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Linear Regression, finding the best-fit line Linear regression models the relationship between a dependent variable and one or more independent variables. The goal is simple: find the best-fitting s
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The Schrödinger Equation. This equation changed reality forever. It doesn’t tell you where a particle is… It tells you where it might be. In quantum mechanics, particles don’t have fixed positions.
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We’ve been optimizing reasoning in AI agents. But the real bottleneck is memory. Most agents today are fundamentally stateless. They can plan, reason, and use tools, but once a session ends, everyth
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Self-organizing LLM agents vs designed structures Most multi-agent systems begin with explicit role assignment, planner, coder, reviewer, critic, assuming that structure improves coordination. This
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Three AI agents were given the same task. All three picked it up. All three worked on it. All three interfered with each other. The result wasn’t speed. It was confusion. A principle from 1967 expl
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Why do averages become predictable? This is the Central Limit Theorem (CLT). It says that if you take repeated samples from any distribution, the average of those samples will tend toward a normal d
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Most people pray like they’re negotiating. Asking. Bargaining. Trying to get something outside of them to rearrange life in their favor. “Make this happen. Take this away. Give me what I want.” And
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OpenAI’s CLIP (Contrastive Language–Image Pretraining) is a multimodal neural network that learns to connect text and images within a shared representation space. Unlike traditional models that class
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“Natural-Language Agent Harnesses” We’ve been focusing a lot on models. Bigger models. Better models. Smarter outputs. But this paper made me pause for a moment. It argues that what actually deter
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How do AI models understand words? They use word embeddings. Each word is converted into a vector in a high-dimensional space. But it’s not random. Words with similar meanings end up close to each
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What is a normal distribution? It’s a bell-shaped curve where most values cluster around the mean. As you move away from the center, fewer observations appear. The curve is perfectly symmetrical, l
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I came across a paper that challenges a very deep assumption in how we train language models. Today, most models are optimized to give one correct answer. But the real world rarely works like that.
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About @daliamalkesh

Malkesh Dalia is a Instagram growing creator in the Entrepreneur category. With 197 followers and 68 posts, daliamalkesh's account shows a follower-to-following ratio of 1.1:1. View all content anonymously on Pictame - no login required.

daliamalkesh's engagement rate sits at 11.68%. That's 134% 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, 50% are videos and 50% are photos. Notably, videos tend to get 91.0x more engagement than photos. New content goes up pretty much every day — a very consistent posting habit. Captions lean detailed, averaging 1148 characters.

11.68% engagement rate — 134% above average for 1K-10K follower accounts.
50% videos, 50% photos. Videos get 91.0x more engagement here.
Posts every day — very consistent.
1.1:1 follower-to-following ratio.

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daliamalkesh
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@daliamalkesh

Malkesh Dalia

PublicEntrepreneur
Posts
68
Followers
197
Top Hashtags
#ArtificialIntelligence#AI#MachineLearning#datascience#Deeplearning#AIResearch#ReinforcementLearning#schrodinger

Explore the most impactful hashtags used in recent posts. These tags highlight the core interests and trending topics that define this profile's digital influence and content strategy.

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Popular Hashtags

#style#instadaily#tbt#followme#repost#like4like#summer#beauty#selfie#food#me#instalike
Following
186
Avg. Likes
23
Avg. Comments
1
Engagement
12.18%

AI/ML Engineer | Applied AI Researcher 💡 I love to train large deep neural networks 🔬 Mensa IQ of 174 🧠 ⚛️ Physics | ...

www.linkedin.com/in/malkeshdal...
#girl
#fun
#follow