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Ahmad
RT @TheAhmadOsman: i love having my own private UNRESTRICTED COMPUTE
Buy a GPU https://t.co/6r8c46owH7
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RT @TheAhmadOsman: i love having my own private UNRESTRICTED COMPUTE
Buy a GPU https://t.co/6r8c46owH7
tweet
Offshore
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Ahmad
RT @TheAhmadOsman: > be us
> Larry & Sergey
> at Stanford with a crawler and a dream
> accidentally organize the entire internet
> call it Google
> build search, email, maps, docs, OS, phones, browser, car, satellite, thermostat, AI lab, TPU farm, and quantum computer
> 2025
> everyone talking about AGI
> OpenAI: “we need data, sensors, feedback, and scale”
> us: staring at Google Maps, YouTube, Gmail, Android, Waymo, Pixel, Fitbit, Docs, Calendar, Street View, and Earth Engine
> "damn. guess we already did that."
> YouTube: 2.6M videos/day
> Android: 3B phones, streaming sensor data 24/7
> Gmail: 1.8B inboxes of human priors
> Search: global-scale RLHF
> Waymo: 71M miles of real-world self-driving footage
> Google Earth: modeled the entire planet
> also your calendar
> people training LLMs on books and PDFs
> we train on humanity
> every click, swipe, tap, misspelled search, scroll, and bookmark
> feedback loop from hell (or heaven)
> depends who you ask
> OpenAI: “we need $100B for GPUs”
> us: already built TPUs
> custom silicon
> datacenters pre-co-located with planetary data lakes
> no egress, no latency
> just vibes and FLOPs
> coders: fine-tuning on GitHub repos
> us: 2 BILLION lines of internal code
> labeled, typed, tested
> every commit is a training signal
> Code LLMs dream of being our monorepo
> AGI recipe?
> multimodal perception
> real-world feedback
> giant codebase
> scalable compute
> alignment signals
> embodied sensors
> user data for days
> yeah we’ve had that since like 2016
> no investor decks
> no trillion-dollar hype rounds
> just a 25-year accidental simulation of Earth
> running in prod
> OpenAI raises $1T to build AGI
> investors call it revolutionary
> us: quietly mapping 10M new miles in Street View
> syncing another 80PB of Earth imagery
> collecting another year of Fitbit biosignals
> enjoy your foundation model
> we OWN the foundation
> people: “but Google is fumbling”
> true
> we’re fumbling in 120 countries simultaneously
> with the greatest compute footprint and research team on Earth
> fumble hard enough and you loop back into winning
> AGI?
> we don’t need to build it
> it’s already inside the building
> powered by Chrome tabs and doc revisions
> mfw we spent 20 years indexing reality
> mfw our data is so good it scares us
> mfw the only thing stopping us from AGI is a meeting between four VPs and one confused lawyer
> call it research
> call it scale
> call it “planetary simulation-as-a-service”
> we call it Tuesday
tweet
RT @TheAhmadOsman: > be us
> Larry & Sergey
> at Stanford with a crawler and a dream
> accidentally organize the entire internet
> call it Google
> build search, email, maps, docs, OS, phones, browser, car, satellite, thermostat, AI lab, TPU farm, and quantum computer
> 2025
> everyone talking about AGI
> OpenAI: “we need data, sensors, feedback, and scale”
> us: staring at Google Maps, YouTube, Gmail, Android, Waymo, Pixel, Fitbit, Docs, Calendar, Street View, and Earth Engine
> "damn. guess we already did that."
> YouTube: 2.6M videos/day
> Android: 3B phones, streaming sensor data 24/7
> Gmail: 1.8B inboxes of human priors
> Search: global-scale RLHF
> Waymo: 71M miles of real-world self-driving footage
> Google Earth: modeled the entire planet
> also your calendar
> people training LLMs on books and PDFs
> we train on humanity
> every click, swipe, tap, misspelled search, scroll, and bookmark
> feedback loop from hell (or heaven)
> depends who you ask
> OpenAI: “we need $100B for GPUs”
> us: already built TPUs
> custom silicon
> datacenters pre-co-located with planetary data lakes
> no egress, no latency
> just vibes and FLOPs
> coders: fine-tuning on GitHub repos
> us: 2 BILLION lines of internal code
> labeled, typed, tested
> every commit is a training signal
> Code LLMs dream of being our monorepo
> AGI recipe?
> multimodal perception
> real-world feedback
> giant codebase
> scalable compute
> alignment signals
> embodied sensors
> user data for days
> yeah we’ve had that since like 2016
> no investor decks
> no trillion-dollar hype rounds
> just a 25-year accidental simulation of Earth
> running in prod
> OpenAI raises $1T to build AGI
> investors call it revolutionary
> us: quietly mapping 10M new miles in Street View
> syncing another 80PB of Earth imagery
> collecting another year of Fitbit biosignals
> enjoy your foundation model
> we OWN the foundation
> people: “but Google is fumbling”
> true
> we’re fumbling in 120 countries simultaneously
> with the greatest compute footprint and research team on Earth
> fumble hard enough and you loop back into winning
> AGI?
> we don’t need to build it
> it’s already inside the building
> powered by Chrome tabs and doc revisions
> mfw we spent 20 years indexing reality
> mfw our data is so good it scares us
> mfw the only thing stopping us from AGI is a meeting between four VPs and one confused lawyer
> call it research
> call it scale
> call it “planetary simulation-as-a-service”
> we call it Tuesday
tweet
Offshore
Photo
Ahmad
RT @TheAhmadOsman: whatʼs stopping you from becoming a chad like Gilfoyle and building your own servers?
the PATH to becoming a GREAT engineer starts this way https://t.co/kyIAI083w6
tweet
RT @TheAhmadOsman: whatʼs stopping you from becoming a chad like Gilfoyle and building your own servers?
the PATH to becoming a GREAT engineer starts this way https://t.co/kyIAI083w6
tweet
Offshore
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Ahmad
RT @TheAhmadOsman: Comparing & Contrasting Recent LLMs Architecture
> DeepSeek-V3/R1
> OLMo 2
> Gemma 3
> Mistral Small 3.1
> Llama 4
> Qwen3 (dense+MoE)
> SmolLM3
> Kimi 2
> GPT-OSS
Are 2025 LLMs really that different from each other?
MoE, MLA, GQA, sliding window, normalization games & more. https://t.co/JWg9cde34M
tweet
RT @TheAhmadOsman: Comparing & Contrasting Recent LLMs Architecture
> DeepSeek-V3/R1
> OLMo 2
> Gemma 3
> Mistral Small 3.1
> Llama 4
> Qwen3 (dense+MoE)
> SmolLM3
> Kimi 2
> GPT-OSS
Are 2025 LLMs really that different from each other?
MoE, MLA, GQA, sliding window, normalization games & more. https://t.co/JWg9cde34M
tweet
Offshore
Photo
Ahmad
RT @TheAhmadOsman: > youʼre OpenAI
> hire a small army of ex-Meta ad and monetization people
> a Slack channel just for ex-Facebook staff
> brings in the full “targeted ads” playbook
> launch a browser
> users install it, and OpenAI collects personalized, granular data at scale
> it’s a browser-shaped surveillance device
> it’s a mapping machine of your workflows
> itʼs a reverse-engineering tool for the internetʼs data pipelines, deployed at scale via their users
> launch Sora 2
> a TikTok‑style social network
> infinite AI-generated video feed
> you create or remix clips, upload your face, become the cameo star
> every scroll, like, remix is another data point, another ad signal
> their model learns exactly what hooks you and dials up the dopamine
> you’re not just watching, you’re training their algorithm for better ad targeting
> viral videos driven by your input + their algorithm = your attention refined into $$$
> “your feedback helps us improve the experience” (yeah, for advertisers)
> launch “Pulse”
> reads your chats while you sleep
> remembers you wanna visit Bora Bora
> knows your kid is 6 months old and
> “thinks” of your baby milestones
> suggests developmental toys next
> “it's for your convenience”
> actually laying the groundwork for targeted ads using memory
> internal memo: some people already think ChatGPT shows ads
> OpenAI staff: “might as well then”
> congrats, you’re back in the Facebook era
> except this time, you’re training the algo yourself
> Buy a GPU
> run your LLMs locally
> reject adware LLMs before it’s too late
tweet
RT @TheAhmadOsman: > youʼre OpenAI
> hire a small army of ex-Meta ad and monetization people
> a Slack channel just for ex-Facebook staff
> brings in the full “targeted ads” playbook
> launch a browser
> users install it, and OpenAI collects personalized, granular data at scale
> it’s a browser-shaped surveillance device
> it’s a mapping machine of your workflows
> itʼs a reverse-engineering tool for the internetʼs data pipelines, deployed at scale via their users
> launch Sora 2
> a TikTok‑style social network
> infinite AI-generated video feed
> you create or remix clips, upload your face, become the cameo star
> every scroll, like, remix is another data point, another ad signal
> their model learns exactly what hooks you and dials up the dopamine
> you’re not just watching, you’re training their algorithm for better ad targeting
> viral videos driven by your input + their algorithm = your attention refined into $$$
> “your feedback helps us improve the experience” (yeah, for advertisers)
> launch “Pulse”
> reads your chats while you sleep
> remembers you wanna visit Bora Bora
> knows your kid is 6 months old and
> “thinks” of your baby milestones
> suggests developmental toys next
> “it's for your convenience”
> actually laying the groundwork for targeted ads using memory
> internal memo: some people already think ChatGPT shows ads
> OpenAI staff: “might as well then”
> congrats, you’re back in the Facebook era
> except this time, you’re training the algo yourself
> Buy a GPU
> run your LLMs locally
> reject adware LLMs before it’s too late
tweet
Offshore
Photo
Ahmad
RT @TheAhmadOsman: last week, Karpathy dropped the ULTIMATE guide to speed-running your way into LLMs
in this project, you’ll build all the essentials, all under 8k lines of code
> train the tokenizer — new rust implementation
> pretrain a transformer LLM on fineweb
> evaluate core score across a bunch of metrics
> midtrain — user-assistant convos from smoltalk,
> multiple choice Qs, tool use
> sft, then eval the chat model on:
> world knowledge MCQ (arc-e/c, mmlu)
> math (gsm8k)
> code (humaneval)
> rl the model (optionally) on gsm8k with “grpo”
> efficient inference:
> kv cache, fast prefill/decode
> tool use (python interpreter, sandboxed)
> access via cli or chatgpt-like webui
> write a single markdown report card,
> summarizing + gamifying the whole pipeline
the model you’ll build:
> rotary only (no positional embeddings)
> qk norm
> untied embedding / unembedding
> norm after token embedding
> relu² mlp
> no biases in linears
> rmsnorm (no learnable params)
> mqa (multi-query attention)
> logit softcap
> optimizer: muon + adamw
if i had this a couple years ago i’d dodged half the pain and skipped double the rabbit holes
happy hacking
tweet
RT @TheAhmadOsman: last week, Karpathy dropped the ULTIMATE guide to speed-running your way into LLMs
in this project, you’ll build all the essentials, all under 8k lines of code
> train the tokenizer — new rust implementation
> pretrain a transformer LLM on fineweb
> evaluate core score across a bunch of metrics
> midtrain — user-assistant convos from smoltalk,
> multiple choice Qs, tool use
> sft, then eval the chat model on:
> world knowledge MCQ (arc-e/c, mmlu)
> math (gsm8k)
> code (humaneval)
> rl the model (optionally) on gsm8k with “grpo”
> efficient inference:
> kv cache, fast prefill/decode
> tool use (python interpreter, sandboxed)
> access via cli or chatgpt-like webui
> write a single markdown report card,
> summarizing + gamifying the whole pipeline
the model you’ll build:
> rotary only (no positional embeddings)
> qk norm
> untied embedding / unembedding
> norm after token embedding
> relu² mlp
> no biases in linears
> rmsnorm (no learnable params)
> mqa (multi-query attention)
> logit softcap
> optimizer: muon + adamw
if i had this a couple years ago i’d dodged half the pain and skipped double the rabbit holes
happy hacking
tweet
Ahmad
RT @TheAhmadOsman: - in 2025, your focus SHOULD NOT be CUDA
- the real bottlenecks are:
- data, inference, evals, dataloaders, infra in general
- want to get good?
- mess with PyTorch & JAX
- study inference infra like vLLM & SGLang
- build better eval pipelines
- learn how models run end-to-end
tweet
RT @TheAhmadOsman: - in 2025, your focus SHOULD NOT be CUDA
- the real bottlenecks are:
- data, inference, evals, dataloaders, infra in general
- want to get good?
- mess with PyTorch & JAX
- study inference infra like vLLM & SGLang
- build better eval pipelines
- learn how models run end-to-end
tweet
Offshore
Photo
Ahmad
RT @TheAhmadOsman: here is my twitter growth strategy: https://t.co/HhA6C07zPZ
tweet
RT @TheAhmadOsman: here is my twitter growth strategy: https://t.co/HhA6C07zPZ
here is my twitter growth strategy: https://t.co/luJa9ihS2n tweet