Noted. Using my Luminette before breakfast affected my sleep-quality more than using it throughout the day.
For anyone interested: I've compiled 8 years of experiments with bright light therapy devices here:
For every 30 minutes of sun exposure before 10am, you fall asleep 20 minutes earlier at night and sleep quality is significantly improved ☀️ 🌑 💤 t.co/ImjZRmjnFJ
Thank you Shirley, Lewis, Shangri-Las, ESG, Martin, Christopher and Deee-Lite for sharing your music, courage and talent with the world. t.co/5KSXHko8ra
PS: And thank you @ChanceswWolves for watching out for us :)
LOL, got 100% nerd-sniped by my friend Sönke this week and wound up building a small spaceship.
On Monday he's like "Hey, what if you found obscure seed phrases embedded in public texts? You'd only need to remember the name of the book and the paragraph and go from there."
I honestly could care less about crypto(currencies) and I'm 100% sure this is like cryptanalysis 101. But, yeah, it seemed like an interesting problem anyways.
First, I downloaded a few hundred books from Gutenberg, wrote a ruby script and found BIP39 word sequences with a tolerable buffer for filler-words.
Then, I was like, okay, gotta now check them against actual addresses. Downloaded a list of funded ETH addresses. Wrote the checker in ruby. Ran it. No hits but this was now definitely weirdly interesting.
Because: And what if I downloaded the whole pg19 text corpus to scan! And what if I'd add BTC addresses! And what if I checked every permutation of the seed phrase!
Everything got really slow once I got to processing 12G of raw text for finding sequences and then checking a few million candidates with 44.000+ variations per candidate.
So, let's rewrite this into C! And since I've got 16 cores, let's parallelize this puppy! And since it's a MacBook, let's use GCD! Optimize all the things!
Lol, so NOW this thing is so fucking FAST. Takes four minutes to go through the full pg19 corpus and generates 64,205,390 "interesting" seed phrases. The fully parallelized checker (see Terminal screenshot) processes 460 derived addresses per second.
I really don't care if I get a match or not. I feel like I started with building a canoo and wound up with a spaceship is in itself just the best thing in the world.
Anyways. Just wanted to share this. If anyone wants, I can put the whole thing on github, too. Let me know!
LOL, got 100% nerd-sniped by my friend Sönke this week and wound up building a small spaceship.
On Monday he's like "Hey, what if you found obscure seed phrases embedded in public texts? You'd only need to remember the name of the book and the paragraph and go from there."
I honestly could care less about crypto(currencies) and I'm 100% sure this is like cryptanalysis 101. But, yeah, it seemed like an interesting problem anyways.
First, I downloaded a few hundred books from Gutenberg, wrote a ruby script and found BIP39 word sequences with a tolerable buffer for filler-words.
Then, I was like, okay, gotta now check them against actual addresses. Downloaded a list of funded ETH addresses. Wrote the checker in ruby. Ran it. No hits but this was now definitely weirdly interesting.
Because: And what if I downloaded the whole pg19 text corpus to scan! And what if I'd add BTC addresses! And what if I checked every permutation of the seed phrase!
Everything got really slow once I got to processing 12G of raw text for finding sequences and then checking a few million candidates with 44.000+ variations per candidate.
So, let's rewrite this into C! And since I've got 16 cores, let's parallelize this puppy! And since it's a MacBook, let's use GCD! Optimize all the things!
Lol, so NOW this thing is so fucking FAST. Takes four minutes to go through the full pg19 corpus and generates 64,205,390 "interesting" seed phrases. The fully parallelized checker (see Terminal screenshot) processes 460 derived addresses per second.
I really don't care if I get a match or not. I feel like I started with building a canoo and wound up with a spaceship is in itself just the best thing in the world.
Anyways. Just wanted to share this. If anyone wants, I can put the whole thing on github, too. Let me know!
Small changes, up from 460 to 12,898 checks per second.
@haydendevs Wrote down an idea around this yesterday - basically have LLMs generate text using homoglyphs. Which would be perfectly readable and verifiably of non-human origin
Idea for "marking" AI-generated text: Make a final pass that replaces the regular letters with UTF8 homoglyphs instead. Still 100% readable and verifiably of non-human origin.
Case in point, here's the homoglyph version of this message:
This is actually the biggest danger: that workers still have to keep it a secret that they use AI at work because so many employers still think AI is still at the GPT-4 level.
Idea for "marking" AI-generated text: Make a final pass that replaces the regular letters with UTF8 homoglyphs instead. Still 100% readable and verifiably of non-human origin.
Case in point, here's the homoglyph version of this message:
LOL, got 100% nerd-sniped by my friend Sönke this week and wound up building a small spaceship.
On Monday he's like "Hey, what if you found obscure seed phrases embedded in public texts? You'd only need to remember the name of the book and the paragraph and go from there."
I honestly could care less about crypto(currencies) and I'm 100% sure this is like cryptanalysis 101. But, yeah, it seemed like an interesting problem anyways.
First, I downloaded a few hundred books from Gutenberg, wrote a ruby script and found BIP39 word sequences with a tolerable buffer for filler-words.
Then, I was like, okay, gotta now check them against actual addresses. Downloaded a list of funded ETH addresses. Wrote the checker in ruby. Ran it. No hits but this was now definitely weirdly interesting.
Because: And what if I downloaded the whole pg19 text corpus to scan! And what if I'd add BTC addresses! And what if I checked every permutation of the seed phrase!
Everything got really slow once I got to processing 12G of raw text for finding sequences and then checking a few million candidates with 44.000+ variations per candidate.
So, let's rewrite this into C! And since I've got 16 cores, let's parallelize this puppy! And since it's a MacBook, let's use GCD! Optimize all the things!
Lol, so NOW this thing is so fucking FAST. Takes four minutes to go through the full pg19 corpus and generates 64,205,390 "interesting" seed phrases. The fully parallelized checker (see Terminal screenshot) processes 460 derived addresses per second.
I really don't care if I get a match or not. I feel like I started with building a canoo and wound up with a spaceship is in itself just the best thing in the world.
Anyways. Just wanted to share this. If anyone wants, I can put the whole thing on github, too. Let me know!
I would say it's pretty performance but, the actual cryptanalysis is very naïve. I call it a cute-force attack 😁
Idea for "marking" AI-generated text: Make a final pass that replaces the regular letters with UTF8 homoglyphs instead. Still 100% readable and verifiably of non-human origin.
Case in point, here's the homoglyph version of this message:
Idea for "marking" AI-generated text: Make a final pass that replaces the regular letters with UTF8 homoglyphs instead. Still 100% readable and verifiably of non-human origin.
Case in point, here's the homoglyph version of this message:
This conversation doesn't need to be covert either. In fact, I think that AI-generated text should have it's own visual voice! And it looks like, for the Latin alphabet at least, there are several 'typefaces" that could be built.
Idea for "marking" AI-generated text: Make a final pass that replaces the regular letters with UTF8 homoglyphs instead. Still 100% readable and verifiably of non-human origin.
Case in point, here's the homoglyph version of this message:
LOL, got 100% nerd-sniped by my friend Sönke this week and wound up building a small spaceship.
On Monday he's like "Hey, what if you found obscure seed phrases embedded in public texts? You'd only need to remember the name of the book and the paragraph and go from there."
I honestly could care less about crypto(currencies) and I'm 100% sure this is like cryptanalysis 101. But, yeah, it seemed like an interesting problem anyways.
First, I downloaded a few hundred books from Gutenberg, wrote a ruby script and found BIP39 word sequences with a tolerable buffer for filler-words.
Then, I was like, okay, gotta now check them against actual addresses. Downloaded a list of funded ETH addresses. Wrote the checker in ruby. Ran it. No hits but this was now definitely weirdly interesting.
Because: And what if I downloaded the whole pg19 text corpus to scan! And what if I'd add BTC addresses! And what if I checked every permutation of the seed phrase!
Everything got really slow once I got to processing 12G of raw text for finding sequences and then checking a few million candidates with 44.000+ variations per candidate.
So, let's rewrite this into C! And since I've got 16 cores, let's parallelize this puppy! And since it's a MacBook, let's use GCD! Optimize all the things!
Lol, so NOW this thing is so fucking FAST. Takes four minutes to go through the full pg19 corpus and generates 64,205,390 "interesting" seed phrases. The fully parallelized checker (see Terminal screenshot) processes 460 derived addresses per second.
I really don't care if I get a match or not. I feel like I started with building a canoo and wound up with a spaceship is in itself just the best thing in the world.
Anyways. Just wanted to share this. If anyone wants, I can put the whole thing on github, too. Let me know!
LOL, forgot to mention.
There was a short-lived exploration into writing a Metal shader for a part of the pre-processing. I got pretty far but I backed out because it was slower than running things in parallel.
LOL, got 100% nerd-sniped by my friend Sönke this week and wound up building a small spaceship.
On Monday he's like "Hey, what if you found obscure seed phrases embedded in public texts? You'd only need to remember the name of the book and the paragraph and go from there."
I honestly could care less about crypto(currencies) and I'm 100% sure this is like cryptanalysis 101. But, yeah, it seemed like an interesting problem anyways.
First, I downloaded a few hundred books from Gutenberg, wrote a ruby script and found BIP39 word sequences with a tolerable buffer for filler-words.
Then, I was like, okay, gotta now check them against actual addresses. Downloaded a list of funded ETH addresses. Wrote the checker in ruby. Ran it. No hits but this was now definitely weirdly interesting.
Because: And what if I downloaded the whole pg19 text corpus to scan! And what if I'd add BTC addresses! And what if I checked every permutation of the seed phrase!
Everything got really slow once I got to processing 12G of raw text for finding sequences and then checking a few million candidates with 44.000+ variations per candidate.
So, let's rewrite this into C! And since I've got 16 cores, let's parallelize this puppy! And since it's a MacBook, let's use GCD! Optimize all the things!
Lol, so NOW this thing is so fucking FAST. Takes four minutes to go through the full pg19 corpus and generates 64,205,390 "interesting" seed phrases. The fully parallelized checker (see Terminal screenshot) processes 460 derived addresses per second.
I really don't care if I get a match or not. I feel like I started with building a canoo and wound up with a spaceship is in itself just the best thing in the world.
Anyways. Just wanted to share this. If anyone wants, I can put the whole thing on github, too. Let me know!
GitHubGitHub - anthropics/skills: Public repository for Agent SkillsPublic repository for Agent Skills. Contribute to anthropics/skills development by creating an account on GitHub.↗ github.com
GitHubGitHub - haykgrigo3/TimeCapsuleLLM: A LLM trained only on data from certain time periods to reduce modern biasA LLM trained only on data from certain time periods to reduce modern bias - haykgrigo3/TimeCapsuleLLM↗ github.com
GitHubGitHub - akaalias/bipscanContribute to akaalias/bipscan development by creating an account on GitHub.↗ github.com
GitHubbipscan/src/c/generate_paragraph_seeds.c at main · akaalias/bipscanContribute to akaalias/bipscan development by creating an account on GitHub.↗ github.com
GitHubbipscan/src/c/find_sequences_worker.c at main · akaalias/bipscanContribute to akaalias/bipscan development by creating an account on GitHub.↗ github.com
GitHubbipscan/src/c/check_seeds_worker_btc.c at main · akaalias/bipscanContribute to akaalias/bipscan development by creating an account on GitHub.↗ github.com
huggingface.coAnthropic/AnthropicInterviewer · Datasets at Hugging FaceWe’re on a journey to advance and democratize artificial intelligence through open source and open science.↗ huggingface.co
finalroundai.comAnthropic Study Finds Most Workers Use AI Daily, but 69 Percent Hide It at WorkAnthropic research shows most workers rely on AI but stay quiet about it, with 69% reporting social stigma around using AI tools at work.↗ finalroundai.com
GistUnicode Look-alikesUnicode Look-alikes. GitHub Gist: instantly share code, notes, and snippets.↗ gist.github.com
