PaschalIfeanyi🕵️

🧠 AI & LLM Explorer | Building at the frontier of
Generative AI, Web3 & Intelligent Systems
| Sharing what I learn, one token at a time ⚡

[Amit Shekhar @amitiitbhu]

New Article: How does Token Streaming work?

Read here: https://t.co/XneZ1PgQQd

[Amit Shekhar @amitiitbhu]

https://t.co/7vn9GQTRPR

[Amit Shekhar @amitiitbhu]

New Article: How does a Reranker work?

Read here: https://t.co/KbdQIQIdc3

[Pallavi @pallavishekhar_]

LLM Inference Optimization:

• KV Cache → Fixes repeated computation, but increases memory usage.

• Paged Attention → Fixes KV Cache memory waste using old-school OS paging ideas.

• Flash Attention → Fixes slow attention by reducing GPU memory movement.

• GQA → Fixes large KV Cache size by sharing Keys and Values across heads.

• Speculative Decoding → Fixes slow token generation using a small draft model.

• Continuous Batching → Fixes GPU underutilization in production.

[PaschalIfeanyi🕵️ @DefiPhilosopher]

I thought i was the only one who is also experiencing it too. you literally get tired of eating it after taking a few bites

[Wendy J @Jessicalevi13]

@IdaraAkpabio882 You get tired after the eating two pieces

[Oluwatosin O. Ayeni @anabury25]

Understanding this file means you're closer to training AI models with your data! It shows how to take raw text and prepare it for machine learning magic.
https://t.co/MxWv5UTUyS

@AbdvllxhMvjxhid @theKingslee

[PaschalIfeanyi🕵️ @DefiPhilosopher]

@howareyoudanny @bluewmist This is not a practical solution for the modern day man

[PaschalIfeanyi🕵️ @DefiPhilosopher]

@remotemondays Got in too

[PaschalIfeanyi🕵️ @DefiPhilosopher]

@tanx034 Link Please

[PaschalIfeanyi🕵️ @DefiPhilosopher]

@KhvichaSzS Solid

[Amit Shekhar @amitiitbhu]

New Article: Variational Autoencoders

Read here: https://t.co/LzMLE9Pots

[Pallavi @pallavishekhar_]

More context is making your AI agent dumber. Let's understand why.

We often think that giving the AI more information will make it smarter. So we keep adding more. The full document. The entire chat history. Every tool output. Just in case.

But the opposite happens. The agent gets confused. It slows down. It starts making mistakes.

Here is the reason.

Context is the text we give the model to work with. The instructions, the documents, the past messages, the tool outputs. Think of it as the model's short-term memory for this one task.

And this memory has a limit. The model cannot pay equal attention to everything inside it.

Let's say we ask a colleague a simple question. But instead of telling them the one line that matters, we hand them a 500-page binder and ask them to find the answer inside it.

They will struggle. The important detail is now buried under noise.

The model behaves the same way. When we stuff the context with everything, the one fact that actually matters gets drowned out.

Think of an AI agent that has read 50 files just to fix one small bug. Now it carries 49 files of distraction.

But, here is another catch. The model pays the most attention to the beginning and the end of the context. The middle often gets ignored. This is called the "Lost in the Middle" problem.

So more context does not mean more intelligence. It often means more distraction.

The solution is Context Engineering.

Context Engineering means giving the model the right context, not the most context.

We retrieve only what is relevant. We trim the old history. We remove the noise. We keep the signal.

Quality of context beats quantity of context, every single time.

This is how we make our AI agents smarter, by giving them less, but better.

[Amit Shekhar @amitiitbhu]

https://t.co/VcpHkj32SH

[Pallavi @pallavishekhar_]

Why does an LLM confidently give you a wrong answer? Let's understand.

We have all faced this. We ask an LLM a question. It replies with a clear, confident answer. And the answer is completely wrong.

So, why does this happen?

Before we answer that, we must understand one thing.

An LLM does not "know" facts. It predicts the next word.

When we type a question, the model reads our words and predicts the most likely next word, then the next, then the next. One word at a time. That is all it does.

It is like a very smart auto-complete.

Now, here is the catch.

The model has no built-in sense of truth. It does not check a fact against any database. It simply produces the words that sound most likely to come next.

So, where does the confidence come from?

The model learned from text written by humans. And humans write in a confident tone. Books, articles, answers - they rarely say "I am not sure." So the model learns to sound sure, even when it has no real information.

Confidence in the tone is not the same as correctness in the facts.

Let's say we ask about a person who barely appears in its training data. The model still wants to give the most likely-sounding answer. So it fills the gap with words that fit the pattern, not words that are true.

This is called hallucination. The model is not lying. It is doing exactly what it was built to do - predict, not verify.

This is how an LLM can be confidently wrong. By default, it gives us the most likely answer, not the "I am not sure" answer.

That is why we must always verify the important answers we get from an LLM.

[Outcome School @outcome_school]

Causal Masking in Attention

Read here: https://t.co/BqyP3rjcnO

[Amit Shekhar @amitiitbhu]

New Article: Cross Attention in Transformers

Read here: https://t.co/0M6NOF13Qk

[PaschalIfeanyi🕵️ @DefiPhilosopher]

@tinytoms_ Curiosity is Power

[PaschalIfeanyi🕵️ @DefiPhilosopher]

@connectwithtola Correct scores at FT is 3-1

[Pallavi @pallavishekhar_]

Recursive Language Models (RLMs)

Read here: https://t.co/TT4ccjE4nV

[PaschalIfeanyi🕵️ @DefiPhilosopher]

Another Read for the Day

[Amit Shekhar @amitiitbhu]

New Article: Self Attention in Transformers

Read here: https://t.co/DNgnCUTiH0