Yes, it is deceptively easy to post fake information on the internet, and with social media and AI, it is even easier to spread them.

I wonder however, would prompting a LLM to use only reliable sources return the fake stats website?

It definitely is a small help. Although the lab I was working with had supercalculators to generate microtubule-binding proteins, They were kind of limited in how many they could simulate and try, so being able to go through 318K molecules is useful. Our lab was then tasked with doing the testing (I designed the automated assay) and this part is going on right now, I expect it to last around a year (small lab, many tests to run).

Once potential medical targets have been found, the actual work takes several years as you have mentionned as you would switch to cell cultures, then animal models, then human trials. That matches the 6-7 years timeline you mentionned.

So overall, you can crunch a ~6-months period into 1 month, which is definitely a time saver.

Find “potential leads” is something we did plenty of with computational chemistry. We’d get leads that we a “slam dunk” and they’d fail horribly in clinical trials.

Finding leads is easy, finding leads that reliably pan out in the lab is not

There are already several of these "proofs" that have been retracted because they did not make sense.

I would have no problem accepting this whole ordeal if the humans behind each proof took the time to re-read, understand and synthesize the result in a readable paper.

There is ample evidence of how damaging fake information can be, and paper retraction never fully undoes the damage (see the Andrew Wakefield and his antivax "paper" for instance).

I always wondered why so much of discussed politics centers around race and gender, but I think this is a good hypothesis.

"More generally, you can have a fruitful discussion about a topic only if it doesn't engage the identities of any of the participants."

This has always been obvious to me, but I wish more people would understand this: uncalibrated decision models are basically worthless. The model clearly doesn't "understand" the semantics of the question since the result heavily depends on how you word it.

It is nothing new thought. Before that, a lot of people would ask LLM for confidence scores while classifying text (I even saw a paper from a team at Google).

So with that in mind, I really don't get the hype behind decision models. If you need to curate a dataset to build a classifier anyway, why not train it yourself and have an even faster model?

If you are lucky enough to have very straight and packed teeth, there is very little room between your teeth for any food to be lodged in. I used to never floss then one day I tried it and it took me 3 weeks of flossing before the wire came out clean and odorless.

From what I gathered, a simple harness like the one from Pi is sufficient.

Similarly, I would advise against using tons of skills and keep the ones you genuienly find useful (they are just .md files so you can copy them). The only skill I use is /strategic-compact from everything claude code (ecc) because I like to read the summary before a manual compaction.