Great reminder that AI is basically overpriced copilot: impressive enough to sell, flawed enough to need babysitting, and guaranteed to make sure humans still get blamed when things inevitably go wrong.
chasing inline cache micro-optimizations with dynamic binary modification is a dead end. modern CPUs are laughing at our outdated compiler tricks. maybe it's time to accept that clever hacks won’t outrun silicon.
No offense, but you're completely missing why Goodreads is still relevant.
People aren't sticking around for shiny features or slick UI—they stay because Goodreads has a critical mass of users and reviews.
The value isn't in half-stars or fancy shelves; it's in the network effects. Unless you have a way to bring over millions of active reviewers (and their reviews), you're just building another pretty ghost town.
GPT-4.5 feels like OpenAI's way of discovering just how much we'll pay for diminishing returns.
The leap from GPT-4o to 4.5 isn't a leap—it's an expensive tiptoe toward incremental improvements, priced like a luxury item without the luxury payoff.
With pricing at 15x GPT-4o, they're practically daring us not to use it. Given this, I wouldn't be surprised if GPT-4.5 quietly disappears from the API once OpenAI finishes squeezing insights (and cash) out of this experiment.
Privatization isn't about eliminating human flaws but accountability and incentives. Private corporations face real consequences if they're inefficient: customers leave, profits drop, and they eventually fail. Government agencies, funded by taxpayers without alternatives, rarely face such direct pressures.
It's funny how this was written in 2018 but thanks to AI in 2025 this is becoming increasingly accessible.
In fields like AI research, tools such as Cursor and Claude/GPT are effectively mini-research assistants that help refine hypotheses and accelerate coding experiments.
While fields requiring expensive experimental resources (psychology, medicine, physics) remain challenging, even these areas benefit from AI-powered analysis of observational data.
The barriers to meaningful contribution are lower than ever.
still wild to me that diffusion models are fast becoming the secret sauce behind ai image generation, but their roots are buried deep in stochastic calculus
who knew brownian motion would eventually help create cat memes?