- Direct filesystem reads from the view
- Direct function composition in the style of PHP, despite network
- No API, no client side database, no frontend state at all (except DOM), no frontend/backend.
- Literally all accidental complexity GONE
- 100% of server data sync is managed by Electric
- Rows have arbitrary server local data access, here querying file metadata #clojure
- Optimized and no scroll jank – DOM elements are fixed in place and reused
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// ==UserScript== | |
// @name Hcaptcha Solver with Browser Trainer(Automatically solves Hcaptcha in browser) | |
// @namespace Hcaptcha Solver | |
// @version 10.0 | |
// @description Hcaptcha Solver in Browser | Automatically solves Hcaptcha in browser | |
// @match https://*.hcaptcha.com/*hcaptcha-challenge* | |
// @match https://*.hcaptcha.com/*checkbox* | |
// @grant GM_xmlhttpRequest | |
// @grant GM_setValue | |
// @grant GM_getValue |
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export XDG_CONFIG_HOME="$HOME/.config" | |
export XDG_DATA_HOME="$HOME/.local/share" | |
export XDG_CACHE_HOME="$HOME/.cache" | |
export XDG_STATE_HOME="$HOME/.local/state" | |
export PATH="$PATH:$HOME/.local/bin" | |
export BROWSER='firefox' | |
export EDITOR='nvim' | |
export PAGER='less' | |
export READER='zathura' |
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// ==UserScript== | |
// @name Hcaptcha Solver with Browser Trainer(Automatically solves Hcaptcha in browser) | |
// @namespace Hcaptcha Solver | |
// @version 10.0 | |
// @description Hcaptcha Solver in Browser | Automatically solves Hcaptcha in browser | |
// @author Md ubeadulla | |
// @match https://*.hcaptcha.com/*hcaptcha-challenge* | |
// @match https://*.hcaptcha.com/*checkbox* | |
// @grant GM_xmlhttpRequest | |
// @grant GM_setValue |
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#!/data/data/com.termux/files/usr/bin/bash | |
echo "Enter URL here:" | |
read URL | |
echo "Enter START_TIME here:" | |
read START_TIME | |
echo "Enter END_TIME here:" | |
read END_TIME |
In order to keep filters up to date, please use this repo.
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import torch | |
import torch.utils.dlpack | |
import jax | |
import jax.dlpack | |
# A generic mechanism for turning a JAX function into a PyTorch function. | |
def j2t(x_jax): | |
x_torch = torch.utils.dlpack.from_dlpack(jax.dlpack.to_dlpack(x_jax)) | |
return x_torch |
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# ==== Non-communicating processes | |
# 4x 1 chip per process: | |
os.environ["TPU_CHIPS_PER_PROCESS_BOUNDS"] = "1,1,1" | |
os.environ["TPU_PROCESS_BOUNDS"] = "1,1,1" | |
# Different per process: | |
os.environ["TPU_VISIBLE_DEVICES"] = "0" # "1", "2", "3" | |
# 1-liner for bash: TPU_CHIPS_PER_PROCESS_BOUNDS=1,1,1 TPU_PROCESS_BOUNDS=1,1,1 TPU_VISIBLE_DEVICES=0 TPU_MESH_CONTROLLER_ADDRESS=localhost:8476 TPU_MESH_CONTROLLER_PORT=8476 |
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Param( | |
# (*)クリエイターID: URLから推測して指定 | |
[Parameter(Mandatory, ValueFromPipeline)] | |
[ValidateScript({ $_ -gt 0 })] | |
[int]$CreatorId, | |
# ページ指定: 投稿記事の一覧表示における何ページから何ページまでをDLするかどうか。未指定時は全ページ | |
[ValidateScript({ $_ -gt 0 })] | |
[int]$PageStart = 1, | |
[int]$PageEnd, |
They basically all suggest that apparent improvements to the state of the art in ML and related fields are often not real, or at least the result of factors other than what the authors claim.
The state of sparsity in deep neural networks
What is the state of neural network pruning?
On the State of the Art of Evaluation in Neural Language Models
Do Transformer Modifications Transfer Across Implementations and Applications?