Shikhar

Upcoming SWE at American express | Ex- NASDAQ, Futurepedia | 250k+ user base | Design and code

[Shikhar @Nodlehs73]

https://t.co/8LkSGjZsBH
Hey everyone , this is my github account, I am trying to explore world of source, let's connect and make the open source journey amazing!!!! 🚀

[Shikhar @Nodlehs73]

Losing interest in everything. It’s wrong but feels okay.
Life must have more to offer than this for sure.

[Elon Musk @elonmusk]

This hits home

[Elon Musk @elonmusk]

Bullseye

[Yuval Avrahami @yuvalavra]

We hacked the AWS JavaScript SDK, a core library powering the entire @AWScloud ecosystem - including the AWS Console itself 🤯

How did we do it? Just two missing characters was all it took.

This is the story of #CodeBreach 🧵👇

[Shikhar @Nodlehs73]

This so true

[Ben Holmes @BHolmesDev]

Hot take: If your git commit message has an "and" in it, it should probably be two commits

[Shikhar @Nodlehs73]

Real

[Sam Lambert @samlambert]

I love coding with AI. I am more productive than ever. I will never not use AI to code but you are a moron if you believe people are using one line prompts to build fully functional apps that can be maintained and evolve into real products.

[Shikhar @Nodlehs73]

@supriyaa321 Travis remixes @FRGTNMUSIC

[Rhys @RhysSullivan]

monorepo:
frontend/
backend/

polyamorous repo:
app/
marketing/
constants/
utils
api/
auth/
db/

[Souptik Debnath @souptikdn]

stitched with care

[Shikhar @Nodlehs73]

@awesomekling Winrar

[Shikhar @Nodlehs73]

@icanvardar user_id

[Pratham @Prathkum]

Vibe coding saves you time, until you realize you built nothing worth shipping.

[Elon Musk @elonmusk]

Cancel Netflix for the health of your kids

[Amisha Aggarwal 📌 @awwmishaaa]

Office romance must be cute. You can just have snacks in the cafe and go for a walk in the tech park after a tiring day.

[Shikhar @Nodlehs73]

Kicking off my first engineering story today. Let's build.
#EngineeringJourney

[Shikhar @Nodlehs73]

@Mohitsharma202 Feels to me like choosing peace over opportunity.

[Shikhar @Nodlehs73]

Masti to dekho

[anshuman @athleticKoder]

She dumped me last night.

Not because I don't listen.
Not because I'm always on my phone.
Not even because I forgot our anniversary (twice).

But because,

in her exact words:

"You only pay attention to the parts of what I say that you think are important."

I stared at her for a moment and realized...

She just perfectly described the attention mechanism in transformers.

Turns out I wasn't being a bad boyfriend. I was being mathematically optimal.

See, in conversations (and transformers), you don't give equal weight to every word. Some words matter more for understanding context. Attention figures out exactly HOW important each word should be.

Here's the beautiful math:

Attention(Q, K, V) = softmax(QK^T / √d_k)V

Breaking it down:

Q (Query): "What am I looking for?"
K (Key): "What info is available?"
V (Value): "What is that info?"
d_k: Key dimension (for scaling)

Think library analogy:

You have a question (Query). Books have titles (Keys) and content (Values). Attention finds which books are most relevant.

Step-by-step with "The cat sat on the mat":

Step 1: Create Q, K, VEach word → three vectors via learned matrices W_Q, W_K, W_V
For "cat":

Query: "What should I attend to when processing 'cat'?"
Key: "I am 'cat'"
Value: "Here's cat info"

Step 2: Calculate scoresQK^T = how much each word should attend to others

Processing "sat"? High similarity with "cat" (cats sit) and "mat" (where sitting happens).

Step 3: Scale by √d_kPrevents dot products from getting too large, keeps softmax balanced.

Step 4: SoftmaxConverts scores to probabilities:

"cat": 0.4 (subject)
"sat": 0.3 (action)
"mat": 0.2 (location)
"on": 0.1 (preposition)
"the": 0.1 (article)

Step 5: Weight valuesMultiply each word's value by attention weight, sum up. Now "sat" knows it's most related to "cat" and "mat".

Multi-Head Magic:Transformers do this multiple times in parallel:

Head 1: Subject-verb relationships
Head 2: Spatial ("on", "in", "under")
Head 3: Temporal ("before", "after")
Head 4: Semantic similarity

Each head learns different relationship types.

Why This Changed Everything:

Before: RNNs = reading with flashlight (one word at a time, forget the beginning)

After: Attention = floodlights on entire sentence with dimmer switches
This is why ChatGPT can:

Remember 50 messages ago

Know "it" refers to something specific
Understand "bank" = money vs river based on context
The Kicker:Models learn these patterns from data alone. Nobody programmed grammar rules. It figured out language structure just by predicting next words.
Attention is how AI learned to read between the lines.

Just like my therapist helped me understand my focus patterns, maybe understanding transformers helps us see how we decide what matters.

Now if only I could implement multi-head attention in dating... 🤖

Still waiting for "scaled dot-product listening" to be invented.

[Shikhar @Nodlehs73]

Well, well… looks like @claudeai finally got hit with the design stick — and it shows. 👏 Quite the glow-up!

[Jay @Jay_s6a]

Opus 4.1 is a much better sense of design than other models. Here is a single html page it made of some made up design firm.

[Shikhar @Nodlehs73]

@whhygee Common sense.

[Shikhar @Nodlehs73]

@mounica_sruthi Ohh ? Summer vacation started early this year, ig 🤣