I’ve said this before and I’ll say it again:
Using LLMs-written content professionally will spoil your career, especially if you’re in early stages. Everyone worth their salt that I know is hugely aversive to reading slop and despite your smart attempts, they can sense signatures of AI writing (even if they can’t articulate what gave it away).
When you delegate writing, you delegate thinking and that delegation takes away from you the very thing you need for your future career advancement: careful thinking.
I often hear.. “but my writing is bad”. Well, if you keep on delegating, how will it get better? It’s like complaining that you don’t go to gym because you’re unfit.
Write the first damn draft yourself and then take feedback from AI. Do not ask it to rewrite, but rather ask it point errors and suggestions for improvement.
Writing using LLMs is analogous to binge watching short videos, or eating junk food. Pleasurable dopamine hits in the short term, but complete dependence in the long term.
hello there the jacobian conjecture is false thanx to my close friend akhil for asking about it and my other close friend fable for working during the world cup final
((1+xy)^3 z + y^2 (1+xy) (4+3xy), y + 3 x (1+xy)^2 z + 3 x y^2 (4+3xy), 2 x - 3 x^2 y - x^3 z): \C^3\to \C^3, has jacobian determinant -2, and sends (0, 0, -1/4), (1, -3/2, 13/2), and (-1, 3/2, 13/2) to (-1/4, 0, 0)
Q1 earnings are in: 2026 is off to a terrific start.
Our AI investments and full stack approach are lighting up every part of the business: Search queries are at an all-time high with AI continuing to drive usage. Google Cloud revenue grew 63%, Gemini models have incredible momentum, and it was our strongest quarter ever for consumer AI subs, driven by @GeminiApp.
Thanks to our partners + employees around the world. Much more to share on our earnings call in 20 minutes… and at Google I/O in 20 days!
<think>The starbucks barista just told me goodbye. Let me think about this. First, I need to consider common responses in such situations. When someone says goodbye, typical replies are "Goodbye," "Have a nice day," or something friendly like that. Ok, that..</think>
Y-you too!
The Internet is a globally integrated market, and everyone really is in competition (and collaboration!) with everyone else.
After all: your followers are global, your customers are global, your supply chain is global, and your contractors are global.
Billions of people do remote work, video chat, social networking, and online payments every day.
They log into their apps much more frequently than they salute their flag. They know fellow social network users, but not their next door neighbors.
No borders of legacy states were set up with the Internet in mind. They all assume people who live near each other share the same values, but they don’t anymore. Only social network neighbors do.
So: neither socialists nor nationalists have the conceptual framework to deal with the Internet, because all their ideas were developed when there was a land but no cloud.
There are, however, two factions who take the Internet seriously: China and Crypto.
Because they have the Great Firewall and the Blockchain respectively. These are very different types of fortifications that both treat the digital realm as something to be defended, walled off, and protected.
The Chinese strategy is to vertically integrate their nation, state, and network. 99% of the Han Chinese nation are governed by the Communist Party-State and use only the apps allowed on the Chinese network.
The crypto strategy, by contrast, is to make software so secure that it can run in the open, on every computer in the world, and be simultaneously available to every individual and secure against any state attack.
China represents nationalist socialism.
Crypto represents international capitalism.
Why can't they both go together? Is there any evolutionary reason for this?
I've been wondering if it's possible to create a learning platform that's as addictive and dopamine-inducing as IG Reels.
Today OpenAI announced o3, its next-gen reasoning model. We've worked with OpenAI to test it on ARC-AGI, and we believe it represents a significant breakthrough in getting AI to adapt to novel tasks.
It scores 75.7% on the semi-private eval in low-compute mode (for $20 per task in compute ) and 87.5% in high-compute mode (thousands of $ per task). It's very expensive, but it's not just brute -- these capabilities are new territory and they demand serious scientific attention.
"Reasoning by first principles >>> reasoning by analogy" hits hard when you spend hours debugging, thinking the root cause should be similar as last week's occurrence of same issue.
the existence of "experts" is a myth
the fact that certain skills are "deep" is a meme. you can learn anything in one or two weeks. the only thing that exists is depth