INSTEAD OF WATCHING AN HOUR OF NETFLIX TONIGHT.
This 60-minute Cambridge lecture by Demis Hassabis will teach you more about the future of AI than most people will learn in the next 5 years.
Bookmark it and give it an hour, no matter what.
Instead of watching an hour of Netflix, watch this 2 hour hour Stanford lecture will teach you more about how LLMs like ChatGPT and Claude are built than most people working at top AI companies learn in their entire careers.
Anthropic pays engineers $750,000+ a year to understand how LLMs work.
Stanford just put a 2 hour lecture that covers 80% of it for FREE.
Bookmark this. Give it 2 hours today.
It might be the highest ROI thing you do this month:
> I donโt understand why people are still paying in dollars to learn LLMs.
> these 9 lectures from Stanford are a pure goldmine for anyone wanting to understand LLMs in depth.
๐ ๏ธ๐ค How to build AI agents from scratch
(Even if you've never done it before.)
๐ง๐ต๐ฒ๐๐ฒ ๐ฎ๐ฟ๐ฒ ๐๐ต๐ฒ ๐ด ๐๐๐ฒ๐ฝ๐ ๐๐ผ ๐๐ฎ๐ธ๐ฒ, ๐ณ๐ฟ๐ผ๐บ ๐ฝ๐๐ฟ๐ฝ๐ผ๐๐ฒ ๐๐ผ ๐จ๐.
step-by-step LLM Engineering Projects
each project = one concept learned the hard (i.e. real) way
Tokenization & Embeddings
> build byte-pair encoder + train your own subword vocab
> write a โtoken visualizerโ to map words/chunks to IDs
> one-hot vs learned-embedding: plot cosine distances
Positional Embeddings
> classic sinusoidal vs learned vs RoPE vs ALiBi: demo all four
> animate a toy sequence being โposition-encodedโ in 3D
> ablate positionsโwatch attention collapse
Self-Attention & Multihead Attention
> hand-wire dot-product attention for one token
> scale to multi-head, plot per-head weight heatmaps
> mask out future tokens, verify causal property
transformers, QKV, & stacking
> stack the Attention implementations with LayerNorm and residuals โ single-block transformer
> generalize: n-block โmini-formerโ on toy data
> dissect Q, K, V: swap them, break them, see what explodes
Sampling Parameters: temp/top-k/top-p
> code a sampler dashboard โ interactively tune temp/k/p and sample outputs
> plot entropy vs output diversity as you sweep params
> nuke temp=0 (argmax): watch repetition
KV Cache (Fast Inference)
> record & reuse KV states; measure speedup vs no-cache
> build a โcache hit/missโ visualizer for token streams
> profile cache memory cost for long vs short sequences
Long-Context Tricks: Infini-Attention / Sliding Window
> implement sliding window attention; measure loss on long docs
> benchmark โmemory-efficientโ (recompute, flash) variants
> plot perplexity vs context length; find context collapse point
Mixture of Experts (MoE)
> code a 2-expert router layer; route tokens dynamically
> plot expert utilization histograms over dataset
> simulate sparse/dense swaps; measure FLOP savings
Grouped Query Attention
> convert your mini-former to grouped query layout
> measure speed vs vanilla multi-head on large batch
> ablate number of groups, plot latency
Normalization & Activations
> hand-implement LayerNorm, RMSNorm, SwiGLU, GELU
> ablate eachโwhat happens to train/test loss?
> plot activation distributions layerwise
Pretraining Objectives
> train masked LM vs causal LM vs prefix LM on toy text
> plot loss curves; compare which learns โEnglishโ faster
> generate samples from each โ note quirks
Finetuning vs Instruction Tuning vs RLHF
> fine-tune on a small custom dataset
> instruction-tune by prepending tasks (โSummarize: ...โ)
> RLHF: hack a reward model, use PPO for 10 steps, plot reward
Scaling Laws & Model Capacity
> train tiny, small, medium models โ plot loss vs size
> benchmark wall-clock time, VRAM, throughput
> extrapolate scaling curve โ how โdumbโ can you go?
Quantization
> code PTQ & QAT; export to GGUF/AWQ; plot accuracy drop
Inference/Training Stacks:
> port a model from HuggingFace to Deepspeed, vLLM, ExLlama
> profile throughput, VRAM, latency across all three
Synthetic Data
> generate toy data, add noise, dedupe, create eval splits
> visualize model learning curves on real vs synth
each project = one core insight. build. plot. break. repeat.
> donโt get stuck too long in theory
> code, debug, ablate, even meme your graphs lol
> finish each and post what you learned
your future self will thank you later
๐๐This 200-Page LLM Paper Is a ๐๐ผ๐น๐ฑ๐บ๐ถ๐ป๐ฒ โ and itโll save you months
๐ฃ๐ฟ๐ผ๐บ๐ฝ๐๐ถ๐ป๐ด, ๐๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด, ๐ฎ๐น๐ถ๐ด๐ป๐บ๐ฒ๐ป๐ โ finally crystal clear.
If you donโt have time to read all 200+ pages, here are the most valuable ๐๐ฎ๐ธ๐ฒ๐ฎ๐๐ฎ๐๐ โ
ใ ๐ฃ๐ฟ๐ฒ-๐ง๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด:
How AI Gets Smart Before It Gets Useful Before an LLM can generate anything meaningful, it must pre-trainโabsorbing patterns from vast datasets. This paper breaks it down:
โธ Unsupervised, Supervised, and Self-Supervised Pre-training โ Why AI learns better with less human labeling.
โธ Encoder vs. Decoder vs. Encoder-Decoder Models โ The three fundamental architectures and when to use them.
โธ BERT & Transformers โ How they rewrote the rules of AI understanding.
ใ ๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐:
Where AI Stops Memorizing and Starts Creating
Pre-training gives LLMs knowledge. Generative models give them a voice.
โธ Decoder-Only Transformers (GPT-style models) โ The backbone of AI creativity.
โธ Training & Fine-tuning LLMs โ How models evolve from generalists to specialists.
โธ Alignment & Safety โ Why raw AI outputs need guardrails (and how RLHF fixes it).
ใ๐ฃ๐ฟ๐ผ๐บ๐ฝ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด:
The Skill That Separates AI Users From AI Builders
If youโre not prompting correctly, youโre missing out on 90% of an LLMโs potential. This paper covers:
โธ In-Context Learning โ Teaching AI on the fly without retraining.
โธ Chain of Thought & Self-Refinement โ Making AI reason instead of regurgitate.
โธ RAG & Tool Use โ Giving LLMs external memory for better accuracy.
ใ ๐๐ ๐๐น๐ถ๐ด๐ป๐บ๐ฒ๐ป๐:
Teaching AI to Work for Humans (Not Against Them)
One of the biggest challenges in AI is getting it to follow human intent. The paper breaks down:
โธ Instruction Fine-Tuning โ How models learn from curated data.
โธ Reinforcement Learning with Human Feedback (RLHF) โ Why AI listens to your preferences.
โธ Inference-Time Alignment โ Tweaking responses without retraining the whole model.
โ 200-page paper: https://t.co/ml3bgZrlvS
โฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃ
โซธ๊ Want to build Real-World AI Agents?
Join My ๐๐ฎ๐ป๐ฑ๐-๐ผ๐ป ๐๐ ๐๐ด๐ฒ๐ป๐ ๐ฑ-๐ถ๐ป-๐ญ ๐ง๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด!
โ Build Agents for Healthcare, Finance, Smart Cities & More
โ Master 5 Modules: ๐ ๐๐ฃ ยท LangGraph ยท PydanticAI ยท CrewAI ยท Swarm
โ Includes 9 Full Projects
๐ ๐๐ป๐ฟ๐ผ๐น๐น ๐ก๐ข๐ช (๐ฑ๐ฒ% ๐ข๐๐):
https://t.co/5i2v1fIrhJ
How to become an AI engineer in just 3 months?
A thread,
I literally spend 40 hours to find the perfect roadmap for beginners with a step by step weekly guide.
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