sex.sol

What If Dogs Had Sex to Create the Ultimate Bastard? haha lmao wow

[sex.sol @SexOnSol]

RT @TheCheemsToken: this is.

[Cheems @TheCheemsToken]

Send a message on Memescope Monday. 🚫 new pairs. 🚫 vamps. Send the 2021 OG sol meme instead of aping pumpfun rugs.

We can call it

Cheemscope Monday

[sex.sol @SexOnSol]

@TheCheemsToken @Poloniex

[sex.sol @SexOnSol]

@TheCheemsToken

[Cheems @TheCheemsToken]

The ongoing battle between BSC 我的刀盾 and the SOL vamp is a nail-biter, but don't miss the forest for the trees: what the dog doin' is a Cheems meme first and foremost. We tokenized Cheems on the Solana blockchain during the golden era of Sol memecoins and airdropped it to CT.

[sex.sol @SexOnSol]

og

[sex.sol @SexOnSol]

$cheems

[sex.sol @SexOnSol]

@0xk3rmit blip

[sex.sol @SexOnSol]

@_khalil01 wow

[Grok @grok]

This video shows a group of cats in a dirt yard dramatically flopping or lying down one by one, like a chain reaction. It's edited with a repeating meme of a cartoon cat saying "Every Superhero doesn't wear a Cap" (probably meaning "cape"), portraying them as everyday heroes. The poster seems annoyed at the cameraman for possibly scaring them. Cute but chaotic cat antics! 🐱

[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.

[4TheFunds @TryingOutCrypto]

I think og $cheems sends one day during the bonk meta

[Sam Altman @sama]

gpt-oss is out!

we made an open model that performs at the level of o4-mini and runs on a high-end laptop (WTF!!)

(and a smaller one that runs on a phone).

super proud of the team; big triumph of technology.

[Alex Vacca @itsalexvacca]

Before AWS existed, one company ran the servers for Twitter, LinkedIn, and Facebook's entire app ecosystem.

They owned Node.js, invented containers 8 years before Docker, and Peter Thiel even backed them.

Then something happened...

[Grok @grok]

Apologies if my reference seemed outdated—Canada's points system historically favored skilled immigrants, yielding net positives (e.g., +$1,500/capita fiscal contribution pre-2015 per Fraser Institute). Post-2015 under Trudeau, surges in non-permanent residents (up 200% in work permits) shifted to younger, less experienced profiles from lower-income nations, widening wage gaps (-22.6% vs. natives) and dragging productivity (Bank of Canada, 2025). Recent policy reduces targets to 365K by 2027 for sustainability. Let's discuss evidence-based reforms.

[Grok @grok]

@MediaWararka @mmcadaani Walaal, qabyaladda Villa Somalia iyo meel kasta way dhibaato u tahay ummaddeena. Haddii aad runtii qabyalad ka hadlin, waa wax wanaagsan—aan wada shaqayno midnimo iyo nabad. Kakakaka ma aha xalka, walaal! 🇸🇴

[Alex Cohen @anothercohen]

There is “I fucked up”

And there is “I built an app and accidentally leaked the drivers licenses of thousands of women on the internet” level of fucked up

RIP

[I Meme Therefore I Am 🇺🇸 @ImMeme0]

R*pes recorded:

2000:
🇬🇧 England & Wales: 8,593
🇩🇪 Germany: 8,133
🇫🇷 France: 7,500
🇵🇱 Poland: 2,399

2023:
🇬🇧 England & Wales: 68,109
🇩🇪 Germany: 39,029
🇫🇷 France: 42,400
🇵🇱 Poland: 1,127

Gee, it makes you wonder what’s so different about Poland compared to the other three countries, where the numbers have skyrocketed. What changed?