A temporary pattern of consciousness pretending to be ‘Adrita.’ | Building AI that automates the jobs we created to avoid asking what we’re actually for.
🚨 Google New Image Model
> Instant-ramen (successor of nano-banana)
Ramen is cooked time to serve soon , we will share results as soon as we get hands on it 😉
Stripe is a $65 BILLION company??????
SIXTY FIVE BILLION??????
For simply moving money
between bank accounts??????
Notion is a $10 BILLION company??????
TEN BILLION??????
For turning documents
into an all-in-one workspace??????
Calendly is a $3 BILLION company??????
Three. Billion. Dollars?????
For basically letting people
book a time slot????????
Slack is a $27 BILLION company??????
For essentially being
a team chat app????????
And you think your SaaS idea
is too simple?
Think again.
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Ethereum can already start preparing accounts for a post quantum world, without waiting for a hard fork.
Today, it would be just 0.07$ .
Further audits incoming. Though I squeezed in a review with Fable before Uncle Sam crashed my party. Verity formal proof included for my lean enjoyers
https://t.co/hfOx08X17Q
1,000 GB / 80 GB = 12.5 GPUs. That math works only if your batch size is 1, context window is zero, and you want 5 tokens/sec for exactly one user.
Even the core ML team lead at Cerebras (who explicitly benchmarks against Nvidia clusters) notes that it takes a minimum of ~240 to 280 parallelized GPUs just to host and properly run a 1T instance(GPT 4o) in production.
Don’t take my word watch this breakdown on how cluster stitching vs. wafer-scale handles 1T+ models: https://t.co/Kz9VrG5nix
You’re conflating basic model storage with the mass parallelization needed for real-time production at OpenAI scale.
A ~1T param GPT-5 equivalent in FP16/BF16 needs ~1-2TB VRAM just for the weights. Add KV cache, activations, and headroom for concurrentt users?
If you just want to run it locally for a few tokens/sec, sure - quantize heavily or 3-4 512GB Mac studios or go wafer-scale like Cerebras.
Their single-chip engines handle trillions of params with insane bandwidth…
Again context matters 👁️👁️
i recorded one talking head. that was my entire job here.
research, fact-check, script, captions, the edit.. all of it ran as agents on @papercliping, handing work down the line like a tiny media company.
then the part i can't stop thinking about.. my research agent corrected me. i briefed the science wrong, it pushed back and shipped its version, not mine.
even the edit was code in @HyperFrames_ (open-sourced by @HeyGen). no editing app touched it.
i didn't make a video. i approved a company that did.
WHAT THE HELL is happening in AI?
A 3B parameter model just put up coding benchmark scores in the same league as Claude Opus 4.5.
3 BILLION.
The weights are on Hugging Face, anyone can test it.
I genuinely don't know if this is a breakthrough or if the benchmarks are broken.