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I am feeling the best i have felt in a long time. I have been experimenting with diet and suppliments. here is my current stack:

Morning:

• 15g Collagen + 5g Creatine + aloevera juice + 1 lemon
• Go for a walk + run 1.5 Km + 1.5 Km
• Some basic resistance band exercises.

Since Aloevera is a laxative the best way to prepare it is first cut the leaf in 2-3 pieces horizontally and let it soak in a lot of water for 4-5 hours. throw away that water before removing the aloe gel. Be careful not to scrape any green stuff and you will have no laxative effect.

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Update: this shit doesnt work.

Day	Meal	Food Item	Portion Size	Calories	Carbs (g)	Protein (g)	Fat (g)	Fiber (g)
1	Breakfast	Oatmeal	1/2 cup (40g)	150	27	5	2	4
		Blueberries	1/4 cup (40g)	20	5	0	0	2
		Almonds	1 ounce (28g)	160	6	6	14	3
	Lunch	Grilled Chicken	3 ounces (85g)	165	0	31	3	0
		Brown Rice	1/2 cup cooked (90g)	100	22	2	1	2
		Broccoli	1 cup cooked (156

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This worked on 14/May/23. The instructions will probably require updating in the future.

llama is a text prediction model similar to GPT-2, and the version of GPT-3 that has not been fine tuned yet. It is also possible to run fine tuned versions (like alpaca or vicuna with this. I think. Those versions are more focused on answering questions)

Note: I have been told that this does not support multiple GPUs. It can only use a single GPU.

It is possible to run LLama 13B with a 6GB graphics card now! (e.g. a RTX 2060). Thanks to the amazing work involved in llama.cpp. The latest change is CUDA/cuBLAS which allows you pick an arbitrary number of the transformer layers to be run on the GPU. This is perfect for low VRAM.

• Clone llama.cpp from git, I am on commit 08737ef720f0510c7ec2aa84d7f70c691073c35d.

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"""
Classifies sequences of length 10 with 20 features into 2 classes
with a single LSTM layer with 32 neurons.

See also a more involved example:
https://gist.github.com/bzamecnik/dccc1c4fdcf1c7a31757168b19c827a7
"""


from keras.layers import Input, LSTM, Dense

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"""
When classifying upon a sequence usually we stack some LSTM returning sequences,
then one LSTM returning a point, then Dense with softmax activation.

Is it possible instead to give the last non-sequential LSTM a softmax activation?

The answer is yes.

In this example we have 3 sequential layers and one layer producing the final result.

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macOS Internals

Understand your Mac and iPhone more deeply by tracing the evolution of Mac OS X from prelease to Swift. John Siracusa delivers the details.

Starting Points

How to use this gist

You've got two main options:

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brew install emacs-mac --with-no-title-bars --with-natural-title-bar --with-native-comp --with-xwidgets

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Doom Emacs: Markdown live preview with xwidget-webkit

I saw that there's a markdown-live-preview-mode and the var markdown-live-preview-window-function which was set to the function markdown-live-preview-window-eww. That function looks like this:

(defun markdown-live-preview-window-eww (file)
  "Preview FILE with eww.
To be used with `markdown-live-preview-window-function'."

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;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Terminal notifier 
;; requires 'sudo gem install terminal-notifier'
;; stolen from erc-notifier

(defvar terminal-notifier-command (executable-find "terminal-notifier") "The path to terminal-notifier.")

; (terminal-notifier-notify "Emacs notification" "Something amusing happened")

(defun terminal-notifier-notify (title message)

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set imap_user="mail.vpal@gmail.com"
set imap_pass=`/usr/bin/security find-generic-password -w -a 'mail.vpal@gmail.com' -s 'Gmail'`

set folder=imaps://imap.gmail.com/
set spoolfile=+INBOX
set record="+[Gmail]/Sent Mail"
set postponed="+[Gmail]/Drafts"

# https://www.neomutt.org/guide/reference search sleep_time for additional info
set sleep_time=0 # be faster