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something scraping

boo

4,463 subscribers

20,896 views • 1 year ago •via X (Twitter)

Music Arts Science & Technology

Anya Rossi• Live Now

Private livecam show

11 Comments

the logical bio1 year ago

bro thinks she's dog blood 💀

the logical bio1 year ago

GO BEST FRIEND 🗣 THATS MY BEST FRIEND 🗣 THATS MY BEST FRIEND 🗣

Solar Heavy1 year ago

does this remind you of something?

goosie🏳️‍⚧️1 year ago

listened to this while experiencing vertigo and it fit the vibe 10/10

the logical bio1 year ago

damn girl 😭😭

Cavefishes 🐺1 year ago

scraping my brain

the logical bio1 year ago

🫡🫡🫡 that's my job

D ²1 year ago

this goes dummy!!!💗

the logical bio1 year ago

tytytytyty >:3

polygon cove1 year ago

i like this kick

the logical bio1 year ago

it's bad on like earbuds and other smaller speakers ..... need to clean her up a bit

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something scraping (again)

something scraping (again)

bio�

25,936 views • 1 year ago

I’ll give these games iteration of Peter shit all day but I do love playing this game There’s something satisfying about nearly scraping the streets like the scenes from the Raimi films

I’ll give these games iteration of Peter shit all day but I do love playing this game There’s something satisfying about nearly scraping the streets like the scenes from the Raimi films

nirvanam1nd (EOZ-ssj 3)

102,160 views • 5 months ago

Privacy and crypto payments are back in the spotlight. Earlier this year at WalletCon, Trevor Filter explained how card networks rely on scraping personal data, and why Flexa is building something better. Powered by $Amp.

Privacy and crypto payments are back in the spotlight. Earlier this year at WalletCon, Trevor Filter explained how card networks rely on scraping personal data, and why Flexa is building something better. Powered by $Amp.

Amp

13,454 views • 9 months ago

🇬🇧 “Jeremy I’ve been called racist in my time” “If they have to go back 43 years to use something he may or may not have said as a teenager - they are scraping the barrel” Ann Widdecombe absolutely nails this. Imagine making a big deal of this.

🇬🇧 “Jeremy I’ve been called racist in my time” “If they have to go back 43 years to use something he may or may not have said as a teenager - they are scraping the barrel” Ann Widdecombe absolutely nails this. Imagine making a big deal of this.

Concerned Citizen

203,526 views • 7 months ago

$1500/mo for a remote SDR to do what Hermes is doing for me at $250/mo Set it up last night for Nano Flips newsletter ad sales It’s already > scraping competitors newsletters > finding advertisers >looking up right person on their team >reaching out >negotiating rates > and tracking everything in a CRM And I just chat with it through Telegram. Much less clunky than openclaw was. And it gets better on its own - learning which outreach gets opens, who actually buys, how to negotiate better Def not something I would pitch to an enterprise client or mid market firm yet. But for an SMB or freelancer…this is an amazing agent and kind of crazy

$1500/mo for a remote SDR to do what Hermes is doing for me at $250/mo Set it up last night for Nano Flips newsletter ad sales It’s already > scraping competitors newsletters > finding advertisers >looking up right person on their team >reaching out >negotiating rates > and tracking everything in a CRM And I just chat with it through Telegram. Much less clunky than openclaw was. And it gets better on its own - learning which outreach gets opens, who actually buys, how to negotiate better Def not something I would pitch to an enterprise client or mid market firm yet. But for an SMB or freelancer…this is an amazing agent and kind of crazy

James Camp 🛠,🛠

35,018 views • 3 months ago

LichtFeld Studio in Action! GPLv3 - no subscriptions, no data scraping, forever free! - Trains in 20 minutes at 4k for 60k steps with 2m Gaussians and MCMC densification. - With enabled bilateral guidance (CUDA version), it totals 60 minutes. - Pretty much floater free. Makes me quite proud of what we have achieved in not even 2 months! Features: - The world's fastest training! - Supports MCMC and ADC - Pose Optimization - Bilateral Guidance - Train on distorted images with 3DGUT - Multi-ply viewing - Live training preview This isn't even something you could call a beta or alpha version. This wouldn't have been possible without contributions from the wonderful 3DGS community. We are transparent, and everything is open. There is much more to come! Be part of it and start contributing today!

LichtFeld Studio in Action! GPLv3 - no subscriptions, no data scraping, forever free! - Trains in 20 minutes at 4k for 60k steps with 2m Gaussians and MCMC densification. - With enabled bilateral guidance (CUDA version), it totals 60 minutes. - Pretty much floater free. Makes me quite proud of what we have achieved in not even 2 months! Features: - The world's fastest training! - Supports MCMC and ADC - Pose Optimization - Bilateral Guidance - Train on distorted images with 3DGUT - Multi-ply viewing - Live training preview This isn't even something you could call a beta or alpha version. This wouldn't have been possible without contributions from the wonderful 3DGS community. We are transparent, and everything is open. There is much more to come! Be part of it and start contributing today!

MrNeRF

20,299 views • 10 months ago

This lady finds this kinda ugly, black turtle and decides to bring it home, but something about it just felt off. She keeps looking at it, thinking, "Is this really just an ordinary turtle?" Then she starts gently scraping at the shell and boom-surprise, surprise! Turns out, beneath all that she's got a rare beauty on her hands... From plain and rugged to one of the most stunning turtles you'll ever see. It's amazing how sometimes what we see on the outside doesn't even scratch the surface of what's hidden beneath. Reminds me that everyone-and everything-has a little surprise or beauty hidden under the surface if we were patient enough to see it... ☆ Rayyanadam on youtube

This lady finds this kinda ugly, black turtle and decides to bring it home, but something about it just felt off. She keeps looking at it, thinking, "Is this really just an ordinary turtle?" Then she starts gently scraping at the shell and boom-surprise, surprise! Turns out, beneath all that she's got a rare beauty on her hands... From plain and rugged to one of the most stunning turtles you'll ever see. It's amazing how sometimes what we see on the outside doesn't even scratch the surface of what's hidden beneath. Reminds me that everyone-and everything-has a little surprise or beauty hidden under the surface if we were patient enough to see it... ☆ Rayyanadam on youtube

Sarahh

15,407,056 views • 1 year ago

Rear differential service done right by a pro. There is something deeply therapeutic about taking a component that’s been through the ringer—grime-covered, rusted, and neglected—and bringing it back to a factory-fresh standard. This rear differential service wasn't just a quick drain and fill; it was a full restoration of performance. Watching that old, burnt fluid drain out and replacing it with a clean, amber pour of high-quality gear oil is the ultimate "mechanic ASMR." We spent the extra time scraping the mounting surfaces to a mirror finish, ensuring a perfect seal that’ll hold up through the toughest conditions. In a world of "good enough," there’s no substitute for doing it the right way. No shortcuts, no leaks, just smooth gears and a machine that’s ready to eat up the miles again. When you take care of the drivetrain, the drivetrain takes care of you.

Rear differential service done right by a pro. There is something deeply therapeutic about taking a component that’s been through the ringer—grime-covered, rusted, and neglected—and bringing it back to a factory-fresh standard. This rear differential service wasn't just a quick drain and fill; it was a full restoration of performance. Watching that old, burnt fluid drain out and replacing it with a clean, amber pour of high-quality gear oil is the ultimate "mechanic ASMR." We spent the extra time scraping the mounting surfaces to a mirror finish, ensuring a perfect seal that’ll hold up through the toughest conditions. In a world of "good enough," there’s no substitute for doing it the right way. No shortcuts, no leaks, just smooth gears and a machine that’s ready to eat up the miles again. When you take care of the drivetrain, the drivetrain takes care of you.

Giggling Ganon

83,518 views • 2 months ago

A lot of people misunderstand how wealth is built. They think it works like this: you get money, you already have wealth, and then you just turn it into more money—and that’s the whole explanation. But if that were true, it raises an obvious question: Why aren't humans still living in caves? Where did the initial wealth come from? If you need wealth to create more wealth, then why didn’t humans just stay stuck living in caves, scraping by forever? At some point, something changed. We went from barely surviving to building systems, institutions, and societies that can generate ongoing prosperity—at least in some places. This gets at a classic point from Thomas Sowell: the real question isn’t why some societies are poor, but why some are rich. That’s the harder question, and the more interesting one. And I think people miss this. Wealth isn’t just about having money. It requires work, discipline, the right mindset, and the kinds of social and economic conditions that allow it to grow in the first place.

A lot of people misunderstand how wealth is built. They think it works like this: you get money, you already have wealth, and then you just turn it into more money—and that’s the whole explanation. But if that were true, it raises an obvious question: Why aren't humans still living in caves? Where did the initial wealth come from? If you need wealth to create more wealth, then why didn’t humans just stay stuck living in caves, scraping by forever? At some point, something changed. We went from barely surviving to building systems, institutions, and societies that can generate ongoing prosperity—at least in some places. This gets at a classic point from Thomas Sowell: the real question isn’t why some societies are poor, but why some are rich. That’s the harder question, and the more interesting one. And I think people miss this. Wealth isn’t just about having money. It requires work, discipline, the right mindset, and the kinds of social and economic conditions that allow it to grow in the first place.

Rob Henderson

41,595 views • 4 months ago

Coach Thurman and Coach Hart delivered this chilling warning to the team before Friday night's disaster against Stanford. Some of these quotes are brutally honest and direct. "Some of you have the most horrific practice habits I've ever seen." "If you love something or someone, you take care of it. So do you guys love football?" "If you want to have a chance to play pro football, most of y'all in here are gonna have to change drastically and quickly. Or start thinking about that alternative." "We just came here thinking we're gonna get results. But you're not trying to master your practice." "We're barely scraping by and you wonder why you get your ass beat by top-ranked teams. We cannot be result driven, fellas." "We gotta hold each other accountable, coaches included." Colorado is going to have two long weeks to look into the mirror and figure out its true identity. Fortunately, the most memorable lessons come from the most painful mistakes. No one on the team is enjoying the aftermath of this loss, but this might be a well-needed wake up call before the toughest continuous stretch of CU's schedule. #skobuffs🦬

Coach Thurman and Coach Hart delivered this chilling warning to the team before Friday night's disaster against Stanford. Some of these quotes are brutally honest and direct. "Some of you have the most horrific practice habits I've ever seen." "If you love something or someone, you take care of it. So do you guys love football?" "If you want to have a chance to play pro football, most of y'all in here are gonna have to change drastically and quickly. Or start thinking about that alternative." "We just came here thinking we're gonna get results. But you're not trying to master your practice." "We're barely scraping by and you wonder why you get your ass beat by top-ranked teams. We cannot be result driven, fellas." "We gotta hold each other accountable, coaches included." Colorado is going to have two long weeks to look into the mirror and figure out its true identity. Fortunately, the most memorable lessons come from the most painful mistakes. No one on the team is enjoying the aftermath of this loss, but this might be a well-needed wake up call before the toughest continuous stretch of CU's schedule. #skobuffs🦬

Folsom Frenzy Podcast

1,788,336 views • 2 years ago

After scraping John O’Keefe’s clothing, tiny fragments of broken taillight (a match to Karen Read’s taillight) were found embedded in his shirt. Not just “stuck” or “resting on” the fabric. Embedded. This means the fragments were pressed into the fibers of the fabric - forced into the weave of the shirt by impact, pressure or friction. Think of a splinter of glass that slices into a shirt during an accident. The fragments found were about 1/8 inch by 1/16 inch. That’s about the size of: •A tiny wood splinter •A coarse grain of sand •A fragment of crushed red pepper •The leg of a small ant •A piece of cracked nail or shell Imagine trying to manually embed something that small into a shirt. Not tape it on. Not drop it into a pocket. Physically force it into the threads of a cotton shirt. You’d need tools, magnification, and a steady hand and still probably couldn’t do it convincingly without damaging the fabric or leaving evidence. So there’s really only one explanation - those fragments were embedded during an actual, forceful impact. John O’Keefe wasn’t planted in the snow by his friends that night. He was hit and those taillight fragments were driven into his clothing by the force of that collision.

After scraping John O’Keefe’s clothing, tiny fragments of broken taillight (a match to Karen Read’s taillight) were found embedded in his shirt. Not just “stuck” or “resting on” the fabric. Embedded. This means the fragments were pressed into the fibers of the fabric - forced into the weave of the shirt by impact, pressure or friction. Think of a splinter of glass that slices into a shirt during an accident. The fragments found were about 1/8 inch by 1/16 inch. That’s about the size of: •A tiny wood splinter •A coarse grain of sand •A fragment of crushed red pepper •The leg of a small ant •A piece of cracked nail or shell Imagine trying to manually embed something that small into a shirt. Not tape it on. Not drop it into a pocket. Physically force it into the threads of a cotton shirt. You’d need tools, magnification, and a steady hand and still probably couldn’t do it convincingly without damaging the fabric or leaving evidence. So there’s really only one explanation - those fragments were embedded during an actual, forceful impact. John O’Keefe wasn’t planted in the snow by his friends that night. He was hit and those taillight fragments were driven into his clothing by the force of that collision.

Jamz

32,452 views • 1 year ago

Dylan Patel says GPUs are no longer the biggest bottleneck. According to Dylan Patel, now CPUs are the constraint. In the early AI era, CPUs were the laggers. You used them for storage, checkpointing, pre-processing, etc. (pretty light workloads) The models weren't agentic and couldn't go step by step. Just string in and string out (simple inference) Then OpenAI launched O1 preview in September '24, and RL training loops have since tightened every month. - initially it was checking model output with regex - then running classifiers - followed by code unit tests + compilation - and finally agentic flows calling databases & scientific simulations The model outputs to an environment, gets verified, and trains on it. Coding agent revenue went from a couple billion to north of $10B in roughly 6 months. Something like Codex 5.4 can work agentically on its own for 6-7 hrs straight - doing all sorts of calls (databases, cron servers, scraping) That requires insane CPU capabilities. And over the last two quarters, the entire cloud market ran out of CPUs. - GitHub has been really unstable lately - Amazon's CPU server installations 3x'd year over year - Microsoft sold all of its spare CPUs to Anthropic & OpenAI Earlier, it was 100 megawatts of GPUs served by 1 megawatt of CPUs. Now that ratio is getting much closer for both RL training and agentic inference. There's simply no capacity anywhere, and it's causing massive instability.

Dylan Patel says GPUs are no longer the biggest bottleneck. According to Dylan Patel, now CPUs are the constraint. In the early AI era, CPUs were the laggers. You used them for storage, checkpointing, pre-processing, etc. (pretty light workloads) The models weren't agentic and couldn't go step by step. Just string in and string out (simple inference) Then OpenAI launched O1 preview in September '24, and RL training loops have since tightened every month. - initially it was checking model output with regex - then running classifiers - followed by code unit tests + compilation - and finally agentic flows calling databases & scientific simulations The model outputs to an environment, gets verified, and trains on it. Coding agent revenue went from a couple billion to north of $10B in roughly 6 months. Something like Codex 5.4 can work agentically on its own for 6-7 hrs straight - doing all sorts of calls (databases, cron servers, scraping) That requires insane CPU capabilities. And over the last two quarters, the entire cloud market ran out of CPUs. - GitHub has been really unstable lately - Amazon's CPU server installations 3x'd year over year - Microsoft sold all of its spare CPUs to Anthropic & OpenAI Earlier, it was 100 megawatts of GPUs served by 1 megawatt of CPUs. Now that ratio is getting much closer for both RL training and agentic inference. There's simply no capacity anywhere, and it's causing massive instability.

Ivan Burazin

303,474 views • 3 months ago

In the second episode of Scenius Studio's mini-series "The Use-Case", I sit down with Andrej Co-Founder of touch grass. Grass gives users the ability to earn ownership in the Grass network by supplying the protocol with their unused internet bandwidth for data scraping purposes (something that is already happening to most of us and we don’t get paid!). The grass protocol packages this scraped web data and sells it to AI companies who have insufficient data to further develop their models. With over 3 millions users and millions of annualized revenue, Grass is a real commercial business with a roadmap that makes it one of the most exciting projects at the intersection of crypto x AI and data. In this episode we discuss: ➔ Andrej’s background in physics, finance, and sports betting ➔ Big companies using your IP address without your knowledge or permission ➔ How the Grass protocol puts a toll booth on your internet bandwidth highway ➔ Packaging web scraped data and selling it to AI companies building Multi-Modal models ➔ Dynamics between the Grass Protocol and the labs entity developing Grass’ IP ➔ Protocol design decisions to ensure that all tokenholders (VCs, team, and community) are aligned ➔ Why Grass needed to be built on crypto rails to maximize its potential ➔ The future of LLMs and how they will search for context and information Hope you enjoy this episode of Scenius Studio's "The Use-Case". Links to listen in bio or below👇

In the second episode of Scenius Studio's mini-series "The Use-Case", I sit down with Andrej Co-Founder of touch grass. Grass gives users the ability to earn ownership in the Grass network by supplying the protocol with their unused internet bandwidth for data scraping purposes (something that is already happening to most of us and we don’t get paid!). The grass protocol packages this scraped web data and sells it to AI companies who have insufficient data to further develop their models. With over 3 millions users and millions of annualized revenue, Grass is a real commercial business with a roadmap that makes it one of the most exciting projects at the intersection of crypto x AI and data. In this episode we discuss: ➔ Andrej’s background in physics, finance, and sports betting ➔ Big companies using your IP address without your knowledge or permission ➔ How the Grass protocol puts a toll booth on your internet bandwidth highway ➔ Packaging web scraped data and selling it to AI companies building Multi-Modal models ➔ Dynamics between the Grass Protocol and the labs entity developing Grass’ IP ➔ Protocol design decisions to ensure that all tokenholders (VCs, team, and community) are aligned ➔ Why Grass needed to be built on crypto rails to maximize its potential ➔ The future of LLMs and how they will search for context and information Hope you enjoy this episode of Scenius Studio's "The Use-Case". Links to listen in bio or below👇

Ben Jacobs

22,418 views • 1 year ago

I failed to make a web app on both Replit & Lovable (and I code). I still think every PM should try this at least once. Bottom line: • I didn't need to "code," but at some point I was definitely doing engineering. • Building a complex web application takes babysitting • These tools are amazing for expressing ideas and visualizing data • PMs, try this out! It builds intuition for being on the other side of an agent (and now you don't have to be afraid of failing) My background: I can build web apps (though I became a PM because I was pretty bad at it) What I wanted to build: I wanted to replicate Graphtreon (unofficial patreon scraper and public revenue dashboard), but for Maven (where my course is hosted). How it went for me: 🟢 ChatGPT/Claude helped me iterate to a great prompt (literally, "help me write a prompt for an AI LLM prototyping tool to build a web app that...") 🟢 I pasted that into both Lovable and Replit (why not open two tabs) 🟢 Iterating on the UI was awesome. If I kept feedback high-level, it would think of all the little flow details on its own (confirmation dialogs, errors) 🟡 It had backend powers, but needed *a lot* of obvious help on setting up simple backend web scraping (not nobel prize type work) 🟡 I tried to "empower" it with higher-level goals, but kept getting dragged back down to troubleshooting 🔴 I found myself babysitting every error, and building features solely for the purpose of observability and debugging 🔴 I was having fun, but for the wrong reasons (breaking through frustrating walls rather than for outcomes, insights, or innovation) That said, my friend succeeded. A former PM colleague (now founder) successfully built a web app using Lovable, and reported the same experience. He succeeded because he was way more persistent (spent many days fully focused on this) and also smarter. These tools are in a weird, adolescent neither-here-nor-there stage. That said, I'm still super impressed, and can't believe this even exists. Most importantly for product managers: I recommend trying this hands-on ✓ Timebox yourself to an hour ✓ What's a piece of software you didn't get approval to buy? ✓ Allow yourself to get in the weeds, if only as an exercise ✓ Build intuition for what it's like to be on the other side of an agent doing something really complex. Now you don't have to feel bad if you fail :)

I failed to make a web app on both Replit & Lovable (and I code). I still think every PM should try this at least once. Bottom line: • I didn't need to "code," but at some point I was definitely doing engineering. • Building a complex web application takes babysitting • These tools are amazing for expressing ideas and visualizing data • PMs, try this out! It builds intuition for being on the other side of an agent (and now you don't have to be afraid of failing) My background: I can build web apps (though I became a PM because I was pretty bad at it) What I wanted to build: I wanted to replicate Graphtreon (unofficial patreon scraper and public revenue dashboard), but for Maven (where my course is hosted). How it went for me: 🟢 ChatGPT/Claude helped me iterate to a great prompt (literally, "help me write a prompt for an AI LLM prototyping tool to build a web app that...") 🟢 I pasted that into both Lovable and Replit (why not open two tabs) 🟢 Iterating on the UI was awesome. If I kept feedback high-level, it would think of all the little flow details on its own (confirmation dialogs, errors) 🟡 It had backend powers, but needed a lot of obvious help on setting up simple backend web scraping (not nobel prize type work) 🟡 I tried to "empower" it with higher-level goals, but kept getting dragged back down to troubleshooting 🔴 I found myself babysitting every error, and building features solely for the purpose of observability and debugging 🔴 I was having fun, but for the wrong reasons (breaking through frustrating walls rather than for outcomes, insights, or innovation) That said, my friend succeeded. A former PM colleague (now founder) successfully built a web app using Lovable, and reported the same experience. He succeeded because he was way more persistent (spent many days fully focused on this) and also smarter. These tools are in a weird, adolescent neither-here-nor-there stage. That said, I'm still super impressed, and can't believe this even exists. Most importantly for product managers: I recommend trying this hands-on ✓ Timebox yourself to an hour ✓ What's a piece of software you didn't get approval to buy? ✓ Allow yourself to get in the weeds, if only as an exercise ✓ Build intuition for what it's like to be on the other side of an agent doing something really complex. Now you don't have to feel bad if you fail :)

Tal Raviv

41,324 views • 1 year ago

Debunking Coffeezilla’s prediction market claims In his recent video “prediction markets aren’t just gambling,” Coffeezilla made the three untrue or misleading claims below: 1) “The only way you get the news early, by the way, is if it’s insider trading.” 2) “All these prediction markets are doing is aggregating sentiment on the news.” 3) “The only way you can get something not in the news from these markets is if someone with non-news information trades, which is AKA inside information.” I want to start by saying that I’ve really enjoyed Coffeezilla’s content in the past, and I appreciate him blacking out my name in the tweet he screenshared. I also watched the full video and agreed with parts of it (and disagreed with other parts). That said, these three claims are egregiously incorrect, and I want to correct the record. I hope Coffeezilla reads this and reconsiders these points 1) “The only way you get the news early, by the way, is if it’s insider trading.” Merriam-Webster defines insider trading as “the illegal use of information available only to insiders in order to make a profit in financial trading.” This claim is wrong because it is clearly possible to get information early in entirely legal ways. For example, in CPI inflation markets, someone might notice prices rising on goods they regularly buy, or a sophisticated trader might aggregate pricing data across many products and form a forecast before the CPI release. Journalists may later report on inflation, but the information existed beforehand. Markets also react faster to sudden events, like a Trump Truth Social post or an earthquake, than journalists do. Traders are financially incentivized to react in seconds; journalists are not. Recent high-profile examples include Nobel Peace Prize, Spotify, and Time POTY markets, where traders had information before the news broke. In the Nobel case, there was disinformation claiming insider trading, but as far as most observers can tell, the information was obtained legally via web-scraping. Even if the Nobel Committee disliked it, legally obtained information is fair game. 2) “All these prediction markets are doing is aggregating sentiment on the news.” This is easy to debunk. Prediction markets do aggregate information, but not merely sentiment or headlines. That’s likely why CNN and CNBC partnered with Kalshi. News is filtered through editors, incentives, and bias. Taking headlines at face value is not a winning trading strategy. Savvy traders treat news as one input among many variables. If a headline says “Poll X shows Clinton up 10 points,” markets may adjust, but they don’t blindly price the headline. They factor in other variables. I’d argue markets are often smarter than the news. Domer❤️‍🔥 has even argued that Fed markets on Kalshi are more accurate than CME due to traders like himself making them more efficient, and I think he’s right. 3) “The only way you can get something not in the news from these markets is if someone with non-news information trades, which is AKA inside information.” Merriam-Webster defines insider information as “information not known to the public that one has obtained by virtue of being an insider.” You can obtain non-news information without being an insider. This overlaps with point one, but here’s a concrete example. For the recent TN-07 special election, I traveled to TN-07 and spoke with voters leaving early-voting sites and with everyday residents. I learned how little awareness there was that a special election was even happening, and how voters were thinking about the race. That information wasn’t in the news, but it informed how I traded. I’m not an insider. This was “alpha hunting” through firsthand observation. Almost every serious prediction market trader has similar stories. This is certainly not "insider trading." I’m genuinely curious to hear your thoughts, Coffeezilla, and hope for a good-faith dialogue. I hope you are doing well!

Benjamin Freeman

80,021 views • 7 months ago

Candace Owens and nodding donkey Ian Carrol discuss pornography and, in case you haven’t guessed already, she and Ian Carrol both agree that it’s, of course, the Jews who are responsible for not just the popularity of porno but that it was also “created” by the Jews They discuss Al Goldstein, an American from a secular Jewish family who, amongst many other endeavours, was heavily involved in the porn industry. The quote Candace Owens refers to, is of him saying that “The only reason that Jews are in pornography is that we think that Christ sucks” and it is, indeed something he actually said. Now you’d think that someone like Candace, who once championed common sense and making an effort to research things in great detail, would provide some much-needed context for this. But no, she doesn’t. To her and the donkey sitting next to her, it’s all very much one dimentional. Here’s some truth about Al Goldstein, which took me quite literally less than three minutes to research. Al Goldstein was a larger than life character, an arrogant, self-absorbed, nasty idiot who described himself as an “angry Jew”. He didn’t practise Judaism and, at best, was just a cultural Jew. He did indeed despise Christian culture, but how one comes to the conclusion that this reflects the broader views of all Jews is, quite frankly, retarded. Al despised all religions, saying, “I judge religions on the basis of what they do, and I’m afraid to say that the insidious power of religion has been with us for thousands of years. I do not respect irrationality, stupidity, or superstition.” He also made a statement in which he proclaimed that he is a god, “I’m the god of my own universe. I create my own morality, my own reality. The Christians have their God, but I’ve got Screw magazine and my dick.” His whole life was a circus act, and it wasn’t a reflection of real Jewish identity. Fun fact, again one that takes less than a minute to find, is that Jewish Americans, and no doubt Jews elsewhere, have a divorce rate lower than the national average. Regardless of their politics, they’re generally more aligned with conservative values. Al most definitely wasn’t, and his comments regarding “Jews inventing porn” are nonsensical. Porn has been around for thousands of years in one form or another. It is not some exclusive invention of the last few decades. Finally, and quite ironically, Candace mentions Japan and how Japan is not consumed by pornography. But again, the thousands of brain cells she’s lost over the last few years, during which she has become “anti-Zionist”, failed to make her realise that pornography is actually huge in Japan. In fact, Japan’s overall adult industry is estimated to be worth 4-5 trillion yen annually, with anime porn being one of the most searched-for categories on sites like Pornhub. The confirmation bias of people like Owens and Carrol represents the ongoing decline of brain function within our societies. These idiots are driven by an arrogance in which they think they’re highly intelligent, but in reality, each and every time, it makes them look like intellectual dimwits scraping the bottom of a barrel.

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Candace Owens and nodding donkey Ian Carrol discuss pornography and, in case you haven’t guessed already, she and Ian Carrol both agree that it’s, of course, the Jews who are responsible for not just the popularity of porno but that it was also “created” by the Jews They discuss Al Goldstein, an American from a secular Jewish family who, amongst many other endeavours, was heavily involved in the porn industry. The quote Candace Owens refers to, is of him saying that “The only reason that Jews are in pornography is that we think that Christ sucks” and it is, indeed something he actually said. Now you’d think that someone like Candace, who once championed common sense and making an effort to research things in great detail, would provide some much-needed context for this. But no, she doesn’t. To her and the donkey sitting next to her, it’s all very much one dimentional. Here’s some truth about Al Goldstein, which took me quite literally less than three minutes to research. Al Goldstein was a larger than life character, an arrogant, self-absorbed, nasty idiot who described himself as an “angry Jew”. He didn’t practise Judaism and, at best, was just a cultural Jew. He did indeed despise Christian culture, but how one comes to the conclusion that this reflects the broader views of all Jews is, quite frankly, retarded. Al despised all religions, saying, “I judge religions on the basis of what they do, and I’m afraid to say that the insidious power of religion has been with us for thousands of years. I do not respect irrationality, stupidity, or superstition.” He also made a statement in which he proclaimed that he is a god, “I’m the god of my own universe. I create my own morality, my own reality. The Christians have their God, but I’ve got Screw magazine and my dick.” His whole life was a circus act, and it wasn’t a reflection of real Jewish identity. Fun fact, again one that takes less than a minute to find, is that Jewish Americans, and no doubt Jews elsewhere, have a divorce rate lower than the national average. Regardless of their politics, they’re generally more aligned with conservative values. Al most definitely wasn’t, and his comments regarding “Jews inventing porn” are nonsensical. Porn has been around for thousands of years in one form or another. It is not some exclusive invention of the last few decades. Finally, and quite ironically, Candace mentions Japan and how Japan is not consumed by pornography. But again, the thousands of brain cells she’s lost over the last few years, during which she has become “anti-Zionist”, failed to make her realise that pornography is actually huge in Japan. In fact, Japan’s overall adult industry is estimated to be worth 4-5 trillion yen annually, with anime porn being one of the most searched-for categories on sites like Pornhub. The confirmation bias of people like Owens and Carrol represents the ongoing decline of brain function within our societies. These idiots are driven by an arrogance in which they think they’re highly intelligent, but in reality, each and every time, it makes them look like intellectual dimwits scraping the bottom of a barrel.

𝔸η𝐓

518,796 views • 1 year ago

Make Art Not War: The Battle for Creativity It's Adobe's annual Max event in London today and scott belsky's spotlight on AI paints a clear picture: AI isn't just on Adobe's agenda, it is the agenda. Adobe has already scored a home run with generative fill in Photoshop, a feature now spawning entire categories of memes - including my own video, which surprisingly garnered a million views. However, Adobe's ambitions extend beyond still imagery. The Tanker Charges Towards Video At Max, Adobe's Chief Product Officer put an emphasis on AI video generation with Firefly Video. The tech tanker is charging full steam ahead to the next obvious modality for creation, leaving a wake of disruption for any upstarts bold enough to challenge it. The announcement isn't new, but it showcases the emphasis on new product development with a marked increase in the velocity we can expect from the creative tech behemoth. The company that defined the norms of video editing with Premiere, and motion graphics with After Effects, has now entered the realm of AI-powered creation. The wake-up call is clear - the tankers are moving fast. Goliath vs. The Upstarts This development spells a daunting challenge for the numerous start-ups that dared to dethrone Adobe in recent years. For plenty of use cases people have been asking: Why use Photoshop when you have MidJourney? Why use Premiere when you have Descript? Why use After Effects when you have Runway? These aspiring disruptors sought to chip away at Adobe's dominance by offering more specialized, user-friendly solutions - a process that can be characterized as the 'unbundling' of Adobe. Now, they face a head-to-head collision with the very Goliath they sought to topple. Creators' Toolkit: A New Addition But this imminent clash isn't just a tale of corporate competition. This is a story about the tools of creation and their impact on creators themselves and the very canvas of creation. The advent of Firefly, Adobe's AI-driven offering, reflects a broadening recognition of artificial intelligence as an integral part of the creator's toolkit. In other words, Adobe's massive ecosystem of creators needn't wade out into new waters to acquire AI capabilities -- they will simply be infused into the products they already know and (mostly) love, but more critically -- need to use every day to get creative stuff done. The Increasing Stickiness of Adobe's Tools The intersection of AI and creative tools like Photoshop's generative fill is transforming how creators perceive and interact with AI. When they encounter the innovative features of generative fill, they're not primarily thinking about the AI technology that powers it. Instead, they're marveling at the cool new tool that's now part of their beloved Photoshop. This immediate affinity for "Photoshop" masks the sophisticated technology behind it, essentially furthering Adobe's stronghold on the creative industry. Layer in Adobe's stance to training their AI models with sources like Adobe Stock that promise rock-solid data provenance, and you can see Adobe clearly wants to seem like the responsible adults in the room. After all Adobe elected not to put the Behance catalog to work, perhaps rightly so given the ethical backlash to the scraping Artstation imagery. Adobe's Thirty Something Conundrum But it's not all rainbows and sunshine. While Adobe sails ahead full steam, there's an intriguing conundrum waiting in the wings. With 30-year-old codebases forming the foundation of its most popular tools, Adobe faces a significant challenge: its software has back pain. But it's not just a technical problem -- it's also a philosophical one, akin to the ship of Theseus. Can Adobe modernize and refactor its code bases without sacrificing the essence that made these tools indispensable to creators? Can they innovate without alienating their long-time users who've grown accustomed to the 'Adobe way' of doing things? An Unexpected Solution? Interestingly, solutions might emerge from unexpected quarters. Perhaps it'll take an army of developers armed with GitHub Co-Pilot to alleviate Adobe's refactoring nightmare. By automating parts of the refactoring process, it could accelerate the evolution of Adobe's legacy tools, making them more adaptable to the rapidly progressing tech landscape while preserving their core functionality. In a twist of irony, the AI that's reshaping Adobe's offerings might just come to the rescue of its own legacy. As Adobe navigates these murky waters, opportunities are emerging for new entrants in the field. Startups might also find their moment to shine in the midst of Adobe's strategic and technological shifts. With their innovative approaches and less-encumbered platforms, they have the chance to offer alternative solutions to creators seeking novel, efficient, and intuitive tools. The Battle for Creativity The tech giant's journey through a massive transformation at a previously unfathomable speed will set the course for the next era of creative technology. Given the sheer ubiquity of Adobe tools today, it's by far the most common way creators will experience AI. But let's be honest -- this transformation will not be easy. The future of creative tech isn't written yet and as a growing line up of new entrants vie for the prize, one thing's for sure: it's going to be a darn good fight. Make Art Not War In the end, it is the creators who stand to gain the most. As Adobe and its competitors lock horns, they'll strive to deliver increasingly powerful, intuitive, and efficient tools. But, it's up to the creators themselves to harness these innovations. Only by embracing and mastering these new tools can they unlock their full creative potential. So what are you waiting for? Wield these new tools at your disposal and turn your imagination into reality. We are the architects of a new era of creative self expression. If you enjoyed this, drop a like and retweet. Follow Bilawal Sidhu for more writing on creative tech and AI.

Make Art Not War: The Battle for Creativity It's Adobe's annual Max event in London today and scott belsky's spotlight on AI paints a clear picture: AI isn't just on Adobe's agenda, it is the agenda. Adobe has already scored a home run with generative fill in Photoshop, a feature now spawning entire categories of memes - including my own video, which surprisingly garnered a million views. However, Adobe's ambitions extend beyond still imagery. The Tanker Charges Towards Video At Max, Adobe's Chief Product Officer put an emphasis on AI video generation with Firefly Video. The tech tanker is charging full steam ahead to the next obvious modality for creation, leaving a wake of disruption for any upstarts bold enough to challenge it. The announcement isn't new, but it showcases the emphasis on new product development with a marked increase in the velocity we can expect from the creative tech behemoth. The company that defined the norms of video editing with Premiere, and motion graphics with After Effects, has now entered the realm of AI-powered creation. The wake-up call is clear - the tankers are moving fast. Goliath vs. The Upstarts This development spells a daunting challenge for the numerous start-ups that dared to dethrone Adobe in recent years. For plenty of use cases people have been asking: Why use Photoshop when you have MidJourney? Why use Premiere when you have Descript? Why use After Effects when you have Runway? These aspiring disruptors sought to chip away at Adobe's dominance by offering more specialized, user-friendly solutions - a process that can be characterized as the 'unbundling' of Adobe. Now, they face a head-to-head collision with the very Goliath they sought to topple. Creators' Toolkit: A New Addition But this imminent clash isn't just a tale of corporate competition. This is a story about the tools of creation and their impact on creators themselves and the very canvas of creation. The advent of Firefly, Adobe's AI-driven offering, reflects a broadening recognition of artificial intelligence as an integral part of the creator's toolkit. In other words, Adobe's massive ecosystem of creators needn't wade out into new waters to acquire AI capabilities -- they will simply be infused into the products they already know and (mostly) love, but more critically -- need to use every day to get creative stuff done. The Increasing Stickiness of Adobe's Tools The intersection of AI and creative tools like Photoshop's generative fill is transforming how creators perceive and interact with AI. When they encounter the innovative features of generative fill, they're not primarily thinking about the AI technology that powers it. Instead, they're marveling at the cool new tool that's now part of their beloved Photoshop. This immediate affinity for "Photoshop" masks the sophisticated technology behind it, essentially furthering Adobe's stronghold on the creative industry. Layer in Adobe's stance to training their AI models with sources like Adobe Stock that promise rock-solid data provenance, and you can see Adobe clearly wants to seem like the responsible adults in the room. After all Adobe elected not to put the Behance catalog to work, perhaps rightly so given the ethical backlash to the scraping Artstation imagery. Adobe's Thirty Something Conundrum But it's not all rainbows and sunshine. While Adobe sails ahead full steam, there's an intriguing conundrum waiting in the wings. With 30-year-old codebases forming the foundation of its most popular tools, Adobe faces a significant challenge: its software has back pain. But it's not just a technical problem -- it's also a philosophical one, akin to the ship of Theseus. Can Adobe modernize and refactor its code bases without sacrificing the essence that made these tools indispensable to creators? Can they innovate without alienating their long-time users who've grown accustomed to the 'Adobe way' of doing things? An Unexpected Solution? Interestingly, solutions might emerge from unexpected quarters. Perhaps it'll take an army of developers armed with GitHub Co-Pilot to alleviate Adobe's refactoring nightmare. By automating parts of the refactoring process, it could accelerate the evolution of Adobe's legacy tools, making them more adaptable to the rapidly progressing tech landscape while preserving their core functionality. In a twist of irony, the AI that's reshaping Adobe's offerings might just come to the rescue of its own legacy. As Adobe navigates these murky waters, opportunities are emerging for new entrants in the field. Startups might also find their moment to shine in the midst of Adobe's strategic and technological shifts. With their innovative approaches and less-encumbered platforms, they have the chance to offer alternative solutions to creators seeking novel, efficient, and intuitive tools. The Battle for Creativity The tech giant's journey through a massive transformation at a previously unfathomable speed will set the course for the next era of creative technology. Given the sheer ubiquity of Adobe tools today, it's by far the most common way creators will experience AI. But let's be honest -- this transformation will not be easy. The future of creative tech isn't written yet and as a growing line up of new entrants vie for the prize, one thing's for sure: it's going to be a darn good fight. Make Art Not War In the end, it is the creators who stand to gain the most. As Adobe and its competitors lock horns, they'll strive to deliver increasingly powerful, intuitive, and efficient tools. But, it's up to the creators themselves to harness these innovations. Only by embracing and mastering these new tools can they unlock their full creative potential. So what are you waiting for? Wield these new tools at your disposal and turn your imagination into reality. We are the architects of a new era of creative self expression. If you enjoyed this, drop a like and retweet. Follow Bilawal Sidhu for more writing on creative tech and AI.

Bilawal Sidhu

72,192 views • 3 years ago

In honor of Y Combinator's 20th anniversary, here are my 6 favorite lessons: 1. The 7 minute espresso rule: Our first meeting with Sam Altman lasted just seven minutes. The batch hadn’t even officially started yet and my cofounder Ryan Rowe and I drove down from Mountain View to the tiny SF YC outpost to meet him. Sam was making an espresso when we walked in. He opened with – “have you launched”? We said no, too many bugs. Our product, kimono, made it easy to just point and click to build a web scraper. Our promise was to get you an API in 60 seconds, without writing a single line of code. But, the web is vast, and websites are very different. We needed it to work on a large enough spectrum of sites so that our first users would have a good experience. It worked on just a handful of sites at the time. Sam pushed us to make sure we were launched in less than 2 weeks. We debated, highlighting the complexity of the bugs and the limits of underlying headless browsing technology. The espresso finished brewing, he picked it up, looked at us and said, “well, you better get going then and fix those bugs”. We left, launched within those next two weeks and learned one of the most important lessons that day - speed matters. Ship something you’re embarrassed by. 2. There are no experts. Ryan and I were not prepared for the rapid influx of user on launch day. We did 88 user interviews to validate our product idea, and everyone basically said they wouldn’t use it. We thought they were wrong and built it anyway. It got tons of traffic on day 1. We didn’t sleep in the 48 hours following because our servers and database kept crashing. We hadn’t indexed it properly. We realized we weren’t the experts and needed to hire one. We went over to Michael Seibel's apartment for advice. Michael smiled and told us about the early days at SocialCam, that eventually became Twitch – experts thought the streaming video problem was too hard to be possible. The answer wasn’t hiring an expert, but hiring someone young, capable and naïve enough to give it an earnest try. So we opted to just figure it out ourselves. 3. Messages in Pizza Boxes. Startups win through incredible customer service, then through product, not the other way around. Michael Seibel told us how SocialCam’s streaming infrastructure went down while a key teammate was unreachable off-grid in a Tahoe cabin for the weekend. Normal people would have waited until Monday. Not Michael. He called a local pizza delivery place and asked the delivery person to send a large pizza with an urgent message in the box to the cabin. Their infrastructure was back up in hours. 4. The Twinkle can matter more than the TAM. Ambition matters just as much as practicality. Before demo day, we were struggling with the end note of our pitch. We knew the value of our product, and had a fanatical and fast-growing user base, but the market size we calculated either seemed so ridiculously big that it was not plausible, or so narrow that it was equally silly. Paul Graham and Geoff Ralston sat with us and showed us that if we can really pull it off at scale, it would be bigger than Google. So, we could skip the market size, if we really believed it. PG suggested not talking about market size, but just making sure people could see the twinkle in our eyes when we talked about what you might be able to do with a structured copy of the internet that’s larger than Google’s. 5. You’re always at the Origin. Our demo day was successful beyond our wildest beliefs. Afterwards, Geoff Ralston drew a chart for us on a whiteboard. It was a hockey stick. He asked us where we thought we were. It was a rhetorical question. He said we were at the origin, and that the most important thing is to remember that. Sam Altman doubled down. When he invested in Kimono, he gave us a Zimbabwean Trillion dollar note (the result of extreme hyperinflation in Zimbabwe), as a cautionary reminder that the fundraising and valuation mean nothing. We have channeled this into our culture at @TheArenaAI with our ritual around neon shoelaces and a pair of neon track spikes hanging on the wall to remind us that we’re at the Olympic starting line, but we don’t have any medals yet. 6. High bandwidth discussions with users don’t happen over email. The Collison brothers, who founded Stripe, took user engagement to the next level – so much so that “Collison installation” is part of YC lore. John Collison told us that talking to users was essential because email and chat conversations were not “high bandwidth” enough. He emphasized the importance of what you can learn being with users in person or talking to them over Skype (remember, this was early 2014!). We took this to heart, and spoke to hundreds of users at Kimono regularly over Skype. One power user Alexander Chung, who I met this way, has become a close friend and even attended my wedding in India. At Arena, we now go a step further and regularly fly to our users. We over-invest to a degree that is almost crazy in order to be more than a supplier — a true partner to our early customers. YC taught us it’s the only way to understand their problems on the ground and really make sure our products work for them. YC taught us to outsource very little. Own the problem. Own the outcome. And if you do, you get to be stupidly ambitious. If you’re curious about Kimono Labs (which we later sold to Palantir ), here was our launch demo. No-code web scraping before “no code” was even a term: Here's our demo day pitch, from 2014. Forever grateful to those above + Garry Tan Jessica Livingston Trevor Blackwell

In honor of Y Combinator's 20th anniversary, here are my 6 favorite lessons: 1. The 7 minute espresso rule: Our first meeting with Sam Altman lasted just seven minutes. The batch hadn’t even officially started yet and my cofounder Ryan Rowe and I drove down from Mountain View to the tiny SF YC outpost to meet him. Sam was making an espresso when we walked in. He opened with – “have you launched”? We said no, too many bugs. Our product, kimono, made it easy to just point and click to build a web scraper. Our promise was to get you an API in 60 seconds, without writing a single line of code. But, the web is vast, and websites are very different. We needed it to work on a large enough spectrum of sites so that our first users would have a good experience. It worked on just a handful of sites at the time. Sam pushed us to make sure we were launched in less than 2 weeks. We debated, highlighting the complexity of the bugs and the limits of underlying headless browsing technology. The espresso finished brewing, he picked it up, looked at us and said, “well, you better get going then and fix those bugs”. We left, launched within those next two weeks and learned one of the most important lessons that day - speed matters. Ship something you’re embarrassed by. 2. There are no experts. Ryan and I were not prepared for the rapid influx of user on launch day. We did 88 user interviews to validate our product idea, and everyone basically said they wouldn’t use it. We thought they were wrong and built it anyway. It got tons of traffic on day 1. We didn’t sleep in the 48 hours following because our servers and database kept crashing. We hadn’t indexed it properly. We realized we weren’t the experts and needed to hire one. We went over to Michael Seibel's apartment for advice. Michael smiled and told us about the early days at SocialCam, that eventually became Twitch – experts thought the streaming video problem was too hard to be possible. The answer wasn’t hiring an expert, but hiring someone young, capable and naïve enough to give it an earnest try. So we opted to just figure it out ourselves. 3. Messages in Pizza Boxes. Startups win through incredible customer service, then through product, not the other way around. Michael Seibel told us how SocialCam’s streaming infrastructure went down while a key teammate was unreachable off-grid in a Tahoe cabin for the weekend. Normal people would have waited until Monday. Not Michael. He called a local pizza delivery place and asked the delivery person to send a large pizza with an urgent message in the box to the cabin. Their infrastructure was back up in hours. 4. The Twinkle can matter more than the TAM. Ambition matters just as much as practicality. Before demo day, we were struggling with the end note of our pitch. We knew the value of our product, and had a fanatical and fast-growing user base, but the market size we calculated either seemed so ridiculously big that it was not plausible, or so narrow that it was equally silly. Paul Graham and Geoff Ralston sat with us and showed us that if we can really pull it off at scale, it would be bigger than Google. So, we could skip the market size, if we really believed it. PG suggested not talking about market size, but just making sure people could see the twinkle in our eyes when we talked about what you might be able to do with a structured copy of the internet that’s larger than Google’s. 5. You’re always at the Origin. Our demo day was successful beyond our wildest beliefs. Afterwards, Geoff Ralston drew a chart for us on a whiteboard. It was a hockey stick. He asked us where we thought we were. It was a rhetorical question. He said we were at the origin, and that the most important thing is to remember that. Sam Altman doubled down. When he invested in Kimono, he gave us a Zimbabwean Trillion dollar note (the result of extreme hyperinflation in Zimbabwe), as a cautionary reminder that the fundraising and valuation mean nothing. We have channeled this into our culture at @TheArenaAI with our ritual around neon shoelaces and a pair of neon track spikes hanging on the wall to remind us that we’re at the Olympic starting line, but we don’t have any medals yet. 6. High bandwidth discussions with users don’t happen over email. The Collison brothers, who founded Stripe, took user engagement to the next level – so much so that “Collison installation” is part of YC lore. John Collison told us that talking to users was essential because email and chat conversations were not “high bandwidth” enough. He emphasized the importance of what you can learn being with users in person or talking to them over Skype (remember, this was early 2014!). We took this to heart, and spoke to hundreds of users at Kimono regularly over Skype. One power user Alexander Chung, who I met this way, has become a close friend and even attended my wedding in India. At Arena, we now go a step further and regularly fly to our users. We over-invest to a degree that is almost crazy in order to be more than a supplier — a true partner to our early customers. YC taught us it’s the only way to understand their problems on the ground and really make sure our products work for them. YC taught us to outsource very little. Own the problem. Own the outcome. And if you do, you get to be stupidly ambitious. If you’re curious about Kimono Labs (which we later sold to Palantir ), here was our launch demo. No-code web scraping before “no code” was even a term: Here's our demo day pitch, from 2014. Forever grateful to those above + Garry Tan Jessica Livingston Trevor Blackwell

Pratap Ranade

177,308 views • 1 year ago