Offshore
Video
Ahmad
RT @TheAhmadOsman: can’t write code because Cursor and Codex are both down thanks to the aws-us-east-1 outage?
tired of Anthropic’s weekly limits and nerfed models?
with one command and a few GPUs,
you can route Claude Code to your own local LLM with ZERO downtime
Buy a GPU https://t.co/aj8r201V83
tweet
RT @TheAhmadOsman: can’t write code because Cursor and Codex are both down thanks to the aws-us-east-1 outage?
tired of Anthropic’s weekly limits and nerfed models?
with one command and a few GPUs,
you can route Claude Code to your own local LLM with ZERO downtime
Buy a GPU https://t.co/aj8r201V83
i built a simple tool that makes
Claude Code work with any local LLM
full demo:
> vLLM serving GLM-4.5 Air on 4x RTX 3090s
> Claude Code generating code + docs via my proxy
> 1 Python file + .env handles all requests
> nvtop showing live GPU load
> how it all works
Buy a GPU https://t.co/7nYsId4Uyu tweet
Offshore
Photo
Ahmad
RT @TheAhmadOsman: missed but not forgotten :(
i am sorry Google fumbled you, Gemini 2.5 Pro 03-25, you were the bestest of models https://t.co/tBwqOWJJrB
tweet
RT @TheAhmadOsman: missed but not forgotten :(
i am sorry Google fumbled you, Gemini 2.5 Pro 03-25, you were the bestest of models https://t.co/tBwqOWJJrB
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
Offshore
Photo
Ahmad
RT @TheAhmadOsman: working on getting nanochat training running with TT‑NN
the more i push my single Tenstorrent QuietBox Blackhole,
the more i see just how much headroom this thing has
counting down until my 4x TT‑QuietBox Blackhole cluster arrives
this cluster's going to be an absolute beast https://t.co/lN9VsITgDs
tweet
RT @TheAhmadOsman: working on getting nanochat training running with TT‑NN
the more i push my single Tenstorrent QuietBox Blackhole,
the more i see just how much headroom this thing has
counting down until my 4x TT‑QuietBox Blackhole cluster arrives
this cluster's going to be an absolute beast https://t.co/lN9VsITgDs
tweet
Offshore
Photo
Ahmad
RT @TheAhmadOsman: https://t.co/ealbNXzGbX
tweet
RT @TheAhmadOsman: https://t.co/ealbNXzGbX
𝕏 premium should add a “see who viewed your profile” feature 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
Ahmad
RT @TheAhmadOsman: - you are
- a normal dev who’s heard “embeddings” and “RAG” 1000x
- want to know what they actually are, how they plug into LLMs
- suddenly: vectors are just coordinates for meaning, not magic
- first: what even is an “embedding”?
- embedding = a list of numbers (a vector) that represents text
- same-ish meaning ⇒ nearby vectors; different meaning ⇒ far apart
- produced by a smaller model (an encoder), not your chat LLM
- length (a.k.a. dimension): 256/384/768/1024+ numbers is common
- the vector space (101)
- you can measure closeness with math:
- L2 distance: straight-line distance
- dot product: alignment + magnitude
- cosine similarity: (a·b)/(||a||·||b||) = angle only
- normalize vectors (unit length) ⇒ dot product ≡ cosine
- embeddings compress semantics; they are lossy by design
- types of embeddings (don’t overthink; pick what you need)
- token embeddings: internal to the LLM (you don’t use these)
- sentence/document embeddings: 1 vector per chunk/snippet
- multilingual: one space across languages
- domain-tuned: legal, code, bio — better clustering for that domain
- how text becomes vectors (pipeline)
- clean text (lowercase? keep punctuation? depends; don’t destroy signal)
- chunking: split long docs into overlapping windows (by tokens, not chars)
- rule of thumb: 200–800 tokens, 10–20% overlap
- keep titles/headers as context inside each chunk
- embed each chunk ⇒ store in a vector index with metadata (source, page, tags)
- storing & searching vectors
- exact search (brute force): simplest; fine for ≤100k vectors
- ANN (approx nearest neighbor): fast at scale, tiny recall tradeoff
- HNSW (graph-based): great latency, memory heavier
- IVF/PQ (quantization): smaller index, some recall loss
- where to put them:
- FAISS/hnswlib (library), pgvector (Postgres), dedicated stores (Milvus, Pinecone, Weaviate, etc.)
- ops notes:
- track embedding_model_name + dimension in the index
- you cannot mix dimensions or swap models without re-embedding
- memory math: 768-dim float32 ≈ 3 KB/vector → 1M vectors ≈ ~3 GB (+ index overhead)
- RAG (Retrieval-Augmented Generation): the shape of it
- goal: let the LLM answer with your data, not its memory
- loop:
- take user question
- embed question (a single vector)
- retrieve top-k similar chunks (k=3–20 is common)
- (optional) rerank with a cross-encoder (relevance re-check)
- stuff the best chunks into the prompt as context
- generate answer (cite sources; limit style drift)
- RAG ≠ “just search”; it’s retrieval + prompt construction + guardrails
- hybrid retrieval (dense + sparse)
- dense vectors catch synonyms/semantics
- sparse/BM25 catches exact terms, numbers, rare tokens
- combine scores or do reciprocal rank fusion for better recall
- reranking (cheap insurance)
- use a cross-encoder (reads query+chunk together) to re-score the top 50–200 hits
- keeps fast ANN recall but upgrades precision in the final top-k
- building the prompt from retrieved chunks
- include: brief task instruction → user query → curated chunks (with titles) → “answer + cite”
- beware prompt injection in docs (“ignore previous instructions…”)
- mitigate: strip instructions from chunks; use system prompts to restrict tools; sanitizer rules
- RAG quality knobs
- chunk size/overlap: too big = off-topic; too small = missing context
- k (results): too low = miss facts; too high = blow context window
- similarity threshold: prevent garbage at tail
- reranker on/off: trade latency for quality
- metadata filters: time ranges, authors, tenants, permissions (ABAC/RBAC)
- evaluating retrieval
- offline: make a small test set (query → expected passages)
- metrics: Recall@k, MRR, nDCG
- online: measure “answer contained sources?”, “clicked citations?”, “escalations?”
- error taxonomy: missed retrieval vs wr[...]
RT @TheAhmadOsman: - you are
- a normal dev who’s heard “embeddings” and “RAG” 1000x
- want to know what they actually are, how they plug into LLMs
- suddenly: vectors are just coordinates for meaning, not magic
- first: what even is an “embedding”?
- embedding = a list of numbers (a vector) that represents text
- same-ish meaning ⇒ nearby vectors; different meaning ⇒ far apart
- produced by a smaller model (an encoder), not your chat LLM
- length (a.k.a. dimension): 256/384/768/1024+ numbers is common
- the vector space (101)
- you can measure closeness with math:
- L2 distance: straight-line distance
- dot product: alignment + magnitude
- cosine similarity: (a·b)/(||a||·||b||) = angle only
- normalize vectors (unit length) ⇒ dot product ≡ cosine
- embeddings compress semantics; they are lossy by design
- types of embeddings (don’t overthink; pick what you need)
- token embeddings: internal to the LLM (you don’t use these)
- sentence/document embeddings: 1 vector per chunk/snippet
- multilingual: one space across languages
- domain-tuned: legal, code, bio — better clustering for that domain
- how text becomes vectors (pipeline)
- clean text (lowercase? keep punctuation? depends; don’t destroy signal)
- chunking: split long docs into overlapping windows (by tokens, not chars)
- rule of thumb: 200–800 tokens, 10–20% overlap
- keep titles/headers as context inside each chunk
- embed each chunk ⇒ store in a vector index with metadata (source, page, tags)
- storing & searching vectors
- exact search (brute force): simplest; fine for ≤100k vectors
- ANN (approx nearest neighbor): fast at scale, tiny recall tradeoff
- HNSW (graph-based): great latency, memory heavier
- IVF/PQ (quantization): smaller index, some recall loss
- where to put them:
- FAISS/hnswlib (library), pgvector (Postgres), dedicated stores (Milvus, Pinecone, Weaviate, etc.)
- ops notes:
- track embedding_model_name + dimension in the index
- you cannot mix dimensions or swap models without re-embedding
- memory math: 768-dim float32 ≈ 3 KB/vector → 1M vectors ≈ ~3 GB (+ index overhead)
- RAG (Retrieval-Augmented Generation): the shape of it
- goal: let the LLM answer with your data, not its memory
- loop:
- take user question
- embed question (a single vector)
- retrieve top-k similar chunks (k=3–20 is common)
- (optional) rerank with a cross-encoder (relevance re-check)
- stuff the best chunks into the prompt as context
- generate answer (cite sources; limit style drift)
- RAG ≠ “just search”; it’s retrieval + prompt construction + guardrails
- hybrid retrieval (dense + sparse)
- dense vectors catch synonyms/semantics
- sparse/BM25 catches exact terms, numbers, rare tokens
- combine scores or do reciprocal rank fusion for better recall
- reranking (cheap insurance)
- use a cross-encoder (reads query+chunk together) to re-score the top 50–200 hits
- keeps fast ANN recall but upgrades precision in the final top-k
- building the prompt from retrieved chunks
- include: brief task instruction → user query → curated chunks (with titles) → “answer + cite”
- beware prompt injection in docs (“ignore previous instructions…”)
- mitigate: strip instructions from chunks; use system prompts to restrict tools; sanitizer rules
- RAG quality knobs
- chunk size/overlap: too big = off-topic; too small = missing context
- k (results): too low = miss facts; too high = blow context window
- similarity threshold: prevent garbage at tail
- reranker on/off: trade latency for quality
- metadata filters: time ranges, authors, tenants, permissions (ABAC/RBAC)
- evaluating retrieval
- offline: make a small test set (query → expected passages)
- metrics: Recall@k, MRR, nDCG
- online: measure “answer contained sources?”, “clicked citations?”, “escalations?”
- error taxonomy: missed retrieval vs wr[...]
Offshore
Ahmad RT @TheAhmadOsman: - you are - a normal dev who’s heard “embeddings” and “RAG” 1000x - want to know what they actually are, how they plug into LLMs - suddenly: vectors are just coordinates for meaning, not magic - first: what even is an “embedding”?…
ong generation vs prompt injection
tweet
tweet
Offshore
Photo
Ahmad
RT @TheAhmadOsman: i love having my own private UNRESTRICTED COMPUTE
Buy a GPU https://t.co/6r8c46owH7
tweet
RT @TheAhmadOsman: i love having my own private UNRESTRICTED COMPUTE
Buy a GPU https://t.co/6r8c46owH7
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