#CompSc

Matheus Marc Guichard @mmarccs

O voto dessa pessoa tem o mesmo valor que o meu

The Macro Paper @macropaperr

🚨 US LAWMAKERS ARE NOW PREPARING FOR THE AI BUBBLE TO BURST. House Democrats have introduced a resolution opposing government bailouts for AI companies if the industry crashes. Instead of rescuing them with taxpayer money, lawmakers want failed AI companies to go through normal bankruptcy. And the timing is extremely concerning. Some of the world's biggest tech companies are investing billions into AI startups that spend heavily on their products. • Nvidia invests in CoreWeave, which buys Nvidia chips. • Microsoft invests in OpenAI, which spends billions on Microsoft's cloud services. • Amazon invests in Anthropic, which uses Amazon's cloud infrastructure. Billions are moving between companies that are becoming increasingly dependent on each other's spending. And lawmakers are raising concerns about the growing use of borrowed money by Big Tech and Wall Street to finance the AI boom. The resolution is not law, but it shows that some lawmakers are already worried about what happens if the industry collapses. If AI startups run out of funding, they could be forced to slash spending or declare bankruptcy, leaving their investors and suppliers facing massive losses. And without government bailouts, a major failure could spread across chipmakers, cloud providers, banks and the broader stock market. The biggest risk is that one major AI bankruptcy could trigger losses across an entire industry worth trillions.

Rishabh Karnawat @rishabhkio

Many of you don't understand how serious this is, so let me break it down. In computer science there is a famous kind of puzzle called 3-SAT. You are given a long list of yes-or-no switches and a set of rules about how they must be arranged, and you have to find a combination that satisfies every rule. The obvious way to solve it is to try every combination, but each new switch doubles the number of possibilities, so a puzzle with a few hundred switches would take longer than the age of the universe to brute force. For decades, the smartest people in the field found clever tricks that made this a little faster, but every trick still hit the same wall: the time required kept exploding as the puzzle grew. About 25 years ago, two computer scientists, Russell Impagliazzo and Ramamohan Paturi, working with Francis Zane, turned that wall into a formal idea. They proposed the Exponential Time Hypothesis, which says this explosion is not a failure of human cleverness but a law of nature. No algorithm, however brilliant, will ever solve 3-SAT without the time blowing up exponentially. Nobody could prove it, but almost everyone believed it, and it became one of the foundations of modern computer science. Researchers used it as a measuring stick. If you could show that solving some other problem quickly would also crack 3-SAT quickly, you had shown that problem was hopelessly hard too. Hundreds of papers, across scheduling, graphs, logic and much more, rest on this one assumption. Now an internal AI model at OpenAI claims to have broken it. It says it found an algorithm that solves these puzzles meaningfully faster than the hypothesis allows, and it has checked the proof in a computer verification system called Lean. And here is the remarkable part: the very people who proposed the hypothesis, Impagliazzo and Paturi, along with MIT's Ryan Williams, are now leading the public review of whether a machine has disproved their own idea. So what happens next? If the disproof holds, a large part of theoretical computer science has to be revisited. Every result that said "this problem cannot be solved faster, assuming ETH" loses its foundation, and researchers will have to work out which of those walls were real and which were simply where we stopped looking. It does not mean everything becomes easy. The new algorithm is still slow in absolute terms and this is not the famous P versus NP problem being solved. But it means our map of what is possible was wrong in a place we were very confident about, and it was an AI that redrew it. If the disproof fails, the hypothesis survives and the field breathes out, but we will still have watched an AI produce serious, checkable work on one of the deepest questions in the discipline, closely enough that the founders of the idea had to take it seriously. Either way, something has shifted.

Ivan Burazin @ivanburazin

We have a few Mac minis left over from our Daytona event in San Francisco. Thought we'd give 3 away to people building something cool. To enter: 1. Follow @daytonaio 2. Reply with what you'd build with a Mac mini and tag 2 people who'd build something cool with one. We'll randomly pick 3 winners and announce them on October 30th. Winners will be contacted directly via DM. Good luck! No purchase necessary. US residents 18+. Rules below.

JABBA1GER 💍💒 NEWLYWED ❤️ @JABBRWONGR

i would love to tell you but ive been completely dissociated and havent formed a single thought since orientation so i dont know. I Dont Know

Barefoot Student @BarefootStudent

Mark Cuban says Gen Z should stop chasing the perfect job: ‘Just get a job,’ even at McDonald’s, per Fortune.