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Save this if attention feels confusing. Attention is not magic focus. It is weighted information mixing. The pipeline: Query asks what the current token needs. Key tells what each token contains. Va
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Most people say: LLMs predict the next word. Technically, it is deeper than that. LLM generation loop: tokenize embed score choose repeat In this example: function add(a, b) { return ___ The model
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AI can sound confident and still be wrong. That is hallucination. LLMs predict likely tokens based on patterns. But fluent language is not the same as verified truth. LLM Day 01: Why AI Hallucinate
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Big O is not scary math. It is a warning label for growth. Day 05/60: Big O: O(1), O(n), O(n squared) O(1): constant work O(n): one pass O(n squared): n times n comparisons Save this before starti
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Fast code can still be heavy. Space Complexity tells us how much extra memory code uses while running. Day 04/60: Space Complexity In Simple Words O(1): fixed extra memory O(n): memory grows with i
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Your code can feel fast with small data and suddenly become slow when the input grows. Next video: Space Complexity In Simple Words - how much extra memory your code uses while running. Day 03/60: T
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Prompt injection is only one part of LLM app security. Real production AI apps can fail in many tricky ways: user input can override instructions model output can be unsafe RAG data can be poisoned
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Save this if you think DSA is only for interviews. Data Structures matter because real apps keep touching data again and again: find, update, remove, process. The right structure makes the same feat
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Your code is not always slow because of logic. Sometimes your data is messy. Day 01/60: Data Structure = data ka arrangement. Better arrangement = better code. Follow @codersaurayt for one DSA conc
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Time Complexity sounds scary, but the idea is simple. It is not about exact seconds. It is about how steps grow when input grows. Day 03/60: Time Complexity In Simple Words O(1): direct O(n): scan
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Save this before starting DSA. Data Structures are simply different ways to arrange data. Array, Stack, Queue, Map, Tree, Graph - all of them exist because different problems need different arrangem
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New video just dropped on CodersAura! Kubernetes — finally explained the way nobody else has. Most tutorials throw terms at you and hope something sticks. This one uses a City Analogy that makes eve
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About @codersaurayt

CodersAura is a Instagram growing creator in the Science & Tech category. With 101 followers and 12 posts, codersaurayt's account shows a follower-to-following ratio of 101.0:1. View all content anonymously on Pictame - no login required.

codersaurayt's engagement rate sits at 19.31%. That's 286% 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, 58% are videos and 42% are photos. New content goes up pretty much every day — a very consistent posting habit. Captions lean detailed, averaging 547 characters.

19.31% engagement rate — 286% above average for 1K-10K follower accounts.
Posts every day — very consistent.
101.0:1 follower-to-following ratio.

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codersaurayt
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@codersaurayt

CodersAura

PublicScience & Tech
Posts
12
Followers
101
Top Hashtags
#codersaura#datastructures#dsa#coding#programming#codingforbeginners#developers#dailydsa

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Popular Hashtags

#instamood#dogsofinstagram#gym#wedding#foodporn#drawing#likeforlike#beach#insta#cat#memes#nofilter
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18.81%

AI Agents • Full Stack • DevOps Build future-ready skills → Free on YouTube Follow me on YouTube & WhatsApp as well ⬇️

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