Altman miscalculated badly. OpenAI took what could have been amazing publicity, and in a rush to publish, gave reason for users to distrust their core product.
It's like they're allergic to slowing down.
18 karma · joined May 13, 2026
It's like they're allergic to slowing down.
*A misconception is that transition states are local maxima. They are first order saddle points: maxima along one direction but minima along every other direction.
To drive a reaction forward, it doesn't always have to be lowering the transition state energy. Another technique is by destabilizing the resting state. In the analogy, the message would be: to not get too settled into one's comfort zone.
Maybe this could be true about practiced abilities in general. For example, at some point circus entertainers must go from almost never succeeding to succeeding enough to put their lives at stake. It seems that at a certain point, with enough practice or intelligence, you reach a critical threshold where success rate switches from almost never to almost certain.
And I am also a big fan of potential energy surfaces - it always seemed like a huge upgrade going from crude 1D to multi-dimensional reaction coordinates. A big conceptual shift for me was to learn that transition states are not maxima, but first order saddle points.
Though I do wish I could have a better grasp on entropy's role. Also for example PES's connection to diffusion models and flow matching
I am really looking forward to this hitting v2.0. I can't stand uncompressed JSON - so space-inefficient. But heterogenous JSON in parquet files is such a pain because of schema differences causing fields to be silently dropped. Having DuckDB solve this is exactly what I've been looking for.
I'm surprised that hypothesis 2 (that CSV serialization format mangles table columns) was falsified. Back in the gpt-3.5-turbo and gpt-4o era, I did needle-haystack tests and found that table format mattered a lot (csv, tsv, markdown). Most models "could not read vertically" for csv (they were horrible), but they could for markdown. I concluded that serialization format or tokenization played a major role.
Nowadays, LLM performance on csvs is much improved (I'm guessing after being explicitly trained on CSV question-answering.) But I still carry the impression that LLMs read columns only by "memorizing" column positions in a format-dependent manner. Maybe this impression is out of date.
For instance, I have almost completely forgotten how to solve ODEs, even though I had a good command of it when I learned it (by solving practice problems). In that sense, I wish that my prior self had taken good notes, so that I wouldn't have to dig up source material if I wanted to relearn it again.
Everyone likes nicely typeset LaTeX -- why not apply that craftsmanship to preserving academic notes?
There must be some merit to retyping LLM generated code, even verbatim. In school, I would rewrite or re-typeset notes as a study habit. In doing so, I'd review content, detect errors, synthesize concepts simply because rewriting notes forced me to pay attention at the per-word level.
While retyping LLM code is not something I personally do, I'd imagine it could bestow similar benefits.