Million 🇪🇷

Software Engineer | MSc | UoA Alum | Stock & Options Trader 📈| Peace , Prosperity lover 🇪🇷ወዲ አስመራ

[Sulekha Tripathi @sulekhat95]

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.

[Troll Football @TrollFootball]

Arsenal are the Premier League Champions

[AilaunchX @Ai_Tech_tool]

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.

[Jasmin @AI_with_jasmin]

Passive studying is dead!

Claude can train your brain harder than most professors ever will.

Here are 10 Claude prompts to learn anything 10× faster

[Evan Luthra @EvanLuthra]

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:

[Viora Tech @Viora_Tech_Ai]

During a job interview, if they ask: “Do you have any questions for us?”

USE THE GOLDEN RESPONSE:

[Mustafa @oprydai]

> I don’t 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.

[Chidanand Tripathi @thetripathi58]

These are literally best YouTube channels to learn AI from scratch

Try Now: https://t.co/WGC5SBvaRp

[MATT GRAY @matt_gray_]

10 AI courses every founder should take (all free):

[Aakash Gupta @aakashgupta]

🛠️🤖 How to build AI agents from scratch

(Even if you've never done it before.)

𝗧𝗵𝗲𝘀𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝟴 𝘀𝘁𝗲𝗽𝘀 𝘁𝗼 𝘁𝗮𝗸𝗲, 𝗳𝗿𝗼𝗺 𝗽𝘂𝗿𝗽𝗼𝘀𝗲 𝘁𝗼 𝗨𝗜.

[Chidanand Tripathi @thetripathi58]

Microsoft dropped the best free Generative AI course you’ll ever see

[Ahmad @TheAhmadOsman]

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 “token visualizer” 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 “position-encoded” in 3D
> ablate positions—watch 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 → single-block transformer
> generalize: n-block “mini-former” 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 — 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 “cache hit/miss” 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 “memory-efficient” (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 each—what 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 “English” faster
> generate samples from each — note quirks

Finetuning vs Instruction Tuning vs RLHF

> fine-tune on a small custom dataset
> instruction-tune by prepending tasks (“Summarize: ...”)
> RLHF: hack a reward model, use PPO for 10 steps, plot reward

Scaling Laws & Model Capacity

> train tiny, small, medium models — plot loss vs size
> benchmark wall-clock time, VRAM, throughput
> extrapolate scaling curve — how “dumb” 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.

> don’t 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

[Maryam Miradi, PhD @MaryamMiradi]

🏆📚This 200-Page LLM Paper Is a 𝗚𝗼𝗹𝗱𝗺𝗶𝗻𝗲 — and it’ll save you months
𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴, 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴, 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 — finally crystal clear.
If you don’t have time to read all 200+ pages, here are the most valuable 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 ↓

》 𝗣𝗿𝗲-𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴:

How AI Gets Smart Before It Gets Useful Before an LLM can generate anything meaningful, it must pre-train—absorbing patterns from vast datasets. This paper breaks it down:

✸ Unsupervised, Supervised, and Self-Supervised Pre-training – Why AI learns better with less human labeling.

✸ Encoder vs. Decoder vs. Encoder-Decoder Models – The three fundamental architectures and when to use them.

✸ BERT & Transformers – How they rewrote the rules of AI understanding.

》 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝘀:

Where AI Stops Memorizing and Starts Creating
Pre-training gives LLMs knowledge. Generative models give them a voice.

✸ Decoder-Only Transformers (GPT-style models) – The backbone of AI creativity.

✸ Training & Fine-tuning LLMs – How models evolve from generalists to specialists.

✸ Alignment & Safety – Why raw AI outputs need guardrails (and how RLHF fixes it).

》𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴:

The Skill That Separates AI Users From AI Builders
If you’re not prompting correctly, you’re missing out on 90% of an LLM’s potential. This paper covers:

✸ In-Context Learning – Teaching AI on the fly without retraining.

✸ Chain of Thought & Self-Refinement – Making AI reason instead of regurgitate.

✸ RAG & Tool Use – Giving LLMs external memory for better accuracy.

》 𝗔𝗜 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁:

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:

✸ Instruction Fine-Tuning – How models learn from curated data.

✸ Reinforcement Learning with Human Feedback (RLHF) – Why AI listens to your preferences.

✸ Inference-Time Alignment – Tweaking responses without retraining the whole model.

☆ 200-page paper: https://t.co/ml3bgZrlvS

≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣

⫸ꆛ Want to build Real-World AI Agents?

Join My 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝟱-𝗶𝗻-𝟭 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴!

➠ Build Agents for Healthcare, Finance, Smart Cities & More
➠ Master 5 Modules: 𝗠𝗖𝗣 · LangGraph · PydanticAI · CrewAI · Swarm
➠ Includes 9 Full Projects

👉 𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗢𝗪 (𝟱𝟲% 𝗢𝗙𝗙):
https://t.co/5i2v1fIrhJ

[Daily Fashion @genamind]

12 AI skills to master in 2025

[Arde @ardent0X]

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.

(1/n)

[Tyler @tyler_agg]

how to build your first AI agent

[Aakash Gupta @aakashgupta]

This is the only set of study methods you'll ever need