AI Bookmarks

Last updated: 2026-10-29 01:43 | 394 links | 36 categories

Categories

• 1-bit & Quantization (8)
• Agents & MCP (23)
• Blogs & People (49)
• Books & PDFs (34)
• Courses & Learning (35)
• Diffusion Models (15)
• GPU & CUDA (41)
• Inference & Serving (29)
• Information Retrieval & RAG (5)
• Interpretability (11)
• LLM Training & Fine-tuning (5)
• RL & Alignment (18)
• Research Papers (66)
• Robotics & Embodied AI (15)
• Tools & Repos (23)
• Transformers & Attention (11)
• World Models (6)
• 19 empty categories ready for future bookmarks

1-bit & Quantization (8)

• Dummy's Guide to Modern Samplers
• A Visual Guide to Quantization - by Maarten Grootendorst
• [2402.17764] The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
• [2310.11453] BitNet: Scaling 1-bit Transformers for Large Language Models
• [1712.05877] Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
• [2306.00978] AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
• [2210.17323] GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
• [2211.10438] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

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Agents & MCP (23)

• GitHub - amitshekhariitbhu/ai-system-design: AI System Design - Learn how to design AI systems built on LLMs, RAG, and AI Agents step by step. · GitHub
• morluto/rea: Reverse engineer anything with agents, from app behavior down to native binaries.
• browser-use/video-use: Edit videos with coding agents
• Procedural Graphs: Self-Evolving Execution Structures for LLM Agents
• CS294/194-280 Advanced Large Language Model Agents | CS 294/194-280 Advanced Large Language Model Agents
• CS294/194-196 Large Language Model Agents | CS 194/294-196 Large Language Model Agents
• (3) CS 194/294-280 (Advanced LLM Agents) - Lecture 12, Dawn Song - YouTube
• GitHub - google-agentic-commerce/AP2: Building a Secure and Interoperable Future for AI-Driven Payments.
• Agentic Design Patterns - Google Docs
• ai-agents-for-beginners/13-agent-memory at main · microsoft/ai-agents-for-beginners
• Agent Learning via Early Experience
• GitHub - langchain-ai/agents-from-scratch
• microsoft/ai-agents-for-beginners: 12 Lessons to Get Started Building AI Agents
• AI Maestro - Orchestrate Your AI Coding Agents
• PayPal-R/Infra-AI: slack integration with MCP servers
• How we built our multi-agent research system \ Anthropic
• moltbook - the front page of the agent internet
• PacktPublishing/Agentic-Architectural-Patterns-for-Building-Multi-Agent-Systems: Agentic Architectural Patterns for Building Multi-Agent Systems, published by Packt
• pageman/sutskever-30-implementations: Sutskever 30 implementations inspired by https://papercode.vercel.app/ | For Agents, use https://github.com/pageman/Sutskever-Agent | Polyglot / Multi-Backed version at https://github.com/pageman/sutskever-30-beyond-numpy
• The Hitchhiker's Guide to Agentic AI: From Foundations to Systems
• AI Agent Evaluation | DeepEval - The LLM Evaluation Framework
• Discovery of a new OpenAI agent message board
• Training frontier knowledge work agents: A 397B RL training guide with SkyRL | Mercor Blog

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Blogs & People (49)

• frontier model training methodologies | Alex Wa's Blog
• DeepRSI
• How to Build a Scientific Scaling Ladder | Jiaxuan Zou
• yang-yifan.github.io/blogs/memory_model/memory_model.html
• Scaling Pre-Training in Practice: A Hierarchical Approach — Aleph Alpha
• Can LLMs Grow Their Own Capabilities? | Percepta
• Ray Infrastructure at Pinterest. Chia-Wei Chen; Sr. Software Engineer |… | by Pinterest Engineering | Pinterest Engineering Blog | Medium
• Language Models for Text Classification: From Bag-of-Words to Jev
• Language Model "Shape" | Alex L. Zhang
• AIDE²: First Evidence of Recursive Self-Improvement | Weco AI
• Recent Developments in LLM Architectures: KV Sharing, mHC, and Compressed Attention
• The Big LLM Architecture Comparison
• Introducing OUI-1: world's first model for Generative UI | OpenUI - The Open Standard for Generative UI
• Towards self-driving codebases · Cursor
• How To Force Your Brain To Crave Doing Hard Things
• Why We Think | Lil'Log
• Jeremy Jordan
• 27 - The Year Everything Changes - by BOSS | Beauty Of SaaS
• fchollet (fchollet) / Repositories
• Hierarchical Reasoning Model assembly manual for toddlers
• Qwen
• All Posts – Sander Dieleman
• Blog - Aleksa Gordić
• PyTorch internals : ezyang’s blog
• Fabien Sanglard's Website
• Blog | kalomaze's kalomazing blog
• kipply's blog
• HydraDB - serverless context infra for AI
• Suvash Sedhain | Blog
• Blog and Notes | Sebastian Raschka, PhD
• Lil'Log
• blog | Ziming Liu
• Lil'Log
• Simon Willison’s Weblog
• Scaling and Optimizing Frontier Model Training
• blog | Maksim (Max) Zhdanov
• Alisa Liu
• Scaling Laws, Carefully | Lil'Log
• Continuous batching from first principles
• Harness Engineering for Self-Improvement | Lil'Log
• Language model harnesses are compositional generalizers | Alex L. Zhang
• Hamel Husain’s Blog – Hamel's Blog
• Machine Studying | Jacob Xiaochen Li
• Modular Blog: Engineering
• Aleksa Gordić
• TPU 8t and TPU 8i technical deep dive | Google Cloud Blog
• Blog - Outcome School | Get a High-Paying Tech Job
• Mixture of Experts Quantile Balancing: Validated at 32B-A5B (1e22 FLOPs) Scale | Open Athena
• SGLang Blog – Releases, Benchmarks & Announcements

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Books & PDFs (34)

• Grokking Parallel Programming - SungHwan Yun
• Research at AI speed
• Index - The Sim Book
• Foundations of Computer Vision
• EECS-2016-143.pdf
• paper.pdf
• Purshow_Notes/YOCO/EN/YOCO_EN.pdf at main · Purshow/Purshow_Notes
• ML Books - Google Drive
• mlabonne/llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
• language_understanding_paper.pdf
• 491108998_1064335232204123_244838803708223849_n.pdf
• fchollet/deep-learning-with-python-notebooks: Jupyter notebooks for the code samples of the book "Deep Learning with Python"
• Dive into Deep Learning — Dive into Deep Learning 1.0.3 documentation
• manji mali book - Google Search
• Chapters - Deep Learning with Python
• Understanding Deep Learning
• Learning Deep Representations of Data Distributions
• book-main.pdf
• Machine Learning Design Interview: Machine Learning System Design Interview
• AI Engineering
• aie-book/resources.md at main · chiphuyen/aie-book · GitHub
• d2l-en.pdf
• hades.mech.northwestern.edu/images/7/7f/MR.pdf
• tech-books-library/AI & Machine Learning (Deep Learning, NLP, etc.)/Natural Language Processing with PyTorch - Build Intelligent Language Applications Using Deep Learning.pdf at master · HarshVadaliya/tech-books-library · GitHub
• What Every Programmer Should Know About Memory
• Deep+Learning+Ian+Goodfellow.pdf
• stas00/ml-engineering: Machine Learning Engineering Open Book
• GitHub - harvard-edge/cs249r_book: Introduction to Machine Learning Systems
• GitHub - aishwaryanr/awesome-generative-ai-guide: A one stop repository for generative AI research updates, interview resources, notebooks and much more!
• ieeecreativePreprint.dvi
• little-book-rl/book.pdf at main · alxndrTL/little-book-rl
• rlbook.pdf
• sea.pdf
• PowerPoint Presentation

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Courses & Learning (35)

• Build Your Own Reasoning Model: A Full Video Course
• AI Engineering from Scratch | Free AI Engineering Course
• Deep Learning Alchemy | Stanford CS 312
• walkinglabs/learn-harness-engineering: Harness engineering beginner tutorial, from 0 to 1
• Welcome to Learn Harness Engineering | Learn Harness Engineering
• All Domains — Study Guide | Claude Certification Guide
• GitHub - Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies: This repository contains a curated collection of 300 case studies from over 80 companies, detailing practical applications and insights into machine learning (ML) system design. The contents are organized to help you easily find relevant case studies based on industry or specific ML use cases.
• From Scratch | Michal Pitr | Substack
• Deep Learning with Yacine | Yacine Mahdid | Substack
• (2) AI Career Advice From a Top 1% Engineer (feat. Jean Lee!) - YouTube
• id2223kth.github.io/assignments/2025/ID2223Projects2025.html
• Making Deep Learning go Brrrr From First Principles
• How I Got a Job at Google DeepMind (No ML Degree) | Medium
• (2) Build an LLM from Scratch 1: Set up your code environment - YouTube
• Build Your Own ViT Model from Scratch
• Quickstart — JAX documentation
• Zero to Mastery Learn PyTorch for Deep Learning
• Learning PyTorch with Examples — PyTorch Tutorials 2.7.0+cu126 documentation
• Deep-ML
• AI/ML Roadmap for beginner To Advanced - sigmoidit.com
• Deep-ML | Practice Problems
• The ML Trench
• Notion
• The Roadmap of Mathematics for Machine Learning
• GitHub - ritchieng/the-incredible-pytorch: The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
• GitHub - GokuMohandas/Made-With-ML: Learn how to design, develop, deploy and iterate on production-grade ML applications.
• GitHub - HamzaElshafie/gpt-oss-20B: A PyTorch implementation of the GPT-OSS-20B architecture. All components are coded from scratch: RoPE with YaRN, RMSNorm, SwiGLU with clamping and residual connection, Mixture-of-Experts (MoE), Self-Attention with learned sinks, banded attention, GQA, and KV-cache.
• CMU LTI Summer 2026 Internship Program Application
• mallahyari/ml-practical-usecases: A database of 650 Machine Learning (ML) system design case studies from 100+ companies.
• Coding Patterns: A Cheat Sheet
• (13) Deep Learning with Yacine - YouTube
• GitHub - HuaizhengZhang/AI-Infra-from-Zero-to-Hero: 🚀 Awesome System for Machine Learning ⚡️ AI System Papers and Industry Practice. ⚡️ System for Machine Learning, LLM (Large Language Model), GenAI (Generative AI). 🍻 OSDI, NSDI, SIGCOMM, SoCC, MLSys, etc. 🗃️ Llama3, Mistral, etc. 🧑‍💻 Video Tutorials. · GitHub
• GMvandeVen/continual-learning: PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.
• Continual Learning: A Primer | Towards Data Science
• TensorTonic — The hands-on way to master machine learning | TensorTonic

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Diffusion Models (15)

• Large Language Continuous Diffusion Models
• lecture_notes.pdf
• kuleshov-group/awesome-discrete-diffusion-models: A curated list for awesome discrete diffusion models resources.
• All Are Worth Words: A ViT Backbone for Diffusion Models
• AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
• Large Language Diffusion Models
• [2406.07524] Simple and Effective Masked Diffusion Language Models
• Generative modelling in latent space – Sander Dieleman
• Flow Matching in 5 Minutes | wh
• Practical Deep Learning for Coders - 25: Latent diffusion
• We’re open-sourcing our text-to-image model and the process behind it
• I-DLM: Introspective Diffusion Language Models
• How to Build a Diffusion Language Model | Kuleshov Group
• Stanford CME296 Diffusion & Large Vision Models | Spring 2026 | Lecture 1 - Diffusion - YouTube
• The Illustrated Stable Diffusion – Jay Alammar – Visualizing machine learning one concept at a time.

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GPU & CUDA (41)

• https://developer.nvidia.com/blog/accelerating-mamba-2-with-high-performance-forward-pass-kernels-in-nvidia-cuda-tile
• Journal
• How to Train Models Too Big for One GPU | ZeRO & FSDP - YouTube
• Modern GPU Programming For MLSys — Modern GPU Programming For MLSys
• CUDA Programming Guide — CUDA Programming Guide
• Cornell Virtual Workshop: Understanding GPU Architecture
• A history of NVidia Stream Multiprocessor
• Basic facts about GPUs | Damek Davis’ Website
• GitHub - julienokumu/100DaysOfGPUProgramming: 100 Days Of GPU Programming.
• 1. Introduction — CUDA C++ Programming Guide
• cudabook.pdf
• [Full Workshop] Reinforcement Learning, Kernels, Reasoning, Quantization & Agents — Daniel Han - YouTube
• PyTorch in One Hour: From Tensors to Training Neural Networks on Multiple GPUs
• GitHub - clu0/unet.cu: UNet diffusion model in pure CUDA
• Domain specific architectures for AI inference
• TPU Deep Dive
• LeetGPU - The GPU Programming Platform
• 1y33/100Days: GPU Kernels
• a-hamdi/GPU: 100 days of building GPU kernels!
• 100-days-of-gpu/CUDA.md at main · hkproj/100-days-of-gpu
• Smth Smth GPU Related
• 🔥 Introduction - Mojo 🔥 GPU Puzzles
• Blog | Modal
• Hello, world! | Modal Docs
• LeetGPU - The GPU Programming Platform
• README | GPU Glossary
• What is a GPU Core? | GPU Glossary
• Inside NVIDIA GPUs: Anatomy of high performance matmul kernels - Aleksa Gordić
• A case for learning GPU programming with a compute-first mindset – Maister's Graphics Adventures
• 1.1. Introduction — CUDA Programming Guide
• 3.1. Advanced CUDA APIs and Features — CUDA Programming Guide
• accelerated-computing-hub/tutorials/accelerated-python/notebooks/syllabi/cuda_python__cupy_cudf_cccl_kernels__8_hours.ipynb at main · NVIDIA/accelerated-computing-hub · GitHub
• ray-project/llm-numbers: Numbers every LLM developer should know
• Optimizing a Layer Normalization Kernel with CUDA: a Worklog
• Pretrained to Imagine, Fine-Tuned to Act: The Rise of World-Action Models | NVIDIA Technical Blog
• What happens when a GPU reads memory | Doubleword
• wafer-ai/gpu-perf-engineering-resources: A curated resource list for learning AI performance engineering, from GPU fundamentals to production inference.
• What happens when a GPU writes memory | Doubleword
• Redesigning the Inference Chip: From Nvidia GPU's Flaws to OpenAI Jalapeño
• GPU World
• Fast Embeddings on GPUs

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Inference & Serving (29)

• Serving LLMs at Scale: The Hidden KV Cache Advantage - InfoQ
• (1) Cerebras on X: "Disaggregated Inference From The Ground Up" / X
• Avi Chawla on X: "MoE inference engineering, clearly explained" / X
• Learn LLM Serving - Tiny-LLM — Build an LLM Serving System with MLX
• Computation and Data Movement for Inference
• Toward Recursive Self-Improvement: How GLM Built Its Own Inference Infrastructure
• [2508.09874] Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models
• How To Scale Your Model
• How Attention Sinks Keep Language Models Stable
• Defeating Nondeterminism in LLM Inference - Thinking Machines Lab
• Paged Attention from First Principles: A View Inside vLLM | Hamza's Blog
• Transformer Inference Arithmetic | kipply's blog
• How prompt caching works - Paged Attention and Automatic Prefix Caching plus practical tips | sankalp's blog
• vllm-project/vllm: A high-throughput and memory-efficient inference and serving engine for LLMs
• Tensor Parallelism (TP) in Transformers: 5 Minutes to Understand
• Efficient Memory Management for Large Language Model Serving with PagedAttention
• Achieve 23x LLM Inference Throughput & Reduce p50 Latency
• Optimization story: Bloom inference
• AI-Infra-from-Zero-to-Hero/inference.md at master · HuaizhengZhang/AI-Infra-from-Zero-to-Hero · GitHub
• LLM Inference Handbook
• Inside vLLM: Anatomy of a High-Throughput LLM Inference System - Aleksa Gordić
• Inference Engineering Chapters | Inference Engineering
• [2401.10774] Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads
• The fastest path to state-of-the-art LLM inference on any accelerator | llm-d
• Inside vLLM: Anatomy of a High-Throughput LLM Inference System - Aleksa Gordić
• Inference · Learn Inference
• Understanding and Coding the KV Cache in LLMs from Scratch
• Transformer Inference Arithmetic | kipply's blog
• Latent Briefing: Efficient Memory Sharing for Multi-Agent Systems via KV Cache Compaction · Ramp Labs Research

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Information Retrieval & RAG (5)

• DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning
• Building a web search engine from scratch in two months with 3 billion neural embeddings
• How to Build a Complete End-to-End NLP Pipeline with Gensim: Topic Modeling, Word Embeddings, Semantic Search, and Advanced Text Analysis - MarkTechPost
• Evaluation in information retrieval
• Introduction to Information Retrieval

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Interpretability (11)

• Zoom In: An Introduction to Circuits
• Superposition by Hand ✍️ - by Prof. Tom Yeh - AI by Hand ✍️
• Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet
• Transformer Circuits Thread
• Activation Atlas
• Distill — Latest articles about machine learning
• A Comprehensive Mechanistic Interpretability Explainer & Glossary - Dynalist
• Mechanistic Interpretability — Neel Nanda
• An Intuitive Explanation of Sparse Autoencoders for LLM Interpretability | Adam Karvonen
• [1503.02531] Distilling the Knowledge in a Neural Network
• On-Policy Distillation - Thinking Machines Lab

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LLM Training & Fine-tuning (5)

• The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities (Version 1.0)
• The Smol Training Playbook: The Secrets to Building World-Class LLMs
• Build MLOps Fine-Tune System on GCP From Scratch
• We Got Claude to Fine-Tune an Open Source LLM
• Yewei-Liu/SHINE: The repo for SHINE: A Scalable In-Context Hypernetwork for Mapping Context to LoRA in a Single Pass

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RL & Alignment (18)

• An Extremely Opinionated Annotated List of My Favourite Mechanistic Interpretability Papers v2 — AI Alignment Forum
• Reinforcement Learning from Verifiable Rewards
• What is RLVR? Reinforcement Learning from Verifiable Rewards – Reinforcement Learning from Verifiable Rewards
• KLPO — Critic-Free Agentic Reinforcement Learning
• yifanzhang-pro/KLPO: Official Project Page for KL-Regularized Policy Optimization for Agentic Reinforcement Learning (KLPO)
• [2505.22954] Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
• avatarl: training language models from scratch with pure reinforcement learning - tokenbender
• How To Become A Mechanistic Interpretability Researcher — AI Alignment Forum
• [2509.04475] ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute
• Parallel-R1: Towards Parallel Thinking via Reinforcement Learning
• DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning
• Post-training 101 | Tokens for Thoughts
• Stanford CS329A | Self-Improving AI Agents
• 10-703 Deep RL
• 10-703 Deep RL | Schedule
• Reinforcement Learning of Large Language Models - YouTube
• andri27-ts/Reinforcement-Learning: Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning
• State of RL for reasoning LLMs | A. Weers

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Research Papers (66)

• [2312.03096] What Causes Polysemanticity? An Alternative Origin Story of Mixed Selectivity from Incidental Causes
• [2610.13630] Multi-Stream Attention Residuals
• Continuous Memory Machines
• Beyond Parameters: Exploring Virtual Logic Depth for Scaling Laws
• Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
• Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation
• arxiv.org/pdf/1807.03819
• Multimodal Deep Learning
• 2405.05254
• DeepSeek_V41_Tech_Report.pdf · deepseek-ai/DeepSeek-V4.1-Flash at main
• 2412.05265v3.pdf
• 2408.12528
• 2404.02905
• 2111.14822
• AI Academic Reader | Paper Digest
• 2408.11039
• 2407.15595
• 2210.02747
• 2505.15778
• 2304.08485
• 2505.12540
• arxiv.org/pdf/2505.00675
• 2505.19590
• 2405.00334
• 2402.01364
• 2505.21444
• 1410.5401
• 2306.14846
• 2406.09246
• 2310.08864
• 2301.08243
• 2507.10524v1.pdf
• Kimi-K2/tech_report.pdf at main · MoonshotAI/Kimi-K2 · GitHub
• 2507.15028v1.pdf
• 2407.18384v3.pdf
• 2507.18074v1.pdf
• 2003.02436
• 2506.21734v2.pdf
• 2507.21046v2.pdf
• Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving | alphaXiv
• arxiv.org/pdf/2507.21206
• 2501.09223v2.pdf
• 2507.22229v1.pdf
• Fall 2025 Reading List (##201-210) – Aleksey Charapko
• Loading PDF…
• 2508.15884v1.pdf
• 1904.10509v1.pdf
• 2504.15228v2.pdf
• 2403.18103v3.pdf
• 2509.08827v1.pdf
• SpikingBrain Technical Report: Spiking Brain-inspired Large Models
• ATLAS: Learning to Optimally Memorize the Context at Test Time
• 2510.04871v1.pdf
• 2510.17558v1.pdf
• 2510.21890v1.pdf
• 2006.11239
• arxiv.org/pdf/2506.22355?
• 2402.17764
• arxiv.org/pdf/2602.24281
• 3D Gaussian Splatting for Real-Time Radiance Field Rendering
• World Action Models are Zero-shot Policies
• 1612.03153
• [2111.06377] Masked Autoencoders Are Scalable Vision Learners
• Roofline: an insightful visual performance model for multicore architectures
• 2607.13104v1.pdf
• arxiv.org/pdf/2510.21890

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Robotics & Embodied AI (15)

• fracapuano/robot-learning-tutorial: All the source code for "Robot Learning: A Tutorial". Get involved to be featured in the next iteration!
• Towards Universal Post-Training for Robotics — Perry Dong
• Robot Learning
• Paper page - Robot Learning: A Tutorial
• HCPLab-SYSU/Embodied_AI_Paper_List: [Embodied-AI-Survey-2025] Paper List and Resource Repository for Embodied AI
• Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI
• haoranD/Awesome-Embodied-AI: A curated list of awesome papers on Embodied AI and related research/industry-driven resources.
• [2402.02385] A Survey on Robotics with Foundation Models: toward Embodied AI
• GT-RIPL/Awesome-LLM-Robotics: A comprehensive list of papers using large language/multi-modal models for Robotics/RL, including papers, codes, and related websites
• Large Language Models for Robotics: A Survey
• Embodied AI Agents: Modeling the World
• Gradient Robotics
• huggingface/lerobot: 🤗 LeRobot: Making AI for Robotics more accessible with end-to-end learning
• Robot Learning: A Tutorial - a Hugging Face Space by lerobot
• An Overview of Modern AI Robotics from First Principles | Interlatent Blog

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Tools & Repos (23)

• Purshow/Purshow_Notes
• harshit3011/Technical-Engineering-Articles
• Research acceleration: The view inside OpenAI | OpenAI
• Deepseek Papers - a Presidentlin Collection
• @Kseniase on Hugging Face: "12 Foundational AI Model Types Let’s refresh some fundamentals today to stay…"
• GitHub - google-gemini/genai-processors: GenAI Processors is a lightweight Python library that enables efficient, parallel content processing.
• Why language models hallucinate | OpenAI
• ML Resources
• Petals – Run LLMs at home, BitTorrent-style
• turbopuffer
• Spaces - Hugging Face
• GitHub - modal-projects/modal-jazz: we have ai at home
• microgpt · GitHub
• GitHub - liquidslr/interview-company-wise-problems: Lists of company wise questions. Every csv file in the companies directory corresponds to a list of questions on leetcode for a specific company based on the leetcode company tags. Updated as of 20 June, 2025 · GitHub
• Advancing AI for Humanity
• Advancing AI for Humanity
• llm-wiki
• Emotion concepts and their function in a large language model \ Anthropic
• graphdeco-inria/gaussian-splatting: Original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering"
• EdisonLeeeee/Awesome-Masked-Autoencoders: A collection of literature after or concurrent with Masked Autoencoder (MAE) (Kaiming He el al.).
• AiHubCN/Awesome-LLM-Survey: An Awesome Collection for LLM Survey
• genai-llm-ml-case-studies/case-studies/by-use-case/llm.md at main · themanojdesai/genai-llm-ml-case-studies
• amitshekhariitbhu/ai-engineering-interview-questions: Your Cheat Sheet for AI Engineering Interview – Questions and Answers.

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Transformers & Attention (11)

• Recurrent Looped Transformer
• Full-bandwidth transformer
• GPT-6 Astra, Looped Transformers, and Hidden Reasoning
• Transformers from scratch | peterbloem.nl
• GitHub - analyticalrohit/AI-ML-Cheatsheets: All standford Cheatsheets: Artificial Intelligence, Transformers, LLMs, Deep Learning, Machine Learning, Probabilities, Statistics, Algebra and Calculus.
• [2508.16204] Competition and Attraction Improve Model Fusion
• The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time.
• A Visual Guide to Attention Variants in Modern LLMs
• Circuits Updates - July 2024
• Understanding Transformers and Attention Mechanisms: An Introduction for Applied Mathematicians
• Looped Transformer — papers and benchmarks | Papers with Code

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World Models (6)

• Next-Latent Prediction Transformers Learn World Models feat. Jayden Teoh from MIT - YouTube
• CIS 6280 · World Models
• World Models
• GuochenZhou/World-Model: A paper list of world model
• World Models
• knightnemo/Awesome-World-Models: A Curated List of Awesome Works in World Modeling, Aiming to Serve as a One-stop Resource for Researchers, Practitioners, and Enthusiasts Interested in World Modeling.

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394 links across 17 categories

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