Average Patanjali Enjoyer

Avid Patanjali Fan+Gamer+Meme Addict+Occassionally weeb+Marvel/dc hater+ pcmr for life + anti left but not necessarily pro right+ trying to become a researcher

[Average Patanjali Enjoyer @babaramdevstan]

@Priyanshu__2109 Specs wise not worth the price also might suffer with green line who knows, look at OnePlus phones if you can

[Kirk Borne @KirkDBorne]

10 experts' YouTube channels for AI lectures, tutorials, tips, conversations, opinions, guides, lessons learned, explainers, perspectives, mathematics background, etc.:

🎓 1) Andrej Karpathy – Deep yet accessible lectures on deep learning, LLMs, and an intro course on neural networks. https://t.co/jycWqOs7Z7
📊 2) 3Blue1Brown – Stunning visualizations that make abstract mathematical concepts intuitive. https://t.co/y6qOcSPe7N
🎙️ 3) Lex Fridman – In-depth conversations with AI leaders, offering a broader perspective on the field. https://t.co/PahPfkKeUU
🤖 4) Machine Learning Street Talk – Technical deep dives and discussions with top AI researchers. https://t.co/gnWrxWPyTb
📚 5) StatQuest with Joshua Starmer PhD – Beginner-friendly explainers on machine learning and statistics. https://t.co/u3d2EtKCRN
🍉 6) Serrano Academy (Luis Serrano) – Clear and accessible content on ML, deep learning, and AI https://t.co/Qn8Qxez0kq
💻 7) Jeremy Howard – Practical deep learning courses and AI-powered web app tutorials. https://t.co/Z33ddvHdRZ
🛠️ 8) Hamel Husain – Hands-on lessons in LLMs, RAG, fine-tuning, and AI evaluations. https://t.co/Nq6ZAHiSfs
🚀 9) Jason Liu – Expert-led lectures on RAG and AI freelancing tips for ML developers. https://t.co/UQwyMu9hJG
⚙️ 10) Dave Ebbelaar – Practical guides on building AI systems and real-world applications. https://t.co/xyu3dhi9fw

[Pallavi @pallavishekhar_]

AI System Design - A complete guide to learn AI System Design step by step - from LLM inference, GPUs, KV Cache, and caching to RAG, Vector Databases, AI Agents, MCP, Multi-Agent Systems, Voice AI, Guardrails, Evaluation, Observability, Cost Optimization, and a step-by-step framework to crack any AI System Design interview. Everything in one place, explained in simple words, with detailed blogs for every deep dive.

Start here: https://t.co/dmnEFMlkId

[Tech with Mak @techNmak]

These are the best visual AI resources for learning Transformers, LLMs, embeddings, diffusion, inference and model internals ↓

1/ Transformer Explainer - Watch GPT process text through embeddings, attention, MLPs and next-token prediction.
https://t.co/vkyxOqDVll

2/ Brendan Bycroft’s LLM Visualization - Explore an LLM from architecture down to tensors and operations.
https://t.co/ZFGH0pRtP3

3/ 3Blue1Brown - Visual intuition for linear algebra, neural networks, backprop, attention and Transformers.
https://t.co/plaENBG6aQ

4/ The Illustrated Transformer - One of the clearest visual explanations of embeddings, Q/K/V and attention.
https://t.co/tYrJCm17L6

5/ TensorFlow Playground - Watch neural networks learn as you change layers, activations, features and learning rate.
https://t.co/vva9dm1Gnv

6/ Google PAIR AI Explorables - Interactive explainers on LLMs, generalization, interpretability and model behavior.
https://t.co/vva9dm1Gnv

7/ Distill - Exceptional visual essays on t-SNE, feature visualization, GNNs and interpretability.
https://t.co/FuqpUdDwZD

8/ Abhik Sarkar’s Transformer Visualizations - RoPE, KV cache, FlashAttention, MQA, GQA and more.
https://t.co/vva9dm1Gnv

9/ Visual Guide to Attention Variants - MHA, MQA, GQA and MLA visually compared.
https://t.co/nbpKMa0cpT

10/ Visual Guide to Mixture of Experts - Routing, experts, sparse activation and load balancing.
https://t.co/3gu7xq5e3i

11/ Modular LLM Inference Handbook - Prefill, decode, KV cache, batching, quantization and speculative decoding.
https://t.co/itMIfZnPP5

12/ Apple Embedding Atlas - Explore clusters, neighborhoods and outliers in large embedding spaces.
https://t.co/hxKdXSp5I2

13/ Diffusion Explainer - Follow Stable Diffusion step by step.
https://t.co/YVHEG1zslC

14/ Neuronpedia - Explore features, activations, SAE latents and attribution graphs inside real models.
https://t.co/F8P8eNDH3W

15/ Seeing Theory - Probability, Bayes, distributions, regression and inference made interactive.
https://t.co/KeEhGNSMIQ

16/ CNN Explainer - Visualize convolutions, feature maps, activations and pooling.
https://t.co/sCwv59d2S6

17/ GAN Lab - Train a GAN in your browser and watch its generated distribution evolve.
https://t.co/G94RDWMNak

Save this. There’s a serious AI curriculum hiding inside these links.

[Average Patanjali Enjoyer @babaramdevstan]

@rathor7_ Overused joke sto

[Average Patanjali Enjoyer @babaramdevstan]

@maniacriri @chickenalakiev Marketcity?

[Average Patanjali Enjoyer @babaramdevstan]

@chickenalakiev Magnolia?

[Average Patanjali Enjoyer @babaramdevstan]

@WigettaGaming @OutofGalaxyy what f1 widget is that?i need it

[Md Ismail Šojal 🕷️ @0x0SojalSec]

Stop reading attention is all you need.

Transformers (LLMs) clearly explained with visuals:

- Real GPT-2 visualizer
- tokens to embeddings
- 12 attention heads (Q, K, V, scale, mask, softmax)
- multi-head Q/K/V
- residual + MLP
- next-token probabilities

That’s the difference between reading about transformers and seeing one think.

- https://t.co/zBmxt1HW1f

[Average Patanjali Enjoyer @babaramdevstan]

@cneuralnetwork Fold comes pretty close

[Suraj Sharma @suraj_sharma14]

Your AI feed is probably noise. Fix it:

1.) @karpathy
➣ LLM internals, from-scratch builds

2.) @simonw
➣ Building with LLMs, tools, evals

3.) @swyx
➣ AI engineering ecosystem

4.) @emollick
➣ Applied AI experiments

5.) @chipro
➣ Production ML / LLM systems

6.) @rasbt
➣ Model internals & papers

7.) @fchollet
➣ Reasoning, evals, ARC

8.) @DrJimFan
➣ Agents, robotics, embodied AI

9.) @natolambert
➣ Open models & post-training

10.) @hwchase17
➣ Agents

11.) @jerryjliu0
➣ RAG & LlamaIndex

12.) @alexalbert__
➣ Claude / API builder tips

13.) @AmandaAskell
➣ Model behavior

14.) @gdb
➣ OpenAI engineering signal

15.) @sama
➣ OpenAI product drops

16.) @DarioAmodei
➣ Anthropic / scaling

17.) @demishassabis
➣ DeepMind research

18.) @ylecun
➣ Open models & architecture

19.) @AndrewYNg
➣ Applied AI learning paths

20.) @OpenAI
➣ Official launches

21.) @AnthropicAI
➣ Claude updates

22.) @claudeai
➣ Claude product

23.) @GoogleDeepMind
➣ Gemini / research

24.) @xai
➣ Grok / xAI

25.) @AIatMeta
➣ Llama & open weights

26.) @huggingface
➣ Models, datasets, Spaces

27.) @LangChain
➣ Agent stack

28.) @llama_index
➣ RAG stack

29.) @cursor_ai
➣ AI coding workflow

30.) @vercel
➣ AI SDK / shipping AI apps

31.) @shadcn
➣ AI-native UI

32.) @levelsio
➣ Shipping products fast

33.) @amasad
➣ Replit / agent coding

34.) @goodside
➣ Prompting

35.) @jxnlco
➣ Structured outputs

36.) @eugeneyan
➣ Applied ML writing

37.) @HamelHusain
➣ Evals & LLM ops

38.) @lateinteraction
➣ Retrieval / DSPy

39.) @mckaywrigley
➣ AI apps in public

40.) @rowancheung
➣ Fast AI news

41.) @TheRundownAI
➣ Daily AI brief

42.) @ClementDelangue
➣ Hugging Face + open source

43.) @yoheinakajima
➣ Agent ideas

44.) @mattshumer_
➣ Builder tools

45.) @AIHighlight
➣ Tool / paper drops

Save this list. Build your AI builder feed with signal not noise.

[Siddharth @Pseudo_Sid26]

The best free Standford course + book combinations -

1. CS229 + HOML ( a pre requisite course needed )
2. CS224N + NLP with transformers
3. CS230 + DL by Goodfellow
4. CS336 + Build a LLM by Sebastian
5. CME295 + Hugging face LLM course
6. CS329A + Build an AI Agent by Jungjun

[Average Patanjali Enjoyer @babaramdevstan]

@ksr_writes @GabbbarSingh Wells Fargo i suppose?

[Average Patanjali Enjoyer @babaramdevstan]

@filtercoffeee 500D/F/CAs are single handedly being the lifeline of ORR

[Average Patanjali Enjoyer @babaramdevstan]

@canaresehammer @mxtaverse Travelling is costly bruh, ac bus min fare is 15, even metro is the costliest i'd say

[Average Patanjali Enjoyer @babaramdevstan]

@dhinka_cheeka @filtercoffeee Kundli

[SCR @SCR01111]

Goldmine for AI Engineers! 📌

If you're learning AI, ML, LLMs, or AI agents, don't waste hours jumping between random tutorials.

These are 10 repositories I'd actually keep bookmarked - from Python fundamentals to ML, LLMs, agents, and production AI.

1. Python - 100 Days

jackfrued/Python-100-Days

A 100-day Python learning path covering fundamentals, data analysis, web development, and more.

GitHub:
https://t.co/NMCNWjp4GT

2. Generative AI for Beginners

microsoft/generative-ai-for-beginners

A practical introduction to building Generative AI applications.

Covers:

• LLM fundamentals
• Prompt engineering
• RAG
• AI agents
• Fine-tuning
• AI application development

GitHub:
https://t.co/5QXAC1rsUZ

3. LLMs From Scratch

rasbt/LLMs-from-scratch

Want to understand what's actually happening inside an LLM?

Build one step by step.

Covers:

• Tokenization
• Embeddings
• Attention
• Transformers
• Training
• Fine-tuning

GitHub:
https://t.co/iEtPYVPpXV

4. Machine Learning for Beginners

microsoft/ML-For-Beginners

A structured 12-week, 26-lesson curriculum covering classical machine learning.

A good starting point if you want ML fundamentals before jumping into LLMs.

GitHub:
https://t.co/sLijf46NKB

5. OpenAI Cookbook

openai/openai-cookbook

A collection of practical examples and guides for building applications with OpenAI models.

Useful when you want to move from:

Learning → Building

GitHub:
https://t.co/H3ZiMmpMAq

6. Stable Diffusion

CompVis/stable-diffusion

Interested in generative image models?

This repository contains the original Stable Diffusion implementation and research code.

GitHub:
https://t.co/DFwSLqOlyn

7. AI Agents for Beginners

microsoft/ai-agents-for-beginners

A practical course for understanding and building AI agents.

Covers:

• Agentic AI
• RAG
• Agent frameworks
• Tool use
• Multi-agent systems

GitHub:
https://t.co/zNeYlZRQqa

8. AI for Beginners

microsoft/AI-For-Beginners

A structured 12-week, 24-lesson introduction to AI.

Covers:

• Neural networks
• Computer vision
• NLP
• Deep learning
• Classical AI

GitHub:
https://t.co/V7Oilwt7nA

9. LLM App

pathwaycom/llm-app

Focused on building practical LLM applications.

Explore:

• RAG
• AI pipelines
• Enterprise search
• Real-time data
• Vector search

GitHub:
https://t.co/2FdeXjZfQ8

10. Segment Anything

facebookresearch/segment-anything

A foundation model for promptable image segmentation.

Worth exploring if you're interested in computer vision and multimodal AI.

GitHub:
https://t.co/yCSrZH8b5w

Don't bookmark all 10 and forget about them.

Pick based on where you are:

Python → Python-100-Days

ML → ML-For-Beginners

AI Fundamentals → AI-For-Beginners

LLMs → LLMs-from-scratch

Generative AI → Generative-AI-for-Beginners

Agents → AI-Agents-for-Beginners

Building → OpenAI Cookbook / LLM App

Computer Vision → Segment Anything

Pick one.

Build something.

Then move to the next.

[R𝛼m 🦅 @rambuilds_]

The best free AI & ML Courses from Stanford:

❯ CS336 - LLM from Scratch

❯ CS221 - Artificial Intelligence

❯ CS229 - Machine Learning

❯ CS230 - Deep Learning

❯ CS234 - Reinforcement Learning

❯ CS224N - NLP with Deep Learning

[Linas Beliūnas @linasbeliunas]

Instead of watching 2 hours of Netflix tonight, watch this Stanford lecture. It’s the clearest end-to-end explanation I’ve seen of how ChatGPT and Claude are actually built.

From tokenization and BPE all the way to the transformer architecture, training pipeline, and next-token decoding.

Whether you’ve never written a line of AI code or you’ve been shipping agents every day, this 2h 34min session will click things into place that most people take years to figure out.

Bookmark it and clear the time this weekend, because this might be the single most valuable thing you learn all month.

[Hux @huxlab]

今晚别刷Netflix了。

去看这个斯坦福2小时34分钟的讲座。

这是我见过最清晰、端到端讲透ChatGPT和Claude到底怎么建出来的课。

从Tokenization和BPE开始，一路到Transformer架构、训练管线、Next-token解码。

不管你是从没写过一行AI代码，还是天天在ship Agent，看完都会把很多“原来如此”的点一次打通。

真正核心就这几层：
1文本怎么变成模型能吃的数字（BPE tokenization）
2语言模型本质就是在预测下一个token
3Transformer怎么用Attention做消息传递
4训练时用NLL loss把整条序列概率拉高
5生成时怎么一步步解码出来

大多数人花好几年才拼出的全景图，这节课直接给你画完。

把时间空出来，这可能是你这个月最值的一堂课。