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Jet New | 100 Lessons from 1 Year of AI Research
100 years of whatever this will be - apenwarr
How can we develop transformative tools for thought?
How to Remember What You Read
How to understand groups of people
Kierkegaard and Entrepreneurship
Lockhart's Magician's Lament
Reality has a surprising amount of detail
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Shtetl-Optimized » Blog Archive » An alarming trend in K-12 math education: a guest post and an open letter
The Business of Extracting Knowledge from Academic Publications
The Expected Value of Friendships - by Judah - Be Wrong
The Gift We Give To Tomorrow
The Inner Ring
blog
Advice and resources for the agentic high-schooler
How people cope with civilizational doom
Notes on Agency
Yearly Review 2020
books
Fiction
Non Fiction / Learning
notes
Beyond Good and Evil
L'insoutenable légèreté de l'ètre
La Peau de Chagrin - Balzac
La promesse de l'aube
Le Loup des Steppes
Philosophical Astonishment - Jeanne Hersch
Problèmes clés de mathématiques supérieures
Selfish Gene
Structure of Scientific Revolutions
The Anomaly - Hervé Le Tellier
The Art Of Doing Science and Engineering
The Precipice: Existential Risk and the future of humanity
The Theoretical Minimum
The Unconsoled Kazuo Ishiguro
Why Nations Fail
computer-science
6.190 C and Assembly
6.191 MIT
program synthesis notes
AI
6.S898 Deep Learning
AGISF
AI Notes
Autocast
Building a simplified pytorch in rust
DreamCoder
GAN paper
GPT paper
Grokking and representation learning
Imitating interactive intelligence
Implicit Representations of Meaning in Neural Language Models
In-context Learning and Induction Heads
Polysemanticity and capacity in neural networks
Science of deep learning reading group
Seri Mats 2022
The Computational Limits of Deep Learning
Thoughts on Fast.AI: Deep Learning for Coders
To-learn
Transformers learn in-context by gradient descent
using natural language and program abstractions to instill human inductive biases
Visualizing representations - colah
nlp-course
NLP attention models
NLP Probabilistic Models
NLP Sequence Models
Vector Spaces in NLP1
RL
A (Long) Peek into Reinforcement Learning
safety
Inference Time Intervention: Eliciting Truthful Answers from a Language Model
Messy Research Journal
Roadmap and links
Survey paper interp
algorithms
6.1220 Notes
Competitive Programming Method
bin-trees
Dynamic maximum
General
Priority Queue
language-notes
C++-Lang
Effective Rust
Haskell
Rust-Lang Notes
cybersec
Cool challenges
FCSC 2021 Overview
econ
14.02 Notes
MRU Micro
knowledge-management
AI + KM index
maths
18.022 intuitions
18.701
18.702
Analysis / Measures 18.100B
Animaths - Prep
Binomial coefficients
chaos
Chromatic Numbers
Concours General Solutions
cool solving methods
Game theory
Inequalities
Intersections of sets
Mathematical Summer in Paris Notes
Matrix Calculus 18.S096
Nombres catalans et bijections
Résolution d'équations polynomiales
Similar to binomials
Theory of Computation 18.404
Transport optimal
prepa-ri
Dérivation/Integration
music
Favorite Musicians / Groups
Full Album Listens
Recs for Friends
physics
8.022 Blockings
8.03 Waves
poetry
Poems I enjoyed
The Life of Poetry
random
2.678 Electronics for mechanics
AOTD MIT
Article of the day
Cool Artists
Digital Attic
Learning How to learn
Quotes
Startup School
Vim CheatSheet
Word-Bank
recipes
Mercimek koftesi
school
Act revisions
english
Analysis Pointers
Comparative Essay Identity
OIB revision plan
Oral Howard's end p91-92
Rewrite Personal Helicon Essay
physics
CG prep
rev dst
tle
Cold war notes
GEO
History-GEO-FR
theme 3 hist
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Inference Time Intervention: Eliciting Truthful Answers from a Language Model
06-12-23
