Moaz Alba

AI & ML Explorer 🚀 | Life, Learning & Curiosity | Projects & Thoughts

[قرآن | Quran @MajdMry77995]

سوره الملك رتويت لعلها تشفع لك..

[Moaz Alba @moishere5]

@AzizMns @Hamidgx027 صحيح
بس اقصد الغرور يلي عنده بالبدايات و الصلابة ساعدته بمواجهة الجمهور

[Moaz Alba @moishere5]

@Hamidgx027 💯
هون بتبرز قوة شخصية جوبز
مغرور لدرجة الكذب و ما بيتأثر بهيك حالات

[Moaz Alba @moishere5]

@Hamidgx027 فيه حشود عم يراقبوه ب ابل بارك هاد غير كوكب الأرض كله عم يشوفه اونلاين و هو بحدث تجاري اي خطأ ممكن يطلع بالمنتج بيضرب سمعة الشركة و اول مرة له ك CEO

[Moaz Alba @moishere5]

@zd_651 @Hamidgx027 💯

[Ameera Hamada | UX UI Designer @ameerahamadaa]

جبت لكم مكتبة برومبتات خرافية 🤯

إذا كنت تصمم مواقع، فيها برومبتات جاهزة لأقسام مواقع جميلة وLanding Pages متحركة.

انسخ، الصق، وابدأ…
ووفّر على نفسك ساعات من الشغل

جربها بنفسك: 👇

[Moaz Alba @moishere5]

@paulg I think it’s

RonanRx

[blueprint.io @tryblueprint_io]

Software has become easier to build, yet hardware still feels really hard

That's why we're building https://t.co/sIMZ9ounSw enter a prompt and get a wiring diagram, BOM, Mech view, and step by step build instructions

It's the easiest way to go from idea to prototype

[Moaz Alba @moishere5]

@johnternus Hi !!

[SCR @SCR01111]

STANFORD'S COMPLETE LLM COURSE IS FREE 📌

If you're serious about Generative AI, LLM Engineering, or Agentic AI, this is a great course to save.

COURSE OUTLINE

1. Transformers
https://t.co/2tfLyPr924

2. Transformer-Based Models & Tricks
https://t.co/CyhfPK48aB

3. Large Language Models
https://t.co/YK4hj4p7LK

4. LLM Training
https://t.co/VtbIUQwXGy

5. LLM Tuning
https://t.co/oa8nrJmiLN

6. LLM Reasoning
https://t.co/q0OTU5Wm0W

7. Agentic LLMs
https://t.co/LKiPqaetfv

8. LLM Evaluation
https://t.co/SumxFSYlbm

9. Recap & Current Trends
https://t.co/FsBsEGv01D

OFFICIAL COURSE SYLLABUS

https://t.co/Sle9oL0Z6x

The official Stanford syllabus confirms all 9 lectures and their topics, including RAG, tool calling, agents, LLM evaluation, RLHF, LoRA, reasoning, and current trends.

If you already know the basics, pay extra attention to:

Lecture 7 → Agentic LLMs

Lecture 8 → LLM Evaluation

[SCR @SCR01111]

Problem list:📌

1. Sliding Window: 3, 76, 209, 424, 567, 904

2. Two Pointers: 11, 15, 16, 18, 42, 167

3. Fast/Slow Pointers (Linked List): 141, 142, 19, 876, 160, 234

4. Binary Search on Sorted Data: 33, 34, 35, 153, 162, 704

5. Binary Search on Answer: 875, 1011, 410, 774, 1283, 1482

6. Hashing / Frequency Maps: 1, 49, 128, 217, 242, 347

7. Prefix Sum / Running Sum: 303, 560, 724, 930, 974, 523

8. Difference Array / Range Updates: 370, 1094, 1109, 1893, 1943, 2381

9. Monotonic Stack: 739, 496, 503, 84, 85, 901

10. Monotonic Queue / Deque: 239, 862, 1425, 1438, 1499, 1696

11. Heap / Top K: 215, 347, 692, 703, 973, 1046

12. Intervals: 56, 57, 252, 253, 435, 452

13. Greedy Scheduling / Sorting: 45, 55, 406, 621, 763, 134

14. Linked List Manipulation: 21, 23, 24, 25, 92, 138

15. Tree DFS: 104, 112, 113, 543, 124, 226

16. Tree BFS / Level Order: 102, 103, 199, 515, 637, 116

17. BST Problems: 98, 99, 230, 235, 450, 700

18. Backtracking Basics: 46, 47, 77, 78, 90, 39

19. Backtracking with Constraints: 40, 17, 79, 131, 51, 52

20. Graph BFS / DFS: 200, 695, 733, 994, 1091, 1254

21. Topological Sort / DAG: 207, 210, 802, 1462, 1203, 2115

22. Union Find / DSU: 547, 684, 1319, 1579, 990, 1202

23. Shortest Path: 743, 787, 1514, 1631, 1334, 1976

24. MST / Graph Greedy: 1584, 1135, 1168, 1489, 778, 1102

25. Trie: 208, 211, 212, 648, 677, 1268

26. Bit Manipulation: 136, 137, 191, 338, 268, 190

27. 1D DP Basics: 70, 198, 213, 322, 279, 300

28. 2D DP / Grid DP: 62, 63, 64, 120, 221, 1143

[GREG ISENBERG @gregisenberg]

Grok Bot might be the first tool that lets one non-technical person run an entire business with a team of AI agents.

My friend Billy runs his whole newsletter business on Grok Bot agents, and I think we're about to see 100,000+ businesses like his.

BEST PRACTICES:

1. The agents run on a shared cloud computer, so running your newsletter, your X, and your receipts all in one place creates context bloat and burns tokens fast. One mission per setup.

2. Start with a Chief of Staff. Give it access to your existing docs (Notion, Slack, Gmail), have it audit the business, then tell you the top three agents to build first to drive revenue.

3. Perfect a task with the Chief of Staff before spinning up a new agent. Have it do the outbound sales once, review it, and only then say "now build a bot that does exactly that." You earn each new hire by proving the task works first.

4. Constraints are the feature. You get a limited number of agents, one thread per bot, like DMs with a teammate. It forces you to stay mission-oriented instead of spinning up a bot for every random idea.

5. You make the decisions, not the agent. Billy's team spent three weeks unable to pick where content should live. At some point you say "we're doing Notion, no more tinkering" and move on.

6. Run week one with no new agents. Build the team, learn to fly the plane, just execute. Week three is when you find the real gaps and expand, someone to man the inbox, someone for the Shopify shop.

7. Then add routines so it works while you sleep. Ask your Chief of Staff what recurring jobs would move the business forward overnight, and it builds the automations that run without you.

Thanks to @billyjhowell for sharing the sauce on @startupideaspod (follow for more).

Grokbot is really cool.

Watch below:

https://t.co/9jTXeOiu9x

[Khairallah AL-Awady @eng_khairallah1]

this is f**king dangerous

A normal American student just bought an iPad and Mac Mini for $2,200. Connected them to his MacBook.

Three computers on one desk - dorm roommates thought he was mining crypto.

He just set up the automation and went to sleep.

In the morning the system had already processed hundreds of leads, written personalized emails to each one and filled the CRM without a single touch.

The team that did this before him- cost $7,000 a month

He paid $2,200 once.

There are 360 million companies in the world and 310 of them still pay people for what a machine does better.

And only 100,000 people on the planet know how to use AI and set this up.

[Mark Yi @ SpaceXAi @himarkyi]

if you are ambitious and young, im begging you to read this

[Mo Elgaraihy @EngMoElgaraihy]

كيف تبني وكيل ذكاء اصطناعي في 20 دقيقة وتبيعه لـ Anthropic بـ 100 ألف دولار؟ 💥

​عرض خرافي تم تقديمه مؤخراً في جامعة ستانفورد، يكشف كيف تحول شخص واحد بمساعد كود بسيط إلى "فريق هندسي كامل" تشتري منه كبرى الشركات برمجياتها الجاهزة!

​🧵 إليك التفاصيل الصادمة وكيف تطبقها بنفسك:👇

[Mo Elgaraihy @EngMoElgaraihy]

عصارة خبرة 8 سنوات في عمالقة الذكاء الاصطناعي OpenAI وTesla.. أندريه كارباتي يختصر كل ما تعرفه البشرية عن الذكاء الاصطناعي في محاضرة تاريخية مدتها ساعتان فقط! 💥🔥

​الآلاف يدفعون 15 ألف دولار في معسكرات تدريبية لا تقدم نصف هذه المعرفة المتركزة، والآن أصبحت متاحة مجاناً للجميع. 👇

[MindBranches @MindBranches]

Book Summary: Mathematical Thinking - For People Who Hate Math

[Ashutosh Maheshwari @asmah2107]

For people who keep asking what to build in AI Engineering

> Build your own Reasoner (Chain of Thought implementation)

> Build your own Agent loop (ReAct pattern)

> Build your own Inference Server (in C++/Rust)

> Build your own Transformer from scratch (Attention is all you need)

> Build your own Vector Database (HNSW index)

> Build your own RAG pipeline

> Build your own Flash Attention kernel (CUDA)

> Build your own Quantization library (Int8/FP4 implementation)

> Build your own Mixture of Experts (MoE) routing layer

> Build your own Distributed training loop (FSDP/Tensor Parallelism)

> Build your own KV Cache paging system (like vLLM)

> Build your own Speculative Decoding system

> Build your own State Space Model (Mamba implementation)

> Build your own RLHF pipeline (PPO implementation)

> Build your own Small Language Model (SLM)

> Build your own Matrix Multiplication kernel

> Build your own LoRA (Low-Rank Adaptation) trainer

> Build your own Code interpreter sandbox

> Build your own DPO (Direct Preference Optimization) loss function

> Build your own Graph RAG system

> Build your own Model merger (Model Soups/Spherical Linear Interpolation)

> Build your own Interpretability tool (SAE - Sparse Autoencoders)

> Build your own Synthetic data generator

> Build your own Function Calling router

> Build your own Structured Output parser (Context Free Grammars)

> Build your own Multi-modal projector (CLIP implementation)

> Build your own LLM Eval harness

> Build your own Guardrails system (Input/Output filtering)

> Build your own Prompt caching mechanism

> Build your own Tokenizer (BPE implementation)

> Build your own Autograd engine (like Micrograd)

> Build your own Diffusion model (UNet + Scheduler)

> Build your own Vision Transformer (ViT)

> Build your own Whisper-style ASR model

> Build your own Text-to-Speech pipeline

> Build your own Semantic Router

> Build your own Knowledge Graph builder

> Build your own Data curation pipeline (MinHash/Deduplication)

> Build your own AI Gateway (Load balancing/Failover)

> Build your own Parameter Efficient Fine-Tuning (PEFT) library

> Build your own Text-to-SQL engine

> Build your own Recommendation system (Two-tower architecture)

> Build your own Embedding model

> Build your own Logit Processor

> Build your own Softmax kernel optimization

> Build your own Adversarial attack generator

> Build your own Audio Spectrogram transformer

> Build your own Neural Architecture Search

> Build your own Model Distillation pipeline

> Build your own Feature Store

> Build your own Database driver (for Vectors)

[diva @divaagurlxw]

As an AI engineer, please learn:

>Learn the math behind gradients, attention, embeddings, and probability

>Implement linear regression, decision trees, and neural networks from scratch

>Understand backpropagation from the code, not just diagrams

>Build a tokenizer before relying on an LLM API

>Implement self-attention and a small transformer yourself

>Train, evaluate, and debug a small language model

>Learn how embeddings are created, stored, searched, and evaluated

>Build RAG, then evaluate retrieval separately from generation

>Compare chunking, reranking, hybrid search, and metadata filtering

>Learn fine-tuning tradeoffs: LoRA, QLoRA, and full fine-tuning

>Understand inference: batching, KV cache, quantization, latency, and throughput

>Build a tool-calling agent and inspect every step of its loop

>Learn memory, planning, state management, and failure recovery

>Use structured outputs and validate every model response

>Build evaluations before trusting your demos

>Add traces, metrics, logs, token usage, and cost tracking

>Learn prompt injection, data leakage, permissions, and sandboxing

>Deploy with containers, queues, caches, databases, and model gateways

>Build one end-to-end AI system that can survive real users

>Publish the code, architecture, benchmarks, and everything that failed