vesicularia
@PentelEnergel
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Plans for the next few weeks:
- Compiler
- OS
- 6502 emulator
- Networking protocols
- Write everything I use from scratch (tokenizer, optimizers, etc)
- Implement papers
- Mod Hades 2; MuJoCo
Textbooks
- The C Programming Language
- PRML
- RL (Sutton)
- Misc textbooks
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@lightxvision
Gamify CS education
You spawn in with a thinkpad and a usb with arch
You don’t even get to see your course content until you setup your laptop
Everything happens on a quest basis and everyone gets different hardware so you have to do it yourself
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i remember watching this video when it came out and thinking "who would do this unironically"
little did i know
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what distinguishes good CS unis (CMU, waterloo, berkeley, etc) from the rest, where it's basically the same everywhere?
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exactly 100 pages of notes in april
i kinda wanna make a progress bar that detects what i'm doing and starts recording time then displays the running total as a percentage of 10000 hours (pic rel)
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desperately need one of these to display training loss or KL divergence
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ml this ai that i'm gonna go learn how a computer works brb
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Week 3:
- papers papers papers papers papers
- implementing GPT-2 in mlxGPT
- working through chapter 2 of PRML still and getting the hang of distributions
- implemented LeNet 5
- Started CUDA by example
Geohot streams are great.
Current roadmap:
- Pattern Recognition and Machine Learning
- Train my nanoGPT rewrite in mlx on the Shakespeare dataset; experiment more to build intuition
- Go through the RL lectures by David Silver
- Read 2-3 of the "important papers" every day, on loop
- Implement the papers
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he's roughly 32 watches tall
if you take the average diameter of a sports watch (42mm/1.65") that would make him 134cm/4ft4 tall
respectfully
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It’s gojover I’m past 17 and haven’t published a paper that’s on the cover of Science
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Current roadmap:
- Pattern Recognition and Machine Learning
- Train my nanoGPT rewrite in mlx on the Shakespeare dataset; experiment more to build intuition
- Go through the RL lectures by David Silver
- Read 2-3 of the "important papers" every day, on loop
- Implement the papers
Current high level roadmap:
- Learn how to write basic ML in C++
- Attempt DL stuff
- Learn how to write Triton kernels
- Learn how to use CUDA (big step)
- Try writing kernels myself
- Try implementing papers
- Write my own Triton and CUDA architecture
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reminder for those who need it, this is what to do if an egirl follows you
x dot com is for everything except catching me lacking on the tl
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You can just bruteforce anything
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Reading the os textbook took me a whole month but now we ball
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Boy dinner
in your remaining lifetime, you are going to eat maybe 200, max 400 more rib eye steaks. make sure to enjoy them
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@qtnx_
one of the cool things about ml is that you can literally use it on everything to make it better
reminded me of pmarca
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Week 4:
- More paper reading (3/day) and read the code repo implementations
- Almost done with rewrite of nanoGPT in mlx, only training loop left
- Skimming textbooks
- Kaggle AIMO challenge
End of semester college grind doesn't know how to read the room :(
Current roadmap:
- Pattern Recognition and Machine Learning
- Train my nanoGPT rewrite in mlx on the Shakespeare dataset; experiment more to build intuition
- Go through the RL lectures by David Silver
- Read 2-3 of the "important papers" every day, on loop
- Implement the papers
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Weirdness/abnormality is a measure of a person’s dimensionality
You’d expect a rust dev to be a catboy but not a high ranking army general
The more spheres you inhabit the more varied a person you become and the more associations you break
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Doors is unironically hard as a wallpaper app I have 15 nous wallpapers on shuffle
@kennethnym
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@iamyourboon
If people use the time they spend bookmarking/peacocking about what they’re learning actually consuming the information the average iq would rise faster than the sea levels
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Every engineer should aspire to be like this guy
> designs and assembles his own pcbs
> does frontend, backend, low level, machine learning, drivers, everything
> engineering and cad for his robots
<Peng zhihui> on YouTube btw
limiting yourself to being just frontend or just backend is such an easy way to ruin the fun of the job
fullstack should be the only title you chase, otherwise you're limiting your abilities to serve your userbase
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There’s a cat on my way to the gym and I pet it every time best way to start the day fr
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> internship at a startup
> ask if the product is a gpt wrapper or something new and novel
> they’re confused
> explain that you probably don’t need a company to run a gpt wrapper
> they laugh and say it’s a good product
> look at product
> it’s a gpt wrapper
Many such cases
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Week 2:
- Coded less, deciding next project
- Read ~5 papers a day
- Getting obliterated by Gaussians in PRML Chapter 2
- Ren AI Circulation
I'm experimenting with different approaches to reading papers, lmk if you have a way that works for yourself specifically :)
Current roadmap:
- Pattern Recognition and Machine Learning
- Train my nanoGPT rewrite in mlx on the Shakespeare dataset; experiment more to build intuition
- Go through the RL lectures by David Silver
- Read 2-3 of the "important papers" every day, on loop
- Implement the papers
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Learning how to speak Chinese teaches you how to generalize because if you encounter a word you don’t know that looks similar to one you know you can just read that one in a different tone and it’s usually right
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@nahrzf
There’s a Chinese medicine explanation that my doctor told about liver fire raging at that time of day and it does some shit to your blood and brain
Doesn’t translate well but it’s a known thing
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If you do 100 pages a day a 1000 page textbook can be completed in 10 days
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i finally have time to play with anifusion and these go unreasonably hard holy shit
@EsotericCofe
cooked with gas
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book review (mostly a summary) on why greatness can't be planned
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Week 6:
- Spent revision time trying to fix the loss on GPT2, terrible idea
- Read a total of 5 papers
- Read Henry Ford’s autobiography (recommend) and Jay Gould’s biography
Final week of exams. I’ve been edged by ml for the past month. I will be unfathomably locked in.
Current roadmap:
- Pattern Recognition and Machine Learning
- Train my nanoGPT rewrite in mlx on the Shakespeare dataset; experiment more to build intuition
- Go through the RL lectures by David Silver
- Read 2-3 of the "important papers" every day, on loop
- Implement the papers
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no fucking way i actually got tagged by pentel the company
i might have to change username though
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new uni strat
spend two days of the week just learning all the content for the week, then rest of the week I’m free
it'll be interesting if i just use textbooks (CSAPP, Computer Systems, Intro to Algorithms, SICP)
do all the exercises, do the work, just do it faster.
god i
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it took 3 hours to get the architecture right but now I have the most carefully commented code i've ever written
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Why is there so little work done on genetic algorithms and evolution strategies and just non-backprop methods
And I swear Kenneth O. Stanley’s name is on half the papers I read which is cool because I read his book
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@gion_alt
@EsotericCofe
i miss the old tpot, the ml post tpot
chop up the soul tpot, set on the code tpot
i hate the new tpot, slop in the news tpot
i miss the sweet tpot, arxiv pre-release tpot
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should i get a printer for my dorm room to read papers better
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new strat for reading textbooks
1. read
2. make notes
3. come up with the worst pickup lines possible with the content
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Why make a dedicated wearable when you can make Amadeus
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i'm ashamed about the 20-30 commits i added from debugging my html website
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NeRV is pretty interesting ngl
- it makes a neural representation of a video and returns a frame when given the timestamp
- this already compresses the video, and further model compression methods can be used to make it even smaller (like quantization)
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@yotzol
How do you just start talking to people in any unfamiliar social situation I inevitably start thinking of bocchi the rock
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google colab runtime disconnecting because of inactivity is the worst feature to ever exist how tf am i supposed to do an overnight run
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@nahrzf
In two years dev YouTubers will be following your example and making dingboard tutorials
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LIFE UPDATE: I’m 6’2 now
I am also dying to a pumpkin spice latte it’s been a good run
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When I read anime pfp tweets I see the character talk with the speech bubble
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You can get cracked at literally anything just by doing the right thing over and over and over again
If you don’t know what the right thing is then keep doing reps slightly differently than before until you hit it
You just have to keep doing repetitions repetitions repetitions
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I’ll bet you didn’t know that the souls of the rei clones are made from the lost life savings of Japanese men playing rei (rebuild 3+1) pachinko machines
For everyone calling me a “fake cel” for not knowing Eva’s are made of meat & bone imbued with the spirits of the pilots mother..
I just want to say I have forgotten more about evangelion than you will ever know. I’ve watch end of evangelion twenty times, twice in theaters. I
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actually reading textbooks feel less like that and more like this
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i really really really don't like college courses rn it's getting in the way of the me actually learning
it's not that i don't like math, it's that i want to do other math
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It’s beautiful that there are people who do so much research and go through so much information and just shares the compilations online for free. I love the internet.
For anyone who likes reading about business and companies
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