term context, long-term preference profile, aur episodic retrieval via
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Most engineers test prompts like this: try 5–10 examples, it “looks fine”… ship. Then one small prompt change makes the bot: The fix is simple: treat your prompt like code → you need prompt evals tha
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About @jeetsoni.dev

coding.miner is a Instagram nano influencer. With 4.7K followers and 194 posts, jeetsoni.dev's account shows a follower-to-following ratio of 31.4:1. View all content anonymously on Pictame - no login required.

jeetsoni.dev's engagement rate sits at 1.22%. For accounts with 1K-10K followers, the average is around 5%, so there's some room to grow — though every community is unique.

Out of the last 12 posts, 100% are videos and 0% are photos. New content goes up pretty much every day — a very consistent posting habit. Captions lean detailed, averaging 1327 characters.

1.22% engagement rate — room to grow compared to average for 1K-10K follower accounts.
Posts every day — very consistent.
31.4:1 follower-to-following ratio.

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jeetsoni.dev
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@jeetsoni.dev

coding.miner

Public
Posts
194
Followers
4.7K
Top Hashtags
#llmops#aiengineering#AIEngineering#LLM#mlops#mlengineering#machinelearning#deeplearning

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Popular Hashtags

#style#instadaily#tbt#followme#repost#like4like#summer#beauty#selfie#food#me#instalike
Following
150
Avg. Likes
48
Avg. Comments
10
Engagement
1.23%

AI Engineer | Educator JS → Production AI Systems RAG • AI Agents • Tools • UI Building real-world AI, not demos �...

linktr.ee/jeetsoni.dev
#girl
#fun
#follow