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Sublunary sphere - Wikipedia
10 Months to 10%
The Lost Reading Items · Tensor Labbet
Earning to Save (Give 1%, Save 10%) — EA Forum
Earning to Save (Give 1%, Save 10%) — EA Forum
Stuart J. Russell - Wikipedia
How does it feel to switch from earn-to-give? — LessWrong
"Taking AI Risk Seriously" (thoughts by Critch) — LessWrong
Deliberate Grad School | Andrew Critch
Every Grant is also a Bounty | Applied Divinity Studies
Manifund
Manifund
Explaining Impact Markets
manifoldmarkets/manifund
About | Forethought
Overview: Funders in EA
Support our mission | Founders Pledge
Who we are | Founders Pledge
Proposal: LLMs to trade prediction markets
Build anti-mosquito drones | Manifund
Perfectly Normal
Promote Georgism | Manifund
FQxI - Foundational Questions Institute - FQxI
Seven Laws of Noah - Wikipedia
Deterministic finite automaton - Wikipedia
0-1.pdf
Hava Siegelmann - Wikipedia
[1412.7755] Multiple Object Recognition with Visual Attention
[1502.04623] DRAW: A Recurrent Neural Network For Image Generation
Great Plains - Wikipedia
Killer fungi to spread as climate heats up
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
karpathy/char-rnn: Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch
jcjohnson/torch-rnn: Efficient, reusable RNNs and LSTMs for torch
List of computer science awards - Wikipedia
XML - Wikipedia
Germanic vs Latinate Words in Writing
Dominant Assurance Contract Experiment #2: Berkeley House Dinners
Prioritizing Work — EA Forum
In My Culture — LessWrong
[3Run] [SuperTricks.tk] Evolution - YouTube
I Worked for Pope Francis. | by Daniel B. Gallagher | Apr, 2025 | Medium
[1406.6247] Recurrent Models of Visual Attention
[1410.5401] Neural Turing Machines
Humanoid Robots Being Mass Produced in China - Newsweek
Agibot A2 Humanoid Robot w/ Advanced AI: Buy or Lease at Top3DShop
Digital Manufacturing Integrator and Store Top 3D Shop. 3D Printers, 3D Scanners, CNC Machines, Robots and Custom solutions
Learning long-term dependencies with gradient descent is difficult - Neural Networks, IEEE Transactions on
Blog – The Tangent Bundle
wojzaremba/lstm