Offshore
Photo
Ahmad
> 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 $$$
> “your feedback helps us improve the experience” (yeah, for advertisers)
> launch “Pulse”
> reads your chats while you sleep
> remembers you wanna visit Bora Bora
> knows your kid is 6 months old and
> “thinks” of your baby milestones
> suggests developmental toys next
> “it's for your convenience”
> actually laying the groundwork for targeted ads using memory
> internal memo: some people already think ChatGPT shows ads
> OpenAI staff: “might as well then”
> congrats, you’re back in the Facebook era
> except this time, you’re training the algo yourself
> Buy a GPU
> run your LLMs locally
> reject adware LLMs before it’s too late
tweet
> youʼre OpenAI
> hire a small army of ex-Meta ad and monetization people
> a Slack channel just for ex-Facebook staff
> brings in the full “targeted ads” playbook
> launch a browser
> users install it, and OpenAI collects personalized, granular data at scale
> it’s a browser-shaped surveillance device
> it’s a mapping machine of your workflows
> itʼs a reverse-engineering tool for the internetʼs data pipelines, deployed at scale via their users
> launch Sora 2
> a TikTok‑style social network
> infinite AI-generated video feed
> you create or remix clips, upload your face, become the cameo star
> every scroll, like, remix is another data point, another ad signal
> their model learns exactly what hooks you and dials up the dopamine
> you’re not just watching, you’re training their algorithm for better ad targeting
> viral videos driven by your input + their algorithm = your attention refined into $$$
> “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
here is my twitter growth strategy: https://t.co/HhA6C07zPZ
tweet
here is my twitter growth strategy: https://t.co/HhA6C07zPZ
here is my twitter growth strategy: https://t.co/luJa9ihS2n tweet
Offshore
Photo
Offshore
Photo
Ahmad
RT @TheAhmadOsman: My house has 33 GPUs.
> 21x RTX 3090s
> 4x RTX 4090s
> 4x RTX 5090s
> 4x Tenstorrent Blackhole p150a
Before AGI arrives:
Acquire GPUs.
Go into debt if you must.
But whatever you do, secure the GPUs. https://t.co/8U89OStknt
tweet
RT @TheAhmadOsman: My house has 33 GPUs.
> 21x RTX 3090s
> 4x RTX 4090s
> 4x RTX 5090s
> 4x Tenstorrent Blackhole p150a
Before AGI arrives:
Acquire GPUs.
Go into debt if you must.
But whatever you do, secure the GPUs. https://t.co/8U89OStknt
tweet
Offshore
Photo
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
Ahmad
RT @TheAhmadOsman: - you are
- a random CS grad with 0 clue how LLMs work
- get tired of people gatekeeping with big words and tiny GPUs
- decide to go full monk mode
- 2 years later i can explain attention mechanisms at parties and ruin them
- here’s the forbidden knowledge map
- top to bottom, how LLMs *actually* work
- start at the beginning
- text → tokens
- tokens → embeddings
- you are now a floating point number in 4D space
- vibe accordingly
- positional embeddings:
- absolute: “i am position 5”
- rotary (RoPE): “i am a sine wave”
- alibi: “i scale attention by distance like a hater”
- attention is all you need
- self-attention: “who am i allowed to pay attention to?”
- multihead: “what if i do that 8 times in parallel?”
- QKV: query, key, value
- sounds like a crypto scam
- actually the core of intelligence
- transformers:
- take your inputs
- smash them through attention layers
- normalize, activate, repeat
- dump the logits
- congratulations, you just inferred a token
- sampling tricks for the final output:
- temperature: how chaotic you want to be
- top-k: only sample from the top K options
- top-p: sample from the smallest group of tokens whose probabilities sum to p
- beam search? never ask about beam search
- kv cache = cheat code
- saves past keys & values
- lets you skip reprocessing old tokens
- turns a 90B model from “help me I’m melting” to “real-time genius”
- long context hacks:
- sliding window: move the attention like a scanner
- infini attention: attend sparsely, like a laser sniper
- memory layers: store thoughts like a diary with read access
- mixture of experts (MoE):
- not all weights matter
- route tokens to different sub-networks
- only activate ~3B params out of 80B
- “only the experts reply” energy
- grouped query attention (GQA):
- fewer keys/values than queries
- improves inference speed
- “i want to be fast without being dumb”
- normalization & activations:
- layernorm, RMSnorm
- gelu, silu, relu
- they all sound like failed Pokémon
- but they make the network stable and smooth
- training goals:
- causal LM: guess the next word
- masked LM: guess the missing word
- span prediction, fill-in-the-middle, etc
- LLMs trained on the art of guessing and got good at it
- tuning flavors:
- finetuning: new weights
- instruction tuning: “please act helpful”
- rlhf: reinforcement from vibes and clickbait prompts
- dpo: direct preference optimization — basically “do what humans upvote”
- scaling laws:
- more data, more parameters, more compute
- loss goes down predictably
- intelligence is now a budget line item
- bonus round:
- quantization:
- post-training quantization (PTQ)
- quant-aware training (QAT)
- models shrink, inference gets cheaper
- gguf, awq, gptq — all just zip files with extra spice
- training vs inference stacks:
- deepspeed, megatron, fschat — for pain
- vllm, tgi, tensorRT-LLM — for speed
- everyone has a repo
- nobody reads the docs
- synthetic data:
- generate your own training set
- model teaches itself
- feedback loop of knowledge and hallucination
- welcome to the ouroboros era
- final boss secret:
- you can learn *all of this* in ~2 years
- no PhD
- no 10x compute
- just relentless curiosity, good bookmarks, and late nights
- the elite don’t want you to know this
- but now that you do
- choose to act
- start now
- build the models
tweet
RT @TheAhmadOsman: - you are
- a random CS grad with 0 clue how LLMs work
- get tired of people gatekeeping with big words and tiny GPUs
- decide to go full monk mode
- 2 years later i can explain attention mechanisms at parties and ruin them
- here’s the forbidden knowledge map
- top to bottom, how LLMs *actually* work
- start at the beginning
- text → tokens
- tokens → embeddings
- you are now a floating point number in 4D space
- vibe accordingly
- positional embeddings:
- absolute: “i am position 5”
- rotary (RoPE): “i am a sine wave”
- alibi: “i scale attention by distance like a hater”
- attention is all you need
- self-attention: “who am i allowed to pay attention to?”
- multihead: “what if i do that 8 times in parallel?”
- QKV: query, key, value
- sounds like a crypto scam
- actually the core of intelligence
- transformers:
- take your inputs
- smash them through attention layers
- normalize, activate, repeat
- dump the logits
- congratulations, you just inferred a token
- sampling tricks for the final output:
- temperature: how chaotic you want to be
- top-k: only sample from the top K options
- top-p: sample from the smallest group of tokens whose probabilities sum to p
- beam search? never ask about beam search
- kv cache = cheat code
- saves past keys & values
- lets you skip reprocessing old tokens
- turns a 90B model from “help me I’m melting” to “real-time genius”
- long context hacks:
- sliding window: move the attention like a scanner
- infini attention: attend sparsely, like a laser sniper
- memory layers: store thoughts like a diary with read access
- mixture of experts (MoE):
- not all weights matter
- route tokens to different sub-networks
- only activate ~3B params out of 80B
- “only the experts reply” energy
- grouped query attention (GQA):
- fewer keys/values than queries
- improves inference speed
- “i want to be fast without being dumb”
- normalization & activations:
- layernorm, RMSnorm
- gelu, silu, relu
- they all sound like failed Pokémon
- but they make the network stable and smooth
- training goals:
- causal LM: guess the next word
- masked LM: guess the missing word
- span prediction, fill-in-the-middle, etc
- LLMs trained on the art of guessing and got good at it
- tuning flavors:
- finetuning: new weights
- instruction tuning: “please act helpful”
- rlhf: reinforcement from vibes and clickbait prompts
- dpo: direct preference optimization — basically “do what humans upvote”
- scaling laws:
- more data, more parameters, more compute
- loss goes down predictably
- intelligence is now a budget line item
- bonus round:
- quantization:
- post-training quantization (PTQ)
- quant-aware training (QAT)
- models shrink, inference gets cheaper
- gguf, awq, gptq — all just zip files with extra spice
- training vs inference stacks:
- deepspeed, megatron, fschat — for pain
- vllm, tgi, tensorRT-LLM — for speed
- everyone has a repo
- nobody reads the docs
- synthetic data:
- generate your own training set
- model teaches itself
- feedback loop of knowledge and hallucination
- welcome to the ouroboros era
- final boss secret:
- you can learn *all of this* in ~2 years
- no PhD
- no 10x compute
- just relentless curiosity, good bookmarks, and late nights
- the elite don’t want you to know this
- but now that you do
- choose to act
- start now
- build the models
tweet
Ahmad
RT @TheAhmadOsman: > be you
> want to actually learn how LLMs work
> sick of “just start with linear algebra and come back in 5 years”
> decide to build my own roadmap
> no fluff. no detours. no 200-hour generic ML playlists
> just the stuff that actually gets you from “what’s a token?” to “I trained a mini-GPT with LoRA adapters and FlashAttention”
> goal: build, fine-tune, and ship LLMs
> not vibe with them. not "learn the theory" forever
> build them
> you will:
> > build an autograd engine from scratch
> > write a mini-GPT from scratch
> > implement LoRA and fine-tune a model on real data
> > hate CUDA at least once
> > cry
> > keep going
> 5 phases
> if you already know something? skip
> if you're lost? rewatch
> if you’re stuck? use DeepResearch
> this is a roadmap, not a leash
> by the end: you either built the thing or you didn’t
> phase 0: foundations
> > if matrix multiplication is scary, you’re not ready yet
> > watch 3Blue1Brown’s linear algebra series
> > MIT 18.06 with Strang, yes, he’s still the GOAT
> > code Micrograd from scratch (Karpathy)
> > train a mini-MLP on MNIST
> > no frameworks, no shortcuts, no mercy
> phase 1: transformers
> > the name is scary
> > it’s just stacked matrix multiplies and attention blocks
> > Jay Alammar + 3Blue1Brown for the “aha”
> > Stanford CS224N for the theory
> > read "Attention Is All You Need" only AFTER building mental models
> > Karpathy's "Let's Build GPT" will break your brain in a good way
> > project: build a decoder-only GPT from scratch
> > bonus: swap tokenizers, try BPE/SentencePiece
> phase 2: scaling
> > LLMs got good by scaling, not magic
> > Kaplan paper -> Chinchilla paper
> > learn Data, Tensor, Pipeline parallelism
> > spin up multi-GPU jobs using HuggingFace Accelerate
> > run into VRAM issues
> > fix them
> > welcome to real training hell
> phase 3: alignment & fine-tuning
> > RLHF: OpenAI blog -> Ouyang paper
> > SFT -> reward model -> PPO (don’t get lost here)
> > Anthropic's Constitutional AI = smart constraints
> > LoRA/QLoRA: read, implement, inject into HuggingFace models
> > fine-tune on real data
> > project: fine-tune gpt2 or distilbert with your own adapters
> > not toy examples. real use cases or bust
> phase 4: production
> this is the part people skip to, but you earned it
> inference optimization: FlashAttention, quantization, sub-second latency
> read the paper, test with quantized models
> resources:
> math/coding:
> > 3Blue1Brown, MIT 18.06, Goodfellow’s book
> PyTorch:
> > Karpathy, Zero to Mastery
> > transformers:
> > Alammar, Karpathy, CS224N, Vaswani et al
> > scaling:
> > Kaplan, Chinchilla, HuggingFace Accelerate
> > alignment:
> > OpenAI, Anthropic, LoRA, QLoRA
> > inference:
> > FlashAttention
> the endgame:
> > understand how these models actually work
> > see through hype
> > ignore LinkedIn noise
> > build tooling
> > train real stuff
> > ship your own stack
> > look at a paper and think “yeah I get it”
> > build your own AI assistant, infra, whatever
> make it all the way through?
> ship something real?
> DM me.
> I wanna see what you built.
> happy hacking.
tweet
RT @TheAhmadOsman: > be you
> want to actually learn how LLMs work
> sick of “just start with linear algebra and come back in 5 years”
> decide to build my own roadmap
> no fluff. no detours. no 200-hour generic ML playlists
> just the stuff that actually gets you from “what’s a token?” to “I trained a mini-GPT with LoRA adapters and FlashAttention”
> goal: build, fine-tune, and ship LLMs
> not vibe with them. not "learn the theory" forever
> build them
> you will:
> > build an autograd engine from scratch
> > write a mini-GPT from scratch
> > implement LoRA and fine-tune a model on real data
> > hate CUDA at least once
> > cry
> > keep going
> 5 phases
> if you already know something? skip
> if you're lost? rewatch
> if you’re stuck? use DeepResearch
> this is a roadmap, not a leash
> by the end: you either built the thing or you didn’t
> phase 0: foundations
> > if matrix multiplication is scary, you’re not ready yet
> > watch 3Blue1Brown’s linear algebra series
> > MIT 18.06 with Strang, yes, he’s still the GOAT
> > code Micrograd from scratch (Karpathy)
> > train a mini-MLP on MNIST
> > no frameworks, no shortcuts, no mercy
> phase 1: transformers
> > the name is scary
> > it’s just stacked matrix multiplies and attention blocks
> > Jay Alammar + 3Blue1Brown for the “aha”
> > Stanford CS224N for the theory
> > read "Attention Is All You Need" only AFTER building mental models
> > Karpathy's "Let's Build GPT" will break your brain in a good way
> > project: build a decoder-only GPT from scratch
> > bonus: swap tokenizers, try BPE/SentencePiece
> phase 2: scaling
> > LLMs got good by scaling, not magic
> > Kaplan paper -> Chinchilla paper
> > learn Data, Tensor, Pipeline parallelism
> > spin up multi-GPU jobs using HuggingFace Accelerate
> > run into VRAM issues
> > fix them
> > welcome to real training hell
> phase 3: alignment & fine-tuning
> > RLHF: OpenAI blog -> Ouyang paper
> > SFT -> reward model -> PPO (don’t get lost here)
> > Anthropic's Constitutional AI = smart constraints
> > LoRA/QLoRA: read, implement, inject into HuggingFace models
> > fine-tune on real data
> > project: fine-tune gpt2 or distilbert with your own adapters
> > not toy examples. real use cases or bust
> phase 4: production
> this is the part people skip to, but you earned it
> inference optimization: FlashAttention, quantization, sub-second latency
> read the paper, test with quantized models
> resources:
> math/coding:
> > 3Blue1Brown, MIT 18.06, Goodfellow’s book
> PyTorch:
> > Karpathy, Zero to Mastery
> > transformers:
> > Alammar, Karpathy, CS224N, Vaswani et al
> > scaling:
> > Kaplan, Chinchilla, HuggingFace Accelerate
> > alignment:
> > OpenAI, Anthropic, LoRA, QLoRA
> > inference:
> > FlashAttention
> the endgame:
> > understand how these models actually work
> > see through hype
> > ignore LinkedIn noise
> > build tooling
> > train real stuff
> > ship your own stack
> > look at a paper and think “yeah I get it”
> > build your own AI assistant, infra, whatever
> make it all the way through?
> ship something real?
> DM me.
> I wanna see what you built.
> happy hacking.
tweet
Ahmad
RT @TheAhmadOsman: GLM 4.5 > KIMI K2 > QWEN 3 235B NON-THINKING > Qwen 3 CODER 480B
For Agentic coding tools
GLM 4.5 with Claude Code is the closest thing to Opus 4 imo
tweet
RT @TheAhmadOsman: GLM 4.5 > KIMI K2 > QWEN 3 235B NON-THINKING > Qwen 3 CODER 480B
For Agentic coding tools
GLM 4.5 with Claude Code is the closest thing to Opus 4 imo
tweet
Offshore
Photo
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