> Generate a realistic image of a bad fitting suit without looking obviously fake

Create a new chat and attach the image generated previously:

> Please rate my suit

You will receive an answer that reads something like this:

>7.5/10 — clean, professional, and well coordinated.

Yeah, I don't think superintelligence is imminent.

I'm not so sure about that. A patient absolutely must critically evaluate the LLM's suggestions. A naive user risks severe complications. A user with that kind of competence, however, doesn't need an LLM for such trivial adjustments - they're obvious

These are exactly the kinds of people who should NOT base their diabetes management on the "suggestions" of LLMs! There is the real risk that such users lack the competence or judgement to critically assess the convincing-sounding output of an LLM.

I'm a T1D myself and like to experiment with ChatGPT (or Opus). My experiences are mixed

LLMs are overly cautious when it comes to correcting with insulin. They regularly advise against correcting before going to bed, even if this means that my blood glucose remains above 140 mg/dl for the whole night.

I am following a low to medium carb diet (<100g a day). ChatGPT always nudges me to consume more carbohydrates, even though I have a TIR of 90% (70-150 mg/dl). Why would I change my diet if it currently works very well for me? Still, most LLMs seem to favor carbs for some reason.

I am using Fiasp as my fast acting insulin. Typically, I inject around 1 to 4 IUs of Fiasp. Its glucose-lowering effect typically lasts for roughly 2-3 hours. Therefore, I know that it is safe to re-inject after three hours without risking insulin stacking. But ChatGPT regularly advises against that and wants me to wait another 1-2 hours.

I am not against automating diabetes management. In fact, I really appreciate projects that help with that. But I don't consider LLMs to be helpful in this regard. Their combination of training data bias, liability aversion, lack of context, and one-size-fits-all thinking disqualifies them from such tasks.

Nope. But last winter it got really cold (< -10°C). Also the Honda was aggressively heating the battery

- the range was miserable

- the software quality was bad

- no OTA updates ever (despite Honda's promises)

- slow charging

- poor public charging infrastructure in Germany

I should have known that a 35 kW battery wouldn't deliver great range or charging speed. But I didn't fully appreciate how limiting it would be.

Last year, I bought a new Mini Cooper e. Larger battery. Better software. BMW's quality actually delivered this time. The car feels objectively nice. The software is polished. There are updates. Few bugs. But the range still leaves something to be desired. In summer it's okay. During winter 30-40% of the range just melts away.

Public charging in Northern Germany still sucks:

- too few public chargers

- chargers are often broken or out of service

- pricing is intransparent

Municipal utility companies ("Stadtwerke") seem especially bad at maintaining their charger fleets. Every second charger that I want to use is out of service. The one next to my apartment has been labeled as "defective" for a couple of weeks now. Nobody seems to care...

I still like (love during summer) my car. It's a cool car. It feels luxurious. It's comfortable. It's fun to tear around corners. It's still compact enough to maneuver through the city. And it looks cool. But it also costs 40-50k EUR and only has limited range. And public charging really needs to improve.

I've developed this weird addiction to making notes in Obsidian. It wasn't really about learning or understanding anything. I bought into the illusion that having notes in my PKM meant I had actual knowledge. Bigger graph = smarter me, or so I thought. I even started reading books just to feed the system: Look at me with my 3,587 notes this year - aren't I clever!"

Currently, I am just taking notes where it really matters: Readme, documentation and some loosely organised markdown files